First Implementation of an All-in-One Solution With Fully Automatic Workflow for Rectal Cancer Based on an Integrated CT-linac.
Authors
Affiliations (6)
Affiliations (6)
- Cancer Center, Peking University Third Hospital, Beijing, China; Department of Radiation Oncology, Peking University Third Hospital, Beijing, China; Beijing Key Laboratory for Interdisciplinary Research in Gastrointestinal Oncology, Peking University Third Hospital, Beijing, China.
- Collaborative Innovation Department of Radiotherapy Business Unit, Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China.
- Department of Oncology, Chengyang People's Hospital, Qingdao, China.
- Cancer Center, Peking University Third Hospital, Beijing, China; Department of Radiation Oncology, Peking University Third Hospital, Beijing, China; Beijing Key Laboratory for Interdisciplinary Research in Gastrointestinal Oncology, Peking University Third Hospital, Beijing, China. Electronic address: [email protected].
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China. Electronic address: [email protected].
- Cancer Center, Peking University Third Hospital, Beijing, China; Department of Radiation Oncology, Peking University Third Hospital, Beijing, China; Beijing Key Laboratory for Interdisciplinary Research in Gastrointestinal Oncology, Peking University Third Hospital, Beijing, China. Electronic address: [email protected].
Abstract
This study aimed to prospectively evaluate, in a single-center pilot study, the feasibility, workflow efficiency, and dosimetric performance of an integrated All-in-One (AIO) radiation therapy solution for rectal cancer that combines computed tomography (CT) simulation, automatic segmentation, planning, and treatment delivery on a single CT-linac platform. A total of 20 patients with rectal cancer underwent AIO radiation therapy on an integrated CT-linac system. The workflow comprised CT simulation, artificial intelligence-based autosegmentation, autoplanning, online verification, and beam delivery, all performed sequentially without patient repositioning. Dosimetric parameters for target volumes and organs at risk, together with gamma analysis results, were collected. Autosegmentation accuracy was assessed using Dice similarity coefficient and average surface distance. Short-term toxicities, surgical outcomes, and tumor regression grade were assessed at follow-up. The AIO workflow was successfully completed in all 20 patients. The mean total treatment time was 37.5 ± 6.9 minutes, with autosegmentation and planning accounting for the largest proportion of workflow time. The AIO autoplanning system achieved clinically acceptable target coverage and organs at risk sparing in most cases, indicating stable and reproducible plan quality across the cohort. The mean Dice similarity coefficients for the gross tumor volume, clinical target volume 45, and clinical target volume 50 were 0.83, 0.95, and 0.89, respectively. Gamma analysis demonstrated excellent delivery accuracy, with 3-dimensional pass rates exceeding 95% in all patients, and two-dimensional pass rates across gantry angles ranging from 84.0% to 100.0% under the 3%/3 mm criterion. Hematologic toxicity was the most common adverse event, with leukopenia observed in 55.0% of patients. Among 20 patients who completed follow-up, 19 underwent surgery and 1 achieved a clinical complete response. The overall complete response rate was 15.0%, and 68.4% of the surgical patients achieved tumor regression grade 0-1. This pilot study demonstrates the feasibility of a fully automated, time-efficient, and clinically applicable radiation therapy workflow for rectal cancer. Favorable target conformity, stable plan quality, and promising early outcomes support further clinical implementation.