Reviews and CommentaryFree Access

Review

Emerging and Evolving Concepts in Cancer Immunotherapy Imaging

From the Department of Radiology, New York Presbyterian Hospital–Columbia University Medical Center, 630 W 168th St, New York, NY 10032 (L.D., S.S., L.H.S.); Department of Nuclear Medicine, Institut Curie, Paris, France (R.D.S.); DMU Smart Imaging, Department of Medical Imaging, Assistance Publique-Hôpitaux de Paris, GH Université Paris-Saclay, Raymond Poincaré Teaching Hospital, Garches, France (A.M.); Gustave Roussy-Centrale Supélec-Therapanacea Centre of Artificial Intelligence in Radiation Therapy and Oncology, Gustave Roussy Cancer Campus, Villejuif, France (R.S.); Radiomics Team, Molecular Radiation Therapy INSERM U1030, Paris-Sud University, Gustave Roussy Cancer Campus, and University of Paris-Saclay, Villejuif, France (R.S.); Departments of Radiation Oncology (R.S.) and Interventional Radiology (L.T.), Gustave Roussy Cancer Campus, Villejuif, France; Department of Oncology, Henri Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France (S.H.); Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif, France (A.B.T.); Department of Radiology, Cochin Hospital, APHP, France (F.M.B.); Department of Nuclear Medicine, University Hospital, INSERM 1199 ANTICIPE, Normandy University, Caen, France (N.A.); Department of Nuclear Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Louis, Paris, France (L.V., A.R.); Department of Nuclear Medicine, Centre Eugène Marquis, Université Rennes 1, Rennes, France (A.G.); Department of Radiology, Rangueil University Hospital, Toulouse, France (F.Z.M.); Department of Hematology, University Hospital of Rennes, U1236, INSERM, Rennes, France (G.M., R.H.); EANM Oncology Committee, Vienna, Austria (E.L.); Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, via Manzoni 56, 20089 Rozzano, Milan, Italy (E.L.); Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.Y.); and Department of Medical Imaging, Diagnostic Imaging Service, Gustave Roussy, Université Paris Saclay, Villejuif, France (S.A.).
Address correspondence to L.D. (email: [email protected]).

Published Online:Dec 6 2022https://doi.org/10.1148/radiol.210518

Correction(s) for this article

Abstract

Criteria based on measurements of lesion diameter at CT have guided treatment with historical therapies due to the strong association between tumor size and survival. Clinical experience with immune checkpoint modulators shows that editing immune system function can be effective in various solid tumors. Equally, novel immune-related phenomena accompany this novel therapeutic paradigm. These effects of immunotherapy challenge the association of tumor size with response or progression and include risks and adverse events that present new demands for imaging to guide treatment decisions. Emerging and evolving approaches to immunotherapy highlight further key issues for imaging evaluation, such as dissociated response following local administration of immune checkpoint modulators, pseudoprogression due to immune infiltration in the tumor environment, and premature death due to hyperprogression. Research that may offer tools for radiologists to meet these challenges is reviewed. Different modalities are discussed, including immuno-PET, as well as new applications of CT, MRI, and fluorodeoxyglucose PET, such as radiomics and imaging of hematopoietic tissues or anthropometric characteristics. Multilevel integration of imaging and other biomarkers may improve clinical guidance for immunotherapies and provide theranostic opportunities.

Online supplemental material is available for this article.

An earlier incorrect version of this article appeared online. This article was corrected on January 10, 2023.

Summary

This review highlights the distinct imaging patterns associated with immunotherapy response and progression and considers novel imaging approaches to evaluate emerging immunotherapies.

Essentials

■ Immunotherapy produces new phenomena that challenge size-based criteria for tumor response and progression, including prolonged response, pseudoprogression, dissociated or mixed response, abscopal effect, hyperprogression, and fast progression.
■ Hyperprogression, an atypical flare-up of tumor growth kinetics, can lead to premature death and requires clinical management that must be guided by successful detection at imaging.
■ The early diagnosis of immune-related adverse events is critical to initiate appropriate management, and they should not be misdiagnosed as treatment failure due to disease progression.
■ None of the proposed modifications to Response Evaluation Criteria in Solid Tumors for immunotherapy assessment are well-suited to provide guidance for the emerging generation of immunotherapies.
■ Artificial intelligence may enable clinical decision support algorithms combining biomarkers developed with use of new imaging modalities, such as immuno-PET, and new analytic approaches, such as radiomics.

Introduction

Measuring tumor diameter has been key to assessing the efficacy of historical systemic cancer therapies targeting uncontrolled cell division. However, immunotherapies act on the concept that cancer produces and exploits disorder of the immune system, which compels re-evaluation of fundamental assumptions in radiology of what constitutes response or progression. Imaging paradigms developed to assess chemotherapeutic agents must shift to become sensitive to the different response patterns produced by these modern agents. These immune-related phenomena include unusual dynamics in tumor size change on immunotherapy, such as a transient increase in diameter due to local inflammation (pseudoprogression) or treatment-induced acceleration of tumor growth (hyperprogression). Failure of imaging to adapt will strangle efforts in discovery of the next generation of immunotherapies. Success could dramatically expand the capabilities and role of radiologists in the area of cancer care.

This review will summarize the imaging evidence for distinct patterns associated with response and disease progression on immunotherapy. We will explore how these patterns confound current guidelines for evaluating response and progression and survey emerging therapeutic tools for editing immune function. We will also discuss how these therapies in novel combinations and routes of administration might be monitored by new proposed immunotherapy response criteria and the importance of these criteria for evaluating emerging immunotherapies in clinical trials. Finally, this review will survey research progress that could improve imaging assessment of immunotherapy and enable image-guided theragnosis.

Old and New Concepts in Cancer Imaging

The diverse mechanisms of action of historical treatments converge on promoting cancer cell death and interrupting cancer cell division, and their ability to promote survival correlates with how well they shrink target lesions and prevent new lesions from appearing. Because tumor size is easy to measure at CT, the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines adopted specific size criteria for progression, stability, partial response, and complete response to standardize tumor assessment in patients (1). RECIST guidelines were later generalized to other tumor types (ie, Response Assessment in Neurooncology, or RANO, and the Cheson criteria in hematology) and shaped the clinical concepts of this era (Table 1). Clinical experience with immunotherapy has demonstrated that these guidelines incorporated assumptions true only for the treatments they were designed to assess. In addition, they shaped further drug development by assuming that the effectiveness of novel therapies could be gauged by their ability to produce imaging phenomena that fit the conceptual categories validated using historical therapies.

**Table 1: Role of Imaging in Assessing and Guiding Therapy**

ImmunothFerapy has shifted the paradigm in cancer therapy by restoring the antitumor capacity of the immune system (2). Our experience with the imaging phenomena associated with this radically different mechanism of action comes mostly from monotherapy using the first agents to gain clinical approval (ie, immune checkpoint modulators), which disrupt the negative regulation of T-cell activation by cancer cells seeking to evade the immune system. Other therapies building on the concept of editing immune system function are being pursued and may produce their own characteristic changes at imaging. Evaluating these new approaches will be difficult because immunotherapy challenges the concepts of response and progression often used to determine the success of clinical trials (Table 1).

Patterns of Response to Immunotherapy

Our concept of response assumes that the clinical benefit of a therapy can be assessed categorically by the degree and timing of shrinkage it produces in measurable tumors. While this was applicable to historical therapies, clinical benefit from immunotherapies may be associated with a transitory increase in tumor diameter (pseudoprogression), shrinkage of some lesions but not others (dissociated response), or no response at all according to current size-based criteria. Novel patterns of immunotherapy response (2) are illustrated in Figures 1–3 and discussed in the following sections.

Figure 1: Intratumoral immunotherapy. Axial CT images obtained at the level of abdomen in a 50-year-old man with metastatic melanoma. At baseline, one liver metastasis, which is the lesion in segment VII-VIII (arrows on the top and middle rows), was injected with intratumoral immunotherapy with immune checkpoint modulators. At month 4, a local response was observed in the injected lesion and a response distant from the injected site was observed, such as in the liver lesion displayed on the bottom row (arrows).

