145 related articles for article (PubMed ID: 36471752)
1. Development of a Novel Deep Learning-Based Prediction Model for the Prognosis of Operable Cervical Cancer.
Dong T; Wang L; Li R; Liu Q; Xu Y; Wei Y; Jiao X; Li X; Zhang Y; Zhang Y; Song K; Yang X; Cui B
Comput Math Methods Med; 2022; 2022():4364663. PubMed ID: 36471752
[TBL] [Abstract][Full Text] [Related]
2. A novel deep learning prognostic system improves survival predictions for stage III non-small cell lung cancer.
Yang L; Fan X; Qin W; Xu Y; Zou B; Fan B; Wang S; Dong T; Wang L
Cancer Med; 2022 Nov; 11(22):4246-4255. PubMed ID: 35491970
[TBL] [Abstract][Full Text] [Related]
3. Survival outcome prediction in cervical cancer: Cox models vs deep-learning model.
Matsuo K; Purushotham S; Jiang B; Mandelbaum RS; Takiuchi T; Liu Y; Roman LD
Am J Obstet Gynecol; 2019 Apr; 220(4):381.e1-381.e14. PubMed ID: 30582927
[TBL] [Abstract][Full Text] [Related]
4. Preoperative platelet count improves the prognostic prediction of the FIGO staging system for operable cervical cancer patients.
Zheng RR; Huang XX; Jin C; Zhuang XX; Ye LC; Zheng FY; Lin F
Clin Chim Acta; 2017 Oct; 473():198-203. PubMed ID: 27836106
[TBL] [Abstract][Full Text] [Related]
5. Development and Validation of a Personalized Survival Prediction Model for Uterine Adenosarcoma: A Population-Based Deep Learning Study.
Qu W; Liu Q; Jiao X; Zhang T; Wang B; Li N; Dong T; Cui B
Front Oncol; 2020; 10():623818. PubMed ID: 33680946
[TBL] [Abstract][Full Text] [Related]
6. Prognostic model based on magnetic resonance imaging, whole-tumour apparent diffusion coefficient values and HPV genotyping for stage IB-IV cervical cancer patients following chemoradiotherapy.
Lin G; Yang LY; Lin YC; Huang YT; Liu FY; Wang CC; Lu HY; Chiang HJ; Chen YR; Wu RC; Ng KK; Hong JH; Yen TC; Lai CH
Eur Radiol; 2019 Feb; 29(2):556-565. PubMed ID: 30051142
[TBL] [Abstract][Full Text] [Related]
7. The pathological risk score: A new deep learning-based signature for predicting survival in cervical cancer.
Chen C; Cao Y; Li W; Liu Z; Liu P; Tian X; Sun C; Wang W; Gao H; Kang S; Wang S; Jiang J; Chen C; Tian J
Cancer Med; 2023 Jan; 12(2):1051-1063. PubMed ID: 35762423
[TBL] [Abstract][Full Text] [Related]
8. Nomograms predicting the overall survival and cancer-specific survival of patients with stage IIIC1 cervical cancer.
Feng Y; Wang Y; Xie Y; Wu S; Li Y; Li M
BMC Cancer; 2021 Apr; 21(1):450. PubMed ID: 33892663
[TBL] [Abstract][Full Text] [Related]
9. A Novel Prognostic Risk Model for Cervical Cancer Based on Immune Checkpoint HLA-G-Driven Differentially Expressed Genes.
Xu HH; Wang HL; Xing TJ; Wang XQ
Front Immunol; 2022; 13():851622. PubMed ID: 35924232
[TBL] [Abstract][Full Text] [Related]
10. Development and validation of a deep learning survival model for cervical adenocarcinoma patients.
Li R; Qu W; Liu Q; Tan Y; Zhang W; Hao Y; Jiang N; Mao Z; Ye J; Jiao J; Gao Q; Cui B; Dong T
BMC Bioinformatics; 2023 Apr; 24(1):146. PubMed ID: 37055729
[TBL] [Abstract][Full Text] [Related]
11. Prognosis prediction of extremity and trunk wall soft-tissue sarcomas treated with surgical resection with radiomic analysis based on random survival forest.
Yang Y; Ma X; Wang Y; Ding X
Updates Surg; 2022 Feb; 74(1):355-365. PubMed ID: 34003477
[TBL] [Abstract][Full Text] [Related]
12. The Application and Comparison of Machine Learning Models for the Prediction of Breast Cancer Prognosis: Retrospective Cohort Study.
Xiao J; Mo M; Wang Z; Zhou C; Shen J; Yuan J; He Y; Zheng Y
JMIR Med Inform; 2022 Feb; 10(2):e33440. PubMed ID: 35179504
[TBL] [Abstract][Full Text] [Related]
13. Development and Validation of a Deep Learning Radiomics Model Predicting Lymph Node Status in Operable Cervical Cancer.
Dong T; Yang C; Cui B; Zhang T; Sun X; Song K; Wang L; Kong B; Yang X
Front Oncol; 2020; 10():464. PubMed ID: 32373511
[No Abstract] [Full Text] [Related]
14. Development and validation of a SEER-based prognostic nomogram for cervical cancer patients below the age of 45 years.
Liu Q; Li W; Xie M; Yang M; Xu M; Yang L; Sheng B; Peng Y; Gao L
Bosn J Basic Med Sci; 2021 Oct; 21(5):620-631. PubMed ID: 33485294
[TBL] [Abstract][Full Text] [Related]
15. Calculating the overall survival probability in patients with cervical cancer: a nomogram and decision curve analysis-based study.
Xie G; Wang R; Shang L; Qi C; Yang L; Huang L; Yang W; Chung MC
BMC Cancer; 2020 Sep; 20(1):833. PubMed ID: 32873257
[TBL] [Abstract][Full Text] [Related]
16. Construction of Immune-Associated Nomogram for Predicting the Recurrence Survival Risk of Stage I Cervical Cancer.
Wang Y; Zhang L; Wang B; Cheng Y
Biomed Res Int; 2021; 2021():6699131. PubMed ID: 34337046
[TBL] [Abstract][Full Text] [Related]
17. Explainable deep learning-based survival prediction for non-small cell lung cancer patients undergoing radical radiotherapy.
Astley JR; Reilly JM; Robinson S; Wild JM; Hatton MQ; Tahir BA
Radiother Oncol; 2024 Apr; 193():110084. PubMed ID: 38244779
[TBL] [Abstract][Full Text] [Related]
18. Deep-Learning-Based Survival Prediction of Patients in Coronary Care Units.
Yang R; Huang T; Wang Z; Huang W; Feng A; Li L; Lyu J
Comput Math Methods Med; 2021; 2021():5745304. PubMed ID: 34976110
[TBL] [Abstract][Full Text] [Related]
19. Nomogram Predicting Overall Survival in Operable Cervical Cancer Patients.
Zheng RR; Huang XW; Liu WY; Lin RR; Zheng FY; Lin F
Int J Gynecol Cancer; 2017 Jun; 27(5):987-993. PubMed ID: 28498238
[TBL] [Abstract][Full Text] [Related]
20. Deep learning-based survival prediction of oral cancer patients.
Kim DW; Lee S; Kwon S; Nam W; Cha IH; Kim HJ
Sci Rep; 2019 May; 9(1):6994. PubMed ID: 31061433
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]