BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

245 related articles for article (PubMed ID: 35284996)

  • 1. Deep learning method with a convolutional neural network for image classification of normal and metastatic axillary lymph nodes on breast ultrasonography.
    Ozaki J; Fujioka T; Yamaga E; Hayashi A; Kujiraoka Y; Imokawa T; Takahashi K; Okawa S; Yashima Y; Mori M; Kubota K; Oda G; Nakagawa T; Tateishi U
    Jpn J Radiol; 2022 Aug; 40(8):814-822. PubMed ID: 35284996
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning.
    Zhou LQ; Wu XL; Huang SY; Wu GG; Ye HR; Wei Q; Bao LY; Deng YB; Li XR; Cui XW; Dietrich CF
    Radiology; 2020 Jan; 294(1):19-28. PubMed ID: 31746687
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Deep Learning Prediction of Axillary Lymph Node Metastasis in Breast Cancer Patients Using Clinical Implication-Applied Preprocessed CT Images.
    Park TY; Kwon LM; Hyeon J; Cho BJ; Kim BJ
    Curr Oncol; 2024 Apr; 31(4):2278-2288. PubMed ID: 38668072
    [No Abstract]   [Full Text] [Related]  

  • 4. Classification of Breast Masses on Ultrasound Shear Wave Elastography using Convolutional Neural Networks.
    Fujioka T; Katsuta L; Kubota K; Mori M; Kikuchi Y; Kato A; Oda G; Nakagawa T; Kitazume Y; Tateishi U
    Ultrason Imaging; 2020; 42(4-5):213-220. PubMed ID: 32501152
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Axillary lymph node metastasis status prediction of early-stage breast cancer using convolutional neural networks.
    Lee YW; Huang CS; Shih CC; Chang RF
    Comput Biol Med; 2021 Mar; 130():104206. PubMed ID: 33421823
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Dual-modal computer-assisted evaluation of axillary lymph node metastasis in breast cancer patients on both real-time elastography and B-mode ultrasound.
    Zhang Q; Suo J; Chang W; Shi J; Chen M
    Eur J Radiol; 2017 Oct; 95():66-74. PubMed ID: 28987700
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network.
    Fujioka T; Kubota K; Mori M; Kikuchi Y; Katsuta L; Kasahara M; Oda G; Ishiba T; Nakagawa T; Tateishi U
    Jpn J Radiol; 2019 Jun; 37(6):466-472. PubMed ID: 30888570
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Computer-aided diagnostic models to classify lymph node metastasis and lymphoma involvement in enlarged cervical lymph nodes using PET/CT.
    Yang Y; Zheng B; Li Y; Li Y; Ma X
    Med Phys; 2023 Jan; 50(1):152-162. PubMed ID: 35925871
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Role of diffusion-weighted MRI: predicting axillary lymph node metastases in breast cancer.
    Chung J; Youk JH; Kim JA; Gweon HM; Kim EK; Ryu YH; Son EJ
    Acta Radiol; 2014 Oct; 55(8):909-16. PubMed ID: 24234236
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Attention-based Deep Learning for the Preoperative Differentiation of Axillary Lymph Node Metastasis in Breast Cancer on DCE-MRI.
    Gao J; Zhong X; Li W; Li Q; Shao H; Wang Z; Dai Y; Ma H; Shi Y; Zhang H; Duan S; Zhang K; Yang P; Zhao F; Zhang H; Xie H; Mao N
    J Magn Reson Imaging; 2023 Jun; 57(6):1842-1853. PubMed ID: 36219519
    [TBL] [Abstract][Full Text] [Related]  

  • 11. In vitro diagnosis of axillary lymph node metastases in breast cancer by spectrum analysis of radio frequency echo signals.
    Tateishi T; Machi J; Feleppa EJ; Oishi R; Jucha J; Yanagihara E; McCarthy LJ; Noritomi T; Shirouzu K
    Ultrasound Med Biol; 1998 Oct; 24(8):1151-9. PubMed ID: 9833584
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Quantitative ultrasound image analysis of axillary lymph node status in breast cancer patients.
    Drukker K; Giger M; Meinel LA; Starkey A; Janardanan J; Abe H
    Int J Comput Assist Radiol Surg; 2013 Nov; 8(6):895-903. PubMed ID: 23526445
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Multitask Deep Learning-Based Whole-Process System for Automatic Diagnosis of Breast Lesions and Axillary Lymph Node Metastasis Discrimination from Dynamic Contrast-Enhanced-MRI: A Multicenter Study.
    Zhou H; Hua Z; Gao J; Lin F; Chen Y; Zhang S; Zheng T; Wang Z; Shao H; Li W; Liu F; Li Q; Chen J; Wang X; Zhao F; Qu N; Xie H; Ma H; Zhang H; Mao N
    J Magn Reson Imaging; 2024 May; 59(5):1710-1722. PubMed ID: 37497811
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning.
    Wang Y; Choi EJ; Choi Y; Zhang H; Jin GY; Ko SB
    Ultrasound Med Biol; 2020 May; 46(5):1119-1132. PubMed ID: 32059918
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset.
    Ha R; Chang P; Karcich J; Mutasa S; Fardanesh R; Wynn RT; Liu MZ; Jambawalikar S
    J Digit Imaging; 2018 Dec; 31(6):851-856. PubMed ID: 29696472
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Convolutional Neural Network Detection of Axillary Lymph Node Metastasis Using Standard Clinical Breast MRI.
    Ren T; Cattell R; Duanmu H; Huang P; Li H; Vanguri R; Liu MZ; Jambawalikar S; Ha R; Wang F; Cohen J; Bernstein C; Bangiyev L; Duong TQ
    Clin Breast Cancer; 2020 Jun; 20(3):e301-e308. PubMed ID: 32139272
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Ultrasound Imaging for Detecting Metastasis to Level II and III Axillary Lymph Nodes after Axillary Lymph Node Dissection for Invasive Breast Cancer.
    Lin X; An X; Xiang H; Pei X; Li A; Tang G
    J Ultrasound Med; 2019 Nov; 38(11):2925-2934. PubMed ID: 30912182
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Convolutional Neural Network of Multiparametric MRI Accurately Detects Axillary Lymph Node Metastasis in Breast Cancer Patients With Pre Neoadjuvant Chemotherapy.
    Ren T; Lin S; Huang P; Duong TQ
    Clin Breast Cancer; 2022 Feb; 22(2):170-177. PubMed ID: 34384696
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Deep-learning approach with convolutional neural network for classification of maximum intensity projections of dynamic contrast-enhanced breast magnetic resonance imaging.
    Fujioka T; Yashima Y; Oyama J; Mori M; Kubota K; Katsuta L; Kimura K; Yamaga E; Oda G; Nakagawa T; Kitazume Y; Tateishi U
    Magn Reson Imaging; 2021 Jan; 75():1-8. PubMed ID: 33045323
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Comparison of Traditional Radiomics, Deep Learning Radiomics and Fusion Methods for Axillary Lymph Node Metastasis Prediction in Breast Cancer.
    Li X; Yang L; Jiao X
    Acad Radiol; 2023 Jul; 30(7):1281-1287. PubMed ID: 36376154
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.