机构：[1]College of Medicine and Biomedical Information Engineering, Northeastern University, Shenyang, Liaoning, China.[2]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.[3]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China.[4]Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China.[5]Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, Henan, China.[6]People's Hospital of Zhengzhou University, Zhengzhou, Henan, China.[7]People's Hospital of Henan University, Zhengzhou, Henan, China.[8]Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China.[9]Department of Electrical and Computer Engineering, University of Texas at El Paso.[10]University of Chinese Academy of Sciences, Beijing, China.[11]Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China.
Accurate identification of lymph node metastasis preoperatively and noninvasively in patients with cervical cancer can avoid unnecessary surgical intervention and benefit treatment planning.To develop a deep learning model using preoperative magnetic resonance imaging for prediction of lymph node metastasis in cervical cancer.This diagnostic study developed an end-to-end deep learning model to identify lymph node metastasis in cervical cancer using magnetic resonance imaging (MRI). A total of 894 patients with stage IB to IIB cervical cancer who underwent radical hysterectomy and pelvic lymphadenectomy were reviewed. All patients underwent radical hysterectomy and pelvic lymphadenectomy, received pelvic MRI within 2 weeks before the operations, had no concurrent cancers, and received no preoperative treatment. To achieve the optimal model, the diagnostic value of 3 MRI sequences was compared, and the outcomes in the intratumoral and peritumoral regions were explored. To mine tumor information from both image and clinicopathologic levels, a hybrid model was built and its prognostic value was assessed by Kaplan-Meier analysis. The deep learning model and hybrid model were developed on a primary cohort consisting of 338 patients (218 patients from Sun Yat-sen University Cancer Center, Guangzhou, China, between January 2011 and December 2017 and 120 patients from Henan Provincial People's Hospital, Zhengzhou, China, between December 2016 and June 2018). The models then were evaluated on an independent validation cohort consisting of 141 patients from Yunnan Cancer Hospital, Kunming, China, between January 2011 and December 2017.The primary diagnostic outcome was lymph node metastasis status, with the pathologic characteristics diagnosed by lymphadenectomy. The secondary primary clinical outcome was survival. The primary diagnostic outcome was assessed by receiver operating characteristic (area under the curve [AUC]) analysis; the primary clinical outcome was assessed by Kaplan-Meier survival analysis.A total of 479 patients (mean [SD] age, 49.1 [9.7] years) fulfilled the eligibility criteria and were enrolled in the primary (n = 338) and validation (n = 141) cohorts. A total of 71 patients (21.0%) in the primary cohort and 32 patients (22.7%) in the validation cohort had lymph node metastais confirmed by lymphadenectomy. Among the 3 image sequences, the deep learning model that used both intratumoral and peritumoral regions on contrast-enhanced T1-weighted imaging showed the best performance (AUC, 0.844; 95% CI, 0.780-0.907). These results were further improved in a hybrid model that combined tumor image information mined by deep learning model and MRI-reported lymph node status (AUC, 0.933; 95% CI, 0.887-0.979). Moreover, the hybrid model was significantly associated with disease-free survival from cervical cancer (hazard ratio, 4.59; 95% CI, 2.04-10.31; P < .001).The findings of this study suggest that deep learning can be used as a preoperative noninvasive tool to diagnose lymph node metastasis in cervical cancer.
基金：
National Natural Science Foundation of China grants 81922040,
81930053, 81227901, and 61702087; Beijing Natural Science Foundation grant 7182109; National Key R&D
Program of China grants 2017YFA0205200 and 2017YFA0700401; Strategic Priority Research Program of
Chinese Academy of Sciences grants XDB32030200 and XDB01030200; and Youth Innovation Promotion
Association CAS grant 2019136.
第一作者机构：[1]College of Medicine and Biomedical Information Engineering, Northeastern University, Shenyang, Liaoning, China.[2]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
共同第一作者：
通讯作者：
通讯机构：[1]College of Medicine and Biomedical Information Engineering, Northeastern University, Shenyang, Liaoning, China.[2]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.[3]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China.[10]University of Chinese Academy of Sciences, Beijing, China.[*1]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China, No. 95 Zhongguancun East Road, Beijing 100190, China[*2]College of Medicine and Biomedical Information Engineering, Northeastern University, Shenyang, Liaoning 110819, China
推荐引用方式(GB/T 7714)：
Wu Qingxia,Wang Shuo,Zhang Shuixing,et al.Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer[J].JAMA NETWORK OPEN.2020,3(7):doi:10.1001/jamanetworkopen.2020.11625.
APA：
Wu Qingxia,Wang Shuo,Zhang Shuixing,Wang Meiyun,Ding Yingying...&Tian Jie.(2020).Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer.JAMA NETWORK OPEN,3,(7)
MLA：
Wu Qingxia,et al."Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer".JAMA NETWORK OPEN 3..7(2020)
医学