Immune response and redefinition of progression as a spectrum. Three patients with non–small cell lung cancer were treated with immune checkpoint modulators and experienced atypical patterns of response and progression. The first patient had a pseudoprogression. Axial CT images obtained at the level of the chest show transitory increase in tumor size during treatment with immune checkpoint modulators. The second patient had an abscopal response: The local treatment of one segment IV liver lesion triggered an objective response in all other lesions, as displayed on axial CT images obtained at the level of the abdomen (top and middle rows) and pelvis (bottom row) at baseline and month 3. The third patient had hyperprogression, as demonstrated by fluorine 18 fluorodeoxyglucose (18F-FDG) PET (top and middle rows: axial 18F-FDG PET/CT images; bottom row: maximum intensity projection images). He was treated with first-line chemotherapy and second-line anti–programmed cell death protein 1 (PD1) agent. There was an acceleration in tumor size and metabolism after initiation of treatment with immune checkpoint modulators. The patients were evaluated four times: at the end of the first line of chemotherapy (E0), at baseline before the initiation of treatment with immune checkpoint modulators (E1), and during treatment with immune checkpoint modulators (E2 and E3). With use of existing response criteria, several metrics were compared: maximum standardized uptake value (SUVmax) at 18F-FDG PET, total metabolic tumor volume at 18F-FDG PET, and the diameters of target lesions at CT. The relative change between two timepoints (ie, E0E1, E1E2, or E2E3) was evaluated using all these metrics. The treatment-induced change in tumor growth rate (TGR) (ie, E1E2/E2E3) was computed to demonstrate the acceleration in TGR after the initiation of treatment with immune checkpoint modulators. iRECIST = immune-modified RECIST, irRC = Immune-related Response Criteria, irRECIST = immune-related RECIST, MTV = metabolic tumor volume, RECIST = Response Evaluation Criteria in Solid Tumors. — Figure 2: Immune response and redefinition of progression as a spectrum. Three patients with non–small cell lung cancer were treated with immune checkpoint modulators and experienced atypical patterns of response and progression. The first patient had a pseudoprogression. Axial CT images obtained at the level of the chest show transitory increase in tumor size during treatment with immune checkpoint modulators. The second patient had an abscopal response: The local treatment of one segment IV liver lesion triggered an objective response in all other lesions, as displayed on axial CT images obtained at the level of the abdomen (top and middle rows) and pelvis (bottom row) at baseline and month 3. The third patient had hyperprogression, as demonstrated by fluorine 18 fluorodeoxyglucose (¹⁸F-FDG) PET (top and middle rows: axial ¹⁸F-FDG PET/CT images; bottom row: maximum intensity projection images). He was treated with first-line chemotherapy and second-line anti–programmed cell death protein 1 (PD1) agent. There was an acceleration in tumor size and metabolism after initiation of treatment with immune checkpoint modulators. The patients were evaluated four times: at the end of the first line of chemotherapy (E0), at baseline before the initiation of treatment with immune checkpoint modulators (E1), and during treatment with immune checkpoint modulators (E2 and E3). With use of existing response criteria, several metrics were compared: maximum standardized uptake value (SUVmax) at ¹⁸F-FDG PET, total metabolic tumor volume at ¹⁸F-FDG PET, and the diameters of target lesions at CT. The relative change between two timepoints (ie, E0E1, E1E2, or E2E3) was evaluated using all these metrics. The treatment-induced change in tumor growth rate (TGR) (ie, E1E2/E2E3) was computed to demonstrate the acceleration in TGR after the initiation of treatment with immune checkpoint modulators. iRECIST = immune-modified RECIST, irRC = Immune-related Response Criteria, irRECIST = immune-related RECIST, MTV = metabolic tumor volume, RECIST = Response Evaluation Criteria in Solid Tumors.

Figure 3: Dissociated/mixed response. Patient 1 was treated with immune checkpoint modulators for metastatic ovarian carcinoma. Axial CT images obtained at the level of the abdomen (top row) and pelvis (bottom row) show progression of hepatic metastases but a response of peritoneal lesions (arrows). Patient 2 was treated with immune checkpoint modulators for metastatic clear cell carcinoma. Axial CT images obtained at the level of the chest (top row) and abdomen (bottom row) show progression of peritoneal lesions (long arrows) but a response of pulmonary lesions (short arrows).

Pseudoprogression

Immunotherapy may produce a transitory enlargement of lesions that meets size-based criteria for progression, followed by stabilization or shrinkage of tumors and associated clinical benefit (3,4). This unconventional pattern and timing of imaging presentations may be due to delayed activation of the immune system, local inflammation causing edema, and/or infiltration of tumor lesions and microenvironment by immune cells. Meta-analysis has established an approximately 6% overall incidence of pseudoprogression (5), varying with type of tumor and immunotherapy. Evaluating response in melanoma may be especially complicated by pseudoprogression, with one study noting pseudoprogression in 17.9% of patients with progression treated with pembrolizumab who were imaged and continued therapy after progression (6). Clinical trial data suggest that outcomes for patients with pseudoprogression better resembled treatment-sensitive patients than those with true progression, indicating a need to improve on current clinical guidance (5).

Dissociated Response

Also known as mixed response or disproportionate response, growth in some target lesions may qualify as progression even as others decrease or stabilize (7) (Fig 3). Dissociated response may reflect the heterogeneity of tissue-specific tumor microenvironments and has been reported in less than 10% of patients (8). Durable clinical benefit from continued immunotherapy despite RECIST1.1 progression has been shown retrospectively and prospectively (9), indicating the potential for novel imaging to help identify patients in whom tumor size increase should not indicate treatment cessation.

Absence of Response

Long-term survivors can benefit durably from immunotherapy regardless of RECIST response (10). Failure of size-based criteria to predict clinical outcome is also represented as a plateau in the right tail of survival curves, which has been observed since the earliest clinical trials of immune checkpoint modulators. The low incidence of pseudoprogression in these patients means that it alone cannot account for the discrepancy between survival and its prediction by the most used imaging end point in oncology research, progression-free survival.

Abscopal Response

The discovery that local treatment can release systemic tumor antigens that activate the antitumor immune system was first observed in radiation therapy, where abscopal response is defined as the regression of cancer outside of the radiation field (11). This spatially and temporally complex dynamic of treatment response is poorly recognized by existing guidelines, indicating the need for imaging concepts to adapt to the novel patterns produced by therapeutic modulation of the immune system.

Patterns of Progression with Immunotherapy

Our current concept of progression assumes that the worst possible treatment outcome is a failure to halt the course of disease, reflected in a categorically defined increase in the diameter of measurable tumors. This is no longer true for immunotherapy, which might cause a fatal increase in tumor growth or life-threatening adverse effects elsewhere in the body. To guide modern clinical decisions, we must decrease our reliance on size-based categories and reconceptualize progression as a spectrum from acceleration to deceleration. The previously discussed response patterns of pseudoprogression and dissociated response can overlap with the concept of progression and coexist with new phenomena of rapid progression observed with immunotherapy, which are presented in the following sections.

Hyperprogression

Immunotherapy may cause an atypical flare-up of tumor growth kinetics leading to premature death (12–14). Due to its recent emergence as a clinical phenomenon in 2016, hyperprogression may be underdiagnosed. Reported risk factors are age (14) and the presence of specific aberration or amplification profiles (15). The incidence of hyperprogression in solid tumors treated with immune checkpoint modulators differs widely (12,15), with estimates in the literature ranging from 4% (15) to 29% (16) of patients.

The difficulty in establishing the frequency of hyperprogression is in part because the term is currently used to define two distinct pathophysiologic phenomena that we will call accelerated progression and fast progression. These require different management (17,18) but are indistinguishably categorized as true progression by current response criteria. New imaging tools that can differentiate between types of accelerated growth with immunotherapy are urgently needed to guide critical clinical decisions.

Hyperprogression by Accelerated Progression

Accelerated progression is a detrimental effect of immunotherapy that shortens patient survival as a result of treatment-induced acceleration of tumor growth. While accelerated progression is relatively uncommon (19), its detection is essential in preventing early death associated with immunotherapy. Establishing diagnosis using the original definition, which describes accelerated progression as a twofold increase in tumor growth rate (TGR) at treatment plus RECIST-defined progression at the first evaluation before therapy initiation, was difficult because it required medical imaging before, at the start of, and during immunotherapy. A recent comparison of five proposed definitions suggested that hyperprogression can be sufficiently diagnosed by establishing RECIST progression during therapy combined with a twofold increase in TGR (13).

