436 related articles for article (PubMed ID: 31746687)
21. 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]
22. Machine Learning Prediction of Lymph Node Metastasis in Breast Cancer: Performance of a Multi-institutional MRI-based 4D Convolutional Neural Network.
Polat DS; Nguyen S; Karbasi P; Hulsey K; Cobanoglu MC; Wang L; Montillo A; Dogan BE
Radiol Imaging Cancer; 2024 May; 6(3):e230107. PubMed ID: 38607282
[TBL] [Abstract][Full Text] [Related]
23. Axillary lymph node metastasis prediction by contrast-enhanced computed tomography images for breast cancer patients based on deep learning.
Liu Z; Ni S; Yang C; Sun W; Huang D; Su H; Shu J; Qin N
Comput Biol Med; 2021 Sep; 136():104715. PubMed ID: 34388460
[TBL] [Abstract][Full Text] [Related]
24. 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]
25. 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]
26. 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]
27. 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]
28. 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]
29. The role of ultrasonography and FDG-PET in axillary lymph node staging of breast cancer.
Ahn JH; Son EJ; Kim JA; Youk JH; Kim EK; Kwak JY; Ryu YH; Jeong J
Acta Radiol; 2010 Oct; 51(8):859-65. PubMed ID: 20670083
[TBL] [Abstract][Full Text] [Related]
30. Computer-aided prediction model for axillary lymph node metastasis in breast cancer using tumor morphological and textural features on ultrasound.
Moon WK; Chen IL; Yi A; Bae MS; Shin SU; Chang RF
Comput Methods Programs Biomed; 2018 Aug; 162():129-137. PubMed ID: 29903479
[TBL] [Abstract][Full Text] [Related]
31. Cortical morphologic features of axillary lymph nodes as a predictor of metastasis in breast cancer: in vitro sonographic study.
Bedi DG; Krishnamurthy R; Krishnamurthy S; Edeiken BS; Le-Petross H; Fornage BD; Bassett RL; Hunt KK
AJR Am J Roentgenol; 2008 Sep; 191(3):646-52. PubMed ID: 18716089
[TBL] [Abstract][Full Text] [Related]
32. Deep learning to predict breast cancer sentinel lymph node status on INSEMA histological images.
Marmé F; Krieghoff-Henning E; Gerber B; Schmitt M; Zahm DM; Bauerschlag D; Forstbauer H; Hildebrandt G; Ataseven B; Brodkorb T; Denkert C; Stachs A; Krug D; Heil J; Golatta M; Kühn T; Nekljudova V; Gaiser T; Schönmehl R; Brochhausen C; Loibl S; Reimer T; Brinker TJ
Eur J Cancer; 2023 Dec; 195():113390. PubMed ID: 37890350
[TBL] [Abstract][Full Text] [Related]
33. Investigation of synthetic MRI with quantitative parameters for discriminating axillary lymph nodes status in invasive breast cancer.
Qu M; Feng W; Liu X; Li Z; Li Y; Lu X; Lei J
Eur J Radiol; 2024 Jun; 175():111452. PubMed ID: 38604092
[TBL] [Abstract][Full Text] [Related]
34. 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]
35. 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]
36. Role of Elastography in Axillary Examination of Patients With Breast Cancer.
Chang W; Jia W; Shi J; Yuan C; Zhang Y; Chen M
J Ultrasound Med; 2018 Mar; 37(3):699-707. PubMed ID: 29344976
[TBL] [Abstract][Full Text] [Related]
37. 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]
38. Routine use of standard breast MRI compared to axillary ultrasound for differentiating between no, limited and advanced axillary nodal disease in newly diagnosed breast cancer patients.
van Nijnatten TJA; Ploumen EH; Schipper RJ; Goorts B; Andriessen EH; Vanwetswinkel S; Schavemaker M; Nelemans P; de Vries B; Beets-Tan RGH; Smidt ML; Lobbes MBI
Eur J Radiol; 2016 Dec; 85(12):2288-2294. PubMed ID: 27842679
[TBL] [Abstract][Full Text] [Related]
39. 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]
40. RESOLVE-DWI-based deep learning nomogram for prediction of normal-sized lymph node metastasis in cervical cancer: a preliminary study.
Qian W; Li Z; Chen W; Yin H; Zhang J; Xu J; Hu C
BMC Med Imaging; 2022 Dec; 22(1):221. PubMed ID: 36528577
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
