UPMA: unspecified pretraining and modular adaptation for context-aware target delineation in adaptive esophageal radiotherapy

Abstract

Abstract Esophageal cancer, a serious malignancy, is primarily treated with radiotherapy (RT). Accurate delineation of gross tumor volume (GTV) and clinical target volume (CTV) is essential, yet manual contouring is time-consuming and automatic methods remain challenging. Adaptive radiotherapy enables personalized target adjustments, but tumor changes following initial RT complicate delineation, and automatic second-phase replanning is underexplored. We propose the Unspecified Pretraining and Modular Adaptation (UPMA) framework for context-aware second-phase esophageal target delineation. UPMA pretrains its network on diverse datasets to comprehend tumor and anatomical features, then modularly adapts it for second-phase tasks with minimal parameters. The method improves Dice scores by over 23% for GTV and over 5% for CTV, while reducing learnable parameters by over 73% during adaptation. In clinical evaluations, 73.7% and 52.6% of the predicted GTVs and CTVs required only minor edits. UPMA generalizes across diverse patient characteristics and imaging factors, supporting automated adaptive radiotherapy for precise, personalized treatment.