A prognostic and predictive model based on deep learning to identify optimal candidates for intensity-modulated radiotherapy alone in patients with stage II nasopharyngeal carcinoma: A retrospective multicenter study.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PMID: 39645201

Abstract

PURPOSE: To develop and validate a prognostic and predictive model integrating deep learning MRI features and clinical information in patients with stage II nasopharyngeal carcinoma (NPC) to identify patients with a low risk of progression for whom intensity-modulated radiotherapy (IMRT) alone is sufficient.

Authors

  • Jiong-Lin Liang
    State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China. Electronic address: liangjl@sysucc.org.cn.
  • Yue-Feng Wen
    Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou 510095, China. Electronic address: wenyf@gzhmu.edu.cn.
  • Ying-Ping Huang
    State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China. Electronic address: huangyingp@sysucc.org.cn.
  • Jia Guo
    Department of Radiology, Stanford University, Stanford, CA, USA.
  • Yun He
    Metanotitia Inc., Shenzhen, China.
  • Hong-Wei Xing
    State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; Department of Information, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China. Electronic address: xinghw@sysucc.org.cn.
  • Ling Guo
    Department of Nephrology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Ji'nan, China. Electronic address: gulixiji@sdu.edu.cn.
  • Hai-Qiang Mai
    Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, P R China.
  • Qi Yang
    Department of Radiology, The First Hospital of Jilin University, No.1, Xinmin Street, Changchun 130021, China (Y.W., M.L., Z.M., J.W., K.H., Q.Y., L.Z., L.M., H.Z.).

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