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The pathological risk score: A new deep learning-based signature for predicting survival in cervical cancer

文献类型：期刊论文

作者Chen, Chi7,8; Cao, Yuye6; Li, Weili6; Liu, Zhenyu5,7; Liu, Ping6; Tian, Xin6; Sun, Caixia7,8; Wang, Wuliang4; Gao, Han7,8; Kang, Shan3
刊名CANCER MEDICINE
出版日期2022-06-28
页码13
关键词cervical cancer deep learning disease-free survival overall survival whole slide image
ISSN号2045-7634
DOI10.1002/cam4.4953
通讯作者Jiang, Jingying(jingyingjiang@buaa.edu.cn) ; Chen, Chunlin(ccl1@smu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要Purpose To develop and validate a deep learning-based pathological risk score (RS) with an aim of predicting patients' prognosis to investigate the potential association between the information within the whole slide image (WSI) and cervical cancer prognosis. Methods A total of 251 patients with the International Federation of Gynecology and Obstetrics (FIGO) Stage IA1-IIA2 cervical cancer who underwent surgery without any preoperative treatment were enrolled in this study. Both the clinical characteristics and WSI of each patient were collected. To construct a prognosis-associate RS, high-dimensional pathological features were extracted using a convolutional neural network with an autoencoder. With the score threshold selected by X-tile, Kaplan-Meier survival analysis was applied to verify the prediction performance of RS in overall survival (OS) and disease-free survival (DFS) in both the training and testing datasets, as well as different clinical subgroups. Results For the OS and DFS prediction in the testing cohort, RS showed a Harrell's concordance index of higher than 0.700, while the areas under the curve (AUC) achieved up to 0.800 in the same cohort. Furthermore, Kaplan-Meier survival analysis demonstrated that RS was a potential prognostic factor, even in different datasets or subgroups. It could further distinguish the survival differences after clinicopathological risk stratification. Conclusion In the present study, we developed an effective signature in cervical cancer for prognosis prediction and patients' stratification in OS and DFS.
WOS关键词PELVIC RADIATION-THERAPY ; SQUAMOUS-CELL CARCINOMA ; TUMOR-STROMA RATIO ; INDEPENDENT PREDICTOR ; DIGITAL PATHOLOGY ; STAGE ; HYSTERECTOMY ; EXPRESSION
资助项目Guangzhou Municipal Science and Technology Bureau[158100075] ; National Natural Science Foundation of China[81922040] ; National Natural Science Foundation of China[81971662] ; National Natural Science Foundation of China[92059103] ; National Science and Technology Program during the Twelfth Five-year Plan Period[2014BAI05B03] ; Natural Science Foundation of Beijing Municipality[7202105] ; Natural Science Foundation of Guangdong Province[2015A030311024] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences[2019136]
WOS研究方向Oncology
语种英语
WOS记录号WOS:000817095600001
出版者WILEY
资助机构Guangzhou Municipal Science and Technology Bureau ; National Natural Science Foundation of China ; National Science and Technology Program during the Twelfth Five-year Plan Period ; Natural Science Foundation of Beijing Municipality ; Natural Science Foundation of Guangdong Province ; Youth Innovation Promotion Association of the Chinese Academy of Sciences
源URL[http://ir.ia.ac.cn/handle/173211/49197]  
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Jiang, Jingying; Chen, Chunlin; Tian, Jie
作者单位1.Beihang Univ, Minist Ind & Informat Technol, Key Lab Big Data Based Precis Med, Beijing, Peoples R China
2.Yantai Yuhuangding Hosp, Dept Gynecol, Yantai, Peoples R China
3.Hebei Med Univ, Hosp 4, Dept Gynecol, Shijiazhuang, Hebei, Peoples R China
4.Henan Med Univ, Affiliated Hosp 2, Dept Obstet & Gynecol, Zhengzhou, Peoples R China
5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
6.Southern Med Univ, Nanfang Hosp, Dept Obstet & Gynecol, 1838 Guangzhou Ave North, Guangzhou 510515, Peoples R China
7.Chinese Acad Sci, Beijing Key Lab Mol Imaging, State Key Lab Management & Control Complex Syst, CAS Key Lab Mol Imaging,Inst Automat, Beijing, Peoples R China
8.Beihang Univ, Sch Med & Engn, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Chi,Cao, Yuye,Li, Weili,et al. The pathological risk score: A new deep learning-based signature for predicting survival in cervical cancer[J]. CANCER MEDICINE,2022:13.
APA Chen, Chi.,Cao, Yuye.,Li, Weili.,Liu, Zhenyu.,Liu, Ping.,...&Tian, Jie.(2022).The pathological risk score: A new deep learning-based signature for predicting survival in cervical cancer.CANCER MEDICINE,13.
MLA Chen, Chi,et al."The pathological risk score: A new deep learning-based signature for predicting survival in cervical cancer".CANCER MEDICINE (2022):13.

入库方式： OAI收割

来源：自动化研究所

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