Radiomics and machine learning with multiparametric breast MRI for improved diagnostic accuracy in breast cancer diagnosis
I Daimiel Naranjo, P Gibbs, JS Reiner, R Lo Gullo… - Diagnostics, 2021 - mdpi.com
The purpose of this multicenter retrospective study was to evaluate radiomics analysis
coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion …
coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion …
The Kaiser score reliably excludes malignancy in benign contrast-enhancing lesions classified as BI-RADS 4 on breast MRI high-risk screening exams
RI Milos, F Pipan, A Kalovidouri, P Clauser… - European …, 2020 - Springer
Objectives MRI is an integral part of breast cancer screening in high-risk patients. We
investigated whether the application of the Kaiser score, a clinical decision-support tool, may …
investigated whether the application of the Kaiser score, a clinical decision-support tool, may …
Machine learning in breast cancer imaging: a review on data, models and methods
MAVM Grinet, AIR Gouveia… - Computer Methods in …, 2024 - Taylor & Francis
Medical imaging research has experienced significant growth over the past decade,
particularly in the fields of computer vision and pattern recognition. Computational …
particularly in the fields of computer vision and pattern recognition. Computational …
Improved differential diagnosis based on BI-RADS descriptors and apparent diffusion coefficient for breast lesions: a multiparametric MRI analysis as compared to …
L Meng, X Zhao, J Guo, L Lu, M Cheng, Q Xing… - Academic …, 2023 - Elsevier
Rationale and Objectives To develop the nomogram utilizing the American College of
Radiology BI-RADS descriptors, clinical features, and apparent diffusion coefficient (ADC) to …
Radiology BI-RADS descriptors, clinical features, and apparent diffusion coefficient (ADC) to …
Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers
R Lo Gullo, I Daimiel, C Rossi Saccarelli… - European …, 2020 - Springer
Objectives To investigate whether radiomics features extracted from MRI of BRCA-positive
patients with sub-centimeter breast masses can be coupled with machine learning to …
patients with sub-centimeter breast masses can be coupled with machine learning to …
Assessing PD-L1 expression status using radiomic features from contrast-enhanced breast MRI in breast cancer patients: Initial results
R Lo Gullo, H Wen, JS Reiner, R Hoda, V Sevilimedu… - Cancers, 2021 - mdpi.com
Simple Summary To our knowledge, this is the first study assessing radiomics coupled with
machine learning from MRI-derived features to predict PD-L1 expression status in biopsy …
machine learning from MRI-derived features to predict PD-L1 expression status in biopsy …
Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic …
H Yin, Y Jiang, Z Xu, H Jia, G Lin - Journal of Cancer Research and …, 2023 - Springer
Purpose To investigate the value of the combined diagnosis of multiparametric MRI-based
deep learning models to differentiate triple-negative breast cancer (TNBC) from …
deep learning models to differentiate triple-negative breast cancer (TNBC) from …
Diagnostic value of radiomics and machine learning with dynamic contrast-enhanced magnetic resonance imaging for patients with atypical ductal hyperplasia in …
R Lo Gullo, K Vincenti, C Rossi Saccarelli… - Breast Cancer Research …, 2021 - Springer
Purpose To investigate whether radiomics features extracted from magnetic resonance
imaging (MRI) of patients with biopsy-proven atypical ductal hyperplasia (ADH) coupled with …
imaging (MRI) of patients with biopsy-proven atypical ductal hyperplasia (ADH) coupled with …
A comparative assessment of MR BI-RADS 4 breast lesions with Kaiser score and apparent diffusion coefficient value
L Meng, X Zhao, L Lu, Q Xing, K Wang, Y Guo… - Frontiers in …, 2021 - frontiersin.org
Objectives To investigate the diagnostic performance of the Kaiser score and apparent
diffusion coefficient (ADC) to differentiate Breast Imaging Reporting and Data System (BI …
diffusion coefficient (ADC) to differentiate Breast Imaging Reporting and Data System (BI …
Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility study
B Burger, M Bernathova, P Seeböck, CF Singer… - European Radiology …, 2023 - Springer
Background International societies have issued guidelines for high-risk breast cancer (BC)
screening, recommending contrast-enhanced magnetic resonance imaging (CE-MRI) of the …
screening, recommending contrast-enhanced magnetic resonance imaging (CE-MRI) of the …