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 *

263 related articles for article (PubMed ID: 38044998)

  • 1. Development of an interpretable machine learning model for Ki-67 prediction in breast cancer using intratumoral and peritumoral ultrasound radiomics features.
    Wang J; Gao W; Lu M; Yao X; Yang D
    Front Oncol; 2023; 13():1290313. PubMed ID: 38044998
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Enhancing Ki-67 Prediction in Breast Cancer: Integrating Intratumoral and Peritumoral Radiomics From Automated Breast Ultrasound via Machine Learning.
    Li F; Zhu TW; Lin M; Zhang XT; Zhang YL; Zhou AL; Huang DY
    Acad Radiol; 2024 Jul; 31(7):2663-2673. PubMed ID: 38182442
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Radiomics Analysis of Intratumoral and Various Peritumoral Regions From Automated Breast Volume Scanning for Accurate Ki-67 Prediction in Breast Cancer Using Machine Learning.
    Hu B; Xu Y; Gong H; Tang L; Li H
    Acad Radiol; 2024 Sep; ():. PubMed ID: 39256084
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Machine Learning Model for Predicting Axillary Lymph Node Metastasis in Clinically Node Positive Breast Cancer Based on Peritumoral Ultrasound Radiomics and SHAP Feature Analysis.
    Wang SR; Cao CL; Du TT; Wang JL; Li J; Li WX; Chen M
    J Ultrasound Med; 2024 Sep; 43(9):1611-1625. PubMed ID: 38808580
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Intratumoral and Peritumoral Analysis of Mammography, Tomosynthesis, and Multiparametric MRI for Predicting Ki-67 Level in Breast Cancer: a Radiomics-Based Study.
    Jiang T; Song J; Wang X; Niu S; Zhao N; Dong Y; Wang X; Luo Y; Jiang X
    Mol Imaging Biol; 2022 Aug; 24(4):550-559. PubMed ID: 34904187
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Intratumoral and peritumoral radiomics model based on abdominal ultrasound for predicting Ki-67 expression in patients with hepatocellular cancer.
    Qian H; Shen Z; Zhou D; Huang Y
    Front Oncol; 2023; 13():1209111. PubMed ID: 37711208
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Predicting Ki-67 expression levels in breast cancer using radiomics-based approaches on digital breast tomosynthesis and ultrasound.
    Liu J; Yan C; Liu C; Wang Y; Chen Q; Chen Y; Guo J; Chen S
    Front Oncol; 2024; 14():1403522. PubMed ID: 39055558
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Based on Intratumoral and Peritumoral DCE-MRI Radiomics Nomogram.
    Liu Y; Li X; Zhu L; Zhao Z; Wang T; Zhang X; Cai B; Li L; Ma M; Ma X; Ming J
    Contrast Media Mol Imaging; 2022; 2022():6729473. PubMed ID: 36051932
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Ultrasound-based radiomics model for predicting molecular biomarkers in breast cancer.
    Xu R; You T; Liu C; Lin Q; Guo Q; Zhong G; Liu L; Ouyang Q
    Front Oncol; 2023; 13():1216446. PubMed ID: 37583930
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Predicting axillary lymph node metastasis in breast cancer patients: A radiomics-based multicenter approach with interpretability analysis.
    Liu Z; Hong M; Li X; Lin L; Tan X; Liu Y
    Eur J Radiol; 2024 Jul; 176():111522. PubMed ID: 38805883
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Application of Interpretable Machine Learning Models Based on Ultrasonic Radiomics for Predicting the Risk of Fibrosis Progression in Diabetic Patients with Nonalcoholic Fatty Liver Disease.
    Meng F; Wu Q; Zhang W; Hou S
    Diabetes Metab Syndr Obes; 2023; 16():3901-3913. PubMed ID: 38077485
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Intra- and Peritumoral Radiomics Model Based on Early DCE-MRI for Preoperative Prediction of Molecular Subtypes in Invasive Ductal Breast Carcinoma: A Multitask Machine Learning Study.
    Zhang S; Wang X; Yang Z; Zhu Y; Zhao N; Li Y; He J; Sun H; Xie Z
    Front Oncol; 2022; 12():905551. PubMed ID: 35814460
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Intratumoral and peritumoral radiomics for preoperatively predicting the axillary non-sentinel lymph node metastasis in breast cancer on the basis of contrast-enhanced mammography: a multicenter study.
    Lin F; Li Q; Wang Z; Shi Y; Ma H; Zhang H; Zhang K; Yang P; Zhang R; Duan S; Gu Y; Mao N; Xie H
    Br J Radiol; 2023 Mar; 96(1143):20220068. PubMed ID: 36542866
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Intratumoral and Peritumoral Radiomics Based on Functional Parametric Maps from Breast DCE-MRI for Prediction of HER-2 and Ki-67 Status.
    Li C; Song L; Yin J
    J Magn Reson Imaging; 2021 Sep; 54(3):703-714. PubMed ID: 33955619
    [TBL] [Abstract][Full Text] [Related]  

  • 15. An XGBoost Machine Learning Based Model for Predicting Ki-67 Value ≥ 15% in T
    Lu Y; Yang F; Tao Y; An P
    Technol Cancer Res Treat; 2024; 23():15330338241265989. PubMed ID: 39051517
    [No Abstract]   [Full Text] [Related]  

  • 16. Noninvasive prediction of perineural invasion in intrahepatic cholangiocarcinoma by clinicoradiological features and computed tomography radiomics based on interpretable machine learning: a multicenter cohort study.
    Liu Z; Luo C; Chen X; Feng Y; Feng J; Zhang R; Ouyang F; Li X; Tan Z; Deng L; Chen Y; Cai Z; Zhang X; Liu J; Liu W; Guo B; Hu Q
    Int J Surg; 2024 Feb; 110(2):1039-1051. PubMed ID: 37924497
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Radiomic Machine Learning in Invasive Ductal Breast Cancer: Prediction of Ki-67 Expression Level Based on Radiomics of DCE-MRI.
    Yang H; Wang W; Cheng Z; Zheng T; Cheng C; Cheng M; Wang Z
    Technol Cancer Res Treat; 2024; 23():15330338241288751. PubMed ID: 39431304
    [TBL] [Abstract][Full Text] [Related]  

  • 18. The CT-based intratumoral and peritumoral machine learning radiomics analysis in predicting lymph node metastasis in rectal carcinoma.
    Yuan H; Xu X; Tu S; Chen B; Wei Y; Ma Y
    BMC Gastroenterol; 2022 Nov; 22(1):463. PubMed ID: 36384504
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Integrating intratumoral and peritumoral radiomics with deep transfer learning for DCE-MRI breast lesion differentiation: A multicenter study comparing performance with radiologists.
    Yu T; Yu R; Liu M; Wang X; Zhang J; Zheng Y; Lv F
    Eur J Radiol; 2024 Aug; 177():111556. PubMed ID: 38875748
    [TBL] [Abstract][Full Text] [Related]  

  • 20. An Automated Breast Volume Scanner-Based Intra- and Peritumoral Radiomics Nomogram for the Preoperative Prediction of Expression of Ki-67 in Breast Malignancy.
    Wu Y; Ma Q; Fan L; Wu S; Wang J
    Acad Radiol; 2024 Jan; 31(1):93-103. PubMed ID: 37544789
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 14.