347 related articles for article (PubMed ID: 27080203)
1. Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy.
Aghaei F; Tan M; Hollingsworth AB; Zheng B
J Magn Reson Imaging; 2016 Nov; 44(5):1099-1106. PubMed ID: 27080203
[TBL] [Abstract][Full Text] [Related]
2. Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy.
Aghaei F; Tan M; Hollingsworth AB; Qian W; Liu H; Zheng B
Med Phys; 2015 Nov; 42(11):6520-8. PubMed ID: 26520742
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. Multilevel analysis of spatiotemporal association features for differentiation of tumor enhancement patterns in breast DCE-MRI.
Lee SH; Kim JH; Cho N; Park JS; Yang Z; Jung YS; Moon WK
Med Phys; 2010 Aug; 37(8):3940-56. PubMed ID: 20879557
[TBL] [Abstract][Full Text] [Related]
6. A computerized global MR image feature analysis scheme to assist diagnosis of breast cancer: a preliminary assessment.
Yang Q; Li L; Zhang J; Shao G; Zheng B
Eur J Radiol; 2014 Jul; 83(7):1086-1091. PubMed ID: 24743001
[TBL] [Abstract][Full Text] [Related]
7. Computer-aided diagnosis of breast DCE-MRI images using bilateral asymmetry of contrast enhancement between two breasts.
Yang Q; Li L; Zhang J; Shao G; Zhang C; Zheng B
J Digit Imaging; 2014 Feb; 27(1):152-60. PubMed ID: 24043592
[TBL] [Abstract][Full Text] [Related]
8. A new quantitative image analysis method for improving breast cancer diagnosis using DCE-MRI examinations.
Yang Q; Li L; Zhang J; Shao G; Zheng B
Med Phys; 2015 Jan; 42(1):103-9. PubMed ID: 25563251
[TBL] [Abstract][Full Text] [Related]
9. Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study.
Ginsburg SB; Algohary A; Pahwa S; Gulani V; Ponsky L; Aronen HJ; Boström PJ; Böhm M; Haynes AM; Brenner P; Delprado W; Thompson J; Pulbrock M; Taimen P; Villani R; Stricker P; Rastinehad AR; Jambor I; Madabhushi A
J Magn Reson Imaging; 2017 Jul; 46(1):184-193. PubMed ID: 27990722
[TBL] [Abstract][Full Text] [Related]
10. Multimodality computer-aided breast cancer diagnosis with FFDM and DCE-MRI.
Yuan Y; Giger ML; Li H; Bhooshan N; Sennett CA
Acad Radiol; 2010 Sep; 17(9):1158-67. PubMed ID: 20692620
[TBL] [Abstract][Full Text] [Related]
11. Computer-aided diagnosis for dynamic contrast-enhanced breast MRI of mass-like lesions using a multiparametric model combining a selection of morphological, kinetic, and spatiotemporal features.
Agliozzo S; De Luca M; Bracco C; Vignati A; Giannini V; Martincich L; Carbonaro LA; Bert A; Sardanelli F; Regge D
Med Phys; 2012 Apr; 39(4):1704-15. PubMed ID: 22482596
[TBL] [Abstract][Full Text] [Related]
12. Computerized detection of breast tissue asymmetry depicted on bilateral mammograms: a preliminary study of breast risk stratification.
Wang X; Lederman D; Tan J; Wang XH; Zheng B
Acad Radiol; 2010 Oct; 17(10):1234-41. PubMed ID: 20619697
[TBL] [Abstract][Full Text] [Related]
13. Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI.
Chen W; Giger ML; Bick U; Newstead GM
Med Phys; 2006 Aug; 33(8):2878-87. PubMed ID: 16964864
[TBL] [Abstract][Full Text] [Related]
14. Assessing the performance of benign and malignant breast lesion classification with bilateral TIC differentiation and other effective features in DCE-MRI.
Li H; Sun H; Liu S; Zhang W; Arukalam FM; Ma H; Qian W
J Magn Reson Imaging; 2019 Aug; 50(2):465-473. PubMed ID: 30688398
[TBL] [Abstract][Full Text] [Related]
15. Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI.
Nie K; Chen JH; Yu HJ; Chu Y; Nalcioglu O; Su MY
Acad Radiol; 2008 Dec; 15(12):1513-25. PubMed ID: 19000868
[TBL] [Abstract][Full Text] [Related]
16. Assessment of CAD-generated tumor volumes measured using MRI in breast cancers before and after neoadjuvant chemotherapy.
Takeda K; Kanao S; Okada T; Kataoka M; Ueno T; Toi M; Ishiguro H; Mikami Y; Togashi K
Eur J Radiol; 2012 Oct; 81(10):2627-31. PubMed ID: 22221829
[TBL] [Abstract][Full Text] [Related]
17. Treatment Response Evaluation of Breast Cancer after Neoadjuvant Chemotherapy and Usefulness of the Imaging Parameters of MRI and PET/CT.
An YY; Kim SH; Kang BJ; Lee AW
J Korean Med Sci; 2015 Jun; 30(6):808-15. PubMed ID: 26028936
[TBL] [Abstract][Full Text] [Related]
18. Texture analysis in assessment and prediction of chemotherapy response in breast cancer.
Ahmed A; Gibbs P; Pickles M; Turnbull L
J Magn Reson Imaging; 2013 Jul; 38(1):89-101. PubMed ID: 23238914
[TBL] [Abstract][Full Text] [Related]
19. Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images.
El Adoui M; Drisis S; Benjelloun M
Int J Comput Assist Radiol Surg; 2020 Sep; 15(9):1491-1500. PubMed ID: 32556920
[TBL] [Abstract][Full Text] [Related]
20. A Metric for Reducing False Positives in the Computer-Aided Detection of Breast Cancer from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Screening Examinations of High-Risk Women.
Levman JE; Gallego-Ortiz C; Warner E; Causer P; Martel AL
J Digit Imaging; 2016 Feb; 29(1):126-33. PubMed ID: 26293705
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]