320 related articles for article (PubMed ID: 31005170)
1. Prediction of molecular subtypes of breast cancer using BI-RADS features based on a "white box" machine learning approach in a multi-modal imaging setting.
Wu M; Zhong X; Peng Q; Xu M; Huang S; Yuan J; Ma J; Tan T
Eur J Radiol; 2019 May; 114():175-184. PubMed ID: 31005170
[TBL] [Abstract][Full Text] [Related]
2. Relationships Between MRI Breast Imaging-Reporting and Data System (BI-RADS) Lexicon Descriptors and Breast Cancer Molecular Subtypes: Internal Enhancement is Associated with Luminal B Subtype.
Grimm LJ; Zhang J; Baker JA; Soo MS; Johnson KS; Mazurowski MA
Breast J; 2017 Sep; 23(5):579-582. PubMed ID: 28295860
[TBL] [Abstract][Full Text] [Related]
3. Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms.
Ma M; Liu R; Wen C; Xu W; Xu Z; Wang S; Wu J; Pan D; Zheng B; Qin G; Chen W
Eur Radiol; 2022 Mar; 32(3):1652-1662. PubMed ID: 34647174
[TBL] [Abstract][Full Text] [Related]
4. BI-RADS 3-5 microcalcifications can preoperatively predict breast cancer HER2 and Luminal a molecular subtype.
Cen D; Xu L; Li N; Chen Z; Wang L; Zhou S; Xu B; Liu CL; Liu Z; Luo T
Oncotarget; 2017 Feb; 8(8):13855-13862. PubMed ID: 28099938
[TBL] [Abstract][Full Text] [Related]
5. MR Imaging Findings in Molecular Subtypes of Breast Cancer According to BIRADS System.
Navarro Vilar L; Alandete Germán SP; Medina García R; Blanc García E; Camarasa Lillo N; Vilar Samper J
Breast J; 2017 Jul; 23(4):421-428. PubMed ID: 28067435
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Reproducibility of quantitative high-throughput BI-RADS features extracted from ultrasound images of breast cancer.
Hu Y; Qiao M; Guo Y; Wang Y; Yu J; Li J; Chang C
Med Phys; 2017 Jul; 44(7):3676-3685. PubMed ID: 28409843
[TBL] [Abstract][Full Text] [Related]
8. Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods.
Shan J; Alam SK; Garra B; Zhang Y; Ahmed T
Ultrasound Med Biol; 2016 Apr; 42(4):980-8. PubMed ID: 26806441
[TBL] [Abstract][Full Text] [Related]
9. The value of breast MRI for BI-RADS category 4B mammographic microcalcification: based on the 5
Eun NL; Son EJ; Gweon HM; Youk JH; Kim JA
Clin Radiol; 2018 Aug; 73(8):750-755. PubMed ID: 29853301
[TBL] [Abstract][Full Text] [Related]
10. Classification of mammographic breast density and its correlation with BI-RADS in elder women using machine learning approach.
Lee ZY; Goh YLE; Lai C
J Med Imaging Radiat Sci; 2022 Mar; 53(1):28-34. PubMed ID: 34801440
[TBL] [Abstract][Full Text] [Related]
11. BI-RADS for sonography: positive and negative predictive values of sonographic features.
Hong AS; Rosen EL; Soo MS; Baker JA
AJR Am J Roentgenol; 2005 Apr; 184(4):1260-5. PubMed ID: 15788607
[TBL] [Abstract][Full Text] [Related]
12. Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images.
Fleury E; Marcomini K
Eur Radiol Exp; 2019 Aug; 3(1):34. PubMed ID: 31385114
[TBL] [Abstract][Full Text] [Related]
13. Computer-aided classification of BI-RADS category 3 breast lesions.
Buchbinder SS; Leichter IS; Lederman RB; Novak B; Bamberger PN; Sklair-Levy M; Yarmish G; Fields SI
Radiology; 2004 Mar; 230(3):820-3. PubMed ID: 14739315
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Development and evaluation of a case-based reasoning classifier for prediction of breast biopsy outcome with BI-RADS lexicon.
Bilska-Wolak AO; Floyd CE
Med Phys; 2002 Sep; 29(9):2090-100. PubMed ID: 12349930
[TBL] [Abstract][Full Text] [Related]
16. Use of BI-RADS-MRI descriptors for differentiation between mucinous carcinoma and fibroadenoma.
Igarashi T; Ashida H; Morikawa K; Motohashi K; Fukuda K
Eur J Radiol; 2016 Jun; 85(6):1092-8. PubMed ID: 27161057
[TBL] [Abstract][Full Text] [Related]
17. Breast MRI as an adjunct to mammography: Does it really suffer from low specificity? A retrospective analysis stratified by mammographic BI-RADS classes.
Benndorf M; Baltzer PA; Vag T; Gajda M; Runnebaum IB; Kaiser WA
Acta Radiol; 2010 Sep; 51(7):715-21. PubMed ID: 20707656
[TBL] [Abstract][Full Text] [Related]
18. Nonmasslike enhancement at breast MR imaging: the added value of mammography and US for lesion categorization.
Thomassin-Naggara I; Trop I; Chopier J; David J; Lalonde L; Darai E; Rouzier R; Uzan S
Radiology; 2011 Oct; 261(1):69-79. PubMed ID: 21771958
[TBL] [Abstract][Full Text] [Related]
19. Breast cancer molecular subtype classifier that incorporates MRI features.
Sutton EJ; Dashevsky BZ; Oh JH; Veeraraghavan H; Apte AP; Thakur SB; Morris EA; Deasy JO
J Magn Reson Imaging; 2016 Jul; 44(1):122-9. PubMed ID: 26756416
[TBL] [Abstract][Full Text] [Related]
20.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
[Next] [New Search]