BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

150 related articles for article (PubMed ID: 38468226)

  • 1. A noninvasive method for predicting clinically significant prostate cancer using magnetic resonance imaging combined with PRKY promoter methylation level: a machine learning study.
    Wang Y; Liu W; Chen Z; Zang Y; Xu L; Dai Z; Zhou Y; Zhu J
    BMC Med Imaging; 2024 Mar; 24(1):60. PubMed ID: 38468226
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Magnetic Resonance Imaging Radiomics-Based Machine Learning Prediction of Clinically Significant Prostate Cancer in Equivocal PI-RADS 3 Lesions.
    Hectors SJ; Chen C; Chen J; Wang J; Gordon S; Yu M; Al Hussein Al Awamlh B; Sabuncu MR; Margolis DJA; Hu JC
    J Magn Reson Imaging; 2021 Nov; 54(5):1466-1473. PubMed ID: 33970516
    [TBL] [Abstract][Full Text] [Related]  

  • 3. [The value of machine learning models based on biparametric MRI for diagnosis of prostate cancer and clinically significant prostate cancer].
    Qiao XM; Hu CH; Hu S; Hu CH; Wang XM; Shen JK; Ji LB; Song Y; Bao J
    Zhonghua Yi Xue Za Zhi; 2023 May; 103(19):1446-1454. PubMed ID: 37198106
    [No Abstract]   [Full Text] [Related]  

  • 4. Machine learning-based radiomics model to predict benign and malignant PI-RADS v2.1 category 3 lesions: a retrospective multi-center study.
    Jin P; Shen J; Yang L; Zhang J; Shen A; Bao J; Wang X
    BMC Med Imaging; 2023 Mar; 23(1):47. PubMed ID: 36991347
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A radiomics machine learning-based redefining score robustly identifies clinically significant prostate cancer in equivocal PI-RADS score 3 lesions.
    Hou Y; Bao ML; Wu CJ; Zhang J; Zhang YD; Shi HB
    Abdom Radiol (NY); 2020 Dec; 45(12):4223-4234. PubMed ID: 32740863
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Predicting the Grade of Prostate Cancer Based on a Biparametric MRI Radiomics Signature.
    Zhang L; Zhe X; Tang M; Zhang J; Ren J; Zhang X; Li L
    Contrast Media Mol Imaging; 2021; 2021():7830909. PubMed ID: 35024015
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prostate Cancer Differentiation and Aggressiveness: Assessment With a Radiomic-Based Model vs. PI-RADS v2.
    Chen T; Li M; Gu Y; Zhang Y; Yang S; Wei C; Wu J; Li X; Zhao W; Shen J
    J Magn Reson Imaging; 2019 Mar; 49(3):875-884. PubMed ID: 30230108
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Utility of Clinical-Radiomic Model to Identify Clinically Significant Prostate Cancer in Biparametric MRI PI-RADS V2.1 Category 3 Lesions.
    Jin P; Yang L; Qiao X; Hu C; Hu C; Wang X; Bao J
    Front Oncol; 2022; 12():840786. PubMed ID: 35280813
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Bi-parametric magnetic resonance imaging based radiomics for the identification of benign and malignant prostate lesions: cross-vendor validation.
    Ji X; Zhang J; Shi W; He D; Bao J; Wei X; Huang Y; Liu Y; Chen JC; Gao X; Tang Y; Xia W
    Phys Eng Sci Med; 2021 Sep; 44(3):745-754. PubMed ID: 34075559
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Utility of machine learning of apparent diffusion coefficient (ADC) and T2-weighted (T2W) radiomic features in PI-RADS version 2.1 category 3 lesions to predict prostate cancer diagnosis.
    Lim CS; Abreu-Gomez J; Thornhill R; James N; Al Kindi A; Lim AS; Schieda N
    Abdom Radiol (NY); 2021 Dec; 46(12):5647-5658. PubMed ID: 34467426
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters.
    Woźnicki P; Westhoff N; Huber T; Riffel P; Froelich MF; Gresser E; von Hardenberg J; Mühlberg A; Michel MS; Schoenberg SO; Nörenberg D
    Cancers (Basel); 2020 Jul; 12(7):. PubMed ID: 32630787
    [TBL] [Abstract][Full Text] [Related]  

  • 12. [Application of MRI-based Radiomics Models in the Assessment of Hepatic Metastasis of Rectal Cancer].
    Hu SX; Yang K; Wang XR; Wen DG; Xia CC; Li X; Li ZL
    Sichuan Da Xue Xue Bao Yi Xue Ban; 2021 Mar; 52(2):311-318. PubMed ID: 33829708
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer.
    Abdollahi H; Mofid B; Shiri I; Razzaghdoust A; Saadipoor A; Mahdavi A; Galandooz HM; Mahdavi SR
    Radiol Med; 2019 Jun; 124(6):555-567. PubMed ID: 30607868
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Biparametric MRI-based radiomics classifiers for the detection of prostate cancer in patients with PSA serum levels of 4∼10 ng/mL.
    Lu Y; Li B; Huang H; Leng Q; Wang Q; Zhong R; Huang Y; Li C; Yuan R; Zhang Y
    Front Oncol; 2022; 12():1020317. PubMed ID: 36582803
    [TBL] [Abstract][Full Text] [Related]  

  • 15. MRI-Based Surrogate Imaging Markers of Aggressiveness in Prostate Cancer: Development of a Machine Learning Model Based on Radiomic Features.
    Dominguez I; Rios-Ibacache O; Caprile P; Gonzalez J; San Francisco IF; Besa C
    Diagnostics (Basel); 2023 Aug; 13(17):. PubMed ID: 37685317
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Magnetic resonance imaging radiomics-based prediction of clinically significant prostate cancer in equivocal PI-RADS 3 lesions in the transitional zone.
    Zhao YY; Xiong ML; Liu YF; Duan LJ; Chen JL; Xing Z; Lin YS; Chen TH
    Front Oncol; 2023; 13():1247682. PubMed ID: 38074651
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Development of a nomogram combining multiparametric magnetic resonance imaging and PSA-related parameters to enhance the detection of clinically significant cancer across different region.
    Zhou Z; Liang Z; Zuo Y; Zhou Y; Yan W; Wu X; Ji Z; Li H; Hu M; Ma L
    Prostate; 2022 Apr; 82(5):556-565. PubMed ID: 35098557
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Development and validation of a logistic regression model to distinguish transition zone cancers from benign prostatic hyperplasia on multi-parametric prostate MRI.
    Iyama Y; Nakaura T; Katahira K; Iyama A; Nagayama Y; Oda S; Utsunomiya D; Yamashita Y
    Eur Radiol; 2017 Sep; 27(9):3600-3608. PubMed ID: 28289941
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features.
    Bernatz S; Ackermann J; Mandel P; Kaltenbach B; Zhdanovich Y; Harter PN; Döring C; Hammerstingl R; Bodelle B; Smith K; Bucher A; Albrecht M; Rosbach N; Basten L; Yel I; Wenzel M; Bankov K; Koch I; Chun FK; Köllermann J; Wild PJ; Vogl TJ
    Eur Radiol; 2020 Dec; 30(12):6757-6769. PubMed ID: 32676784
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Peritumoral Radiomics Strategy Based on Ensemble Learning for the Prediction of Gleason Grade Group of Prostate Cancer.
    Qiu Y; Liu YF; Shu X; Qiao XF; Ai GY; He XJ
    Acad Radiol; 2023 Sep; 30 Suppl 1():S1-S13. PubMed ID: 37393175
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.