890 related articles for article (PubMed ID: 32149858)
1. Computer-Aided Diagnosis in Multiparametric Magnetic Resonance Imaging Screening of Women With Extremely Dense Breasts to Reduce False-Positive Diagnoses.
Verburg E; van Gils CH; Bakker MF; Viergever MA; Pijnappel RM; Veldhuis WB; Gilhuijs KGA
Invest Radiol; 2020 Jul; 55(7):438-444. PubMed ID: 32149858
[TBL] [Abstract][Full Text] [Related]
2. [Impact of breast density on computer-aided detection (CAD) of breast cancer].
Yang KY; Liu XJ; Zhai RY
Zhonghua Zhong Liu Za Zhi; 2012 May; 34(5):360-3. PubMed ID: 22883457
[TBL] [Abstract][Full Text] [Related]
3. Validation of Combined Deep Learning Triaging and Computer-Aided Diagnosis in 2901 Breast MRI Examinations From the Second Screening Round of the Dense Tissue and Early Breast Neoplasm Screening Trial.
Verburg E; van Gils CH; van der Velden BHM; Bakker MF; Pijnappel RM; Veldhuis WB; Gilhuijs KGA
Invest Radiol; 2023 Apr; 58(4):293-298. PubMed ID: 36256783
[TBL] [Abstract][Full Text] [Related]
4. Diagnostic value of diffusion-weighted imaging with synthetic b-values in breast tumors: comparison with dynamic contrast-enhanced and multiparametric MRI.
Daimiel Naranjo I; Lo Gullo R; Saccarelli C; Thakur SB; Bitencourt A; Morris EA; Jochelson MS; Sevilimedu V; Martinez DF; Pinker-Domenig K
Eur Radiol; 2021 Jan; 31(1):356-367. PubMed ID: 32780207
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Deep Learning for Automated Triaging of 4581 Breast MRI Examinations from the DENSE Trial.
Verburg E; van Gils CH; van der Velden BHM; Bakker MF; Pijnappel RM; Veldhuis WB; Gilhuijs KGA
Radiology; 2022 Jan; 302(1):29-36. PubMed ID: 34609196
[TBL] [Abstract][Full Text] [Related]
7. Reducing False-Positive Screening MRI Rate in Women with Extremely Dense Breasts Using Prediction Models Based on Data from the DENSE Trial.
den Dekker BM; Bakker MF; de Lange SV; Veldhuis WB; van Diest PJ; Duvivier KM; Lobbes MBI; Loo CE; Mann RM; Monninkhof EM; Veltman J; Pijnappel RM; van Gils CH;
Radiology; 2021 Nov; 301(2):283-292. PubMed ID: 34402665
[TBL] [Abstract][Full Text] [Related]
8. Screening US in patients with mammographically dense breasts: initial experience with Connecticut Public Act 09-41.
Hooley RJ; Greenberg KL; Stackhouse RM; Geisel JL; Butler RS; Philpotts LE
Radiology; 2012 Oct; 265(1):59-69. PubMed ID: 22723501
[TBL] [Abstract][Full Text] [Related]
9. Problem Solving MRI to Reduce False-Positive Biopsy Related to Breast US: Conductivity vs. DWI vs. Abbreviated Contrast-Enhanced MRI.
Kim JH; Kim SY; Cui C; Ji H; Yoen H; Cho N; Kim DH
J Magn Reson Imaging; 2024 Apr; 59(4):1218-1228. PubMed ID: 37477575
[TBL] [Abstract][Full Text] [Related]
10. Evaluation of the applicability of BI-RADS® MRI for the interpretation of contrast-enhanced digital mammography.
Travieso-Aja MM; Maldonado-Saluzzi D; Naranjo-Santana P; Fernández-Ruiz C; Severino-Rondón W; Rodríguez Rodríguez M; Luzardo OP
Radiologia (Engl Ed); 2019; 61(6):477-488. PubMed ID: 31262509
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Recall rate of screening ultrasound with automated breast volumetric scanning (ABVS) in women with dense breasts: a first quarter experience.
Arleo EK; Saleh M; Ionescu D; Drotman M; Min RJ; Hentel K
Clin Imaging; 2014; 38(4):439-444. PubMed ID: 24768327
[TBL] [Abstract][Full Text] [Related]
13. Combined contrast-enhanced magnetic resonance and diffusion-weighted imaging reading adapted to the "Breast Imaging Reporting and Data System" for multiparametric 3-T imaging of breast lesions.
Pinker K; Bickel H; Helbich TH; Gruber S; Dubsky P; Pluschnig U; Rudas M; Bago-Horvath Z; Weber M; Trattnig S; Bogner W
Eur Radiol; 2013 Jul; 23(7):1791-802. PubMed ID: 23504036
[TBL] [Abstract][Full Text] [Related]
14. Independent value of image fusion in unenhanced breast MRI using diffusion-weighted and morphological T2-weighted images for lesion characterization in patients with recently detected BI-RADS 4/5 x-ray mammography findings.
Bickelhaupt S; Tesdorff J; Laun FB; Kuder TA; Lederer W; Teiner S; Maier-Hein K; Daniel H; Stieber A; Delorme S; Schlemmer HP
Eur Radiol; 2017 Feb; 27(2):562-569. PubMed ID: 27193776
[TBL] [Abstract][Full Text] [Related]
15. Fully automatic classification of breast lesions on multi-parameter MRI using a radiomics model with minimal number of stable, interpretable features.
Zhang J; Zhan C; Zhang C; Song Y; Yan X; Guo Y; Ai T; Yang G
Radiol Med; 2023 Feb; 128(2):160-170. PubMed ID: 36670236
[TBL] [Abstract][Full Text] [Related]
16. Multiparametric MR Imaging with High-Resolution Dynamic Contrast-enhanced and Diffusion-weighted Imaging at 7 T Improves the Assessment of Breast Tumors: A Feasibility Study.
Pinker K; Baltzer P; Bogner W; Leithner D; Trattnig S; Zaric O; Dubsky P; Bago-Horvath Z; Rudas M; Gruber S; Weber M; Helbich TH
Radiology; 2015 Aug; 276(2):360-70. PubMed ID: 25751227
[TBL] [Abstract][Full Text] [Related]
17. Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features.
Vogl WD; Pinker K; Helbich TH; Bickel H; Grabner G; Bogner W; Gruber S; Bago-Horvath Z; Dubsky P; Langs G
Eur Radiol Exp; 2019 Apr; 3(1):18. PubMed ID: 31030291
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions.
Yin HL; Jiang Y; Xu Z; Jia HH; Lin GW
J Cancer Res Clin Oncol; 2023 Jun; 149(6):2575-2584. PubMed ID: 35771263
[TBL] [Abstract][Full Text] [Related]
20.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
[Next] [New Search]