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For: Lu M, Fan Z, Xu B, Chen L, Zheng X, Li J, Znati T, Mi Q, Jiang J. Using machine learning to predict ovarian cancer. Int J Med Inform 2020;141:104195. [PMID: 32485554 DOI: 10.1016/j.ijmedinf.2020.104195] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
Number Citing Articles
1 Kaur I, Doja MN, Ahmad T, Ahmad M, Hussain A, Nadeem A, Abd El-Latif AA. An Integrated  Approach for Cancer Survival Prediction Using Data Mining Techniques. Comput Intell Neurosci 2021;2021:6342226. [PMID: 34992648 DOI: 10.1155/2021/6342226] [Reference Citation Analysis]
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3 Rossi M, Aspromonte SM, Kohlhapp FJ, Newman JH, Lemenze A, Pepe RJ, Defina SM, Herzog NL, Donnelly R, Kuzel TM, Reiser J, Guevara-patino JA, Zloza A. Gut Microbial Shifts Indicate Melanoma Presence and Bacterial Interactions in a Murine Model. Diagnostics 2022;12:958. [DOI: 10.3390/diagnostics12040958] [Reference Citation Analysis]
4 Juwono FH, Wong W, Pek HT, Sivakumar S, Acula DD. Ovarian cancer detection using optimized machine learning models with adaptive differential evolution. Biomedical Signal Processing and Control 2022;77:103785. [DOI: 10.1016/j.bspc.2022.103785] [Reference Citation Analysis]
5 Sato M, Sato S, Shintani D, Hanaoka M, Ogasawara A, Miwa M, Yabuno A, Kurosaki A, Yoshida H, Fujiwara K, Hasegawa K. Clinical significance of metabolism-related genes and FAK activity in ovarian high-grade serous carcinoma. BMC Cancer 2022;22. [DOI: 10.1186/s12885-021-09148-x] [Reference Citation Analysis]
6 Laios A, Kalampokis E, Johnson R, Thangavelu A, Tarabanis C, Nugent D, De Jong D. Explainable Artificial Intelligence for Prediction of Complete Surgical Cytoreduction in Advanced-Stage Epithelial Ovarian Cancer. JPM 2022;12:607. [DOI: 10.3390/jpm12040607] [Reference Citation Analysis]
7 Ma J, Yang J, Jin Y, Cheng S, Huang S, Zhang N, Wang Y. Artificial Intelligence Based on Blood Biomarkers Including CTCs Predicts Outcomes in Epithelial Ovarian Cancer: A Prospective Study. Onco Targets Ther 2021;14:3267-80. [PMID: 34040391 DOI: 10.2147/OTT.S307546] [Reference Citation Analysis]
8 Qiu L, Li H, Wang M, Wang X. Gated Graph Attention Network for Cancer Prediction. Sensors (Basel) 2021;21:1938. [PMID: 33801894 DOI: 10.3390/s21061938] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Gao Y, Zeng S, Xu X, Li H, Yao S, Song K, Li X, Chen L, Tang J, Xing H, Yu Z, Zhang Q, Zeng S, Yi C, Xie H, Xiong X, Cai G, Wang Z, Wu Y, Chi J, Jiao X, Qin Y, Mao X, Chen Y, Jin X, Mo Q, Chen P, Huang Y, Shi Y, Wang J, Zhou Y, Ding S, Zhu S, Liu X, Dong X, Cheng L, Zhu L, Cheng H, Cha L, Hao Y, Jin C, Zhang L, Zhou P, Sun M, Xu Q, Chen K, Gao Z, Zhang X, Ma Y, Liu Y, Xiao L, Xu L, Peng L, Hao Z, Yang M, Wang Y, Ou H, Jia Y, Tian L, Zhang W, Jin P, Tian X, Huang L, Wang Z, Liu J, Fang T, Yan D, Cao H, Ma J, Li X, Zheng X, Lou H, Song C, Li R, Wang S, Li W, Zheng X, Chen J, Li G, Chen R, Xu C, Yu R, Wang J, Xu S, Kong B, Xie X, Ma D, Gao Q. Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: a retrospective, multicentre, diagnostic study. The Lancet Digital Health 2022;4:e179-87. [DOI: 10.1016/s2589-7500(21)00278-8] [Reference Citation Analysis]
10 Akazawa M, Hashimoto K. Artificial intelligence in gynecologic cancers: Current status and future challenges - A systematic review. Artif Intell Med 2021;120:102164. [PMID: 34629152 DOI: 10.1016/j.artmed.2021.102164] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]