Hyperprogression by Fast Progression

Fast progression is a phenomenon of rapid tumor growth observed in aggressive tumors without taking into account treatment effect. Patients who show high TGR after the initiation of immune checkpoint modulators (20,21) have poor outcomes similar to accelerated progression (18,20). Detection of fast progression conveniently requires only two response assessments: before and after treatment. However, without pretreatment measurement of TGR to rule out the possibility that a high TGR was already present, these assessments cannot demonstrate causality between progression and immunotherapy.

Immune-related Adverse Events

Immunotherapy can produce a spectrum of toxicity involving all organs due to autoimmune effects resulting from misdirected stimulation of the immune system (22). Well-known effects include pneumonitis, colitis, hepatitis, pancreatitis, thyroiditis, hypophysitis, synovitis, arthritis, and even sarcoid-like granulomatosis and lymphadenopathy (Figs 4–6). Imaging can be used to detect 74% of immune-related adverse events (irAEs) (23,24). The incidence, diagnosis, and specific imaging appearances of irAEs are reviewed elsewhere (25–29) and in Appendix E1(online).

Detection of immune-related adverse events (irAEs) at medical imaging. (A, B) Axial CT images show pulmonary irAEs. Non-specific interstitial pneumonitis pattern. Ground-glass opacities with immediate subpleural sparing and basal predominance (A). Control after corticosteroids shows disappearance of the lesions (B). (C–E) Coronal CT images show cryptogenic organized pneumonia pattern. Patchy consolidation with a subpleural and peribronchial distribution (C). First control shows migratory lesions (D). Control after corticosteroids shows disappearance of the lesions (E). (F) Axial CT image shows hypersensivity pneumonitis pattern: combination of patchy ground-glass opacities, normal regions, and air trapping. (G) Axial CT image shows acute interstitial pneumonitis pattern: ground-glass opacities with septal thickening. (H–K) CT (H, K) and fluorine 18 fluorodeoxyglucose (18F-FDG) PET (I, J) images show abdominal and digestive irAEs. Segmental colitis: segmental bowel wall thickening and segmental fat infiltration (H). Gastritis: 18F-FDG diffuse uptake in the stomach (I). Diffuse colitis: 18F-FDG diffuse colonic uptake (J). Pancreatitis: pancreatic enlargement with loss of lobulations (K). (L–O) Endocrine irAEs. Thyroiditis: heterogenous appearance of the thyroid gland (L) at Doppler US analysis shows marked hypervascularity. Thyroiditis: intense uptake of 18F-FDG by the thyroid gland (M). Hypophysitis: diffuse and symmetric enlargement of the pituitary gland, with homogeneous and intense enhancement after gadolinium administration on sagittal MRI scan (N). Hypophysitis: intense uptake of 18F-FDG on an axial image of the pituitary gland (O). (P–S) Musculoskeletal irAEs are seen on coronal MRI scans. Tenosynovitis: thickening and postcontrast enhancement of the tendon sheath (P). Myositis: skeletal muscle edema (Q). Myocarditis: delayed contrast enhancement involving the subepicardial myocardium on cardiac MRI scans (R, S). (T) Hand MRI scan shows tenosynovitis. (U, V) Sagittal spine MRI scans show myelitis. — Figure 5: Detection of immune-related adverse events (irAEs) at medical imaging. **(A, B)** Axial CT images show pulmonary irAEs. Non-specific interstitial pneumonitis pattern. Ground-glass opacities with immediate subpleural sparing and basal predominance **(A)**. Control after corticosteroids shows disappearance of the lesions **(B). (C–E)** Coronal CT images show cryptogenic organized pneumonia pattern. Patchy consolidation with a subpleural and peribronchial distribution **(C)**. First control shows migratory lesions **(D)**. Control after corticosteroids shows disappearance of the lesions **(E). (F)** Axial CT image shows hypersensivity pneumonitis pattern: combination of patchy ground-glass opacities, normal regions, and air trapping. **(G)** Axial CT image shows acute interstitial pneumonitis pattern: ground-glass opacities with septal thickening. **(H–K)** CT **(H, K)** and fluorine 18 fluorodeoxyglucose (¹⁸F-FDG) PET **(I, J)** images show abdominal and digestive irAEs. Segmental colitis: segmental bowel wall thickening and segmental fat infiltration **(H)**. Gastritis: ¹⁸F-FDG diffuse uptake in the stomach **(I)**. Diffuse colitis: ¹⁸F-FDG diffuse colonic uptake **(J)**. Pancreatitis: pancreatic enlargement with loss of lobulations **(K). (L–O)** Endocrine irAEs. Thyroiditis: heterogenous appearance of the thyroid gland **(L)** at Doppler US analysis shows marked hypervascularity. Thyroiditis: intense uptake of ¹⁸F-FDG by the thyroid gland **(M)**. Hypophysitis: diffuse and symmetric enlargement of the pituitary gland, with homogeneous and intense enhancement after gadolinium administration on sagittal MRI scan **(N)**. Hypophysitis: intense uptake of ¹⁸F-FDG on an axial image of the pituitary gland **(O). (P–S)** Musculoskeletal irAEs are seen on coronal MRI scans. Tenosynovitis: thickening and postcontrast enhancement of the tendon sheath **(P)**. Myositis: skeletal muscle edema **(Q)**. Myocarditis: delayed contrast enhancement involving the subepicardial myocardium on cardiac MRI scans **(R, S). (T)** Hand MRI scan shows tenosynovitis. **(U, V)** Sagittal spine MRI scans show myelitis.

Sarcoid-like granulomatosis and lymphadenopathy induced by immune checkpoint modulators in three patients with advanced melanoma. New mediastinal lymph nodes were observed after treatment initiation (arrows). Lymphadenopathy was biopsy-proven sarcoid-like granulomatosis with a favorable outcome and objective response to immune checkpoint modulators. Sarcoid-like granulomatosis should be considered a differential diagnosis and not be misdiagnosed as progression. (A, B) Axial CT images in patient 1. Baseline scan (A): no intraparenchymal lesions. Scan at 3 months (B): appearance of disseminated intraparenchymal, subpleural, and peribronchial micronodules (ie, lymphatic distribution). (C, D) Coronal CT images in patient 2. Baseline scan (C): absence of mediastinal lymph nodes. Scan at 3 months (D): appearance of bilateral hilar, subcarinal, and aortopulmonary window enlarged lymph nodes. (E–H) Axial fluorine 18 fluorodeoxyglucose (18F-FDG) images in patient 3. Baseline 18F-FDG PET/CT images (E and F) compared with 3-month 18F-FDG PET/CT images (G and H). E, right lower lobe pulmonary nodule; F, pulmonary nodule regression; G, absence of mediastinal lymph nodes; H, new bilateral hilar lymphadenopathy. — Figure 6: Sarcoid-like granulomatosis and lymphadenopathy induced by immune checkpoint modulators in three patients with advanced melanoma. New mediastinal lymph nodes were observed after treatment initiation (arrows). Lymphadenopathy was biopsy-proven sarcoid-like granulomatosis with a favorable outcome and objective response to immune checkpoint modulators. Sarcoid-like granulomatosis should be considered a differential diagnosis and not be misdiagnosed as progression. **(A, B)** Axial CT images in patient 1. Baseline scan **(A)**: no intraparenchymal lesions. Scan at 3 months **(B)**: appearance of disseminated intraparenchymal, subpleural, and peribronchial micronodules (ie, lymphatic distribution). **(C, D)** Coronal CT images in patient 2. Baseline scan **(C)**: absence of mediastinal lymph nodes. Scan at 3 months **(D)**: appearance of bilateral hilar, subcarinal, and aortopulmonary window enlarged lymph nodes. **(E–H)** Axial fluorine 18 fluorodeoxyglucose (¹⁸F-FDG) images in patient 3. Baseline ¹⁸F-FDG PET/CT images (E and F) compared with 3-month ¹⁸F-FDG PET/CT images (G and H). E, right lower lobe pulmonary nodule; F, pulmonary nodule regression; G, absence of mediastinal lymph nodes; H, new bilateral hilar lymphadenopathy.

irAEs are a new key concept in cancer imaging. They can be difficult to distinguish from response and progression because T-cell inflammatory responses can produce swelling in the targeted tumor microenvironment that resembles tumor growth, as well as disease spread in susceptible healthy tissues. Early accurate diagnosis of irAEs is essential to clinical management of immunotherapies. Less serious irAEs can be treated with corticosteroids, but severe reactions can be life-threatening and require guidance to withhold the triggering immunotherapy.

