BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

493 related articles for article (PubMed ID: 33254045)

  • 1. Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients.
    Nazari M; Shiri I; Zaidi H
    Comput Biol Med; 2021 Feb; 129():104135. PubMed ID: 33254045
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT.
    Coy H; Hsieh K; Wu W; Nagarajan MB; Young JR; Douek ML; Brown MS; Scalzo F; Raman SS
    Abdom Radiol (NY); 2019 Jun; 44(6):2009-2020. PubMed ID: 30778739
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Computed Tomography-Based Radiomics Model to Predict Central Cervical Lymph Node Metastases in Papillary Thyroid Carcinoma: A Multicenter Study.
    Li J; Wu X; Mao N; Zheng G; Zhang H; Mou Y; Jia C; Mi J; Song X
    Front Endocrinol (Lausanne); 2021; 12():741698. PubMed ID: 34745008
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Computed tomography-based radiomics prediction of CTLA4 expression and prognosis in clear cell renal cell carcinoma.
    He H; Jin Z; Dai J; Wang H; Sun J; Xu D
    Cancer Med; 2023 Mar; 12(6):7627-7638. PubMed ID: 36397666
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Enhanced computed tomography radiomics-based machine-learning methods for predicting the Fuhrman grades of renal clear cell carcinoma.
    Yin RH; Yang YC; Tang XQ; Shi HF; Duan SF; Pan CJ
    J Xray Sci Technol; 2021; 29(6):1149-1160. PubMed ID: 34657848
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study.
    Abbaspour S; Barahman M; Abdollahi H; Arabalibeik H; Hajainfar G; Babaei M; Iraji H; Barzegartahamtan M; Ay MR; Mahdavi SR
    Biomed Phys Eng Express; 2023 Dec; 10(1):. PubMed ID: 37995359
    [No Abstract]   [Full Text] [Related]  

  • 9. 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]  

  • 10. 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]  

  • 11. Machine learning-based unenhanced CT texture analysis for predicting BAP1 mutation status of clear cell renal cell carcinomas.
    Kocak B; Durmaz ES; Kaya OK; Kilickesmez O
    Acta Radiol; 2020 Jun; 61(6):856-864. PubMed ID: 31635476
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Clear cell renal cell carcinoma: Machine learning-based computed tomography radiomics analysis for the prediction of WHO/ISUP grade.
    Shu J; Wen D; Xi Y; Xia Y; Cai Z; Xu W; Meng X; Liu B; Yin H
    Eur J Radiol; 2019 Dec; 121():108738. PubMed ID: 31756634
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Differentiation of Clear Cell and Non-clear-cell Renal Cell Carcinoma through CT-based Radiomics Models and Nomogram.
    Cheng D; Abudikeranmu Y; Tuerdi B
    Curr Med Imaging; 2023; 19(9):1005-1017. PubMed ID: 36411581
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. 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]  

  • 16. Textural differences between renal cell carcinoma subtypes: Machine learning-based quantitative computed tomography texture analysis with independent external validation.
    Kocak B; Yardimci AH; Bektas CT; Turkcanoglu MH; Erdim C; Yucetas U; Koca SB; Kilickesmez O
    Eur J Radiol; 2018 Oct; 107():149-157. PubMed ID: 30292260
    [TBL] [Abstract][Full Text] [Related]  

  • 17. CT-based peritumoral radiomics signatures for malignancy grading of clear cell renal cell carcinoma.
    Zhou Z; Qian X; Hu J; Ma X; Zhou S; Dai Y; Zhu J
    Abdom Radiol (NY); 2021 Jun; 46(6):2690-2698. PubMed ID: 33427908
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Application of machine learning model to predict osteoporosis based on abdominal computed tomography images of the psoas muscle: a retrospective study.
    Huang CB; Hu JS; Tan K; Zhang W; Xu TH; Yang L
    BMC Geriatr; 2022 Oct; 22(1):796. PubMed ID: 36229793
    [TBL] [Abstract][Full Text] [Related]  

  • 19. The value of a dual-energy CT Iodine map radiomics model for the prediction of collagen fiber content in the ccRCC tumor microenvironment.
    Li Z; Wang N; Bing X; Li Y; Yao J; Li R; Ouyang A
    BMC Med Imaging; 2023 Nov; 23(1):186. PubMed ID: 37968599
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer.
    Gu Q; Feng Z; Liang Q; Li M; Deng J; Ma M; Wang W; Liu J; Liu P; Rong P
    Eur J Radiol; 2019 Sep; 118():32-37. PubMed ID: 31439255
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 25.