BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

141 related articles for article (PubMed ID: 37528301)

  • 1. An automatic texture feature analysis framework of renal tumor: surgical, pathological, and molecular evaluation based on multi-phase abdominal CT.
    Yang H; Liu H; Lin J; Xiao H; Guo Y; Mei H; Ding Q; Yuan Y; Lai X; Wu K; Wu S
    Eur Radiol; 2024 Jan; 34(1):355-366. PubMed ID: 37528301
    [TBL] [Abstract][Full Text] [Related]  

  • 2. An automated surgical decision-making framework for partial or radical nephrectomy based on 3D-CT multi-level anatomical features in renal cell carcinoma.
    Yang H; Wu K; Liu H; Wu P; Yuan Y; Wang L; Liu Y; Zeng H; Li J; Liu W; Wu S
    Eur Radiol; 2023 Nov; 33(11):7532-7541. PubMed ID: 37289245
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Automatic analysis framework based on 3D-CT multi-scale features for accurate prediction of Ki67 expression levels in substantial renal cell carcinoma.
    Yang H; Lin J; Liu H; Yao J; Lin Q; Wang J; Jiang F; Wei L; Lin C; Wu K; Wu S
    Insights Imaging; 2023 Jul; 14(1):130. PubMed ID: 37466878
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A comprehensive texture feature analysis framework of renal cell carcinoma: pathological, prognostic, and genomic evaluation based on CT images.
    Wu K; Wu P; Yang K; Li Z; Kong S; Yu L; Zhang E; Liu H; Guo Q; Wu S
    Eur Radiol; 2022 Apr; 32(4):2255-2265. PubMed ID: 34800150
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses.
    Yap FY; Varghese BA; Cen SY; Hwang DH; Lei X; Desai B; Lau C; Yang LL; Fullenkamp AJ; Hajian S; Rivas M; Gupta MN; Quinn BD; Aron M; Desai MM; Aron M; Oberai AA; Gill IS; Duddalwar VA
    Eur Radiol; 2021 Feb; 31(2):1011-1021. PubMed ID: 32803417
    [TBL] [Abstract][Full Text] [Related]  

  • 6. CT-based radiomic model predicts high grade of clear cell renal cell carcinoma.
    Ding J; Xing Z; Jiang Z; Chen J; Pan L; Qiu J; Xing W
    Eur J Radiol; 2018 Jun; 103():51-56. PubMed ID: 29803385
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics.
    Cui E; Li Z; Ma C; Li Q; Lei Y; Lan Y; Yu J; Zhou Z; Li R; Long W; Lin F
    Eur Radiol; 2020 May; 30(5):2912-2921. PubMed ID: 32002635
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis.
    Luo S; Wei R; Lu S; Lai S; Wu J; Wu Z; Pang X; Wei X; Jiang X; Zhen X; Yang R
    Eur Radiol; 2022 Apr; 32(4):2340-2350. PubMed ID: 34636962
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade.
    Bektas CT; Kocak B; Yardimci AH; Turkcanoglu MH; Yucetas U; Koca SB; Erdim C; Kilickesmez O
    Eur Radiol; 2019 Mar; 29(3):1153-1163. PubMed ID: 30167812
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Radiomics of small renal masses on multiphasic CT: accuracy of machine learning-based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat.
    Yang R; Wu J; Sun L; Lai S; Xu Y; Liu X; Ma Y; Zhen X
    Eur Radiol; 2020 Feb; 30(2):1254-1263. PubMed ID: 31468159
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.
    Lee H; Hong H; Kim J; Jung DC
    Med Phys; 2018 Apr; 45(4):1550-1561. PubMed ID: 29474742
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning.
    Nazari M; Shiri I; Hajianfar G; Oveisi N; Abdollahi H; Deevband MR; Oveisi M; Zaidi H
    Radiol Med; 2020 Aug; 125(8):754-762. PubMed ID: 32193870
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Influence of segmentation margin on machine learning-based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas.
    Kocak B; Ates E; Durmaz ES; Ulusan MB; Kilickesmez O
    Eur Radiol; 2019 Sep; 29(9):4765-4775. PubMed ID: 30747300
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Interpretability of radiomics models is improved when using feature group selection strategies for predicting molecular and clinical targets in clear-cell renal cell carcinoma: insights from the TRACERx Renal study.
    Orton MR; Hann E; Doran SJ; Shepherd STC; Ap Dafydd D; Spencer CE; López JI; Albarrán-Artahona V; Comito F; Warren H; Shur J; Messiou C; Larkin J; Turajlic S; ; Koh DM
    Cancer Imaging; 2023 Aug; 23(1):76. PubMed ID: 37580840
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma.
    Feng Z; Rong P; Cao P; Zhou Q; Zhu W; Yan Z; Liu Q; Wang W
    Eur Radiol; 2018 Apr; 28(4):1625-1633. PubMed ID: 29134348
    [TBL] [Abstract][Full Text] [Related]  

  • 16. CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma.
    Demirjian NL; Varghese BA; Cen SY; Hwang DH; Aron M; Siddiqui I; Fields BKK; Lei X; Yap FY; Rivas M; Reddy SS; Zahoor H; Liu DH; Desai M; Rhie SK; Gill IS; Duddalwar V
    Eur Radiol; 2022 Apr; 32(4):2552-2563. PubMed ID: 34757449
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Predicting the recurrence risk of renal cell carcinoma after nephrectomy: potential role of CT-radiomics for adjuvant treatment decisions.
    Deniffel D; McAlpine K; Harder FN; Jain R; Lawson KA; Healy GM; Hui S; Zhang X; Salinas-Miranda E; van der Kwast T; Finelli A; Haider MA
    Eur Radiol; 2023 Aug; 33(8):5840-5850. PubMed ID: 37074425
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis.
    Erdim C; Yardimci AH; Bektas CT; Kocak B; Koca SB; Demir H; Kilickesmez O
    Acad Radiol; 2020 Oct; 27(10):1422-1429. PubMed ID: 32014404
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification.
    Lee HS; Hong H; Jung DC; Park S; Kim J
    Med Phys; 2017 Jul; 44(7):3604-3614. PubMed ID: 28376281
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Distinguishing common renal cell carcinomas from benign renal tumors based on machine learning: comparing various CT imaging phases, slices, tumor sizes, and ROI segmentation strategies.
    Zhou T; Guan J; Feng B; Xue H; Cui J; Kuang Q; Chen Y; Xu K; Lin F; Cui E; Long W
    Eur Radiol; 2023 Jun; 33(6):4323-4332. PubMed ID: 36645455
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.