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 *

167 related articles for article (PubMed ID: 39343999)

  • 1. Exploring prognostic biomarkers in pathological images of colorectal cancer patients via deep learning.
    Wei B; Li L; Feng Y; Liu S; Fu P; Tian L
    J Pathol Clin Res; 2024 Nov; 10(6):e70003. PubMed ID: 39343999
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Exploring prognostic indicators in the pathological images of hepatocellular carcinoma based on deep learning.
    Shi JY; Wang X; Ding GY; Dong Z; Han J; Guan Z; Ma LJ; Zheng Y; Zhang L; Yu GZ; Wang XY; Ding ZB; Ke AW; Yang H; Wang L; Ai L; Cao Y; Zhou J; Fan J; Liu X; Gao Q
    Gut; 2021 May; 70(5):951-961. PubMed ID: 32998878
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A retrospective analysis using deep-learning models for prediction of survival outcome and benefit of adjuvant chemotherapy in stage II/III colorectal cancer.
    Li X; Jonnagaddala J; Yang S; Zhang H; Xu XS
    J Cancer Res Clin Oncol; 2022 Aug; 148(8):1955-1963. PubMed ID: 35332389
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Construction and validation of a deep learning prognostic model based on digital pathology images of stage III colorectal cancer.
    Zhou X; Lu Y; Wu Y; Yu Y; Liu Y; Wang C; Zhao Z; Wang C; Gao Z; Li Z; Zhao Y; Cao W
    Eur J Surg Oncol; 2024 Jul; 50(7):108369. PubMed ID: 38703632
    [TBL] [Abstract][Full Text] [Related]  

  • 5. N6-Methyladenosine-Related lncRNA Signature Predicts the Overall Survival of Colorectal Cancer Patients.
    Song W; Ren J; Yuan W; Xiang R; Ge Y; Fu T
    Genes (Basel); 2021 Aug; 12(9):. PubMed ID: 34573357
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Construction and Validation of Novel Ferroptosis-related Risk Score Signature and Prognostic Prediction Nomogram for Patients with Colorectal Cancer.
    Liu R; Wang Y; Bu J; Li Q; Chen F; Zhu M; Chi H; Yu G; Zhu T; Zhu X; Zhao G
    Int J Med Sci; 2024; 21(6):1103-1116. PubMed ID: 38774759
    [No Abstract]   [Full Text] [Related]  

  • 7. Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients.
    Wang R; Dai W; Gong J; Huang M; Hu T; Li H; Lin K; Tan C; Hu H; Tong T; Cai G
    J Hematol Oncol; 2022 Jan; 15(1):11. PubMed ID: 35073937
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables.
    Tan Y; Liu R; Xue JW; Feng Z
    Cancer Med; 2024 Apr; 13(7):e6947. PubMed ID: 38545828
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Oncological and prognostic impact of lymphovascular invasion in Colorectal Cancer patients.
    Wang X; Cao Y; Ding M; Liu J; Zuo X; Li H; Fan R
    Int J Med Sci; 2021; 18(7):1721-1729. PubMed ID: 33746588
    [No Abstract]   [Full Text] [Related]  

  • 10. Deep learning with whole slide images can improve the prognostic risk stratification with stage III colorectal cancer.
    Sun C; Li B; Wei G; Qiu W; Li D; Li X; Liu X; Wei W; Wang S; Liu Z; Tian J; Liang L
    Comput Methods Programs Biomed; 2022 Jun; 221():106914. PubMed ID: 35640390
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study.
    Bilal M; Raza SEA; Azam A; Graham S; Ilyas M; Cree IA; Snead D; Minhas F; Rajpoot NM
    Lancet Digit Health; 2021 Dec; 3(12):e763-e772. PubMed ID: 34686474
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Multi gene mutation signatures in colorectal cancer patients: predict for the diagnosis, pathological classification, staging and prognosis.
    Zhuang Y; Wang H; Jiang D; Li Y; Feng L; Tian C; Pu M; Wang X; Zhang J; Hu Y; Liu P
    BMC Cancer; 2021 Apr; 21(1):380. PubMed ID: 33836681
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A Pyroptosis-Related Gene Signature Predicts Prognosis and Tumor Immune Microenvironment in Colorectal Cancer.
    Li L; Li Y; Lin J; Pang W
    Technol Cancer Res Treat; 2024; 23():15330338241277584. PubMed ID: 39155627
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study.
    Kather JN; Krisam J; Charoentong P; Luedde T; Herpel E; Weis CA; Gaiser T; Marx A; Valous NA; Ferber D; Jansen L; Reyes-Aldasoro CC; Zörnig I; Jäger D; Brenner H; Chang-Claude J; Hoffmeister M; Halama N
    PLoS Med; 2019 Jan; 16(1):e1002730. PubMed ID: 30677016
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Development and validation of 3-CpG methylation prognostic signature based on different survival indicators for colorectal cancer.
    Huang H; Zhang L; Fu J; Tian T; Liu X; Liu Y; Sun H; Li D; Zhu L; Xu J; Zheng T; Jia C; Zhao Y
    Mol Carcinog; 2021 Jun; 60(6):403-412. PubMed ID: 33826760
    [TBL] [Abstract][Full Text] [Related]  

  • 16. The clinical value of a nomogram constructed from CEA, CA199, PT, FIB, tumor differentiation and TNM stage in colorectal cancer.
    Wang K; Ma L; Chen L; Jiang Y; Liu N; Cai J; Zhang Y
    Cancer Biomark; 2023; 38(4):537-549. PubMed ID: 37980649
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Computer-aided detection and prognosis of colorectal cancer on whole slide images using dual resolution deep learning.
    Xu Y; Jiang L; Chen W; Huang S; Liu Z; Zhang J
    J Cancer Res Clin Oncol; 2023 Jan; 149(1):91-101. PubMed ID: 36331654
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Establishment of a Nomogram by Integrating Molecular Markers and Tumor-Node-Metastasis Staging System for Predicting the Prognosis of Hepatocellular Carcinoma.
    Huang XT; Chen LH; Huang CS; Li JH; Cai JP; Chen W; Yin XY
    Dig Surg; 2019; 36(5):426-432. PubMed ID: 30481744
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Development and validation of prognostic nomograms based on De Ritis ratio and clinicopathological features for patients with stage II/III colorectal cancer.
    Fu J; Du F; Tian T; Huang H; Zhang L; Li D; Liu Y; Zhang D; Gao L; Zheng T; Liu Y; Zhao Y
    BMC Cancer; 2023 Jul; 23(1):620. PubMed ID: 37400788
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Spatial analysis of tumor-infiltrating lymphocytes in histological sections using deep learning techniques predicts survival in colorectal carcinoma.
    Xu H; Cha YJ; Clemenceau JR; Choi J; Lee SH; Kang J; Hwang TH
    J Pathol Clin Res; 2022 Jul; 8(4):327-339. PubMed ID: 35484698
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.