Pretreatment CT and PET radiomics predicting rectal cancer patients in response to neoadjuvant chemoradiotherapy

Abstract

background: The purpose of this study was to characterize pre-treatment non-contrast computed tomography (cT) and 18F-fluorodeoxyglucose positron emission tomography (peT) based radiomics signatures predictive of pathological response and clinical outcomes in rectal cancer patients treated with neoadjuvant chemoradiotherapy (NacrT). Materials and methods: an exploratory analysis was performed using pre-treatment non-contrast cT and peT imaging dataset. The association of tumor regression grade (TrG) and neoadjuvant rectal (Nar) score with pre-treatment cT and peT features was assessed using machine learning algorithms. Three separate predictive models were built for composite features from cT + peT. results: The patterns of pathological response were TrG 0 (n = 13; 19.7%), 1 (n = 34; 51.5%), 2 (n = 16; 24.2%), and 3 (n = 3; 4.5%). There were 20 (30.3%) patients with low, 22 (33.3%) with intermediate and 24 (36.4%) with high Nar scores. Three separate predictive models were built for composite features from cT + peT and analyzed separately for clinical endpoints. composite features with α = 0.2 resulted in the best predictive power using logistic regression. For pathological response prediction, the signature resulted in 88.1% accuracy in predicting TrG 0 vs. TrG 1-3; 91% accuracy in predicting TrG 0-1 vs. TrG 2-3. For the surrogate of DFs and Os, it resulted in 67.7% accuracy in predicting low vs. intermediate vs. high Nar scores. conclusion: The pre-treatment composite radiomics signatures were highly predictive of pathological response in rectal cancer treated with NacrT. a larger cohort is warranted for further validation.