Predicting disease recurrence following trimodality therapy in non-small cell lung cancer using computed tomography derived radiomic features and clinico-pathologic features

Abstract

Embodiments include operations, apparatus, methods and other embodiments that access a baseline CT image of a region of tissue (ROT) demonstrating non-small cell lung cancer (NSCLC), segment a tumoral region represented in the baseline CT image; define a peritumoral region by dilating the tumoral boundary; extract a set of tumoral radiomic features from the tumoral region, a set of peritumoral radiomic features from the peritumoral region, and a set of clinico-pathologic features from the baseline CT image; provide the set of tumoral radiomic features, peritumoral radiomic features, and clinico-pathologic features to a machine learning classifier; receive, from the machine learning classifier, a time-to-recurrence post trimodality therapy (TMT) prediction, based on the set of tumoral radiomic features, peritumoral radiomic features, and clinico-pathologic features; generate a classification of the ROT as an MPR responder or MPR non-responder based, at least in part, on the time-to-recurrence post-TMT prediction; and display the classification.