BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

393 related articles for article (PubMed ID: 31061433)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 5. 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]  

  • 6. 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]  

  • 7. Machine learning-based prediction of 1-year mortality for acute coronary syndrome
    Hadanny A; Shouval R; Wu J; Gale CP; Unger R; Zahger D; Gottlieb S; Matetzky S; Goldenberg I; Beigel R; Iakobishvili Z
    J Cardiol; 2022 Mar; 79(3):342-351. PubMed ID: 34857429
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Overexpression of cysteine-glutamate transporter and CD44 for prediction of recurrence and survival in patients with oral cavity squamous cell carcinoma.
    Lee JR; Roh JL; Lee SM; Park Y; Cho KJ; Choi SH; Nam SY; Kim SY
    Head Neck; 2018 Nov; 40(11):2340-2346. PubMed ID: 30303590
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Development and Assessment of a Machine Learning Model to Help Predict Survival Among Patients With Oral Squamous Cell Carcinoma.
    Karadaghy OA; Shew M; New J; Bur AM
    JAMA Otolaryngol Head Neck Surg; 2019 Dec; 145(12):1115-1120. PubMed ID: 31045212
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A Three Long Noncoding RNA-Based Signature for Oral Squamous Cell Carcinoma Prognosis Prediction.
    Zhao C; Zou H; Wang J; Shen J; Liu H
    DNA Cell Biol; 2018 Nov; 37(11):888-895. PubMed ID: 30234381
    [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. 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]  

  • 14. Deep-learning model for predicting the survival of rectal adenocarcinoma patients based on a surveillance, epidemiology, and end results analysis.
    Yu H; Huang T; Feng B; Lyu J
    BMC Cancer; 2022 Feb; 22(1):210. PubMed ID: 35216571
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network.
    Katzman JL; Shaham U; Cloninger A; Bates J; Jiang T; Kluger Y
    BMC Med Res Methodol; 2018 Feb; 18(1):24. PubMed ID: 29482517
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Multimodal artificial intelligence-based pathogenomics improves survival prediction in oral squamous cell carcinoma.
    Vollmer A; Hartmann S; Vollmer M; Shavlokhova V; Brands RC; Kübler A; Wollborn J; Hassel F; Couillard-Despres S; Lang G; Saravi B
    Sci Rep; 2024 Mar; 14(1):5687. PubMed ID: 38453964
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Prognostic implications of mandibular invasion in oral cancer.
    Ash CS; Nason RW; Abdoh AA; Cohen MA
    Head Neck; 2000 Dec; 22(8):794-8. PubMed ID: 11084640
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Survival prediction for oral tongue cancer patients via probabilistic genetic algorithm optimized neural network models.
    Pan X; Zhang T; Yang Q; Yang D; Rwigema JC; Qi XS
    Br J Radiol; 2020 Aug; 93(1112):20190825. PubMed ID: 32520585
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 20.