Instant plan quality prediction on transrectal ultrasound for high-dose-rate prostate brachytherapy.

Abstract

Purpose: We investigated the feasibility of AI to provide an instant feedback of the potential plan quality based on live needle placement, and before planning is initiated.

Materials and methods: We utilized YOLOv8 to perform automatic organ segmentation and needle detection on 2D transrectal ultrasound images. The segmentation and detection results for each patient were then fed into a plan quality prediction model based on ResNet101. Its outputs are values of selected dose volume metrics. Imaging and plan data from 504 prostate HDR boost patients (456 for training, 24 for validation, and 24 for testing) treated in our clinic were included in this study. The segmentation, needle detection, and prediction results were compared to the clinical results (ground truth).

Results: For prediction model, the p-values of t-test between the predicted values and ground truth for either rectum D2cc or urethra D20% were larger than 0.8. The sensitivity of prediction model in finding implant geometries resulting in below-median rectum D2cc and urethra D20% were 83% and 87%.

Conclusion: The proposed method has great potential to facilitate the current prostate HDR brachytherapy workflows by providing valuable feedback during needle insertion, and facilitating decision making of where and if additional needles are required.