142 related articles for article (PubMed ID: 37497811)
1. 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]
2. 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]
3. Differentiating axillary lymph node metastasis in invasive breast cancer patients: A comparison of radiomic signatures from multiparametric breast MR sequences.
Chai R; Ma H; Xu M; Arefan D; Cui X; Liu Y; Zhang L; Wu S; Xu K
J Magn Reson Imaging; 2019 Oct; 50(4):1125-1132. PubMed ID: 30848041
[TBL] [Abstract][Full Text] [Related]
4. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study.
Yu Y; He Z; Ouyang J; Tan Y; Chen Y; Gu Y; Mao L; Ren W; Wang J; Lin L; Wu Z; Liu J; Ou Q; Hu Q; Li A; Chen K; Li C; Lu N; Li X; Su F; Liu Q; Xie C; Yao H
EBioMedicine; 2021 Jul; 69():103460. PubMed ID: 34233259
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast-enhanced-MRI-based radiomics.
Liu Z; Feng B; Li C; Chen Y; Chen Q; Li X; Guan J; Chen X; Cui E; Li R; Li Z; Long W
J Magn Reson Imaging; 2019 Sep; 50(3):847-857. PubMed ID: 30773770
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. MRI-Based Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid CNN-RNN Model.
Guo YJ; Yin R; Zhang Q; Han JQ; Dou ZX; Wang PB; Lu H; Liu PF; Chen JJ; Ma WJ
J Magn Reson Imaging; 2024 Jan; ():. PubMed ID: 38205712
[TBL] [Abstract][Full Text] [Related]
9. Non-invasive prediction model of axillary lymph node status in patients with early-stage breast cancer: a feasibility study based on dynamic contrast-enhanced-MRI radiomics.
Chen W; Lin G; Kong C; Wu X; Hu Y; Chen M; Xia S; Lu C; Xu M; Ji J
Br J Radiol; 2024 Feb; 97(1154):439-450. PubMed ID: 38308028
[TBL] [Abstract][Full Text] [Related]
10. Multi-modality radiomics model predicts axillary lymph node metastasis of breast cancer using MRI and mammography.
Wang Q; Lin Y; Ding C; Guan W; Zhang X; Jia J; Zhou W; Liu Z; Bai G
Eur Radiol; 2024 Feb; ():. PubMed ID: 38337068
[TBL] [Abstract][Full Text] [Related]
11. A delta-radiomic lymph node model using dynamic contrast enhanced MRI for the early prediction of axillary response after neoadjuvant chemotherapy in breast cancer patients.
Liu S; Du S; Gao S; Teng Y; Jin F; Zhang L
BMC Cancer; 2023 Jan; 23(1):15. PubMed ID: 36604679
[TBL] [Abstract][Full Text] [Related]
12. Difference of DCE-MRI Parameters at Different Time Points and Their Predictive Value for Axillary Lymph Node Metastasis of Breast Cancer.
Ya G; Wen F; Xing-Ru L; Zhuan-Zhuan G; Jun-Qiang L
Acad Radiol; 2022 Jan; 29 Suppl 1():S79-S86. PubMed ID: 33504446
[TBL] [Abstract][Full Text] [Related]
13. Diagnostic accuracy of pre-operative breast magnetic resonance imaging (MRI) in predicting axillary lymph node metastasis: variations in intrinsic subtypes, and strategy to improve negative predictive value-an analysis of 2473 invasive breast cancer patients.
Chen ST; Lai HW; Chang JH; Liao CY; Wen TC; Wu WP; Wu HK; Lin YJ; Chang YJ; Chen ST; Chen DR; Huang HI; Hung CL
Breast Cancer; 2023 Nov; 30(6):976-985. PubMed ID: 37500823
[TBL] [Abstract][Full Text] [Related]
14. Magnetic resonance imaging features for predicting axillary lymph node metastasis in patients with breast cancer.
Zhao M; Wu Q; Guo L; Zhou L; Fu K
Eur J Radiol; 2020 Aug; 129():109093. PubMed ID: 32512504
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Development of MRI-Based Deep Learning Signature for Prediction of Axillary Response After NAC in Breast Cancer.
Zhang B; Yu Y; Mao Y; Wang H; Lv M; Su X; Wang Y; Li Z; Zhang Z; Bian T; Wang Q
Acad Radiol; 2024 Mar; 31(3):800-811. PubMed ID: 37914627
[TBL] [Abstract][Full Text] [Related]
17. Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: A multicenter study.
Gu J; Tong T; Xu D; Cheng F; Fang C; He C; Wang J; Wang B; Yang X; Wang K; Tian J; Jiang T
Cancer; 2023 Feb; 129(3):356-366. PubMed ID: 36401611
[TBL] [Abstract][Full Text] [Related]
18. Automated deep learning method for whole-breast segmentation in diffusion-weighted breast MRI.
Zhang L; Mohamed AA; Chai R; Guo Y; Zheng B; Wu S
J Magn Reson Imaging; 2020 Feb; 51(2):635-643. PubMed ID: 31301201
[TBL] [Abstract][Full Text] [Related]
19. Radiomics nomogram for predicting axillary lymph node metastasis in breast cancer based on DCE-MRI: A multicenter study.
Zhang J; Zhang Z; Mao N; Zhang H; Gao J; Wang B; Ren J; Liu X; Zhang B; Dou T; Li W; Wang Y; Jia H
J Xray Sci Technol; 2023; 31(2):247-263. PubMed ID: 36744360
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
