Clinical evaluation of deep learning-based clinical target volume three-channel auto-segmentation algorithm for adaptive radiotherapy in cervical cancer.

Objectives: Accurate contouring of the clinical target volume (CTV) is a key element of radiotherapy in cervical cancer. We validated a novel deep learning (DL)-based auto-segmentation algorithm for CTVs in cervical cancer called the three-channel adaptive auto-segmentation network (TCAS).

Methods: A total of 107 cases were collected and contoured by senior radiation oncologists (ROs).

Deep learning-based auto-segmentation of clinical target volumes for radiotherapy treatment of cervical cancer.

Objectives: Because radiotherapy is indispensible for treating cervical cancer, it is critical to accurately and efficiently delineate the radiation targets. We evaluated a deep learning (DL)-based auto-segmentation algorithm for automatic contouring of clinical target volumes (CTVs) in cervical cancers.

Methods: Computed tomography (CT) datasets from 535 cervical cancers treated with definitive or postoperative radiotherapy were collected.