Although potentially lethal, irAEs are linked with survival benefit in many cancers, including melanoma (30) and non–small cell lung cancer (31). This may be because responders to therapy generally have longer treatment duration and thus a higher risk of irAEs (32); however, a recent analysis found that the relationship between irAEs and immunotherapy response was not confounded by time on therapy (33). New concepts of imaging immune function are needed to untangle the relationships between irAEs, response, and progression on immunotherapy.

Response Criteria and Emerging Immunotherapies

Novel response criteria proposed to assess unusual imaging patterns of response and progression on immunotherapy have mostly been slight modifications of historical criteria (RECIST1.1, RANO, Cheson) while adding the requirement to follow-up with subsequent imaging in clinically stable progressive patients to diagnose pseudoprogression. This is partially because size-based concepts are central to radiology, and technology for measuring tumor diameter at CT is ubiquitous in cancer care. Furthermore, unusual reactions to immune checkpoint modulators in monotherapy have been relatively rare; therefore, the powerful association between tumor size and survival has been confounded rather than eliminated by current-generation immunotherapies.

Recent reviews offer an excellent survey of existing proposals for new guidelines to assess immunotherapy with use of CT (25,26) and/or metabolic imaging with fluorine 18 fluorodeoxyglucose (¹⁸F-FDG) PET (27,34). Summaries of these novel criteria are listed in Table 2. Differences between them are detailed in Appendix E1 (online) and Tables E1 and E2 (online). Our discussion herein considers how well these criteria might perform if certain developments in emerging immunotherapies prove more likely to produce specific phenomena that are rarely observed in current clinical experience.

**Table 2: History and Adoption of Immune-related Response Criteria**

New Mechanisms and Pseudoprogression

Avoiding premature treatment cessation due to pseudoprogression is the most common goal for new response criteria. However, emerging immunotherapies are based on entirely new mechanisms for altering the immune system and tumor dynamic. Adoptive cell therapy, in which engineered immune cells are infused into patients with cancer, has proven viable, and cancer vaccines and oncolytic viruses are in development. Therefore, many proposed response criteria for pseudoprogression may not consider the potential benefit of new treatments. This is the case for Immune-related Response Criteria (35), immune-modified RECIST (ie, iRECIST) (36), and immune-related RECIST (ie, irRECIST)(37) for solid tumors; refined Lymphoma Response to Immunomodulatory Therapy Criteria, or LYRIC, for lymphoma (38); and immunotherapy response assessment for neuro-oncology (ie, iRANO) for brain tumors (39). All alter the historical timing of treatment decisions. They require re-evaluation using a follow-up scan when there is growth in tumor burden without substantial clinical deterioration. This “wait and see” strategy prevents pseudoprogression from being mistaken for true progression but delays evaluation. Novel concepts are needed to identify imaging biomarkers of pseudoprogression that manifest before changes in tumor size.

New Routes of Administration and Greater Differentiation between Target Lesions

The unique ability of irRECIST to be used in the evaluation of both distant and local responses to immunotherapy is likely to become more important with new developments in immunotherapy. The synergy of immunotherapy with radiation therapy has produced promising results in using the abscopal effect to increase response rates. Similarly, using immune checkpoint modulators as local intratumoral immunotherapy can increase drug concentration in the injected tumor while reducing systemic irAEs. These methods create a need to differentiate local effect in the injected lesions from systemic effect in noninjected lesions (40). Figure 6 depicts a metastasis to the liver that exhibited a local and distant response at 4 months following intratumoral immunotherapy (41). In Response Criteria for Intratumoral Immunotherapy in Solid Tumors, or itRECIST, the redefinition of the concept of overall response to differentiate systemic from local response to immunotherapy (42) could improve clinical guidance for patients with oligoprogressive lesions by supporting continued treatment and/or indicating the integration of local treatments, such as surgery, radiation therapy, and interventional radiology procedures (43).

Greater Potency and Need for Clinical Guidance

Regardless of their mechanisms of action, emerging immunotherapies are likely to converge on producing more powerful activation of the immune system to result in greater cancer-killing potency. The association between irAEs and clinical benefit has shown that more powerful immune activation may produce more life-threatening complications. Likewise, impact of new therapeutic agents is likely to increase risk of hyperprogression. Current response criteria are poorly suited to provide rapid assessment of irAEs and hyperprogression. If the next generation of immunotherapies offers risks to patients proportional to their benefit, radiologists will need new guidelines for clinical decision-making, such as ending a life-threatening therapy without waiting to see its possibly misleading effect on tumor size.

Need for New Surrogate Imaging End Points in Clinical Research

Until new immunotherapies enter widespread clinical use, their unique imaging patterns cannot be determined. However, until new response criteria are adopted in drug discovery, the different patterns produced by novel approaches compared with historical therapies will make it more difficult for these immunotherapies to enter clinical use.

Current treatment regimes, especially immunotherapy, have increased cancer survival and the number of therapies attempted per patient. This makes it increasingly difficult for clinical trials to assess the impact of novel therapies on overall survival, the reference standard for clinical benefit. Accordingly, there is a growing reliance on surrogate imaging end points to provide an earlier prediction of trial outcome.

Current end points like progression-free survival are centered on size-based criteria derived from RECIST1.1. As shown, their association with survival was adequate for historical treatments, but the challenges associated with immunotherapy mean that the cost and failure rate of clinical trials will rise as novel mechanisms of action produce novel dynamics of response and progression.

New concepts are needed to identify imaging biomarkers of survival that can provide new surrogate end points for clinical research in immunology. Can the response criteria proposed to date fill this need? Table 2 provides an estimate by examining how many times each is listed among the outcomes studied in a clinical trial. iRECIST and European Organization for Research and Treatment of Cancer, or EORTC, criteria appear in more clinical trials than any other sets of criteria, with an average of 97 and 189 trials per year, respectively. Their likelihood of replacing RECIST can be gauged by comparison with RECIST1.1-derived progression-free survival, which has been used in 1272 trials each year since its regulatory approval was obtained in 2005. The enormous databases of standardized CT measurements gathered during such trials may help in the discovery of the next generation of surrogate end points, which is discussed in the following section.

We have reviewed the ways that immunotherapies challenge current concepts of response and progression and how these concepts must evolve to provide clinical guidance and empower clinical trials of emerging approaches to editing immune function. New imaging modalities offer promise to address these challenges, but we begin by considering the less obvious advances in image analysis.

New Information from Existing CT Scans

RECIST1.1 became standard in patient care for solid tumors in part because the association between tumor diameter and survival was adequate for historical therapies. Additionally, tumor diameter could be reliably measured at CT with use of the technology available at the time. Current use of image workstations, rather than ruler and film, to provide more sophisticated lesion measurements on images routinely acquired in cancer care allows better evaluation of response and progression on immunotherapy.

Radiomics and Artificial Intelligence

Radiomics and artificial intelligence (AI) are rapidly growing disciplines that convert digital medical images into high-dimensional quantitative data. A growing body of research supports the potential of AI tools to assess the biologic characteristics of tumors and empower new prognostic, predictive, and theranostic approaches in patients treated with immunotherapy (44).

While AI can derive imaging biomarkers using any modality, the wealth of existing CT images available to train and validate algorithms makes it the modality most often studied. AI has also been used to investigate MRI and ¹⁸F-FDG PET (45) scans, most often with primary lung cancers and melanoma. Baseline or early response imaging markers discovered with AI have shown promise in predicting prognosis (46), treatment response, tumor response, tumor phenotype (including tumor mutational burden) (47), irAEs (48), and proxies of immune environment, such as CD8 infiltration (49).

