143 related articles for article (PubMed ID: 36201887)
1. Predicting hormone receptors and PAM50 subtypes of breast cancer from multi-scale lesion images of DCE-MRI with transfer learning technique.
Ming W; Li F; Zhu Y; Bai Y; Gu W; Liu Y; Sun X; Liu X; Liu H
Comput Biol Med; 2022 Nov; 150():106147. PubMed ID: 36201887
[TBL] [Abstract][Full Text] [Related]
2. Radiogenomics analysis reveals the associations of dynamic contrast-enhanced-MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer.
Ming W; Zhu Y; Bai Y; Gu W; Li F; Hu Z; Xia T; Dai Z; Yu X; Li H; Gu Y; Yuan S; Zhang R; Li H; Zhu W; Ding J; Sun X; Liu Y; Liu H; Liu X
Front Oncol; 2022; 12():943326. PubMed ID: 35965527
[TBL] [Abstract][Full Text] [Related]
3. Comparison of Dynamic Contrast-Enhanced MRI and Non-Mono-Exponential Model-Based Diffusion-Weighted Imaging for the Prediction of Prognostic Biomarkers and Molecular Subtypes of Breast Cancer Based on Radiomics.
Zhang L; Zhou XX; Liu L; Liu AY; Zhao WJ; Zhang HX; Zhu YM; Kuai ZX
J Magn Reson Imaging; 2023 Nov; 58(5):1590-1602. PubMed ID: 36661350
[TBL] [Abstract][Full Text] [Related]
4. Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response.
Hussain L; Huang P; Nguyen T; Lone KJ; Ali A; Khan MS; Li H; Suh DY; Duong TQ
Biomed Eng Online; 2021 Jun; 20(1):63. PubMed ID: 34183038
[TBL] [Abstract][Full Text] [Related]
5. Machine learning on MRI radiomic features: identification of molecular subtype alteration in breast cancer after neoadjuvant therapy.
Liu HQ; Lin SY; Song YD; Mai SY; Yang YD; Chen K; Wu Z; Zhao HY
Eur Radiol; 2023 Apr; 33(4):2965-2974. PubMed ID: 36418622
[TBL] [Abstract][Full Text] [Related]
6. Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer.
Huang ZH; Chen L; Sun Y; Liu Q; Hu P
J Transl Med; 2024 Mar; 22(1):226. PubMed ID: 38429796
[TBL] [Abstract][Full Text] [Related]
7. High-temporal resolution DCE-MRI improves assessment of intra- and peri-breast lesions categorized as BI-RADS 4.
Liu Y; Wang S; Qu J; Tang R; Wang C; Xiao F; Pang P; Sun Z; Xu M; Li J
BMC Med Imaging; 2023 Apr; 23(1):58. PubMed ID: 37076817
[TBL] [Abstract][Full Text] [Related]
8. The discerning influence of dynamic contrast-enhanced MRI in anticipating molecular subtypes of breast cancer through the artistry of artificial intelligence - a narrative review.
Ameen A; Shaikh K; Khan A; Vohra LM
J Pak Med Assoc; 2024 Apr; 74(4 (Supple-4)):S72-S78. PubMed ID: 38712412
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Differentiation of breast lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using deep transfer learning based on DenseNet201.
Meng M; Zhang M; Shen D; He G
Medicine (Baltimore); 2022 Nov; 101(45):e31214. PubMed ID: 36397422
[TBL] [Abstract][Full Text] [Related]
11. Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution.
Ji Y; Li H; Edwards AV; Papaioannou J; Ma W; Liu P; Giger ML
Cancer Imaging; 2019 Sep; 19(1):64. PubMed ID: 31533838
[TBL] [Abstract][Full Text] [Related]
12. Radiomics Analysis Based on Automatic Image Segmentation of DCE-MRI for Predicting Triple-Negative and Nontriple-Negative Breast Cancer.
Ma M; Gan L; Jiang Y; Qin N; Li C; Zhang Y; Wang X
Comput Math Methods Med; 2021; 2021():2140465. PubMed ID: 34422088
[TBL] [Abstract][Full Text] [Related]
13. Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI.
Lee JY; Lee KS; Seo BK; Cho KR; Woo OH; Song SE; Kim EK; Lee HY; Kim JS; Cha J
Eur Radiol; 2022 Jan; 32(1):650-660. PubMed ID: 34226990
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. 3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients.
Militello C; Rundo L; Dimarco M; Orlando A; Woitek R; D'Angelo I; Russo G; Bartolotta TV
Acad Radiol; 2022 Jun; 29(6):830-840. PubMed ID: 34600805
[TBL] [Abstract][Full Text] [Related]
16. Transfer Learning Strategy Based on Unsupervised Learning and Ensemble Learning for Breast Cancer Molecular Subtype Prediction Using Dynamic Contrast-Enhanced MRI.
Sun R; Hou X; Li X; Xie Y; Nie S
J Magn Reson Imaging; 2022 May; 55(5):1518-1534. PubMed ID: 34668601
[TBL] [Abstract][Full Text] [Related]
17. Deep learning for identifying radiogenomic associations in breast cancer.
Zhu Z; Albadawy E; Saha A; Zhang J; Harowicz MR; Mazurowski MA
Comput Biol Med; 2019 Jun; 109():85-90. PubMed ID: 31048129
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. 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]
20. A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer.
Brancato V; Brancati N; Esposito G; La Rosa M; Cavaliere C; Allarà C; Romeo V; De Pietro G; Salvatore M; Aiello M; Sangiovanni M
Sensors (Basel); 2023 Jan; 23(3):. PubMed ID: 36772592
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]