Robust image-based risk predictions from the deep learning of lung tumors in motion

Abstract

Deep learning (DL) models that use medical images to predict clinical outcomes are poised for clinical translation. For tumors that reside in organs that move, however, the impact of motion (i.e. degenerated object appearance or blur) on DL model accuracy remains unclear. Here we examine the impact of tumor motion on an image-based DL framework that predicts local failure risk for patients with lung cancer receiving stereotactic body radiotherapy. We show that an image-based DL risk score derived from a series of four-dimensional CT images varies in a deterministic, sinusoidal trajectory in phase with the respiratory cycle. Critically, the mean of the scores derived from time series of images and the score obtained from free breathing scans (average tumor position) were highly associated (Pearson r = 0.99). These results indicate that deep learning models of tumors in motion can be robust to fluctuations in object appearance due to movement.