2803 related articles for article (PubMed ID: 30358693)
1. 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]
2. Texture Analysis with 3.0-T MRI for Association of Response to Neoadjuvant Chemotherapy in Breast Cancer.
Eun NL; Kang D; Son EJ; Park JS; Youk JH; Kim JA; Gweon HM
Radiology; 2020 Jan; 294(1):31-41. PubMed ID: 31769740
[TBL] [Abstract][Full Text] [Related]
3. Multiparametric MRI model with dynamic contrast-enhanced and diffusion-weighted imaging enables breast cancer diagnosis with high accuracy.
Zhang M; Horvat JV; Bernard-Davila B; Marino MA; Leithner D; Ochoa-Albiztegui RE; Helbich TH; Morris EA; Thakur S; Pinker K
J Magn Reson Imaging; 2019 Mar; 49(3):864-874. PubMed ID: 30375702
[TBL] [Abstract][Full Text] [Related]
4. Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer.
Li X; Abramson RG; Arlinghaus LR; Kang H; Chakravarthy AB; Abramson VG; Farley J; Mayer IA; Kelley MC; Meszoely IM; Means-Powell J; Grau AM; Sanders M; Yankeelov TE
Invest Radiol; 2015 Apr; 50(4):195-204. PubMed ID: 25360603
[TBL] [Abstract][Full Text] [Related]
5. Diffusion-Weighted Imaging With Apparent Diffusion Coefficient Mapping for Breast Cancer Detection as a Stand-Alone Parameter: Comparison With Dynamic Contrast-Enhanced and Multiparametric Magnetic Resonance Imaging.
Pinker K; Moy L; Sutton EJ; Mann RM; Weber M; Thakur SB; Jochelson MS; Bago-Horvath Z; Morris EA; Baltzer PA; Helbich TH
Invest Radiol; 2018 Oct; 53(10):587-595. PubMed ID: 29620604
[TBL] [Abstract][Full Text] [Related]
6. Machine learning with textural analysis of longitudinal multiparametric MRI and molecular subtypes accurately predicts pathologic complete response in patients with invasive breast cancer.
Syed A; Adam R; Ren T; Lu J; Maldjian T; Duong TQ
PLoS One; 2023; 18(1):e0280320. PubMed ID: 36649274
[TBL] [Abstract][Full Text] [Related]
7. Evaluation of the efficacy of neoadjuvant chemotherapy for breast cancer using diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging.
Xu HD; Zhang YQ
Neoplasma; 2017; 64(3):430-436. PubMed ID: 28253722
[TBL] [Abstract][Full Text] [Related]
8. Breast Cancer Classification on Multiparametric MRI - Increased Performance of Boosting Ensemble Methods.
Vamvakas A; Tsivaka D; Logothetis A; Vassiou K; Tsougos I
Technol Cancer Res Treat; 2022; 21():15330338221087828. PubMed ID: 35341421
[No Abstract] [Full Text] [Related]
9. Investigating the prediction value of multiparametric magnetic resonance imaging at 3 T in response to neoadjuvant chemotherapy in breast cancer.
Minarikova L; Bogner W; Pinker K; Valkovič L; Zaric O; Bago-Horvath Z; Bartsch R; Helbich TH; Trattnig S; Gruber S
Eur Radiol; 2017 May; 27(5):1901-1911. PubMed ID: 27651141
[TBL] [Abstract][Full Text] [Related]
10. Prediction of Tumor Shrinkage Pattern to Neoadjuvant Chemotherapy Using a Multiparametric MRI-Based Machine Learning Model in Patients With Breast Cancer.
Huang Y; Chen W; Zhang X; He S; Shao N; Shi H; Lin Z; Wu X; Li T; Lin H; Lin Y
Front Bioeng Biotechnol; 2021; 9():662749. PubMed ID: 34295877
[No Abstract] [Full Text] [Related]
11. Role of diffusion-weighted imaging as an adjunct to contrast-enhanced breast MRI in evaluating residual breast cancer following neoadjuvant chemotherapy.
Hahn SY; Ko EY; Han BK; Shin JH; Ko ES
Eur J Radiol; 2014 Feb; 83(2):283-8. PubMed ID: 24315957
[TBL] [Abstract][Full Text] [Related]
12. Effect of breast cancer phenotype on diagnostic performance of MRI in the prediction to response to neoadjuvant treatment.
Bufi E; Belli P; Di Matteo M; Terribile D; Franceschini G; Nardone L; Petrone G; Bonomo L
Eur J Radiol; 2014 Sep; 83(9):1631-8. PubMed ID: 24938669
[TBL] [Abstract][Full Text] [Related]
13. Texture analysis using machine learning-based 3-T magnetic resonance imaging for predicting recurrence in breast cancer patients treated with neoadjuvant chemotherapy.
Eun NL; Kang D; Son EJ; Youk JH; Kim JA; Gweon HM
Eur Radiol; 2021 Sep; 31(9):6916-6928. PubMed ID: 33693994
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Additive value of diffusion-weighted MRI in the I-SPY 2 TRIAL.
Li W; Newitt DC; Wilmes LJ; Jones EF; Arasu V; Gibbs J; La Yun B; Li E; Partridge SC; Kornak J; ; Esserman LJ; Hylton NM
J Magn Reson Imaging; 2019 Dec; 50(6):1742-1753. PubMed ID: 31026118
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Quantitative evaluation of breast cancer response to neoadjuvant chemotherapy by diffusion tensor imaging: Initial results.
Furman-Haran E; Nissan N; Ricart-Selma V; Martinez-Rubio C; Degani H; Camps-Herrero J
J Magn Reson Imaging; 2018 Apr; 47(4):1080-1090. PubMed ID: 28901594
[TBL] [Abstract][Full Text] [Related]
18. Apparent diffusion coefficient values may help predict which MRI-detected high-risk breast lesions will upgrade at surgical excision.
Cheeney S; Rahbar H; Dontchos BN; Javid SH; Rendi MH; Partridge SC
J Magn Reson Imaging; 2017 Oct; 46(4):1028-1036. PubMed ID: 28181343
[TBL] [Abstract][Full Text] [Related]
19. Multimodal prediction of neoadjuvant treatment outcome by serial FDG PET and MRI in women with locally advanced breast cancer.
Kazerouni AS; Peterson LM; Jenkins I; Novakova-Jiresova A; Linden HM; Gralow JR; Hockenbery DM; Mankoff DA; Porter PL; Partridge SC; Specht JM
Breast Cancer Res; 2023 Nov; 25(1):138. PubMed ID: 37946201
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
