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 *

409 related articles for article (PubMed ID: 35577699)

  • 21. Radiomics study for predicting the expression of PD-L1 in non-small cell lung cancer based on CT images and clinicopathologic features.
    Sun Z; Hu S; Ge Y; Wang J; Duan S; Song J; Hu C; Li Y
    J Xray Sci Technol; 2020; 28(3):449-459. PubMed ID: 32176676
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Radiomics analysis based on single phase and different phase combinations of radiomics features from tri-phasic CT to distinguish renal oncocytoma from chromophobe renal cell carcinoma.
    Yang S; Jian Y; Yang F; Liu R; Zhang W; Wang J; Tan X; Wu J; Chen Y; Zhou X
    Abdom Radiol (NY); 2024 Jan; 49(1):182-191. PubMed ID: 37907684
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Development and validation of a nonenhanced CT based radiomics model to detect brown adipose tissue.
    Li J; Zuo R; Schoepf UJ; Griffith JP; Wu S; Zhou C; Chen X; Tan W; Zhou Z; Gao H; Zhang L; Yang G
    Theranostics; 2023; 13(5):1584-1593. PubMed ID: 37056567
    [No Abstract]   [Full Text] [Related]  

  • 24. Deep Learning Radiomics Model of Contrast-Enhanced CT for Differentiating the Primary Source of Liver Metastases.
    Jia W; Li F; Cui Y; Wang Y; Dai Z; Yan Q; Liu X; Li Y; Chang H; Zeng Q
    Acad Radiol; 2024 Oct; 31(10):4057-4067. PubMed ID: 38702214
    [TBL] [Abstract][Full Text] [Related]  

  • 25. 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]  

  • 26. 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]  

  • 27. Computed tomography-based radiomics nomogram for the preoperative prediction of perineural invasion in colorectal cancer: a multicentre study.
    Chen Q; Cui Y; Xue T; Peng H; Li M; Zhu X; Duan S; Gu H; Feng F
    Abdom Radiol (NY); 2022 Sep; 47(9):3251-3263. PubMed ID: 35960308
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features.
    Yin P; Mao N; Zhao C; Wu J; Sun C; Chen L; Hong N
    Eur Radiol; 2019 Apr; 29(4):1841-1847. PubMed ID: 30280245
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Prediction of TTF-1 expression in non-small-cell lung cancer using machine learning-based radiomics.
    Zhang R; Huo X; Wang Q; Zhang J; Duan S; Zhang Q; Zhang S
    J Cancer Res Clin Oncol; 2023 Jul; 149(8):4547-4554. PubMed ID: 36151427
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Contrast-enhanced CT radiomics for preoperative prediction of stage in epithelial ovarian cancer: a multicenter study.
    Leng Y; Kan A; Wang X; Li X; Xiao X; Wang Y; Liu L; Gong L
    BMC Cancer; 2024 Mar; 24(1):307. PubMed ID: 38448945
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Radiomics and machine learning for renal tumor subtype assessment using multiphase computed tomography in a multicenter setting.
    Uhlig A; Uhlig J; Leha A; Biggemann L; Bachanek S; Stöckle M; Reichert M; Lotz J; Zeuschner P; Maßmann A
    Eur Radiol; 2024 Oct; 34(10):6254-6263. PubMed ID: 38634876
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Incremental value of automatically segmented perirenal adipose tissue for pathological grading of clear cell renal cell carcinoma: a multicenter cohort study.
    Li S; Zhou Z; Gao M; Liao Z; He K; Qu W; Li J; Kamel IR; Chu Q; Zhang Q; Li Z
    Int J Surg; 2024 Jul; 110(7):4221-4230. PubMed ID: 38573065
    [TBL] [Abstract][Full Text] [Related]  

  • 33. 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]  

  • 34. Differentiating Primary Tumors for Brain Metastasis with Integrated Radiomics from Multiple Imaging Modalities.
    Cao G; Zhang J; Lei X; Yu B; Ai Y; Zhang Z; Xie C; Jin X
    Dis Markers; 2022; 2022():5147085. PubMed ID: 36199819
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Multimodal data integration using machine learning to predict the risk of clear cell renal cancer metastasis: a retrospective multicentre study.
    Yang Y; Wang J; Ren Q; Yu R; Yuan Z; Jiang Q; Guan S; Tang X; Duan T; Meng X
    Abdom Radiol (NY); 2024 Jul; 49(7):2311-2324. PubMed ID: 38879708
    [TBL] [Abstract][Full Text] [Related]  

  • 36. CT-Based Radiomics Analysis of Different Machine Learning Models for Discriminating the Risk Stratification of Pheochromocytoma and Paraganglioma: A Multicenter Study.
    Zhou Y; Zhan Y; Zhao J; Zhong L; Tan Y; Zeng W; Zeng Q; Gong M; Li A; Gong L; Liu L
    Acad Radiol; 2024 Jul; 31(7):2859-2871. PubMed ID: 38302388
    [TBL] [Abstract][Full Text] [Related]  

  • 37. A CT-based radiomics model for predicting renal capsule invasion in renal cell carcinoma.
    Yang L; Gao L; Arefan D; Tan Y; Dan H; Zhang J
    BMC Med Imaging; 2022 Jan; 22(1):15. PubMed ID: 35094674
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram.
    Liu A; Wang Z; Yang Y; Wang J; Dai X; Wang L; Lu Y; Xue F
    Cancer Commun (Lond); 2020 Jan; 40(1):16-24. PubMed ID: 32125097
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions.
    Kirienko M; Cozzi L; Rossi A; Voulaz E; Antunovic L; Fogliata A; Chiti A; Sollini M
    Eur J Nucl Med Mol Imaging; 2018 Sep; 45(10):1649-1660. PubMed ID: 29623375
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Computed Tomography-Based Radiomic Features Could Potentially Predict Microsatellite Instability Status in Stage II Colorectal Cancer: A Preliminary Study.
    Fan S; Li X; Cui X; Zheng L; Ren X; Ma W; Ye Z
    Acad Radiol; 2019 Dec; 26(12):1633-1640. PubMed ID: 30929999
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 21.