Performance evaluation of the AccuLearning system for automated delineation in cervical cancer radiotherapy(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2026年第7期
- Page:
- 847-851
- Research Field:
- 医学放射物理
- Publishing date:
Info
- Title:
- Performance evaluation of the AccuLearning system for automated delineation in cervical cancer radiotherapy
- Author(s):
- ZHANG Zhuxiang; WANG Lina; WANG Ning; ZHANG Pengcheng; QI Ning
- Department of Radiation Oncology, the First Hospital of Lanzhou University (the First School of Clinical Medicine), Lanzhou 730000, China
- Keywords:
- Keywords: cervical cancer radiotherapy AccuLearning system automated delineation manual delineation
- PACS:
- R318；R811.1
- DOI:
- DOI:10.3969/j.issn.1005-202X.2026.07.002
- Abstract:
- Abstract: Objective To evaluate the performance of the AccuLearning system for automated delineation in cervical cancer radiotherapy. Methods Planning CT images and manually delineated structures from 120 cervical cancer patients who received radiotherapy at the First Hospital of Lanzhou University between May 2022 and May 2025 were retrospectively included. Eighty cases were randomly selected as the training set to develop and train the automated delineation model, and the remaining 40 cases served as the independent test set to generate the automated delineation results. Geometric differences between automated delineation and gold-standard manual delineation from the test set were compared using the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), relative absolute volume difference (RAVD), and deviation of centroid (DC). The original radiotherapy plan based on manual delineation was mapped to the automatically delineated contours from the test set. The efficiency (delineation time) and key dosimetric parameters of the two delineation methods for clinical target volume (CTV) and organs-at-risk (OAR) were evaluated. The assessed dosimetric parameters included CTV parameters (D98, D2, V90, V95, Dmean, and HI) and OAR parameters (V30, V40, V50 and Dmean for the intestinal pouch and rectum, V50 and Dmean for the bladder, and the Dmean for the bone marrow and bilateral femoral head). Results The time required for automated delineation by the AccuLearning system was significantly shorter than that for manual delineation (P<0.05). Geometric evaluation of automated delineation results showed the maximum DSC value for the right femoral head and the minimum DSC value for the rectum, with all average DSC value ≥ 0.80. The intestinal pouch demonstrated the maximum CD, HD95, and RAVD, whereas bone marrow had the minimum CD, HD95, and RAVD. Dosimetric comparison revealed statistically significant differences in the D98, V90, V95, Dmean and HI of CTV between automated delineation and manual delineation (P<0.05). For OAR, statistically significant differences were observed in V40 and V50 of the intestinal pouch and V50 of the bladder (P<0.05). Conclusion Automated delineation using the AccuLearning system can significantly improve the delineation efficiency for cervical cancer and achieve high geometric similarity. Additionally, this system exhibits promising application potential for OAR, yet further optimization is required to preserve delineation accuracy for the CTV.
Last Update: 2026-07-22