"skipMainNavigation"
closeDrawerMenuopenDrawerMenu
Search
Quick Search in Journals
Quick Search anywhere
Advanced Search
Radiology
Menu
  • Information
    • Previous
    Next
    Original Research

    Differentiation of Malignant and Benign Pulmonary Nodules with Quantitative First-Pass 320–Detector Row Perfusion CT versus FDG PET/CT

    Published Online:Feb 1 2011https://doi.org/10.1148/radiol.10100245

    Abstract

    Dynamic first-pass area-detector perfusion CT has the potential to be more specific and accurate than integrated fluorine 18 fluorodeoxyglucose PET/CT for differentiation of malignant and benign pulmonary nodules.

    Purpose

    To prospectively compare the capability of quantitative first-pass perfusion 320–detector row computed tomography (CT) (ie, area-detector CT) with that of combined positron emission tomography and CT (PET/CT) for differentiation between malignant and benign pulmonary nodules.

    Materials and Methods

    This prospective study was approved by the institutional review board, and written informed consent was obtained from 50 consecutive patients with 76 pulmonary nodules. All patients underwent dynamic area-detector CT, PET/CT, and microbacterial and/or histopathologic examinations. All pulmonary nodules were divided into three groups: malignant nodules (n = 43), benign nodules with low biologic activity (n = 6), and benign nodules with high biologic activity (n = 27). For each dynamic area-detector CT data set, the perfusion derived by using the maximum slope model (PFMS), extraction fraction derived by using the Patlak plot model (EFPP), and blood volume derived by using the Patlak plot model (BVPP) were calculated. These parameters were statistically compared among the three nodule groups. Receiver operating characteristic (ROC) analyses were used to compare the diagnostic capability of the CT and PET/CT indexes. Finally, the sensitivity, specificity, and accuracy of each index were compared by using the McNemar test.

    Results

    All indexes in the malignant nodule group were significantly different from those in the low-biologic-activity benign nodule group (P < .05). Areas under the ROC curve for PFMS and EFPP were significantly larger than those for BVPP (P < .05) and maximal standard uptake value (SUVmax) (P < .05). The specificity and accuracy of PFMS and EFPP were significantly higher than those of BVPP and SUVmax (P < .05).

    Conclusion

    Dynamic first-pass area-detector perfusion CT has the potential to be more specific and accurate than PET/CT for differentiating malignant from benign pulmonary nodules.

