Automated segmentation in planning-CT for breast cancer radiotherapy: A review of recent advances

Abstract

Postoperative radiotherapy (RT) has been shown to effectively reduce disease recurrence and mortality in breast cancer (BC) treatment. A critical step in the planning workflow is the accurate delineation of clinical target volumes (CTV) and organs-at-risk (OAR). This literature review evaluates recent advancements in deep-learning (DL) and atlas-based auto-contouring techniques for CTVs and OARs in BC planning-CT images for RT. It examines their performance regarding geometrical and dosimetric accuracy, inter-observer variability, and time efficiency. Our findings indicate that both DL- and atlas-based methods generally show comparable performance across OARs and CTVs, with DL methods slightly outperforming in consistency and accuracy. Auto-segmentation of breast and most OARs achieved robust results in both segmentation quality and dosimetric planning. However, lymph node levels (LNLs) presented the greatest challenge in auto-segmentation with significant impact on dosimetric planning. The translation of these findings into clinical practice is limited by the geometric performance metrics and the lack of dose evaluation studies. Additionally, auto-contouring algorithms showed diverse structure sets, while training datasets varied in size, origin, patient positioning and imaging protocols, affecting model sensitivity. Guideline inconsistencies and varying definitions of ground truth led to substantial variability, suggesting a need for a reliable consensus training dataset. Finally, our review highlights the popularity of the U-Net architecture. In conclusion, while automated contouring has proven efficient for many OARs and the breast-CTV, further improvements are necessary in LNL delineation, dosimetric analysis, and consensus building.