Radiomics has shown promise in each of the problem areas envisioned for emerging immunotherapies. AI used early change in tumor phenotype on CT images to predict the survival of patients with metastases treated with systemic therapies in multicenter clinical trials (46). This offers proof of concept that information in existing images could improve guidance for clinicians currently limited to a wait and see strategy to differentiate pseudoprogression from true progression. AI tools designed and validated to correlate baseline CT images with CD8 infiltration predicted outcome and response in patients with advanced cancer treated with combined radiation therapy and immunotherapy (49,50), successfully evaluating distant versus local response and suggesting potential to guide local treatment for oligoprogressive lesions. AI models have also shown promise in predicting irAEs (48) and patient survival (46), helping mitigate the risk that may arise from more potent immunotherapies.

While the literature on radiomics and AI is encouraging, routine clinical implementation will require substantial translational work, as most studies to date were retrospective and recruited patients from a single center. Reducing variance in image acquisition parameters, harmonizing the definition of radiomics features, externally validating AI-derived biomarkers, and standardizing the use of stress tests in radiology all require further research (51).

Imaging Patient Anthropometric Characteristics: Sarcopenia

Patient anthropometric characteristics offer another source of information for clinical guidance that is routinely captured at imaging and clinical examination but typically ignored. Sarcopenia (assessed using the skeletal muscle mass index on CT scans) and cachexia are associated with poorer survival and higher complication rates, regardless of tumor or treatment type. Sarcopenia may be especially associated with immunotherapy outcome, particularly in lung cancer (52). Because sarcopenia is a potential risk factor for hyperprogression after second-line pembrolizumab in lung cancers, imaging anthropometric characteristics might help identify patients with risk of premature death after the initiation of immunotherapy (53).

Treatment-induced changes in patients’ anthropometric characteristics were linked with favorable outcomes in patients treated with immune checkpoint modulators and can be envisioned as a surrogate of treatment efficacy. In patients with Hodgkin lymphoma treated with anti–programmed cell death protein 1 (PD-1) agents, improvement of the skeletal muscle index was mostly noted in long-term responders (19).

Anthropometric characteristics could have theranostic value in immunotherapy. A study using innovative multiatlas segmentation associated low subcutaneous fat mass with significantly worse outcomes in metastatic non–small cell lung cancer treated with nivolumab (54), suggesting that this measurement at pretreatment imaging could provide clinical guidance to determine individual dosage of immune checkpoint modulators.

New Imaging Modalities for Assessing Immunotherapy

The RECIST criteria contributed immeasurably to CT becoming the workhorse of oncology. Will the need to move beyond size-based criteria propel another imaging modality to become as central to cancer care in the immunotherapy era?

MRI for Assessing Immunotherapy

Conventional MRI has mostly found success in neuro-oncology patients (eg, the iRANO criteria) (39). Innovative MRI techniques, including apparent diffusion coefficient, perfusion-weighted imaging, and MR spectroscopy, have been effective in assessing immunotherapy response (55). Chemical exchange saturation transfer, or CEST, MRI visualizes hydrogen protons saturated by a radiofrequency pulse, enabling imaging of metabolites such as glutamate, lactate, alanine, and creatine, which are under investigation to distinguish tumor recurrence from pseudoprogression.

¹⁸F-FDG PET for Assessing Immunotherapy

Based on evidence that metabolic imaging may be more predictive of outcome than change in tumor size at CT (56,57), European and American associations of nuclear medicine have recently published guidelines for using ¹⁸F-FDG PET to evaluate immunotherapy (58).

Metabolic PET imaging may have an advantage over CT in evaluating irAEs, although studies are relatively scarce (59,60). The inflammatory processes induced by thyroiditis and hypophysitis (61); pneumonitis, colitis, and pancreatitis (62); and even musculoskeletal inflammatory processes (27) are associated with markedly increased ¹⁸F-FDG uptake. This imaging biomarker can be used to predict thyroiditis with hypothyroidism (63) and irAEs in patients with a diagnosis of melanoma receiving a combination of two immune checkpoint inhibitors (64) even before the appearance of the usual clinical and biologic indicators required for diagnosis of most irAEs (65).

Like anthropometric criteria for CT, several metabolic biomarkers of response to immunotherapy may be present, but not used, in routine ¹⁸F-FDG PET imaging. Pretreatment measurements of the glucose metabolism of the spleen and/or bone marrow are associated with clinical outcome in some populations: High glucose metabolism in nontumoral hematopoietic tissue at baseline is typically associated with cancer-related systemic immunosuppressive inflammation and unfavorable outcome (Fig 7); hence, metabolism could become a theranostic biomarker providing clinical guidance to suggest additional therapies. There are debatable results regarding how imaging metrics quantifying on-treatment changes in glucose metabolism of this nontumoral hematopoietic tissue could predict the efficacy of immunotherapies and improve stratification and selection of patients who would most benefit from immunotherapy (66–70). On one hand, increased hematopoietic metabolism can be a surrogate marker of immune activation and treatment response. On the other hand, increased hematopoietic metabolism can be associated with immunotherapy resistance.

Figure 7: Assessing the metabolism of hematopoietic tissues. Patients 1–4 had non–small cell lung cancer with high bone marrow glucose metabolism. Maximal intensity projection fluorine 18 fluorodeoxyglucose PET images for initial staging in patients revealed increased bone marrow glucose metabolism. All patients experienced progression or relapse after initial treatment with immune checkpoint modulators (ICMs). Patients 5 and 6 had non–small cell lung cancer with low bone marrow glucose metabolism. These two patients showed prolonged clinical benefit from treatment with immune checkpoint modulators.

Immuno-PET

Cancer imaging to date has focused on tumor cells, but their immune context, determined by the density, composition, functional state, and organization of tumor-infiltrating leukocytes, may have interesting clinical applications (Fig 8). Investigation has shown that immune context can be used to predict the efficacy of immunotherapy and overall prognosis, requiring confirmation in clinical trials.

Diagram shows that imaging modalities quantify important pathways in immuno-oncology in monotherapy or combination. ADC = apparent diffusion coefficient, ASL = arterial spin labeling, BOLD = blood oxygen level–dependent, 11C-MET = carbon 11 methionine, DCE = dynamic contrast-enhanced, DSC = dynamic susceptibility contrast, FAZA = fluoroazomycin arabinoside, FDG = fluorodeoxyglucose, FET = fluoroethyl-l-tyrosine, 18F = fluorine 18, FLT = fluorothymidine, FMISO = fluoromisonidazole, PD-1 = programmed cell death protein 1, PD-L1 = programmed cell death ligand 1, TSPO = translocator protein. — Figure 8: Diagram shows that imaging modalities quantify important pathways in immuno-oncology in monotherapy or combination. ADC = apparent diffusion coefficient, ASL = arterial spin labeling, BOLD = blood oxygen level–dependent, ¹¹C-MET = carbon 11 methionine, DCE = dynamic contrast-enhanced, DSC = dynamic susceptibility contrast, FAZA = fluoroazomycin arabinoside, FDG = fluorodeoxyglucose, FET = fluoroethyl-l-tyrosine, ¹⁸F = fluorine 18, FLT = fluorothymidine, FMISO = fluoromisonidazole, PD-1 = programmed cell death protein 1, PD-L1 = programmed cell death ligand 1, TSPO = translocator protein.

In the future, the optimal strategy may shift from non–immune-specific imaging biomarkers to immune-specific biomarkers assessed with immuno-PET. Human studies have focused on radiotracers targeting programmed death ligand 1, or PD-L1 (lymphocytic exhaustion), CD8 (cytotoxic lymphocytes), or lymphocytic activation, which could enable in vivo assessment of whole-body receptor and ligand density. Promising results suggest that immuno-PET using antibodies, diabodies, and small molecules may provide more comprehensive information than ex vivo immunohistochemistry data derived from a single biopsy sample (55).

