BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

150 related articles for article (PubMed ID: 38499632)

  • 1. Creation of a machine learning-based prognostic prediction model for various subtypes of laryngeal cancer.
    Wang W; Wang W; Zhang D; Zeng P; Wang Y; Lei M; Hong Y; Cai C
    Sci Rep; 2024 Mar; 14(1):6484. PubMed ID: 38499632
    [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. The Development of a Prediction Model Based on Random Survival Forest for the Postoperative Prognosis of Pancreatic Cancer: A SEER-Based Study.
    Lin J; Yin M; Liu L; Gao J; Yu C; Liu X; Xu C; Zhu J
    Cancers (Basel); 2022 Sep; 14(19):. PubMed ID: 36230593
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Which model is better in predicting the survival of laryngeal squamous cell carcinoma?: Comparison of the random survival forest based on machine learning algorithms to Cox regression: analyses based on SEER database.
    Sun H; Wu S; Li S; Jiang X
    Medicine (Baltimore); 2023 Mar; 102(10):e33144. PubMed ID: 36897699
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Development and validation of a random survival forest model for predicting long-term survival of early-stage young breast cancer patients based on the SEER database and an external validation cohort.
    Li LW; Liu X; Shen ML; Zhao MJ; Liu H
    Am J Cancer Res; 2024; 14(4):1609-1621. PubMed ID: 38726282
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Glottic and supraglottic laryngeal carcinoma: differences in epidemiology, clinical characteristics and prognosis.
    Raitiola H; Pukander J; Laippala P
    Acta Otolaryngol; 1999; 119(7):847-51. PubMed ID: 10687946
    [TBL] [Abstract][Full Text] [Related]  

  • 8. [Clinical study of laryngeal cancer].
    Higuchi E; Iizuka K; Shouda H; Takeichi N
    Nihon Jibiinkoka Gakkai Kaiho; 1996 Mar; 99(3):385-94. PubMed ID: 8934773
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. Machine learning-based overall and cancer-specific survival prediction of M0 penile squamous cell carcinoma:A population-based retrospective study.
    Chen D; Liang S; Chen J; Li K; Mi H
    Heliyon; 2024 Jan; 10(1):e23442. PubMed ID: 38163093
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Dual-energy CT may predict post-operative recurrence in early-stage glottic laryngeal cancer: a novel nomogram and risk stratification system.
    Zhang H; Zou Y; Tian F; Li W; Ji X; Guo Y; Li Q; Sun S; Sun F; Shen L; Xia S
    Eur Radiol; 2022 Mar; 32(3):1921-1930. PubMed ID: 34762148
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Impact of stage, management and recurrence on survival rates in laryngeal cancer.
    Brandstorp-Boesen J; Sørum Falk R; Boysen M; Brøndbo K
    PLoS One; 2017; 12(7):e0179371. PubMed ID: 28708883
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Comparison of the cox regression to machine learning in predicting the survival of anaplastic thyroid carcinoma.
    Xu L; Cai L; Zhu Z; Chen G
    BMC Endocr Disord; 2023 Jun; 23(1):129. PubMed ID: 37291551
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. [A clinical study of 1079 patients with laryngeal cancer].
    Fujii T; Sato T; Yoshino K; Inakami K; Nagahara M; Okita J
    Nihon Jibiinkoka Gakkai Kaiho; 1997 Aug; 100(8):856-63. PubMed ID: 9293766
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Development of machine learning prognostic models for overall survival of prostate cancer patients with lymph node-positive.
    Peng ZH; Tian JH; Chen BH; Zhou HB; Bi H; He MX; Li MR; Zheng XY; Wang YW; Chong T; Li ZL
    Sci Rep; 2023 Oct; 13(1):18424. PubMed ID: 37891423
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A prediction model based on random survival forest analysis of the overall survival of elderly female papillary thyroid carcinoma patients: a SEER-based study.
    Lun Y; Yuan H; Ma P; Chen J; Lu P; Wang W; Liang R; Zhang J; Gao W; Ding X; Li S; Wang Z; Guo J; Lu L
    Endocrine; 2024 Apr; ():. PubMed ID: 38558373
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Prediction of lung papillary adenocarcinoma-specific survival using ensemble machine learning models.
    Xia K; Chen D; Jin S; Yi X; Luo L
    Sci Rep; 2023 Sep; 13(1):14827. PubMed ID: 37684259
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.