Introduction
tumor, Colorectal cancer
TUMOR, MALIGNANCIES, BLIND, COLORECTAL CANCER, RECTAL CANCER
Colorectal cancer is one of the top malignancies in terms of incidence and mortality not only in China [Gross tumor volume (GTV) and clinical target volume (CTV) delineation by oncologists are critical steps in radiotherapy (RT) treatment planning. Accurate target delineation is vitally important in ensuring the delivery of a safe and effective radiation dose to the tumor while minimizing damage to surrounding healthy tissues. According to guidelines of rectal cancer RT [To achieve accurate and efficient target delineation for radiotherapy, growing efforts have been directed to automated segmentation methods, particularly with deep learning (DL) as backend. However, the work pertinent to rectal cancer target segmentation is rare. Men et al. [Moreover, to the best of our knowledge, we did not find any relevant work towards automated segmentation of rectal cancer CTV and GTV for neoadjuvant radiotherapy in the same process. This can be partly attributed to the aforementioned clinical routine practice that CTV and GTV are delineated using information from different imaging modalities. Despite this, the routine practice indicates the feasibility to fully automate the target delineation procedure by means of divide-and-conquer.To this end, this is the first study that has developed a DL-based workflow towards fully automated CTV and GTV delineation for rectal cancer neoadjuvant radiotherapy. The workflow was based on the human reasoning process and the target segmentation task was performed in a divide-and-conquer strategy, i.e., CTV in CT domain and GTV in MRI domain respectively. To validate the workflow in more aspects than comparison with ground truth, we employed a multicenter-involved blind and randomized evaluation scheme.