CASIA OpenIR  > 中国科学院分子影像重点实验室
The pathological risk score: A new deep learning-based signature for predicting survival in cervical cancer
Chen, Chi1,2; Cao, Yuye3; Li, Weili3; Liu, Zhenyu2,4; Liu, Ping3; Tian, Xin3; Sun, Caixia1,2; Wang, Wuliang5; Gao, Han1,2; Kang, Shan6; Wang, Shaoguang7; Jiang, Jingying1,8; Chen, Chunlin3; Tian, Jie1,2
Source PublicationCANCER MEDICINE
ISSN2045-7634
2022-06-28
Pages13
Corresponding AuthorJiang, Jingying(jingyingjiang@buaa.edu.cn) ; Chen, Chunlin(ccl1@smu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
AbstractPurpose 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.
Keywordcervical cancer deep learning disease-free survival overall survival whole slide image
DOI10.1002/cam4.4953
WOS KeywordPELVIC RADIATION-THERAPY ; SQUAMOUS-CELL CARCINOMA ; TUMOR-STROMA RATIO ; INDEPENDENT PREDICTOR ; DIGITAL PATHOLOGY ; STAGE ; HYSTERECTOMY ; EXPRESSION
Indexed BySCI
Language英语
Funding ProjectGuangzhou 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]
Funding OrganizationGuangzhou 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
WOS Research AreaOncology
WOS SubjectOncology
WOS IDWOS:000817095600001
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49198
Collection中国科学院分子影像重点实验室
Corresponding AuthorJiang, Jingying; Chen, Chunlin; Tian, Jie
Affiliation1.Beihang Univ, Sch Med & Engn, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
2.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
3.Southern Med Univ, Nanfang Hosp, Dept Obstet & Gynecol, 1838 Guangzhou Ave North, Guangzhou 510515, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
5.Henan Med Univ, Affiliated Hosp 2, Dept Obstet & Gynecol, Zhengzhou, Peoples R China
6.Hebei Med Univ, Hosp 4, Dept Gynecol, Shijiazhuang, Hebei, Peoples R China
7.Yantai Yuhuangding Hosp, Dept Gynecol, Yantai, Peoples R China
8.Beihang Univ, Minist Ind & Informat Technol, Key Lab Big Data Based Precis Med, Beijing, Peoples R China
Recommended Citation
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.
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