Prediction of tumor doubling time of lung adenocarcinoma using radiomic margin characteristics

Abstract

Background Because shape or irregularity along the tumor perimeter can result from interactions between the tumor and the surrounding parenchyma, there could be a difference in tumor growth rate according to tumor margin or shape. However, no attempt has been made to evaluate the correlation between margin or shape features and tumor growth. Methods We evaluated 52 lung adenocarcinoma (ADC) patients who had at least two computed tomographic (CT) examinations before curative resection. Volume-based doubling times (DTs) were calculated based on CT scans, and patients were divided into two groups according to the growth pattern (GP) of their ADCs (gradually growing tumors [GP I] vs. growing tumors with a temporary decrease in DT [GP II]). CT radiomic features reflecting margin characteristics were extracted, and radiomic features reflective of tumor DT were selected. Results Among the 52 patients, 41 (78.8%) were assigned to GP I and 11 (21.2%) to GP II. Of the 94 radiomic features extracted, eccentricity, surface-to-volume ratio, LoG uniformity (sigma = 3.5), and LoG skewness (sigma = 0.5) were ultimately selected for tumor DT prediction. Selected radiomic features in GP I were surface-to-volume ratio, contrast, LoG uniformity (sigma = 3.5), and LoG skewness (sigma = 0.5), similar to those for total subjects, whereas the radiomic features in GP II were solidity, energy, and busyness. Conclusions This study demonstrated the potential of margin-related radiomic features to predict tumor DT in lung ADCs. Key pointsSignificant findings of the study We found a relationship between margin-related radiomic features and tumor doubling time. What this study adds Margin-related radiomic features can potentially be used as noninvasive biomarkers to predict tumor doubling time in lung adenocarcinoma and inform treatment strategies.