Prior Knowledge-Guided U-Net for Automatic CTV Segmentation in Postmastectomy Radiotherapy of Breast Cancer.

Abstract

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 radiotherapy 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 two-stage auto-segmentation process. Initially, the localization network categorizes CT slices based on the anatomical 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 (95HD), average surface distance (ASD), surface Dice similarity coefficient (sDSC). A four-level objective scale evaluation was performed by two experienced radiation oncologists in a randomized, double-blind manner.

Results: Quantitative evaluations revealed that PK-UNet significantly outperformed state-of-the-art (SOTA) segmentation methods (P < 0.01), with a mean DSC of 0.90 ± 0.02 and a 95HD of 2.82 ± 1.29 mm. The mean ASD of PK-UNet was 0.91 ± 0.22 mm and the sDSC was 0.84 ± 0.07, significantly surpassing the performance of AdwU-Net (P < 0.01) 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 to 18.20 min for manual contouring leading to a 94.4% reduction in working time.

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