A full description of the molecular targets that have been studied for immuno-PET, their rationale, and the strategies used to image them can be found in Appendix E1 (online). Herein, we focus on their potential clinical impact. PD-1 and its ligand PD-L1 are major therapeutic targets for immunotherapy. Imaging PD-L1 is being investigated as a prognostic, predictive, and theranostic tool to guide systemic and local immunotherapy approaches (55,71). CD8 is a coreceptor for the T-cell receptor, and tumor uptake of radiolabeled PET agents identifying CD8 has been correlated with response to immunotherapy (72,73). Macrophages are an increasingly important target in cancer therapy, and imaging biomarkers targeting specific tumor-associated macrophages could guide precision approaches. Radiotracers targeting interleukin 2, interferon-γ, and granzyme B have all shown promising results predicting the efficacy of immune checkpoint modulators in mouse models (55). PET radiotracers characterizing specific components of the inflammatory process, such as cyclooxygenase or matrix metalloproteinase, could also decipher the immune context and identify pseudoprogression. A translationally advanced candidate is ¹⁸F–arabinofuranosyl-guanine (74), a PET imaging agent specific to activated T cells whose assessment of response to immunotherapy has been the subject of ongoing clinical trials (ClinicalTrials.gov identifiers NCT04524195, NCT03129061, and NCT04678440).

Theragnosis

Promising theranostic applications characterize baseline and on-treatment changes in imaging phenotype, deciphering the type of immune environment in vivo in all tumors, which provides information on intratumoral heterogeneity. This information could guide decision-making based on the likelihood that immune checkpoint modulators will activate the immune system, hence offering benefit to each individual patient and a potential surrogate end point for measuring response in clinical trials.

First, tumors with elevated lymphocytes killing CD8-expressing cancer cells are most likely to respond. Immuno-PET, and potentially radiomics at CT, could quantify tumor CD8 expression. Second, cancer cells with highly expressed PD-1 levels promote exhaustion of lymphocytes, limiting their tumor-killing function and thus exploiting a defense mechanism that prevents immune cells from permanently damaging chronically inflamed tissues. The use of immuno-PET to quantify PD-L1 expression in the tumor could guide integrated treatments targeting PD-1 and anti–PD-1. Third, patients with cancers promoting immunosuppression who could benefit from additional immunomodulating treatments tend to have distinct phenotypes, such as distinct glucose metabolism in hematopoietic tissues (eg, spleen and bone marrow) and anthropometric characteristics at CT.

Conclusion

Immune checkpoint modulators have revolutionized cancer treatment while challenging concepts in cancer imaging, and new immunotherapies in development are likely to extend this process. Distinguishing true progression from pseudoprogression has inspired multiple proposals to update existing response evaluation criteria, but a deeper modification is necessary to consider progression as a spectrum that includes hyperprogression. irAEs can be detected in various imaging findings and require differentiation from progression and distinct clinical management.

Immune-specific biomarkers derived from new imaging modalities, as well as innovative uses of existing information, offer potential to address challenges in immunotherapy. Multilevel integration of imaging biomarkers with clinical, laboratory, and tissue parameters has proven advantageous with the seven-point prognostic score (75) and the Lung Immune Prognostic Index (76) and its integration with metabolic imaging to form the immune-metabolic-prognostic index score (77). Reliance on a single easily measured imaging biomarker was necessary, given the historical limitations of image analysis technology. Progress has given us both the complex patterns of immunotherapy and the capacity to train artificial intelligence (AI) to discover complex relationships of imaging features. The future of imaging in the age of immunotherapy is likely to involve an AI-discovered combination of existing prognostic scores (66–68). Immuno-PET and other advanced imaging techniques, integrated into a clinical decision support algorithm, may improve clinical guidance and provide theranostic opportunities.

Disclosures of conflicts of interest: L.D. No relevant relationships. S.S. No relevant relationships. R.D.S. No relevant relationships. A.M. No relevant relationships. R.S. Support from Fondation ARC pour la recherche médicale (international mobility grant and grant SIGN’IC20161236437), INSERM, and Fondation Bettencourt Schueller; pending U.S. patent (no. 16630031). L.T. Board membership for Amgen; consulting fees from MedinCell and GE Healthcare; grants or pending grants from the BMS Foundation and Terumo; payment for lectures from Boston Scientific, BMS, and Ipsen. S.H. No relevant relationships. A.B.T. No relevant relationships. F.M.B. No relevant relationships. N.A. No relevant relationships. L.V. No relevant relationships. A.R. No relevant relationships. A.G. No relevant relationships. F.Z.M. No relevant relationships. G.M. No relevant relationships. R.H. No relevant relationships. E.L. Project funding from Fondazione AIRC; lecture fees from ESMIT and MI&T Congressi; royalties from Springer; fees for the development of educational presentations from ESMIT; travel expenses from ESMIT, EANM, and the European Organization for Research and Treatment of Cancer. R.Y. No relevant relationships. S.A. No relevant relationships. L.H.S. Consulting fees from Regeneron and BI; grants or grants pending from Merck, Sanofi, and BMS.

