AI可精准识别前列腺放疗中每日影像的细微变化，提升治疗监测能力。
AI-Based Detection of Temporal Changes in MR-Linac Images Acquired During Routine Prostate Radiotherapy
- 用配对比较的深度学习模型分析纵向MRI序列，捕捉时间演变特征。
- 模型AUC达0.99，准确率95%，优于放射科医生判断。
- 可定位前列腺、膀胱等关键区域变化，适用于实时治疗监控。
目的：探究基于AI的方法是否能检测在前列腺放疗过程中获取的MR-Linac影像中的微小分次间变化，并探索MR-Linac成像的更广泛应用潜力。方法：本回顾性研究纳入761名患者的纵向0.35T MR-Linac影像。采用一种通过成对比较实现时间排序的深度学习模型，该方法此前已被证明在纵向影像研究中有效。模型使用首末分次对（F1-FL）和所有分次对（All-pairs）进行训练。性能通过定量指标（准确率与AUC）评估，并与放射科医生表现对比。定性分析使用显著性图，识别与时间变化相关的解剖区域。结果：F1-FL模型表现优异（AUC=0.99，准确率=0.95），在时间排序任务中超越放射科医生。All-pairs模型同样表现良好（AUC=0.97，准确率=0.91）。贡献预测的关键区域包括前列腺、膀胱和耻骨联合。性能与分次间隔相关，非照射时段（模拟和首分次）的性能下降，提示观察到的变化可能同时反映时间演变与辐射暴露效应。结论：MR-Linac成像似乎能够捕捉前列腺放疗期间的细微变化，即使在约两天的间隔内也可被AI模型识别。模型的高性能及量化与定性分析支持其在临床应用中超越图像引导的潜在价值。
原文摘要 · Abstract (English)
Purpose: To investigate whether an AI-based method can detect subtle inter-fraction changes in MR-Linac images acquired during radiotherapy and explore the broader potential of MRLinac imaging. Methods: This retrospective study included longitudinal 0.35T MR-Linac images from 761 patients. To identify temporal changes, we employed a deep learning model using temporal ordering via pairwise comparison, previously shown effective for longitudinal imaging studies. The model was trained using first-to-last fraction pairs (F1-FL) and all pairs (All-pairs). Performance was assessed using quantitative metrics (accuracy and AUC) and compared against a radiologist's performance. Qualitative evaluation was performed using saliency maps, which identify anatomical regions associated with temporal imaging changes. Results: The F1-FL model demonstrated high performance (AUC=0.99, accuracy=0.95) and outperformed the radiologist in temporal ordering task. The All-pairs model also showed high performance (AUC=0.97, accuracy=0.91). Regions contributing to predictions included the prostate, bladder, and pubic symphysis. The performance was correlated to fractional intervals and was reduced for non-radiation-exposed timepoints (Sim and F1), suggesting that observed changes may reflect both temporal variation and radiation exposure. Conclusion: MR-Linac imaging appears capable of capturing subtle changes during prostate radiotherapy that can be detected by AI models, even over approximately two-day intervals. The model's high performance, together with quantitative and qualitative analyses, supports a potential role for MR-Linac in clinical applications beyond image guidance.
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