Prior Knowledge-Guided U-Net for Automatic Clinical Target Volume Segmentation in Postmastectomy Radiation Therapy of Breast Cancer

Purpose: This study aimed to design and evaluate a prior-knowledge-guided U-Net (PK-UNet) for automatic clinical target volume (CTV) segmentation in postmastectomy radiation therapy for breast cancer.

Methods and materials: A total of 102 computed tomography (CT) scans from breast cancer patients who underwent postmastectomy were retrospectively collected. Of these, 80 scans were used for training with 5-fold cross-validation, and 22 scans for independent testing. The CTV included the chest wall, supraclavicular region, and axillary group III. The proposed PK-UNet method employs a 2-stage auto-segmentation process. Initially, the localization network categorizes CT slices based on the anatomic information of the CTV and generates prior knowledge labels. These outputs, along with the CT images, were fed into the final segmentation network. Quantitative evaluation was conducted using the mean Dice similarity coefficient (DSC), 95% Hausdorff distance, average surface distance, and surface DSC. A four-level objective scale evaluation was performed by 2 experienced radiation oncologists in a randomized double-blind manner.

Results: Quantitative evaluations revealed that PK-UNet significantly outperformed state-of-the-art segmentation methods (P < .01), with a mean DSC of 0.90 ± 0.02 and a 95% Hausdorff distance of 2.82 ± 1.29 mm. The mean average surface distance of PK-UNet was 0.91 ± 0.22 mm and the surface DSC was 0.84 ± 0.07, significantly surpassing the performance of AdwU-Net (P < .01) and showing comparable results to other models. Clinical evaluation confirmed the efficacy of PK-UNet, with 81.8% of the predicted contours being acceptable for clinical application. The advantages of the auto-segmentation capability of PK-UNet were most evident in the superior and inferior slices and slices with discontinuities at the junctions of different subregions. The average manual correction time was reduced to 1.02 min, compared with 18.20 min for manual contouring leading to a 94.4% reduction in working time.

Conclusions: This study introduced the pioneering integration of prior medical knowledge into a deep learning framework for postmastectomy radiation therapy. This strategy addresses the challenges of CTV segmentation in postmastectomy radiation therapy and improves clinical workflow efficiency.