Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer

doi:10.1001/jamanetworkopen.2020.11625

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer
	Wu, Qingxia 1,2,7,8,9; Wang, Shuo 1,11; Zhang, Shuixing 10; Wang, Meiyun 7,8,9; Ding, Yingying 6; Fang, Jin 10; Qian, Wei 5; Liu, Zhenyu 1,4; Sun, Kai 3; Jin, Yan 6
刊名	JAMA NETWORK OPEN
	2020-07-24
卷号	3 期号:7 页码:13
ISSN号	2574-3805
DOI	10.1001/jamanetworkopen.2020.11625
通讯作者	Ma, He(mahe@bmie.neu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要	Importance Accurate identification of lymph node metastasis preoperatively and noninvasively in patients with cervical cancer can avoid unnecessary surgical intervention and benefit treatment planning. Objective To develop a deep learning model using preoperative magnetic resonance imaging for prediction of lymph node metastasis in cervical cancer. Design, Setting, and Participants 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. Main Outcomes and Measures 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. Results 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). Conclusions and Relevance 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.
资助项目	National Natural Science Foundation of China[81922040] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[61702087] ; Beijing Natural Science Foundation[7182109] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFA0700401] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB32030200] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB01030200] ; Youth Innovation Promotion Association CAS[2019136]
WOS关键词	VESSEL DENSITY ; PROGNOSTIC-SIGNIFICANCE ; RADIOMICS ; CARCINOMA ; NOMOGRAM ; WOMEN ; RISK ; MRI
WOS研究方向	General & Internal Medicine
语种	英语
出版者	AMER MEDICAL ASSOC
WOS记录号	WOS:000555947800006
资助机构	National Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Key R&D Program of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/40362]
专题	自动化研究所_中国科学院分子影像重点实验室
通讯作者	Ma, He; Tian, Jie
作者单位	1.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 2.Northeastern Univ, Coll Med & Biomed Informat Engn, Shenyang 110819, Liaoning, Peoples R China 3.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Shaanxi, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China 5.Univ Texas El Paso, Dept Elect & Comp Engn, El Paso, TX USA 6.Kunming Med Univ, Yunnan Canc Hosp, Dept Radiol, Affiliated Hosp 3, Kunming, Yunnan, Peoples R China 7.Henan Univ, Peoples Hosp, Zhengzhou, Henan, Peoples R China 8.Zhengzhou Univ, Peoples Hosp, Zhengzhou, Henan, Peoples R China 9.Henan Prov Peoples Hosp, Dept Med Imaging, Zhengzhou, Henan, Peoples R China 10.Jinan Univ, Med Imaging Ctr, Affiliated Hosp 1, Guangzhou, Peoples R 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):13.
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),13.
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):13.