146 related articles for article (PubMed ID: 36471752)
21. Analysis of prognostic variables, development of predictive models, and stratification of risk groups in surgically treated FIGO early-stage (IA-IIA) carcinoma cervix.
Singh P; Tripcony L; Nicklin J
Int J Gynecol Cancer; 2012 Jan; 22(1):115-22. PubMed ID: 21997176
[TBL] [Abstract][Full Text] [Related]
22. Postoperative nomogram for the prediction of disease-free survival in lymph node-negative stage I-IIA cervical cancer patients treated with radical hysterectomy.
Gülseren V; Kocaer M; Çakır İ; Özdemir İA; Sancı M; Güngördük K
J Obstet Gynaecol; 2020 Jul; 40(5):699-704. PubMed ID: 31607197
[TBL] [Abstract][Full Text] [Related]
23. Using deep learning to predict survival outcome in non-surgical cervical cancer patients based on pathological images.
Zhang K; Sun K; Zhang C; Ren K; Li C; Shen L; Jing D
J Cancer Res Clin Oncol; 2023 Aug; 149(9):6075-6083. PubMed ID: 36653539
[TBL] [Abstract][Full Text] [Related]
24. The nomograms for predicting overall and cancer-specific survival in elderly patients with early-stage lung cancer: A population-based study using SEER database.
Yu G; Liu X; Li Y; Zhang Y; Yan R; Zhu L; Wang Z
Front Public Health; 2022; 10():946299. PubMed ID: 36159305
[TBL] [Abstract][Full Text] [Related]
25. A nomogram model based on the number of examined lymph nodes-related signature to predict prognosis and guide clinical therapy in gastric cancer.
Li H; Lin D; Yu Z; Li H; Zhao S; Hainisayimu T; Liu L; Wang K
Front Immunol; 2022; 13():947802. PubMed ID: 36405735
[TBL] [Abstract][Full Text] [Related]
26. Prognostic Model for Survival and Recurrence in Patients with Early-Stage Cervical Cancer: A Korean Gynecologic Oncology Group Study (KGOG 1028).
Paik ES; Lim MC; Kim MH; Kim YH; Song ES; Seong SJ; Suh DH; Lee JM; Lee C; Choi CH
Cancer Res Treat; 2020 Jan; 52(1):320-333. PubMed ID: 31401822
[TBL] [Abstract][Full Text] [Related]
27. A novel prognostic nomogram utilizing the 2018 FIGO staging system for cervical cancer: A large multicenter study.
Tang X; Guo C; Liu S; Guo J; Hua K; Qiu J
Int J Gynaecol Obstet; 2021 Oct; 155(1):86-94. PubMed ID: 33587753
[TBL] [Abstract][Full Text] [Related]
28. Association of MRI-derived radiomic biomarker with disease-free survival in patients with early-stage cervical cancer.
Fang J; Zhang B; Wang S; Jin Y; Wang F; Ding Y; Chen Q; Chen L; Li Y; Li M; Chen Z; Liu L; Liu Z; Tian J; Zhang S
Theranostics; 2020; 10(5):2284-2292. PubMed ID: 32089742
[TBL] [Abstract][Full Text] [Related]
29. Nomogram prediction for overall survival of patients diagnosed with cervical cancer.
Polterauer S; Grimm C; Hofstetter G; Concin N; Natter C; Sturdza A; Pötter R; Marth C; Reinthaller A; Heinze G
Br J Cancer; 2012 Sep; 107(6):918-24. PubMed ID: 22871885
[TBL] [Abstract][Full Text] [Related]
30. Oncologic outcomes in the era of modern radiation therapy using FIGO 2018 staging system for cervical cancer.
Brodeur MN; Dejean R; Beauchemin MC; Samouëlian V; Cormier B; Bacha OM; Warkus T; Barkati M
Gynecol Oncol; 2021 Aug; 162(2):277-283. PubMed ID: 34059350
[TBL] [Abstract][Full Text] [Related]
31. An MRI-based radiomics signature and clinical characteristics for survival prediction in early-stage cervical cancer.
Zheng RR; Cai MT; Lan L; Huang XW; Yang YJ; Powell M; Lin F
Br J Radiol; 2022 Jan; 95(1129):20210838. PubMed ID: 34797703
[TBL] [Abstract][Full Text] [Related]
32. Deep Learning for Prediction of N2 Metastasis and Survival for Clinical Stage I Non-Small Cell Lung Cancer.
Zhong Y; She Y; Deng J; Chen S; Wang T; Yang M; Ma M; Song Y; Qi H; Wang Y; Shi J; Wu C; Xie D; Chen C;
Radiology; 2022 Jan; 302(1):200-211. PubMed ID: 34698568
[TBL] [Abstract][Full Text] [Related]
33. Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [
Shen WC; Chen SW; Wu KC; Hsieh TC; Liang JA; Hung YC; Yeh LS; Chang WC; Lin WC; Yen KY; Kao CH
Eur Radiol; 2019 Dec; 29(12):6741-6749. PubMed ID: 31134366
[TBL] [Abstract][Full Text] [Related]
34. Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer.
Wu Q; Wang S; Zhang S; Wang M; Ding Y; Fang J; Wu Q; Qian W; Liu Z; Sun K; Jin Y; Ma H; Tian J
JAMA Netw Open; 2020 Jul; 3(7):e2011625. PubMed ID: 32706384
[TBL] [Abstract][Full Text] [Related]
35. Integrative nomogram of CT imaging, clinical, and hematological features for survival prediction of patients with locally advanced non-small cell lung cancer.
Wang L; Dong T; Xin B; Xu C; Guo M; Zhang H; Feng D; Wang X; Yu J
Eur Radiol; 2019 Jun; 29(6):2958-2967. PubMed ID: 30643940
[TBL] [Abstract][Full Text] [Related]
36. Deep learning models for predicting the survival of patients with chondrosarcoma based on a surveillance, epidemiology, and end results analysis.
Yan L; Gao N; Ai F; Zhao Y; Kang Y; Chen J; Weng Y
Front Oncol; 2022; 12():967758. PubMed ID: 36072795
[TBL] [Abstract][Full Text] [Related]
37. Development and Validation of a Personalized Prognostic Prediction Model for Patients With Spinal Cord Astrocytoma.
Yang S; Yang X; Wang H; Gu Y; Feng J; Qin X; Feng C; Li Y; Liu L; Fan G; Liao X; He S
Front Med (Lausanne); 2021; 8():802471. PubMed ID: 35118095
[TBL] [Abstract][Full Text] [Related]
38. Deep learning based prediction of prognosis in nonmetastatic clear cell renal cell carcinoma.
Byun SS; Heo TS; Choi JM; Jeong YS; Kim YS; Lee WK; Kim C
Sci Rep; 2021 Jan; 11(1):1242. PubMed ID: 33441830
[TBL] [Abstract][Full Text] [Related]
39. Automatic T Staging Using Weakly Supervised Deep Learning for Nasopharyngeal Carcinoma on MR Images.
Yang Q; Guo Y; Ou X; Wang J; Hu C
J Magn Reson Imaging; 2020 Oct; 52(4):1074-1082. PubMed ID: 32583578
[TBL] [Abstract][Full Text] [Related]
40. Deep learning-based survival analysis for brain metastasis patients with the national cancer database.
Bice N; Kirby N; Bahr T; Rasmussen K; Saenz D; Wagner T; Papanikolaou N; Fakhreddine M
J Appl Clin Med Phys; 2020 Sep; 21(9):187-192. PubMed ID: 32790207
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]