Abstract
We evaluate radiomic phenotypes derived from CT scans as early predictors of overall survival (OS) after chemoradiation in stage III primary lung adenocarcinoma. We retrospectively analyzed 110 thoracic CT scans acquired between April 2012-October 2018. Patients received a median radiation dose of 66.6 Gy at 1.8 Gy/fraction delivered with proton (55.5%) and photon (44.5%) beam treatment, as well as concurrent chemotherapy (89%) with carboplatin-based (55.5%) and cisplatin-based (36.4%) doublets. A total of 56 death events were recorded. Using manual tumor segmentations, 107 radiomic features were extracted. Feature harmonization using ComBat was performed to mitigate image heterogeneity due to the presence or lack of intravenous contrast material and variability in CT scanner vendors. A binary radiomic phenotype to predict OS was derived through the unsupervised hierarchical clustering of the first principal components explaining 85% of the variance of the radiomic features. C-scores and likelihood ratio tests (LRT) were used to compare the performance of a baseline Cox model based on ECOG status and age, with a model integrating the radiomic phenotype with such clinical predictors. The model integrating the radiomic phenotype (C-score = 0.69, 95% CI = (0.62, 0.77)) significantly improved (p<0.005) upon the baseline model (C-score = 0.65, CI = (0.57, 0.73)). Our results suggest that harmonized radiomic phenotypes can significantly improve OS prediction in stage III NSCLC after chemoradiation.
Highlights
Lung cancer is the most common cause of cancer-related deaths in the United States [1], with over 135,000 lung cancer-related deaths estimated in 2020 [2]
We investigate the association between overall survival (OS) and radiomic features extracted from chest CT scans of a relatively homogeneous cohort of stage III Non-small cell lung cancer (NSCLC) adenocarcinoma patients at our institution using a predictive pipeline that explicitly accounts for image heterogeneity due to image acquisition protocols, vendors and intravenous contrast material
We report our findings of the use of radiomic features to predict OS using pre-treatment chest CT scans from a relatively homogeneous cohort of stage III NSCLC adenocarcinoma patients treated with definitive chemoradiotherapy at our institution
Summary
Lung cancer is the most common cause of cancer-related deaths in the United States [1], with over 135,000 lung cancer-related deaths estimated in 2020 [2]. Non-small cell lung cancer (NSCLC) represents over 80% of lung cancer cases [3], with the most common subtype being adenocarcinoma [4]. Numerous additional subtypes have been identified as novel mutation and biomarker testing has become available, allowing personalization of treatment options [5]. Data supporting current treatment approaches are based on heterogeneous populations with limited follow-up duration and inadequate statistical power [20,21]. We propose a machine learning approach based on imaging phenotypes aimed at predicting the overall survival (OS) of stage III NSCLC adenocarcinoma patients receiving chemoradiation, to establish novel and relevant predictive biomarkers of treatment response with potential applications in lung cancer management
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