159 related articles for article (PubMed ID: 38745321)
1. Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity.
Zhang X; Teng X; Zhang J; Lai Q; Cai J
Breast Cancer Res; 2024 May; 26(1):77. PubMed ID: 38745321
[TBL] [Abstract][Full Text] [Related]
2. Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI.
Liu C; Ding J; Spuhler K; Gao Y; Serrano Sosa M; Moriarty M; Hussain S; He X; Liang C; Huang C
J Magn Reson Imaging; 2019 Jan; 49(1):131-140. PubMed ID: 30171822
[TBL] [Abstract][Full Text] [Related]
3. [Preoperative prediction of HER-2 expression status in breast cancer based on MRI radiomics model].
Zhang Y; Huang H; Yin L; Wang ZX; Lu SY; Wang XX; Xiang LL; Zhang Q; Zhang JL; Shan XH
Zhonghua Zhong Liu Za Zhi; 2024 May; 46(5):428-437. PubMed ID: 38742356
[No Abstract] [Full Text] [Related]
4. Deep learning radiomic analysis of DCE-MRI combined with clinical characteristics predicts pathological complete response to neoadjuvant chemotherapy in breast cancer.
Li Y; Fan Y; Xu D; Li Y; Zhong Z; Pan H; Huang B; Xie X; Yang Y; Liu B
Front Oncol; 2022; 12():1041142. PubMed ID: 36686755
[TBL] [Abstract][Full Text] [Related]
5. 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]
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. Radiomics Based on Dynamic Contrast-Enhanced MRI to Early Predict Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Therapy.
Zeng Q; Ke M; Zhong L; Zhou Y; Zhu X; He C; Liu L
Acad Radiol; 2023 Aug; 30(8):1638-1647. PubMed ID: 36564256
[TBL] [Abstract][Full Text] [Related]
8. Radiomic analysis of DCE-MRI for prediction of response to neoadjuvant chemotherapy in breast cancer patients.
Fan M; Wu G; Cheng H; Zhang J; Shao G; Li L
Eur J Radiol; 2017 Sep; 94():140-147. PubMed ID: 28712700
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Assessment of Lymphovascular Invasion in Breast Cancer Using a Combined MRI Morphological Features, Radiomics, and Deep Learning Approach Based on Dynamic Contrast-Enhanced MRI.
Yang X; Fan X; Lin S; Zhou Y; Liu H; Wang X; Zuo Z; Zeng Y
J Magn Reson Imaging; 2024 Jun; 59(6):2238-2249. PubMed ID: 37855421
[TBL] [Abstract][Full Text] [Related]
11. Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using radiomics of pretreatment dynamic contrast-enhanced MRI.
Yoshida K; Kawashima H; Kannon T; Tajima A; Ohno N; Terada K; Takamatsu A; Adachi H; Ohno M; Miyati T; Ishikawa S; Ikeda H; Gabata T
Magn Reson Imaging; 2022 Oct; 92():19-25. PubMed ID: 35636571
[TBL] [Abstract][Full Text] [Related]
12. Early prediction of pathologic complete response of breast cancer after neoadjuvant chemotherapy using longitudinal ultrafast dynamic contrast-enhanced MRI.
Cao Y; Wang X; Li L; Shi J; Zeng X; Huang Y; Chen H; Jiang F; Yin T; Nickel D; Zhang J
Diagn Interv Imaging; 2023 Dec; 104(12):605-614. PubMed ID: 37543490
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Textural radiomic features and time-intensity curve data analysis by dynamic contrast-enhanced MRI for early prediction of breast cancer therapy response: preliminary data.
Fusco R; Granata V; Maio F; Sansone M; Petrillo A
Eur Radiol Exp; 2020 Feb; 4(1):8. PubMed ID: 32026095
[TBL] [Abstract][Full Text] [Related]
15. Prediction of pathological complete response to neoadjuvant chemotherapy in patients with breast cancer using a combination of contrast-enhanced ultrasound and dynamic contrast-enhanced magnetic resonance imaging.
Han X; Yang H; Jin S; Sun Y; Zhang H; Shan M; Cheng W
Cancer Med; 2023 Jan; 12(2):1389-1398. PubMed ID: 35822639
[TBL] [Abstract][Full Text] [Related]
16. Early prediction of pathological complete response to neoadjuvant chemotherapy combining DCE-MRI and apparent diffusion coefficient values in breast Cancer.
Liang X; Chen X; Yang Z; Liao Y; Wang M; Li Y; Fan W; Dai Z; Zhang Y
BMC Cancer; 2022 Dec; 22(1):1250. PubMed ID: 36460972
[TBL] [Abstract][Full Text] [Related]
17. Intratumoral and peritumoral radiomics based on dynamic contrast-enhanced MRI for preoperative prediction of intraductal component in invasive breast cancer.
Xu H; Liu J; Chen Z; Wang C; Liu Y; Wang M; Zhou P; Luo H; Ren J
Eur Radiol; 2022 Jul; 32(7):4845-4856. PubMed ID: 35079887
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Four-Dimensional Machine Learning Radiomics for the Pretreatment Assessment of Breast Cancer Pathologic Complete Response to Neoadjuvant Chemotherapy in Dynamic Contrast-Enhanced MRI.
Caballo M; Sanderink WBG; Han L; Gao Y; Athanasiou A; Mann RM
J Magn Reson Imaging; 2023 Jan; 57(1):97-110. PubMed ID: 35633290
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]