148 related articles for article (PubMed ID: 38098850)
1. Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme.
Zhang D; Luan J; Liu B; Yang A; Lv K; Hu P; Han X; Yu H; Shmuel A; Ma G; Zhang C
Front Med (Lausanne); 2023; 10():1271687. PubMed ID: 38098850
[TBL] [Abstract][Full Text] [Related]
2. Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangiocarcinoma.
Wang D; Pan B; Huang JC; Chen Q; Cui SP; Lang R; Lyu SC
Front Oncol; 2023; 13():1106029. PubMed ID: 37007095
[TBL] [Abstract][Full Text] [Related]
3. Deep learning model for predicting the survival of patients with primary gastrointestinal lymphoma based on the SEER database and a multicentre external validation cohort.
Wang F; Chen L; Liu L; Jia Y; Li W; Wang L; Zhi J; Liu W; Li W; Li Z
J Cancer Res Clin Oncol; 2023 Oct; 149(13):12177-12189. PubMed ID: 37428248
[TBL] [Abstract][Full Text] [Related]
4. Development and Validation of a MRI-Based Radiomics Prognostic Classifier in Patients with Primary Glioblastoma Multiforme.
Chen X; Fang M; Dong D; Liu L; Xu X; Wei X; Jiang X; Qin L; Liu Z
Acad Radiol; 2019 Oct; 26(10):1292-1300. PubMed ID: 30660472
[TBL] [Abstract][Full Text] [Related]
5. A radiomics nomogram based on multiparametric MRI might stratify glioblastoma patients according to survival.
Zhang X; Lu H; Tian Q; Feng N; Yin L; Xu X; Du P; Liu Y
Eur Radiol; 2019 Oct; 29(10):5528-5538. PubMed ID: 30847586
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme.
Lao J; Chen Y; Li ZC; Li Q; Zhang J; Liu J; Zhai G
Sci Rep; 2017 Sep; 7(1):10353. PubMed ID: 28871110
[TBL] [Abstract][Full Text] [Related]
8. Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study.
Yang X; Qiu H; Wang L; Wang X
J Med Internet Res; 2023 Oct; 25():e44417. PubMed ID: 37883174
[TBL] [Abstract][Full Text] [Related]
9. Multiple Survival Outcome Prediction of Glioblastoma Patients Based on Multiparametric MRI.
Wang B; Zhang S; Wu X; Li Y; Yan Y; Liu L; Xiang J; Li D; Yan T
Front Oncol; 2021; 11():778627. PubMed ID: 34900728
[TBL] [Abstract][Full Text] [Related]
10. The role of deep learning-based survival model in improving survival prediction of patients with glioblastoma.
Moradmand H; Aghamiri SMR; Ghaderi R; Emami H
Cancer Med; 2021 Oct; 10(20):7048-7059. PubMed ID: 34453413
[TBL] [Abstract][Full Text] [Related]
11. Development and validation of survival prediction model for gastric adenocarcinoma patients using deep learning: A SEER-based study.
Zeng J; Li K; Cao F; Zheng Y
Front Oncol; 2023; 13():1131859. PubMed ID: 36959782
[TBL] [Abstract][Full Text] [Related]
12. Comparison of time-to-event machine learning models in predicting oral cavity cancer prognosis.
Adeoye J; Hui L; Koohi-Moghadam M; Tan JY; Choi SW; Thomson P
Int J Med Inform; 2022 Jan; 157():104635. PubMed ID: 34800847
[TBL] [Abstract][Full Text] [Related]
13. Prognosis prediction for glioblastoma multiforme patients using machine learning approaches: Development of the clinically applicable model.
Kim Y; Kim KH; Park J; Yoon HI; Sung W
Radiother Oncol; 2023 Jun; 183():109617. PubMed ID: 36921767
[TBL] [Abstract][Full Text] [Related]
14. Machine learning methods for accurately predicting survival and guiding treatment in stage I and II hepatocellular carcinoma.
Li X; Bao H; Shi Y; Zhu W; Peng Z; Yan L; Chen J; Shu X
Medicine (Baltimore); 2023 Nov; 102(45):e35892. PubMed ID: 37960763
[TBL] [Abstract][Full Text] [Related]
15. A Multi-parametric MRI-Based Radiomics Signature and a Practical ML Model for Stratifying Glioblastoma Patients Based on Survival Toward Precision Oncology.
Osman AFI
Front Comput Neurosci; 2019; 13():58. PubMed ID: 31507398
[No Abstract] [Full Text] [Related]
16. Comparison of deep learning-based recurrence-free survival with random survival forest and Cox proportional hazard models in Stage-I NSCLC patients.
Kar İ; Kocaman G; İbrahimov F; Enön S; Coşgun E; Elhan AH
Cancer Med; 2023 Sep; 12(18):19272-19278. PubMed ID: 37644818
[TBL] [Abstract][Full Text] [Related]
17. Comparison of Radiomics-Based Machine-Learning Classifiers in Diagnosis of Glioblastoma From Primary Central Nervous System Lymphoma.
Chen C; Zheng A; Ou X; Wang J; Ma X
Front Oncol; 2020; 10():1151. PubMed ID: 33042784
[No Abstract] [Full Text] [Related]
18. Predicting overall survival in chordoma patients using machine learning models: a web-app application.
Cheng P; Xie X; Knoedler S; Mi B; Liu G
J Orthop Surg Res; 2023 Sep; 18(1):652. PubMed ID: 37660044
[TBL] [Abstract][Full Text] [Related]
19. A Comparative and Summative Study of Radiomics-based Overall Survival Prediction in Glioblastoma Patients.
Ruan Z; Mei N; Lu Y; Xiong J; Li X; Zheng W; Liu L; Yin B
J Comput Assist Tomogr; 2022 May-Jun 01; 46(3):470-479. PubMed ID: 35405713
[TBL] [Abstract][Full Text] [Related]
20. Predicting the survival of patients with pancreatic neuroendocrine neoplasms using deep learning: A study based on Surveillance, Epidemiology, and End Results database.
Jiang C; Wang K; Yan L; Yao H; Shi H; Lin R
Cancer Med; 2023 Jun; 12(11):12413-12424. PubMed ID: 37165971
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
