The distribution of volume doubling times of cancers diagnosed in annual rounds of CT screening is not significantly different from that previously reported in an earlier study for all cancers (P = .51) or for non–small cell cancers only (P = .69).
To empirically address the distribution of the volume doubling time (VDT) of lung cancers diagnosed in repeat annual rounds of computed tomographic (CT) screening in the International Early Lung Cancer Action Program (I-ELCAP), first and foremost with respect to rates of tumor growth but also in terms of cell types.
Materials and Methods
All CT screenings in I-ELCAP from 1993 to 2009 were performed according to HIPAA-compliant protocols approved by the institutional review boards of the collaborating institutions. All instances of first diagnosis of primary lung cancer after a negative screening result 7–18 months earlier were identified, with symptom-prompted diagnoses included. Lesion diameter was calculated by using the measured length and width of each cancer at the time when the nodule was first identified for further work-up and at the time of the most recent prior screening, 7–18 months earlier. The length and width were measured a second time for each cancer, and the geometric mean of the two calculated diameters was used to calculate the VDT. The χ2 statistic was used to compare the VDT distributions.
The median VDT for 111 cancers was 98 days (interquartile range, 108). For 56 (50%) cancers it was less than 100 days, and for three (3%) cancers it was more than 400 days. Adenocarcinoma was the most frequent cell type (50%), followed by squamous cell carcinoma (19%), small cell carcinoma (19%), and others (12%). Lung cancers manifesting as subsolid nodules had significantly longer VDTs than those manifesting as solid nodules (P < .0001).
Lung cancers diagnosed in annual repeat rounds of CT screening, as manifest by the VDT and cell-type distributions, are similar to those diagnosed in the absence of screening.
© RSNA, 2012
- 1 . Computed tomography screening for lung cancer: review of screening principles and update on current status. Cancer 2007;110(11):2370–2384. Crossref, Medline, Google Scholar
- 2 . Overdiagnosis in lung cancer: different perspectives, definitions, implications. Thorax 2008;63(4):298–300. Crossref, Medline, Google Scholar
- 3 ; American College of Chest Physicians. Screening for lung cancer: ACCP evidence-based clinical practice guidelines (2nd edition). Chest 2007;132(3 Suppl):69S–77S. Crossref, Medline, Google Scholar
- 4 . Statistical models of disease natural history: their use in the evaluation of screening programmes. In: Prorok PCMiller AB, eds. Screening for cancer. I. General on evaluation of screening for cancer and screening for lung, bladder, and oral cancer. UICC Technical Report Series No 78. Geneva, Switzerland: Union for International Cancer Control, 1984; 55–70. Google Scholar
- 5 . Screening in chronic disease. New York, NY: Oxford University Press, 1992. Google Scholar
- 6 . Breast screening, prognostic factors and survival: results from the Swedish two county study. Br J Cancer 1991;64(6):1133–1138. Crossref, Medline, Google Scholar
- 7 . Comparison of pathologic findings of baseline and annual repeat cancers diagnosed on CT screening. Lung Cancer 2007;56(2):193–199. Crossref, Medline, Google Scholar
- 8 : enrollment and screening protocol. http://www.ielcap.org/professionals/docs/ielcap.pdf. Published July 1, 2011. Accessed February 21, 2012. Google Scholar
- 9 . CT screening for lung cancer: frequency and significance of part-solid and nonsolid nodules. AJR Am J Roentgenol 2002;178(5):1053–1057. Crossref, Medline, Google Scholar
- 10 . A biomathematical approach to clinical tumor growth. Cancer 1961;14:1272–1294. Crossref, Medline, Google Scholar
- 11 . Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation. Radiology 2000;217(1):251–256. Link, Google Scholar
- 12 . Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Trans Med Imaging 2003;22(10):1259–1274. Crossref, Medline, Google Scholar
- 13 . Small pulmonary nodules: reproducibility of three-dimensional volumetric measurement and estimation of time to follow-up CT. Radiology 2004;231(2):446–452. Link, Google Scholar
- 14 . On measuring the change in size of pulmonary nodules. IEEE Trans Med Imaging 2006;25(4):435–450. Crossref, Medline, Google Scholar
- 15 . Growth rate of small lung cancers detected on mass CT screening. Br J Radiol 2000;73(876):1252–1259. Crossref, Medline, Google Scholar
- 16 . Early lung cancer action project pathology protocol. Lung Cancer 2003;39(2):231–232. Crossref, Medline, Google Scholar
- 17 . International Early Lung Cancer Action Program: pathology protocol. http://www.ielcap.org/professionals/docs/pathology_protocol.pdf. Published March 1, 2011. Accessed February 21, 2012. Google Scholar
- 18 . TNM classification of malignant tumours. 6th ed. New York, NY: Wiley-Liss, 2002. Google Scholar
- 19 ; American College of Chest Physicians. The solitary pulmonary nodule. Chest 2003;123(1 Suppl):89S–96S. Crossref, Medline, Google Scholar
- 20 . Turning gray: the natural history of lung cancer over time. J Thorac Oncol 2008;3(7):781–792. Crossref, Medline, Google Scholar
- 21 ; American College of Chest Physicians. Evaluation of patients with pulmonary nodules: when is it lung cancer? ACCP evidence-based clinical practice guidelines (2nd edition). Chest 2007;132(3 Suppl):108S–130S. Crossref, Medline, Google Scholar
- 22 . Lung cancer. Radiol Clin North Am 2007;45(1):21–43. Crossref, Medline, Google Scholar
- 23 . Is our natural-history model of lung cancer wrong? Lancet Oncol 2008;9(7):693–697. Crossref, Medline, Google Scholar
Article HistoryReceived February 10, 2011; revision requested March 30; revision received December 6; accepted December 13; final version accepted January 13, 2012.
Published online: May 2012
Published in print: May 2012