Physics and imaging in radiation oncology · 2026
AI-based detection of temporal changes in MR-Linac images acquired during routine prostate radiotherapy.
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Park Seungbin, Wang Peilin, Paik Joshua, Pennell Ryan, Khani Francesca, Nagar Himanshu, McClure Timothy, Weg Emily S, Sabuncu Mert R, Margolis Daniel, Kim Heejong
Аннотация
BACKGROUND AND 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 MR-Linac imaging.
MATERIAL AND 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 ( F 1 - F L ) 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 F 1 - F L 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). The performance was correlated to fractional intervals and was reduced for non-radiation-exposed timepoints ( S i m and F 1 ). Patients who later developed biochemical recurrence exhibited smaller longitudinal changes in model logits during radiotherapy. Regions contributing to predictions included the prostate, bladder, and pubic symphysis.
CONCLUSIONS: These findings demonstrate the feasibility of detecting subtle inter-fractional changes over short periods (two days on average) in routine MR-Linac images during prostate radiotherapy and contribute to the development of imaging biomarkers for treatment response.
MATERIAL AND 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 ( F 1 - F L ) 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 F 1 - F L 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). The performance was correlated to fractional intervals and was reduced for non-radiation-exposed timepoints ( S i m and F 1 ). Patients who later developed biochemical recurrence exhibited smaller longitudinal changes in model logits during radiotherapy. Regions contributing to predictions included the prostate, bladder, and pubic symphysis.
CONCLUSIONS: These findings demonstrate the feasibility of detecting subtle inter-fractional changes over short periods (two days on average) in routine MR-Linac images during prostate radiotherapy and contribute to the development of imaging biomarkers for treatment response.