    © RSNA, 2011

    Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.10100245/-/DC1

    References

    • 1 Henschke CI, McCauley DI, Yankelevitz DF, et al.. Early Lung Cancer Action Project: overall design and findings from baseline screening. Lancet 1999;354(9173):99–105. Crossref, Medline, Google Scholar
    • 2 Swensen SJ, Jett JR, Hartman TE, et al.. CT screening for lung cancer: five-year prospective experience. Radiology 2005;235(1):259–265. Link, Google Scholar
    • 3 Patz EF, Lowe VJ, Hoffman JM, et al.. Focal pulmonary abnormalities: evaluation with F-18 fluorodeoxyglucose PET scanning. Radiology 1993;188(2):487–490. Link, Google Scholar
    • 4 Dewan NA, Gupta NC, Redepenning LS, Phalen JJ, Frick MP. Diagnostic efficacy of PET-FDG imaging in solitary pulmonary nodules: potential role in evaluation and management. Chest 1993;104(4):997–1002. Crossref, Medline, Google Scholar
    • 5 Gückel C, Schnabel K, Deimling M, Steinbrich W. Solitary pulmonary nodules: MR evaluation of enhancement patterns with contrast-enhanced dynamic snapshot gradient-echo imaging. Radiology 1996;200(3):681–686. Link, Google Scholar
    • 6 Swensen SJ, Viggiano RW, Midthun DE, et al.. Lung nodule enhancement at CT: multicenter study. Radiology 2000;214(1):73–80. Link, Google Scholar
    • 7 Tateishi U, Nishihara H, Tsukamoto E, Morikawa T, Tamaki N, Miyasaka K. Lung tumors evaluated with FDG-PET and dynamic CT: the relationship between vascular density and glucose metabolism. J Comput Assist Tomogr 2002;26(2):185–190. Crossref, Medline, Google Scholar
    • 8 Ohno Y, Hatabu H, Takenaka D, Adachi S, Kono M, Sugimura K. Solitary pulmonary nodules: potential role of dynamic MR imaging in management—initial experience. Radiology 2002;224(2):503–511. Link, Google Scholar
    • 9 Schaefer JF, Vollmar J, Schick F, et al.. Solitary pulmonary nodules: dynamic contrast-enhanced MR imaging—perfusion differences in malignant and benign lesions. Radiology 2004;232(2):544–553. Link, Google Scholar
    • 10 Ohno Y, Hatabu H, Takenaka D, et al.. Dynamic MR imaging: value of differentiating subtypes of peripheral small adenocarcinoma of the lung. Eur J Radiol 2004;52(2):144–150. Crossref, Medline, Google Scholar
    • 11 Jeong YJ, Lee KS, Jeong SY, et al.. Solitary pulmonary nodule: characterization with combined wash-in and washout features at dynamic multi–detector row CT. Radiology 2005;237(2):675–683. Link, Google Scholar
    • 12 Bryant AS, Cerfolio RJ. The maximum standardized uptake values on integrated FDG-PET/CT is useful in differentiating benign from malignant pulmonary nodules. Ann Thorac Surg 2006;82(3):1016–1020. Crossref, Medline, Google Scholar
    • 13 Cronin P, Dwamena BA, Kelly AM, Carlos RC. Solitary pulmonary nodules: meta-analytic comparison of cross-sectional imaging modalities for diagnosis of malignancy. Radiology 2008;246(3):772–782. Link, Google Scholar
    • 14 Ohno Y, Koyama H, Takenaka D, et al.. Dynamic MRI, dynamic multidetector-row computed tomography (MDCT), and coregistered 2-[fluorine-18]-fluoro-2-deoxy-D-glucose-positron emission tomography (FDG-PET)/CT: comparative study of capability for management of pulmonary nodules. J Magn Reson Imaging 2008;27(6):1284–1295. Crossref, Medline, Google Scholar
    • 15 Funabashi N, Yoshida K, Tadokoro H, et al.. Cardiovascular circulation and hepatic perfusion of pigs in 4-dimensional films evaluated by 256-slice cone-beam computed tomography. Circ J 2005;69(5):585–589. Crossref, Medline, Google Scholar
    • 16 Rybicki FJ, Otero HJ, Steigner ML, et al.. Initial evaluation of coronary images from 320-detector row computed tomography. Int J Cardiovasc Imaging 2008;24(5):535–546. Crossref, Medline, Google Scholar
    • 17 Murayama K, Katada K, Nakane M, et al.. Whole-brain perfusion CT performed with a prototype 256-detector row CT system: initial experience. Radiology 2009;250(1):202–211. Link, Google Scholar
    • 18 Kandel S, Kloeters C, Meyer H, Hein P, Hilbig A, Rogalla P. Whole-organ perfusion of the pancreas using dynamic volume CT in patients with primary pancreas carcinoma: acquisition technique, post-processing and initial results. Eur Radiol 2009;19(11):2641–2646. Crossref, Medline, Google Scholar
    • 19 Hoffman EA, Tajik JK, Kugelmass SD. Matching pulmonary structure and perfusion via combined dynamic multislice CT and thin-slice high-resolution CT. Comput Med Imaging Graph 1995;19(1):101–112. Crossref, Medline, Google Scholar
    • 20 Kiessling F, Boese J, Corvinus C, et al.. Perfusion CT in patients with advanced bronchial carcinomas: a novel chance for characterization and treatment monitoring? Eur Radiol 2004;14(7):1226–1233. Crossref, Medline, Google Scholar
    • 21 Ng QS, Goh V, Fichte H, et al.. Lung cancer perfusion at multi-detector row CT: reproducibility of whole tumor quantitative measurements. Radiology 2006;239(2):547–553. Link, Google Scholar
    • 22 Ng QS, Goh V, Milner J, Padhani AR, Saunders MI, Hoskin PJ. Acute tumor vascular effects following fractionated radiotherapy in human lung cancer: in vivo whole tumor assessment using volumetric perfusion computed tomography. Int J Radiat Oncol Biol Phys 2007;67(2):417–424. Crossref, Medline, Google Scholar
    • 23 Sitartchouk I, Roberts HC, Pereira AM, Bayanati H, Waddell T, Roberts TP. Computed tomography perfusion using first pass methods for lung nodule characterization. Invest Radiol 2008;43(6):349–358. Crossref, Medline, Google Scholar
    • 24 Nakayama Y, Awai K, Funama Y, et al.. Abdominal CT with low tube voltage: preliminary observations about radiation dose, contrast enhancement, image quality, and noise. Radiology 2005;237(3):945–951. Link, Google Scholar
    • 25 Yamao Y, Yamakado K, Takaki H, et al.. Optimal scan parameters for CT fluoroscopy in lung interventional radiologic procedures: relationship between radiation dose and image quality. Radiology 2010;255(1):233–241. Link, Google Scholar
    • 26 Patlak CS, Blasberg RG, Fenstermacher JD. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J Cereb Blood Flow Metab 1983;3(1):1–7. Crossref, Medline, Google Scholar
    • 27 Tsushima Y, Funabasama S, Sanada S, Aoki J, Endo K. Development of perfusion CT software for personal computers. Acad Radiol 2002;9(8):922–926. Crossref, Medline, Google Scholar
    • 28 Groves AM, Goh V, Rajasekharan S, et al.. CT coronary angiography: quantitative assessment of myocardial perfusion using test bolus data—initial experience. Eur Radiol 2008;18(10):2155–2163. Crossref, Medline, Google Scholar
    • 29 Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1(8476):307–310. Crossref, Medline, Google Scholar
    • 30 Ohno Y, Murase K, Higashino T, et al.. Assessment of bolus injection protocol with appropriate concentration for quantitative assessment of pulmonary perfusion by dynamic contrast-enhanced MR imaging. J Magn Reson Imaging 2007;25(1):55–65. Crossref, Medline, Google Scholar
    • 31 Miles KA, Kelley BB. CT measurements of capillary permeability within nodal masses: a potential technique for assessing the activity of lymphoma. Br J Radiol 1997;70:74–79. Crossref, Medline, Google Scholar
    • 32 Miles KA, Leggett DA, Bennett GA. CT derived Patlak images of the human kidney. Br J Radiol 1999;72(854):153–158. Crossref, Medline, Google Scholar
    • 33 Littleton JT, Durizch ML, Moeller G, Herbert DE. Pulmonary masses: contrast enhancement. Radiology 1990;177(3):861–871. Link, Google Scholar
    • 34 Cotran RSKumar VCollins T, eds. Acute and chronic inflammation. In: Robbins pathologic basis of disease. 6th ed. Philadelphia, Pa: Saunders, 1999; 50–88. Google Scholar
    • 35 Levitzky MG. Blood flow to the lung. In: Pulmonary physiology. 5th ed. New York, NY: McGraw-Hill, 1999; 85–111. Google Scholar

    Article History

    Received February 1, 2010; revision requested April 2; revision received July 7; accepted August 4; final version accepted September 13.
    Published online: Feb 2011
    Published in print: Feb 2011