These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

178 related articles for article (PubMed ID: 38163093)

  • 1. Machine learning-based overall and cancer-specific survival prediction of M0 penile squamous cell carcinomaļ¼šA 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]  

  • 2. Development and validation of HPV-associated and HPV-independent penile squamous cell carcinoma prognostic nomogram.
    Liu S; Shen Z; Yang H; Wang J; Wang X; Gong Y; Liu S; Lu Z; Huang T
    Int Urol Nephrol; 2024 Sep; 56(9):2929-2944. PubMed ID: 38679654
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 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. Explainable machine learning predicts survival of retroperitoneal liposarcoma: A study based on the SEER database and external validation in China.
    Wang M; Li Z; Zeng S; Wang Z; Ying Y; He W; Zhang Z; Wang H; Xu C
    Cancer Med; 2024 Jun; 13(11):e7324. PubMed ID: 38847519
    [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. A Clinical Nomogram for Predicting Overall Survival in Patients With T1/T2 Penile Squamous Cell Carcinoma.
    Qian S; Liu C; Zhao Y; Jin H; Li X; Zhao X
    Clin Genitourin Cancer; 2024 Oct; 22(5):102114. PubMed ID: 38959838
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Racial/ethnic disparities in penile squamous cell carcinoma incidences, clinical characteristics, and outcomes: A population-based study, 2004-2016.
    Huang T; Xu Y; Hu G; Zhu D; Xiao S; He R
    Urol Oncol; 2020 Aug; 38(8):688.e11-688.e19. PubMed ID: 32340796
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. Development and validation of a novel nomogram model for predicting the survival of patients with T2-4a, N0-x, M0 bladder cancer: a retrospective cohort study.
    Xia Y; Liu X; Ma B; Huang T; Xu D; Zhao C
    Am J Clin Exp Urol; 2023; 11(6):500-515. PubMed ID: 38148935
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Prognostic Value of Neutrophil-to-Lymphocyte Ratio in Penile Squamous Cell Carcinoma Patients Undergoing Inguinal Lymph Node Dissection.
    Azizi M; Peyton CC; Boulware DC; Chipollini J; Juwono T; Pow-Sang JM; Spiess PE
    Eur Urol Focus; 2019 Nov; 5(6):1085-1090. PubMed ID: 29937330
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?
    Anderson AB; Grazal CF; Balazs GC; Potter BK; Dickens JF; Forsberg JA
    Clin Orthop Relat Res; 2020 Jul; 478(7):0-1618. PubMed ID: 32282466
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Incidence trends and survival outcomes of penile squamous cell carcinoma: evidence from the Surveillance, Epidemiology and End Results population-based data.
    Qi F; Wei X; Zheng Y; Ren X; Li X; Zhao E
    Ann Transl Med; 2020 Nov; 8(21):1428. PubMed ID: 33313173
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A Clinical Model of Bone Angiosarcoma Patients: A Population-based Analysis of Epidemiology, Prognosis, and Treatment.
    Wang B; Chen LJ; Wang XY
    Orthop Surg; 2020 Dec; 12(6):1652-1662. PubMed ID: 32914587
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Survival prediction in second primary breast cancer patients with machine learning: An analysis of SEER database.
    Wu Y; Zhang Y; Duan S; Gu C; Wei C; Fang Y
    Comput Methods Programs Biomed; 2024 Sep; 254():108310. PubMed ID: 38996803
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Human papillomavirus infection affects treatment outcomes and the immune microenvironment in patients with advanced penile squamous cell carcinoma receiving programmed cell death protein 1 inhibitor-based combination therapy.
    Wei L; Li Z; Guo S; Ma H; Shi Y; An X; Huang K; Xiong L; Xue T; Zhang Z; Yao K; Luo J; Han H
    Cancer; 2024 May; 130(9):1650-1662. PubMed ID: 38157276
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Surveillance of prognostic risk factors in patients with SCCB using artificial intelligence: a retrospective study.
    Zhanghuang C; Zhang Z; Wang J; Yao Z; Ji F; Wu C; Ma J; Yang Z; Xie Y; Tang H; Yan B
    Sci Rep; 2023 May; 13(1):8727. PubMed ID: 37253772
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.