Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery

被引：0
|
作者：
Seung Yeun Chung
Jee Suk Chang
Min Seo Choi
Yongjin Chang
Byong Su Choi
Jaehee Chun
Ki Chang Keum
Jin Sung Kim
Yong Bae Kim
机构：
[1] Yonsei University College of Medicine,Department of Radiation Oncology, Yonsei Cancer Center
[2] Ajou University School of Medicine,Department of Radiation Oncology
[3] CorelineSoft,undefined
[4] Co,undefined
来源：
Radiation Oncology | / 16卷
关键词：
Breast cancer; Auto-segmentation; Deep learning; Clinical target volume; Organs-at-risk;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [21] Clinical Evaluation of Atlas and Deep Learning-Based Automatic Contouring of Multiple Organs at Risk and Clinical Target Volumes for Breast Cancer
    Choi, M.
    Choi, B.
    Chung, S.
    Kim, N.
    Chun, J.
    Kim, Y.
    Chang, J.
    Kim, J.
    MEDICAL PHYSICS, 2020, 47 (06) : E588 - E588
  • [22] Comparative Clinical Evaluation Of Deep-Learning-Based Algorithms In Auto-Segmentation Of Organs-At-Risk For Head And Neck Cancers
    Liu, A.
    Li, R.
    Han, C.
    Du, D.
    Sampath, S.
    Amini, A.
    Glaser, S. M.
    Wong, J. Y. C.
    INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2020, 108 (03): : E817 - E817
  • [23] A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy
    Chen, Xuming
    Sun, Shanlin
    Bai, Narisu
    Han, Kun
    Liu, Qianqian
    Yao, Shengyu
    Tang, Hao
    Zhang, Chupeng
    Lu, Zhipeng
    Huang, Qian
    Zhao, Guoqi
    Xu, Yi
    Chen, Tingfeng
    Xie, Xiaohui
    Liu, Yong
    RADIOTHERAPY AND ONCOLOGY, 2021, 160 : 175 - 184
  • [24] Clinical Target Volume Auto-Segmentation of Esophageal Cancer for Radiotherapy After Radical Surgery Based on Deep Learning
    Cao, Ruifen
    Pei, Xi
    Ge, Ning
    Zheng, Chunhou
    TECHNOLOGY IN CANCER RESEARCH & TREATMENT, 2021, 20
  • [25] VALIDATION OF DEEP LEARNING-BASED AUTO-SEGMENTATION FOR ORGANS AT RISK AND GROSS TUMOUR VOLUMES IN LUNG STEREOTACTIC BODY RADIOTHERAPY
    Wong, Jordan
    Huang, Vicky
    Teke, Tony
    Giambattista, Joshua
    Atrchian, Siavash
    RADIOTHERAPY AND ONCOLOGY, 2019, 139 : S54 - S54
  • [26] Validation of Deep Learning-based Auto-Segmentation for Organs at Risk and Gross Tumor Volumes in Lung Stereotactic Body Radiotherapy
    Wong, J.
    Huang, V.
    Giambattista, J. A.
    Teke, T.
    Atrchian, S.
    INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2019, 105 (01): : E140 - E140
  • [27] Interobserver study of deep learning-based segmentation for nodal target volumes in breast cancer
    Adriaans, C.
    Dankers, F.
    Papalazarou, C.
    Hoekstra, N.
    RADIOTHERAPY AND ONCOLOGY, 2023, 182 : S1322 - S1324
  • [28] Clinical evaluation of a deep learning based auto-segmentation model for intracranial organs at risk
    Poel, Robert
    Ruefenacht, Elias
    Fix, Michael
    Scheib, Stefan
    Reyes, Mauricio
    Ermis, Ekin
    RADIOTHERAPY AND ONCOLOGY, 2024, 194 : S821 - S822
  • [29] Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer
    Kosmin, M.
    Ledsam, J.
    Romera-Paredes, B.
    Mendes, R.
    Moinuddin, S.
    de Souza, D.
    Gunn, L.
    Kelly, C.
    Hughes, C. O.
    Karthikesalingam, A.
    Nutting, C.
    Sharma, R. A.
    RADIOTHERAPY AND ONCOLOGY, 2019, 135 : 130 - 140
  • [30] Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer
    Mohammadi, Reza
    Shokatian, Iman
    Salehi, Mohammad
    Arabi, Hossein
    Shiri, Isaac
    Zaidi, Habib
    RADIOTHERAPY AND ONCOLOGY, 2021, 159 : 231 - 240
12345