Prediction of tumor volumes using an exponential model

Artit C. Jirapatnakul; Anthony P. Reeves; Tatiyana V. Apanasovich; Matthew D. Cham M.D.; David F. Yankelevitz M.D.; Claudia I. Henschke M.D.

doi:10.1117/12.710371

30 March 2007 Prediction of tumor volumes using an exponential model

Artit C. Jirapatnakul, Anthony P. Reeves, Tatiyana V. Apanasovich, Matthew D. Cham M.D., David F. Yankelevitz M.D., Claudia I. Henschke M.D.

Author Affiliations +

Proceedings Volume 6514, Medical Imaging 2007: Computer-Aided Diagnosis; 65143A (2007) https://doi.org/10.1117/12.710371
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

Measurement of pulmonary nodule growth rate is important for the evaluation of lung cancer treatment. The change in nodule growth rate can be used as an indicator of the efficacy of a prescribed treatment. However, a change in growth rate may be due to actual physiological change, or it may be simply due to measurement error. To address this issue, we propose the use of an exponential model to predict the volume of a tumor based on two earlier scans. We examined 11 lung cancers presenting as solid pulmonary nodules that were not treated. Using 5 of these with optimal scan parameters, thin-slice (1.0mm or 1.25mm) with same axial resolution, we found an error ranging from 1.7% to 27.7%, with an average error of 14.9%. This indicates that we can estimate the growth of a lung cancer, as measured by CT, which includes the actual growth as well as the error due to the technique, by the amount indicated above. Using scans with non-optimal parameters, either thick-slice or different resolution thin-slice scans, resulted in errors ranging from 30% to 600%, suggesting that same resolution thin-slice CT scans are necessary for accurate measurement of nodule growth.

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Artit C. Jirapatnakul, Anthony P. Reeves, Tatiyana V. Apanasovich, Matthew D. Cham M.D., David F. Yankelevitz M.D., and Claudia I. Henschke M.D. "Prediction of tumor volumes using an exponential model", Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65143A (30 March 2007); https://doi.org/10.1117/12.710371

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