References

1. Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 2009;45(2):228–247.
Medline Google Scholar
2. Borcoman E, Kanjanapan Y, Champiat S, et al. Novel patterns of response under immunotherapy. Ann Oncol 2019;30(3):385–396.
Medline Google Scholar
3. Chiou VL, Burotto M. Pseudoprogression and immune-related response in solid tumors. J Clin Oncol 2015;33(31):3541–3543.
Medline Google Scholar
4. Hodi FS, Hwu WJ, Kefford R, et al. Evaluation of immune-related response criteria and RECIST v1.1 in patients with advanced melanoma treated with pembrolizumab. J Clin Oncol 2016;34(13):1510–1517.
Medline Google Scholar
5. Nishino M, Ramaiya NH, Hatabu H, Hodi FS. Monitoring immune-checkpoint blockade: response evaluation and biomarker development. Nat Rev Clin Oncol 2017;14(11):655–668.
Medline Google Scholar
6. Ahmed FS, Dercle L, Goldmacher GV, et al. Comparing RECIST 1.1 and iRECIST in advanced melanoma patients treated with pembrolizumab in a phase II clinical trial. Eur Radiol 2021;31(4):1853–1862.
Medline Google Scholar
7. Humbert O, Chardin D. Dissociated response in metastatic cancer: an atypical pattern brought into the spotlight with immunotherapy. Front Oncol 2020;10:566297.
Medline Google Scholar
8. Bernard-Tessier A, Baldini C, Castanon E, et al. Patterns of progression in patients treated for immuno-oncology antibodies combination. Cancer Immunol Immunother 2021;70(1):221–232.
Medline Google Scholar
9. Tozuka T, Kitazono S, Sakamoto H, et al. Dissociated responses at initial computed tomography evaluation is a good prognostic factor in non-small cell lung cancer patients treated with anti-programmed cell death-1/ligand 1 inhibitors. BMC Cancer 2020;20(1):207.
Medline Google Scholar
10. Nishino M, Giobbie-Hurder A, Manos MP, et al. Immune-related tumor response dynamics in melanoma patients treated with pembrolizumab: identifying markers for clinical outcome and treatment decisions. Clin Cancer Res 2017;23(16):4671–4679.
Medline Google Scholar
11. Michot JM, Mazeron R, Dercle L, et al. Abscopal effect in a Hodgkin lymphoma patient treated by an anti-programmed death 1 antibody. Eur J Cancer 2016;66:91–94.
Medline Google Scholar
12. Ferrara R, Mezquita L, Texier M, et al. Hyperprogressive disease in patients with advanced non-small cell lung cancer treated with PD-1/PD-L1 inhibitors or with single-agent chemotherapy. JAMA Oncol 2018;4(11):1543–1552.
Medline Google Scholar
13. Kas B, Talbot H, Ferrara R, et al. Clarification of definitions of hyperprogressive disease during immunotherapy for non-small cell lung cancer. JAMA Oncol 2020;6(7):1039–1046.
Medline Google Scholar
14. Champiat S, Dercle L, Ammari S, et al. Hyperprogressive disease is a new pattern of progression in cancer patients treated by anti-PD-1/PD-L1. Clin Cancer Res 2017;23(8):1920–1928.
Medline Google Scholar
15. Kato S, Goodman A, Walavalkar V, Barkauskas DA, Sharabi A, Kurzrock R. Hyperprogressors after immunotherapy: analysis of genomic alterations associated with accelerated growth rate. Clin Cancer Res 2017;23(15):4242–4250.
Medline Google Scholar
16. Saâda-Bouzid E, Defaucheux C, Karabajakian A, et al. Hyperprogression during anti-PD-1/PD-L1 therapy in patients with recurrent and/or metastatic head and neck squamous cell carcinoma. Ann Oncol 2017;28(7):1605–1611.
Medline Google Scholar
17. Ferrara R, Mezquita L, Texier M, et al. Comparison of fast-progression, hyperprogressive disease, and early deaths in advanced non-small-cell lung cancer treated with PD-1/PD-L1 inhibitors or chemotherapy. JCO Precis Oncol 2020;4:829–840.
Medline Google Scholar
18. Seban RD, Schwartz LH, Bonardel G, Dercle L. Diagnosis of hyperprogressive disease in patients treated with checkpoint inhibitors using ¹⁸F-FDG PET/CT. J Nucl Med 2020;61(9):1404–1405.
Medline Google Scholar
19. Dercle L, Seban RD, Lazarovici J, et al. ¹⁸F-FDG PET and CT scans detect new imaging patterns of response and progression in patients with Hodgkin lymphoma treated by anti-programmed death 1 immune checkpoint inhibitor. J Nucl Med 2018;59(1):15–24.
Medline Google Scholar
20. Castello A, Lopci E. Reply: Diagnosis of hyperprogressive disease in patients treated with checkpoint inhibitors using ¹⁸F-FDG PET/CT. J Nucl Med 2020;61(9):1405.
Medline Google Scholar
21. Lo Russo G, Moro M, Sommariva M, et al. Antibody-Fc/FcR interaction on macrophages as a mechanism for hyperprogressive disease in non-small cell lung cancer subsequent to PD-1/PD-L1 blockade. Clin Cancer Res 2019;25(3):989–999.
Medline Google Scholar
22. Baxi S, Yang A, Gennarelli RL, et al. Immune-related adverse events for anti-PD-1 and anti-PD-L1 drugs: systematic review and meta-analysis. BMJ 2018;360:k793.
Medline Google Scholar
23. Mekki A, Dercle L, Lichtenstein P, et al. Detection of immune-related adverse events by medical imaging in patients treated with anti-programmed cell death 1. Eur J Cancer 2018;96:91–104.
Medline Google Scholar
24. Petrelli F, Grizzi G, Ghidini M, et al. Immune-related adverse events and survival in solid tumors treated with immune checkpoint inhibitors: a systematic review and meta-analysis. J Immunother 2020;43(1):1–7.
Medline Google Scholar
25. Nishino M, Hatabu H, Hodi FS. Imaging of cancer immunotherapy: current approaches and future directions. Radiology 2019;290(1):9–22.
Google Scholar
26. Carter BW, Bhosale PR, Yang WT. Immunotherapy and the role of imaging. Cancer 2018;124(14):2906–2922.
Medline Google Scholar
27. Costa LB, Queiroz MA, Barbosa FG, et al. Reassessing patterns of response to immunotherapy with PET: from morphology to metabolism. RadioGraphics 2021;41(1):120–143.
Google Scholar
28. Postow MA, Sidlow R, Hellmann MD. Immune-related adverse events associated with immune checkpoint blockade. N Engl J Med 2018;378(2):158–168.
Medline Google Scholar
29. Ramos-Casals M, Brahmer JR, Callahan MK, et al. Immune-related adverse events of checkpoint inhibitors. Nat Rev Dis Primers 2020;6(1):38.
Medline Google Scholar
30. Nakamura Y, Tanaka R, Asami Y, et al. Correlation between vitiligo occurrence and clinical benefit in advanced melanoma patients treated with nivolumab: a multi-institutional retrospective study. J Dermatol 2017;44(2):117–122.
Medline Google Scholar
31. Haratani K, Hayashi H, Chiba Y, et al. Association of immune-related adverse events with nivolumab efficacy in non-small-cell lung cancer. JAMA Oncol 2018;4(3):374–378.
Medline Google Scholar
32. Das S, Johnson DB. Immune-related adverse events and anti-tumor efficacy of immune checkpoint inhibitors. J Immunother Cancer 2019;7(1):306.
Medline Google Scholar
33. Schadendorf D, Wolchok JD, Hodi FS, et al. Efficacy and safety outcomes in patients with advanced melanoma who discontinued treatment with nivolumab and ipilimumab because of adverse events: a pooled analysis of randomized phase II and III trials. J Clin Oncol 2017;35(34):3807–3814.
Medline Google Scholar
34. Ehlerding EB, England CG, McNeel DG, Cai W. Molecular imaging of immunotherapy targets in cancer. J Nucl Med 2016;57(10):1487–1492.
Medline Google Scholar
35. Wolchok JD, Hoos A, O’Day S, et al. Guidelines for the evaluation of immune therapy activity in solid tumors: immune-related response criteria. Clin Cancer Res 2009;15(23):7412–7420.
Medline Google Scholar
36. Seymour L, Bogaerts J, Perrone A, et al. iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics. Lancet Oncol 2017;18(3):e143–e152. [Published correction appears in Lancet Oncol 2019;20(5):e242.]
Medline Google Scholar
37. Le Lay J, Jarraya H, Lebellec L, Penel N. irRECIST and iRECIST: the devil is in the details. Ann Oncol 2017;28(7):1676–1678.
Medline Google Scholar
38. Cheson BD, Ansell S, Schwartz L, et al. Refinement of the Lugano Classification lymphoma response criteria in the era of immunomodulatory therapy. Blood 2016;128(21):2489–2496.
Medline Google Scholar
39. Okada H, Weller M, Huang R, et al. Immunotherapy response assessment in neuro-oncology: a report of the RANO working group. Lancet Oncol 2015;16(15):e534–e542.
Medline Google Scholar
40. Marabelle A, Andtbacka R, Harrington K, et al. Starting the fight in the tumor: expert recommendations for the development of human intratumoral immunotherapy (HIT-IT). Ann Oncol 2018;29(11):2163–2174.
Medline Google Scholar
41. Champiat S, Tselikas L, Farhane S, et al. Intratumoral immunotherapy: from trial design to clinical practice. Clin Cancer Res 2021;27(3):665–679.
Medline Google Scholar
42. Goldmacher GV, Khilnani AD, Andtbacka RHI, et al. Response Criteria for Intratumoral Immunotherapy in Solid Tumors: itRECIST. J Clin Oncol 2020;38(23):2667–2676.
Medline Google Scholar
43. Klemen ND, Wang M, Feingold PL, et al. Patterns of failure after immunotherapy with checkpoint inhibitors predict durable progression-free survival after local therapy for metastatic melanoma. J Immunother Cancer 2019;7(1):196.
