BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

289 related articles for article (PubMed ID: 33177794)

  • 1. Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning.
    Jang HJ; Lee A; Kang J; Song IH; Lee SH
    World J Gastroenterol; 2020 Oct; 26(40):6207-6223. PubMed ID: 33177794
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach.
    Jang HJ; Lee A; Kang J; Song IH; Lee SH
    World J Gastroenterol; 2021 Nov; 27(44):7687-7704. PubMed ID: 34908807
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Feasibility of deep learning-based fully automated classification of microsatellite instability in tissue slides of colorectal cancer.
    Lee SH; Song IH; Jang HJ
    Int J Cancer; 2021 Aug; 149(3):728-740. PubMed ID: 33851412
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. PPsNet: An improved deep learning model for microsatellite instability high prediction in colorectal cancer from whole slide images.
    Lou J; Xu J; Zhang Y; Sun Y; Fang A; Liu J; Mur LAJ; Ji B
    Comput Methods Programs Biomed; 2022 Oct; 225():107095. PubMed ID: 36057226
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality.
    Zhou J; Foroughi Pour A; Deirawan H; Daaboul F; Aung TN; Beydoun R; Ahmed FS; Chuang JH
    EBioMedicine; 2023 Aug; 94():104726. PubMed ID: 37499603
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study.
    Yamashita R; Long J; Longacre T; Peng L; Berry G; Martin B; Higgins J; Rubin DL; Shen J
    Lancet Oncol; 2021 Jan; 22(1):132-141. PubMed ID: 33387492
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Spatially aware graph neural networks and cross-level molecular profile prediction in colon cancer histopathology: a retrospective multi-cohort study.
    Ding K; Zhou M; Wang H; Zhang S; Metaxas DN
    Lancet Digit Health; 2022 Nov; 4(11):e787-e795. PubMed ID: 36307192
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Utility of artificial intelligence with deep learning of hematoxylin and eosin-stained whole slide images to predict lymph node metastasis in T1 colorectal cancer using endoscopically resected specimens; prediction of lymph node metastasis in T1 colorectal cancer.
    Song JH; Hong Y; Kim ER; Kim SH; Sohn I
    J Gastroenterol; 2022 Sep; 57(9):654-666. PubMed ID: 35802259
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Deep learning captures selective features for discrimination of microsatellite instability from pathologic tissue slides of gastric cancer.
    Lee SH; Lee Y; Jang HJ
    Int J Cancer; 2023 Jan; 152(2):298-307. PubMed ID: 36054320
    [TBL] [Abstract][Full Text] [Related]  

  • 11. SAMPLER: unsupervised representations for rapid analysis of whole slide tissue images.
    Mukashyaka P; Sheridan TB; Foroughi Pour A; Chuang JH
    EBioMedicine; 2024 Jan; 99():104908. PubMed ID: 38101298
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Clinical actionability of triaging DNA mismatch repair deficient colorectal cancer from biopsy samples using deep learning.
    Jiang W; Mei WJ; Xu SY; Ling YH; Li WR; Kuang JB; Li HS; Hui H; Li JB; Cai MY; Pan ZZ; Zhang HZ; Li L; Ding PR
    EBioMedicine; 2022 Jul; 81():104120. PubMed ID: 35753152
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A novel deep learning-based algorithm combining histopathological features with tissue areas to predict colorectal cancer survival from whole-slide images.
    Li YJ; Chou HH; Lin PC; Shen MR; Hsieh SY
    J Transl Med; 2023 Oct; 21(1):731. PubMed ID: 37848862
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on CT imaging.
    He K; Liu X; Li M; Li X; Yang H; Zhang H
    BMC Med Imaging; 2020 Jun; 20(1):59. PubMed ID: 32487083
    [TBL] [Abstract][Full Text] [Related]  

  • 15. [Identifying Molecular Subtypes of Whole-Slide Image in Colorectal Cancer via Deep Learning].
    Liao J; Feng XB; Wang YH; Guo LC
    Sichuan Da Xue Xue Bao Yi Xue Ban; 2021 Jul; 52(4):686-692. PubMed ID: 34323050
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Deep learning can predict lymph node status directly from histology in colorectal cancer.
    Kiehl L; Kuntz S; Höhn J; Jutzi T; Krieghoff-Henning E; Kather JN; Holland-Letz T; Kopp-Schneider A; Chang-Claude J; Brobeil A; von Kalle C; Fröhling S; Alwers E; Brenner H; Hoffmeister M; Brinker TJ
    Eur J Cancer; 2021 Nov; 157():464-473. PubMed ID: 34649117
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Prediction of prognosis and treatment response in ovarian cancer patients from histopathology images using graph deep learning: a multicenter retrospective study.
    Yang Z; Zhang Y; Zhuo L; Sun K; Meng F; Zhou M; Sun J
    Eur J Cancer; 2024 Mar; 199():113532. PubMed ID: 38241820
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Deep learning radiopathomics based on preoperative US images and biopsy whole slide images can distinguish between luminal and non-luminal tumors in early-stage breast cancers.
    Huang Y; Yao Z; Li L; Mao R; Huang W; Hu Z; Hu Y; Wang Y; Guo R; Tang X; Yang L; Wang Y; Luo R; Yu J; Zhou J
    EBioMedicine; 2023 Aug; 94():104706. PubMed ID: 37478528
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Biased data, biased AI: deep networks predict the acquisition site of TCGA images.
    Dehkharghanian T; Bidgoli AA; Riasatian A; Mazaheri P; Campbell CJV; Pantanowitz L; Tizhoosh HR; Rahnamayan S
    Diagn Pathol; 2023 May; 18(1):67. PubMed ID: 37198691
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Deep Learning-Based Classification of Uterine Cervical and Endometrial Cancer Subtypes from Whole-Slide Histopathology Images.
    Song J; Im S; Lee SH; Jang HJ
    Diagnostics (Basel); 2022 Oct; 12(11):. PubMed ID: 36359467
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.