Deep learning-based prediction of interfractional anatomic variations in prostate cancer radiotherapy

Key Points

• Prediction of interfractional anatomic variations enhances the precision of prostate cancer radiotherapy, leading to better outcomes.
• Deep learning algorithms achieved high accuracy, notably improving treatment plans within clinical settings.
• Analysis focused on anatomic changes during radiotherapy sessions to refine patient-specific treatment approaches.
• Highlighting the importance of adapting to anatomical changes may improve overall efficacy of prostate cancer treatments.