Medline Google Scholar
44. Dercle L, McGale J, Sun S, et al. Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy. J Immunother Cancer. 2022;10(9):e005292.
Medline Google Scholar
45. Polverari G, Ceci F, Bertaglia V, et al. ¹⁸F-FDG PET parameters and radiomics features analysis in advanced NSCLC treated with immunotherapy as predictors of therapy response and survival. Cancers (Basel) 2020;12(5):1163.
Medline Google Scholar
46. Dercle L, Fronheiser M, Lu L, et al. Identification of non-small cell lung cancer sensitive to systemic cancer therapies using radiomics. Clin Cancer Res 2020;26(9):2151–2162.
Medline Google Scholar
47. He B, Dong D, She Y, et al. Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker. J Immunother Cancer 2020;8(2):e000550.
Medline Google Scholar
48. Mekki A, Dercle L, Lichtenstein P, et al. Machine learning defined diagnostic criteria for differentiating pituitary metastasis from autoimmune hypophysitis in patients undergoing immune checkpoint blockade therapy. Eur J Cancer 2019;119:44–56.
Medline Google Scholar
49. Sun R, Limkin EJ, Vakalopoulou M, et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol 2018;19(9):1180–1191.
Medline Google Scholar
50. Sun R, Sundahl N, Hecht M, et al. Radiomics to predict outcomes and abscopal response of patients with cancer treated with immunotherapy combined with radiotherapy using a validated signature of CD8 cells. J Immunother Cancer 2020;8(2):e001429.
Medline Google Scholar
51. Eche T, Schwartz LH, Mokrane FZ, Dercle L. Toward generalizability in the deployment of artificial intelligence in radiology: role of computation stress testing to overcome underspecification. Radiol Artif Intell 2021;3(6):e210097.
Google Scholar
52. Roch B, Coffy A, Jean-Baptiste S, et al. Cachexia - sarcopenia as a determinant of disease control rate and survival in non-small lung cancer patients receiving immune-checkpoint inhibitors. Lung Cancer 2020;143:19–26.
Medline Google Scholar
53. Petrova MP, Donev IS, Radanova MA, et al. Sarcopenia and high NLR are associated with the development of hyperprogressive disease after second-line pembrolizumab in patients with non-small-cell lung cancer. Clin Exp Immunol 2020;202(3):353–362.
Medline Google Scholar
54. Popinat G, Cousse S, Goldfarb L, et al. Sub-cutaneous fat mass measured on multislice computed tomography of pretreatment PET/CT is a prognostic factor of stage IV non-small cell lung cancer treated by nivolumab. OncoImmunology 2019;8(5):e1580128.
Medline Google Scholar
55. Sinigaglia M, Assi T, Besson FL, et al. Imaging-guided precision medicine in glioblastoma patients treated with immune checkpoint modulators: research trend and future directions in the field of imaging biomarkers and artificial intelligence. EJNMMI Res 2019;9(1):78.
Medline Google Scholar
56. Chen A, Mokrane FZ, Schwartz LH, et al. Early ¹⁸F-FDG PET/CT response predicts survival in relapsed or refractory Hodgkin lymphoma treated with nivolumab. J Nucl Med 2020;61(5):649–654.
Medline Google Scholar
57. Mokrane FZ, Chen A, Schwartz LH, et al. Performance of CT compared with ¹⁸F-FDG PET in predicting the efficacy of nivolumab in relapsed or refractory Hodgkin lymphoma. Radiology 2020;295(3):651–661.
Medline Google Scholar
58. Lopci E, Hicks RJ, Dimitrakopoulou-Strauss A, et al. Joint EANM/SNMMI/ANZSNM practice guidelines/procedure standards on recommended use of [¹⁸F]FDG PET/CT imaging during immunomodulatory treatments in patients with solid tumors version 1.0. Eur J Nucl Med Mol Imaging 2022;49(7):2323–2341.
Medline Google Scholar
59. Nobashi T, Baratto L, Reddy SA, et al. Predicting response to immunotherapy by evaluating tumors, lymphoid cell-rich organs, and immune-related adverse events using FDG-PET/CT. Clin Nucl Med 2019;44(4):e272–e279.
Medline Google Scholar
60. Wachsmann JW, Ganti R, Peng F. Immune-mediated disease in ipilimumab immunotherapy of melanoma with FDG PET-CT. Acad Radiol 2017;24(1):111–115.
Medline Google Scholar
61. Karantanis D, Bogsrud TV, Wiseman GA, et al. Clinical significance of diffusely increased ¹⁸F-FDG uptake in the thyroid gland. J Nucl Med 2007;48(6):896–901.
Medline Google Scholar
62. Kwak JJ, Tirumani SH, Van den Abbeele AD, Koo PJ, Jacene HA. Cancer immunotherapy: imaging assessment of novel treatment response patterns and immune-related adverse events. RadioGraphics 2015;35(2):424–437.
Google Scholar
63. Eshghi N, Garland LL, Nia E, Betancourt R, Krupinski E, Kuo PH. ¹⁸F-FDG PET/CT can predict development of thyroiditis due to immunotherapy for lung cancer. J Nucl Med Technol 2018;46(3):260–264.
Medline Google Scholar
64. Iravani A, Osman MM, Weppler AM, et al. FDG PET/CT for tumoral and systemic immune response monitoring of advanced melanoma during first-line combination ipilimumab and nivolumab treatment. Eur J Nucl Med Mol Imaging 2020;47(12):2776–2786.
Medline Google Scholar
65. Darnell EP, Mooradian MJ, Baruch EN, Yilmaz M, Reynolds KL. Immune-related adverse events (irAEs): diagnosis, management, and clinical pearls. Curr Oncol Rep 2020;22(4):39.
Medline Google Scholar
66. Seban RD, Champion L, Muneer I, Synn S, Schwartz LH, Dercle L. Potential theranostic role of bone marrow glucose metabolism on baseline ¹⁸F-FDG PET/CT in metastatic melanoma. J Nucl Med 2021;63(1):166.
Medline Google Scholar
67. Seban RD, Moya-Plana A, Antonios L, et al. Prognostic 18F-FDG PET biomarkers in metastatic mucosal and cutaneous melanoma treated with immune checkpoint inhibitors targeting PD-1 and CTLA-4. Eur J Nucl Med Mol Imaging 2020;47(10):2301–2312.
Medline Google Scholar
68. Seban RD, Nemer JS, Marabelle A, et al. Prognostic and theranostic 18F-FDG PET biomarkers for anti-PD1 immunotherapy in metastatic melanoma: association with outcome and transcriptomics. Eur J Nucl Med Mol Imaging 2019;46(11):2298–2310.
Medline Google Scholar
69. Seban RD, Synn S, Muneer I, Champion L, Schwartz LH, Dercle L. Spleen glucose metabolism on [18F]-FDG PET/CT for cancer drug discovery and development cannot be overlooked. Curr Cancer Drug Targets 2021;21(11):944–952.
Medline Google Scholar
70. Seban RD, Champion L, Schwartz LH, Dercle L. Spleen glucose metabolism on [¹⁸F]-FDG PET/CT: a dynamic double-edged biomarker predicting outcome in cancer patients. Eur J Nucl Med Mol Imaging 2021;48(8):2309–2311.
Medline Google Scholar
71. Bensch F, van der Veen EL, Lub-de Hooge MN, et al. ⁸⁹Zr-atezolizumab imaging as a non-invasive approach to assess clinical response to PD-L1 blockade in cancer. Nat Med 2018;24(12):1852–1858.
Medline Google Scholar
72. Farwell MD, Gamache RF, Babazada H, et al. CD8-targeted PET imaging of tumor-infiltrating T Cells in patients with cancer: a phase I first-in-humans study of ⁸⁹Zr-Df-IAB22M2C, a radiolabeled anti-CD8 minibody. J Nucl Med 2022;63(5):720–726.
Medline Google Scholar
73. Pandit-Taskar N, Postow MA, Hellmann MD, et al. First-in-humans imaging with ⁸⁹Zr-Df-IAB22M2C anti-CD8 minibody in patients with solid malignancies: preliminary pharmacokinetics, biodistribution, and lesion targeting. J Nucl Med 2020;61(4):512–519.
Medline Google Scholar
74. Namavari M, Chang YF, Kusler B, Yaghoubi S, Mitchell BS, Gambhir SS. Synthesis of 2´-deoxy-2´-[18F]fluoro-9-β-D-arabinofuranosylguanine: a novel agent for imaging T-cell activation with PET. Mol Imaging Biol 2011;13(5):812–818.
Medline Google Scholar
75. Dercle L, Ammari S, Champiat S, et al. Rapid and objective CT scan prognostic scoring identifies metastatic patients with long-term clinical benefit on anti-PD-1/-L1 therapy. Eur J Cancer 2016;65:33–42.
Medline Google Scholar
76. Mezquita L, Auclin E, Ferrara R, et al. Association of the Lung Immune Prognostic Index with immune checkpoint inhibitor outcomes in patients with advanced non-small cell lung cancer. JAMA Oncol 2018;4(3):351–357.
Medline Google Scholar
77. Castello A, Toschi L, Rossi S, Mazziotti E, Lopci E. The immune-metabolic-prognostic index and clinical outcomes in patients with non-small cell lung carcinoma under checkpoint inhibitors. J Cancer Res Clin Oncol 2020;146(5):1235–1243.
Medline Google Scholar
78. Oxnard GR, Morris MJ, Hodi FS, et al. When progressive disease does not mean treatment failure: reconsidering the criteria for progression. J Natl Cancer Inst 2012;104(20):1534–1541.
Medline Google Scholar

Article History

Received: Mar 11 2021
Revision requested: May 25 2021
Revision received: Aug 12 2022
Accepted: Aug 25 2022
Published online: Dec 06 2022
Published in print: Jan 2023

Vol. 306, No. 1

Supplemental Material

Abbreviations

Abbreviations:
AI	artificial intelligence
¹⁸F-FDG	fluorine 18 fluorodeoxyglucose
irAE	immune-related adverse event
PD-1	programmed cell death protein 1
RECIST	Response Evaluation Criteria in Solid Tumors
TGR	tumor growth rate

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