nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
1 |
A Breast Radiology Department-operated, Proactive Same-day Program Identifies Pathogenic Breast Cancer Mutations in Unaffected Women
|
Loving, Vilert A. |
|
|
29 |
S1 |
p. S239-S245 |
artikel |
2 |
An Exploratory Multi-reader, Multi-case Study Comparing Transmission Ultrasound to Mammography on Recall Rates and Detection Rates for Breast Cancer Lesions
|
Malik, Bilal |
|
|
29 |
S1 |
p. S10-S18 |
artikel |
3 |
Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non–Fat-Sat Images and Tested on Fat-Sat Images
|
Zhang, Yang |
|
|
29 |
S1 |
p. S135-S144 |
artikel |
4 |
BI-RADS Ultrasound Lexicon Descriptors and Stromal Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer
|
Candelaria, Rosalind P. |
|
|
29 |
S1 |
p. S35-S41 |
artikel |
5 |
Breast Imaging Boot Camp Meets Milestones 2.0: A Match Made in Clinic
|
Jordan, Sheryl G. |
|
|
29 |
S1 |
p. S246-S254 |
artikel |
6 |
Comparison of Abbreviated MRI with Mammography and Ultrasound in Women with a Personal History of Breast Cancer
|
Baek, Seung Jin |
|
|
29 |
S1 |
p. S19-S25 |
artikel |
7 |
Diagnostic Accuracy of Shear Wave Elastography as an Adjunct Tool in Detecting Axillary Lymph Nodes Metastasis
|
Ng, Wei Lin |
|
|
29 |
S1 |
p. S69-S78 |
artikel |
8 |
Difference of DCE-MRI Parameters at Different Time Points and Their Predictive Value for Axillary Lymph Node Metastasis of Breast Cancer
|
Ya, Gao |
|
|
29 |
S1 |
p. S79-S86 |
artikel |
9 |
Effect of Dose Level on Radiologists’ Detection of Microcalcifications in Digital Breast Tomosynthesis: An Observer Study with Breast Phantoms
|
Chan, Heang-Ping |
|
|
29 |
S1 |
p. S42-S49 |
artikel |
10 |
Evaluation of Multiparametric Shear Wave Elastography Indices in Malignant and Benign Breast Lesions
|
Tekcan Sanli, Deniz Esin |
|
|
29 |
S1 |
p. S50-S61 |
artikel |
11 |
Evaluation of the Effect of Age, Menopausal Status, and Parity on Breast Parenchyma Stiffness by Multiparametric Shear Wave Elastography
|
Sanli, Deniz Esin Tekcan |
|
|
29 |
S1 |
p. S62-S68 |
artikel |
12 |
Gail Model Improves the Diagnostic Performance of the Fifth Edition of Ultrasound BI-RADS for Predicting Breast Cancer: A Multicenter Prospective Study
|
Gao, Lu-Ying |
|
|
29 |
S1 |
p. S1-S7 |
artikel |
13 |
Improving Performance of Breast Cancer Risk Prediction by Incorporating Optical Density Image Feature Analysis
|
Yan, Shiju |
|
|
29 |
S1 |
p. S199-S210 |
artikel |
14 |
Improving the Accuracy of Screening Dense Breasted Women for Breast Cancer By Combining Clinically Based Risk Assessment Models with Ultrasound Imaging
|
Lenkinski, Robert E. |
|
|
29 |
S1 |
p. S8-S9 |
artikel |
15 |
Increasing Imaging Value to Breast Cancer Care Through Prognostic Modeling of Multiparametric MRI Features in Patients Undergoing Neoadjuvant Chemotherapy
|
Dodelzon, Katerina |
|
|
29 |
S1 |
p. S164-S165 |
artikel |
16 |
Magnetic Resonance Imaging Phenotypes of Breast Cancer Molecular Subtypes: A Systematic Review
|
Ab Mumin, Nazimah |
|
|
29 |
S1 |
p. S89-S106 |
artikel |
17 |
Microwave Imaging in Breast Cancer – Results from the First-In-Human Clinical Investigation of the Wavelia System
|
Moloney, Brian M. |
|
|
29 |
S1 |
p. S211-S222 |
artikel |
18 |
MRI Radiomics for Assessment of Molecular Subtype, Pathological Complete Response, and Residual Cancer Burden in Breast Cancer Patients Treated With Neoadjuvant Chemotherapy
|
Choudhery, Sadia |
|
|
29 |
S1 |
p. S145-S154 |
artikel |
19 |
MRI Radiomics of Breast Cancer: Machine Learning-Based Prediction of Lymphovascular Invasion Status
|
Kayadibi, Yasemin |
|
|
29 |
S1 |
p. S126-S134 |
artikel |
20 |
Nomogram for Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Dynamic Contrast-enhanced and Diffusion-weighted MRI
|
Zhao, Rui |
|
|
29 |
S1 |
p. S155-S163 |
artikel |
21 |
Optimizing the Peritumoral Region Size in Radiomics Analysis for Sentinel Lymph Node Status Prediction in Breast Cancer
|
Ding, Jie |
|
|
29 |
S1 |
p. S223-S228 |
artikel |
22 |
Patient preferences regarding use of contrast-enhanced imaging for breast cancer screening
|
Son, Daniel |
|
|
29 |
S1 |
p. S229-S238 |
artikel |
23 |
Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Intra-peritumoral Textural Transition Analysis based on Dynamic Contrast-enhanced Magnetic Resonance Imaging
|
Zhan, Chenao |
|
|
29 |
S1 |
p. S107-S115 |
artikel |
24 |
Radioproteomics in Breast Cancer: Prediction of Ki-67 Expression With MRI-based Radiomic Models
|
Kayadibi, Yasemin |
|
|
29 |
S1 |
p. S116-S125 |
artikel |
25 |
Reporting and Perceptions of Breast Arterial Calcification on Mammography: A Survey of ACR Radiologists
|
Brown, Ann L. |
|
|
29 |
S1 |
p. S192-S198 |
artikel |
26 |
Retrospective Review of a Mobile Mammography Screening Program in an Underserved Population within a Large Metropolitan Area
|
Spak, David A. |
|
|
29 |
S1 |
p. S173-S179 |
artikel |
27 |
Standardization of Quantitative DCE-MRI Parameters Measurement: An Urgent Need for Breast Cancer Imaging
|
Romeo, Valeria |
|
|
29 |
S1 |
p. S87-S88 |
artikel |
28 |
The Utility of the Fifth Edition of the BI-RADS Ultrasound Lexicon in Category 4 Breast Lesions: A Prospective Multicenter Study in China
|
Gu, Yang |
|
|
29 |
S1 |
p. S26-S34 |
artikel |
29 |
True and Missed Interval Cancer in Organized Mammographic Screening: A Retrospective Review Study of Diagnostic and Prior Screening Mammograms
|
Hovda, Tone |
|
|
29 |
S1 |
p. S180-S191 |
artikel |
30 |
Weakly Supervised Deep Learning Approach to Breast MRI Assessment
|
Liu, Michael Z |
|
|
29 |
S1 |
p. S166-S172 |
artikel |