Evaluation of the accuracy of automated segmentation based on deep learning for prostate cancer patients

被引：0
作者：
Miura, Hideharu [1 ,2 ]
Ishihara, Soichiro [1 ]
Kenjo, Masahiro [1 ]
Nakao, Minoru [1 ,2 ]
Ozawa, Shuichi [1 ,2 ]
Kagemoto, Masayuki [1 ]
机构：
[1] Hiroshima High Precis Radiotherapy Canc Ctr, 3-2-2 Futabanosato,Higashiku Ku, Hiroshima 7320057, Japan
[2] Hiroshima Univ, Grad Sch Biomed & Hlth Sci, Dept Radiat Oncol, 1-2-3 Kasumi,Minami Ku, Hiroshima 7348551, Japan
来源：
MEDICAL DOSIMETRY | 2025年 / 50卷 / 01期
关键词：
Automated segmentation; Deep learning; Prostate; INTEROBSERVER VARIABILITY; RADIATION-THERAPY; AUTO-SEGMENTATION; RISK;
D O I：
10.1016/j.meddos.2024.09.002
中图分类号：
R73 [肿瘤学];
学科分类号：
100214 ;
摘要：
Purpose: This study evaluated the accuracy of a commercial deep learning (DL)-based algorithm for segmenting the prostate, seminal vesicles (SV), and organs at risk (OAR) in patients with prostate cancer. Methods: Ten patients with prostate cancer were selected to compare automated and manual segmentation. The prostate, SV, and OAR, including the bladder, rectum, left and right femoral heads, and penile bulb, were delineated and reviewed according to our institutional protocols by radiation oncologists. The CT and MR images were fused to the prostate, and the prostate and penile bulb were manually delineated on the CT and MR images. The remaining organs were delineated on the CT images without the MR images. MVision AI Contour + was used to perform DL-based automated segmentation. The dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95%) were evaluated for comparison with manual delineations. Results: The mean DSC values for the prostate, SV, bladder, rectum, both femoral heads, and penile bulb were 0.86, 0.80, 0.96, 0.92, 0.97, and 0.64, respectively. The HD95% for all the organs was less than 3 mm. Conclusions: The commercial DL-based auto segmentation solution provided high-quality contours in patients with prostate cancer. (c) 2024 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
引用
收藏
页码：91 / 95
页数：5
相关论文
共 29 条
  • [1] Siegel R.L., Miller K.D., Wagle N.S., Et al., Cancer statistics, 2023, CA Cancer J Clin, 73, 1, pp. 17-48, (2023)
  • [2] Wang L., Lu B., He M., Et al., Prostate cancer incidence and mortality: Global status and temporal trends in 89 countries from 2000 to 2019, Front Public Health, 10, (2022)
  • [3] Wong J., Huang V., Wells D., Et al., Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: A workflow study at two cancer centers, Radiat Oncol, 16, 1, (2021)
  • [4] Zabel W.J., Conway J.L., Gladwish A., Et al., Clinical evaluation of deep learning and atlas-based auto-contouring of bladder and rectum for prostate radiation therapy, Pract Radiat Oncol, 11, 1, pp. e80-e89, (2021)
  • [5] Urago Y., Okamoto H., Kaneda T., Et al., Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models, Radiat Oncol, 16, 1, (2021)
  • [6] Duan J., Bernard M., Downes L., Et al., Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process, Med Phys, 49, 4, pp. 2570-2581, (2022)
  • [7] Palazzo G., Mangili P., Deantoni C., Et al., Real-world validation of artificial intelligence-based computed tomography auto-contouring for prostate cancer radiotherapy planning, Phys Imaging Radiat Oncol, 28, (2023)
  • [8] Maduro Bustos L.A., Sarkar A., Doyle L.A., Et al., Feasibility evaluation of novel AI-based deep-learning contouring algorithm for radiotherapy, J Appl Clin Med Phys, 24, 11, (2023)
  • [9] Pera O., Martinez A., Mohler C., Et al., Clinical validation of Siemens' Syngo.via automatic contouring system, Adv Radiat Oncol, 8, 3, (2023)
  • [10] Duan J., Vargas C.E., Yu N.Y., Et al., Incremental retraining, clinical implementation, and acceptance rate of deep learning auto-segmentation for male pelvis in a multiuser environment, Med Phys, 50, 7, pp. 4079-4091, (2023)
123