166 related articles for article (PubMed ID: 32373531)
1. Texture Analysis of Breast DCE-MRI Based on Intratumoral Subregions for Predicting HER2 2+ Status.
Lu H; Yin J
Front Oncol; 2020; 10():543. PubMed ID: 32373531
[No Abstract] [Full Text] [Related]
2. Preliminary study on discriminating HER2 2+ amplification status of breast cancers based on texture features semi-automatically derived from pre-, post-contrast, and subtraction images of DCE-MRI.
Song L; Lu H; Yin J
PLoS One; 2020; 15(6):e0234800. PubMed ID: 32555662
[TBL] [Abstract][Full Text] [Related]
3. Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors.
Zhang B; Song L; Yin J
Front Oncol; 2021; 11():688182. PubMed ID: 34307153
[TBL] [Abstract][Full Text] [Related]
4. DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers.
Fan M; Cheng H; Zhang P; Gao X; Zhang J; Shao G; Li L
J Magn Reson Imaging; 2018 Jul; 48(1):237-247. PubMed ID: 29219225
[TBL] [Abstract][Full Text] [Related]
5. Dynamic contrast-enhanced magnetic resonance imaging radiomics analysis based on intratumoral subregions for predicting luminal and nonluminal breast cancer.
Feng S; Yin J
Quant Imaging Med Surg; 2023 Oct; 13(10):6735-6749. PubMed ID: 37869317
[TBL] [Abstract][Full Text] [Related]
6. The Potential Use of DCE-MRI Texture Analysis to Predict HER2 2+ Status.
Jiang Z; Song L; Lu H; Yin J
Front Oncol; 2019; 9():242. PubMed ID: 31032222
[No Abstract] [Full Text] [Related]
7. Texture Analysis Using Semiquantitative Kinetic Parameter Maps from DCE-MRI: Preoperative Prediction of HER2 Status in Breast Cancer.
Song L; Li C; Yin J
Front Oncol; 2021; 11():675160. PubMed ID: 34168994
[TBL] [Abstract][Full Text] [Related]
8. Intratumor partitioning and texture analysis of dynamic contrast-enhanced (DCE)-MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy.
Wu J; Gong G; Cui Y; Li R
J Magn Reson Imaging; 2016 Nov; 44(5):1107-1115. PubMed ID: 27080586
[TBL] [Abstract][Full Text] [Related]
9. Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.
Huang Y; Wei L; Hu Y; Shao N; Lin Y; He S; Shi H; Zhang X; Lin Y
Front Oncol; 2021; 11():706733. PubMed ID: 34490107
[TBL] [Abstract][Full Text] [Related]
10. Invasive ductal breast cancer molecular subtype prediction by MRI radiomic and clinical features based on machine learning.
Sheng W; Xia S; Wang Y; Yan L; Ke S; Mellisa E; Gong F; Zheng Y; Tang T
Front Oncol; 2022; 12():964605. PubMed ID: 36172153
[TBL] [Abstract][Full Text] [Related]
11. Multi-parametric MRI lesion heterogeneity biomarkers for breast cancer diagnosis.
Tsarouchi MI; Vlachopoulos GF; Karahaliou AN; Vassiou KG; Costaridou LI
Phys Med; 2020 Dec; 80():101-110. PubMed ID: 33137621
[TBL] [Abstract][Full Text] [Related]
12. Radiomics Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Sentinel Lymph Node Metastasis in Breast Cancer.
Liu J; Sun D; Chen L; Fang Z; Song W; Guo D; Ni T; Liu C; Feng L; Xia Y; Zhang X; Li C
Front Oncol; 2019; 9():980. PubMed ID: 31632912
[No Abstract] [Full Text] [Related]
13. Quantification of breast tumor heterogeneity for ER status, HER2 status, and TN molecular subtype evaluation on DCE-MRI.
Chang RF; Chen HH; Chang YC; Huang CS; Chen JH; Lo CM
Magn Reson Imaging; 2016 Jul; 34(6):809-819. PubMed ID: 26968141
[TBL] [Abstract][Full Text] [Related]
14. Radiomics Analysis of Contrast-Enhanced Breast MRI for Optimized Modelling of Virtual Prognostic Biomarkers in Breast Cancer.
Polat DS; Xi Y; Hulsey K; Lewis M; Dogan BE
Eur J Breast Health; 2024 Apr; 20(2):122-128. PubMed ID: 38571687
[TBL] [Abstract][Full Text] [Related]
15. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.
Braman NM; Etesami M; Prasanna P; Dubchuk C; Gilmore H; Tiwari P; Plecha D; Madabhushi A
Breast Cancer Res; 2017 May; 19(1):57. PubMed ID: 28521821
[TBL] [Abstract][Full Text] [Related]
16. Prediction of prostate cancer aggressiveness with a combination of radiomics and machine learning-based analysis of dynamic contrast-enhanced MRI.
Liu B; Cheng J; Guo DJ; He XJ; Luo YD; Zeng Y; Li CM
Clin Radiol; 2019 Nov; 74(11):896.e1-896.e8. PubMed ID: 31495546
[TBL] [Abstract][Full Text] [Related]
17. Efficacy of texture analysis in determining the gene amplification status of HER2 2+ for invasive ductal carcinoma cases.
Zheng X; Yin J
Minerva Med; 2023 Dec; 114(6):832-838. PubMed ID: 32239879
[TBL] [Abstract][Full Text] [Related]
18. Radiomic analysis of imaging heterogeneity in tumours and the surrounding parenchyma based on unsupervised decomposition of DCE-MRI for predicting molecular subtypes of breast cancer.
Fan M; Zhang P; Wang Y; Peng W; Wang S; Gao X; Xu M; Li L
Eur Radiol; 2019 Aug; 29(8):4456-4467. PubMed ID: 30617495
[TBL] [Abstract][Full Text] [Related]
19. Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.
Tahmassebi A; Wengert GJ; Helbich TH; Bago-Horvath Z; Alaei S; Bartsch R; Dubsky P; Baltzer P; Clauser P; Kapetas P; Morris EA; Meyer-Baese A; Pinker K
Invest Radiol; 2019 Feb; 54(2):110-117. PubMed ID: 30358693
[TBL] [Abstract][Full Text] [Related]
20. Breast cancer Ki67 expression prediction by DCE-MRI radiomics features.
Ma W; Ji Y; Qi L; Guo X; Jian X; Liu P
Clin Radiol; 2018 Oct; 73(10):909.e1-909.e5. PubMed ID: 29970244
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
