Deterministic small-scale undulations of image-based risk predictions from the deep learning of lung tumors in motion.
Journal:
Medical physics
Published Date:
Sep 23, 2022
Abstract
INTRODUCTION: 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. We examine the impact of tumor motion on an image-based DL framework that predicts local failure risk after lung stereotactic body radiotherapy (SBRT).
