BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

115 related articles for article (PubMed ID: 38377634)

  • 21. Machine learning model based on enhanced CT radiomics for the preoperative prediction of lymphovascular invasion in esophageal squamous cell carcinoma.
    Wang Y; Bai G; Huang M; Chen W
    Front Oncol; 2024; 14():1308317. PubMed ID: 38549935
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Machine learning analysis for the noninvasive prediction of lymphovascular invasion in gastric cancer using PET/CT and enhanced CT-based radiomics and clinical variables.
    Fan L; Li J; Zhang H; Yin H; Zhang R; Zhang J; Chen X
    Abdom Radiol (NY); 2022 Apr; 47(4):1209-1222. PubMed ID: 35089370
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Development and validation of a Radiopathomics model based on CT scans and whole slide images for discriminating between Stage I-II and Stage III gastric cancer.
    Tan Y; Feng LJ; Huang YH; Xue JW; Feng ZB; Long LL
    BMC Cancer; 2024 Mar; 24(1):368. PubMed ID: 38519974
    [TBL] [Abstract][Full Text] [Related]  

  • 24. The predictive potential of contrast-enhanced computed tomography based radiomics in the preoperative staging of cT4 gastric cancer.
    Liu B; Zhang D; Wang H; Wang H; Zhang P; Zhang D; Zhang Q; Zhang J
    Quant Imaging Med Surg; 2022 Nov; 12(11):5222-5238. PubMed ID: 36330185
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Radiographical assessment of tumour stroma and treatment outcomes using deep learning: a retrospective, multicohort study.
    Jiang Y; Liang X; Han Z; Wang W; Xi S; Li T; Chen C; Yuan Q; Li N; Yu J; Xie Y; Xu Y; Zhou Z; Poultsides GA; Li G; Li R
    Lancet Digit Health; 2021 Jun; 3(6):e371-e382. PubMed ID: 34045003
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Value of muscle quality, strength and gait speed in supporting the predictive power of GLIM-defined malnutrition for postoperative outcomes in overweight patients with gastric cancer.
    Huang DD; Wu GF; Luo X; Song HN; Wang WB; Liu NX; Yu Z; Dong QT; Chen XL; Yan JY
    Clin Nutr; 2021 Jun; 40(6):4201-4208. PubMed ID: 33583658
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Quantitative Radiological Features and Deep Learning for the Non-Invasive Evaluation of Programmed Death Ligand 1 Expression Levels in Gastric Cancer Patients: A Digital Biopsy Study.
    Xie W; Jiang Z; Zhou X; Zhang X; Zhang M; Liu R; Zheng L; Xin F; Lu Y; Wang D
    Acad Radiol; 2023 Jul; 30(7):1317-1328. PubMed ID: 36369191
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer.
    Li J; Dong D; Fang M; Wang R; Tian J; Li H; Gao J
    Eur Radiol; 2020 Apr; 30(4):2324-2333. PubMed ID: 31953668
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Deep learning radio-clinical signatures for predicting neoadjuvant chemotherapy response and prognosis from pretreatment CT images of locally advanced gastric cancer patients.
    Hu C; Chen W; Li F; Zhang Y; Yu P; Yang L; Huang L; Sun J; Chen S; Shi C; Sun Y; Ye Z; Yuan L; Chen J; Wei Q; Xu J; Xu H; Tong Y; Bao Z; Huang C; Li Y; Du Y; Xu Z; Cheng X
    Int J Surg; 2023 Jul; 109(7):1980-1992. PubMed ID: 37132183
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.
    Chen X; Feng B; Xu K; Chen Y; Duan X; Jin Z; Li K; Li R; Long W; Liu X
    Eur Radiol; 2023 Oct; 33(10):6804-6816. PubMed ID: 37148352
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Impact of malnutrition diagnosed using Global Leadership Initiative on Malnutrition criteria on clinical outcomes of patients with gastric cancer.
    Xu LB; Shi MM; Huang ZX; Zhang WT; Zhang HH; Shen X; Chen XD
    JPEN J Parenter Enteral Nutr; 2022 Feb; 46(2):385-394. PubMed ID: 33908649
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Application of machine learning model in predicting the likelihood of blood transfusion after hip fracture surgery.
    Chen X; Pan J; Li Y; Tang R
    Aging Clin Exp Res; 2023 Nov; 35(11):2643-2656. PubMed ID: 37733228
    [TBL] [Abstract][Full Text] [Related]  

  • 33. The GLIM criteria as an effective tool for nutrition assessment and survival prediction in older adult cancer patients.
    Zhang X; Tang M; Zhang Q; Zhang KP; Guo ZQ; Xu HX; Yuan KT; Yu M; Braga M; Cederholm T; Li W; Barazzoni R; Shi HP
    Clin Nutr; 2021 Mar; 40(3):1224-1232. PubMed ID: 32826109
    [TBL] [Abstract][Full Text] [Related]  

  • 34. A comprehensive radiopathological nomogram for the prediction of pathological staging in gastric cancer using CT-derived and WSI-based features.
    Tan Y; Feng LJ; Huang YH; Xue JW; Long LL; Feng ZB
    Transl Oncol; 2024 Feb; 40():101864. PubMed ID: 38141376
    [TBL] [Abstract][Full Text] [Related]  

  • 35. CT-based deep learning radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer.
    Chen W; Gong M; Zhou D; Zhang L; Kong J; Jiang F; Feng S; Yuan R
    Front Oncol; 2022; 12():1019749. PubMed ID: 36544709
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Predictive validity of GLIM malnutrition diagnosis in patients with colorectal cancer.
    da Silva Couto A; Gonzalez MC; Martucci RB; Feijó PM; Rodrigues VD; de Pinho NB; Souza NC
    JPEN J Parenter Enteral Nutr; 2023 Mar; 47(3):420-428. PubMed ID: 36645343
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Evaluation of malnutrition in patients undergoing major abdominal surgery using GLIM criteria and comparing CT and BIA for muscle mass measurement.
    Wobith M; Herbst C; Lurz M; Haberzettl D; Fischer M; Weimann A
    Clin Nutr ESPEN; 2022 Aug; 50():148-154. PubMed ID: 35871916
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Impact of GLIM Defined Malnutrition on Long Term Prognosis in Patients With Gastric Cancer After Gastrectomy.
    Matsui R; Inaki N; Tsuji T
    Anticancer Res; 2022 Sep; 42(9):4611-4618. PubMed ID: 36039451
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Computed Tomography-Based Deep Learning Nomogram Can Accurately Predict Lymph Node Metastasis in Gastric Cancer.
    Guan X; Lu N; Zhang J
    Dig Dis Sci; 2023 Apr; 68(4):1473-1481. PubMed ID: 35909203
    [TBL] [Abstract][Full Text] [Related]  

  • 40. T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma.
    Zheng HD; Huang QY; Huang QM; Ke XT; Ye K; Lin S; Xu JH
    World J Gastrointest Oncol; 2024 Mar; 16(3):819-832. PubMed ID: 38577440
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 6.