Strategies for Offline Adaptive Biology-Guided Radiotherapy (BgRT) on a PET-Linac Platform

作者：Bin Cai, T. Banks, Chenyang Shen, Rameshwar Prasad, Girish Bal, Mu‐Han Lin, Andrew Godley, Arnold Pompoš, Aurélie Garant, Kenneth D. Westover, Tu Dan, Steve Jiang, David J. Sher, Orhan K. Öz, Robert Timmerman, Shahed N. Badiyan · 发表于：Cancers · 年份：2025 · DOI：10.3390/cancers17152470 · 被引用次数：7 · 研究领域：Medical Imaging Techniques and Applications、Advanced Radiotherapy Techniques、Radiomics and Machine Learning in Medical Imaging

Background/Objectives: This study aims to present a structured clinical workflow for offline adaptive Biology-guided Radiotherapy (BgRT) using the RefleXion X1 PET-linac system, addressing challenges introduced by inter-treatment anatomical and biological changes. Methods: We propose a decision tree offline adaptation framework based on real-time assessments of Activity Concentration (AC), Normalized Target Signal (NTS), and bounded dose-volume histogram (bDVH%) metrics. Three offline strategies were developed: (1) preemptive adaptation for minor changes, (2) partial re-simulation for moderate changes, and (3) full re-simulation for major anatomical or metabolic alterations. Two clinical cases demonstrating strategies 1 and 2 are presented. Results: The preemptive adaptation strategy was applied in a case with early tumor shrinkage, maintaining delivery parameters within acceptable limits while updating contours and dose distribution. In the partial re-Simulation case, significant changes in PET signal necessitated a same-day PET functional modeling session and plan re-optimization, effectively restoring safe deliverability. Both cases showed reduced target volumes and improved OAR sparing without additional patient visits or tracer injections. Conclusions: Offline adaptive workflows for BgRT provide practical solutions to address inter-fractional changes in tumor structure and function. These strategies can help maintain the safety and accuracy of BgRT delivery and support c...