Editorial for “A Channel‐Dimensional Feature‐Reconstructed Deep Learning Model for Predicting Breast Cancer Molecular Subtypes on Overall <i>b</i>‐Value Diffusion‐Weighted MRI”-Reference-Cited by-同舟云学术

Editorial for “A Channel‐Dimensional Feature‐Reconstructed Deep Learning Model for Predicting Breast Cancer Molecular Subtypes on Overall b‐Value Diffusion‐Weighted MRI”

Published:2023-07-27 Issue: Volume: Page:
ISSN:1053-1807
Container-title:Journal of Magnetic Resonance Imaging
language:en
Short-container-title:Magnetic Resonance Imaging

Author:

Barzegar Mojtaba¹²³⁴,Schiaffino Simone⁵^ORCID

Affiliation:

1. National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation Doha Qatar

2. Brain mapping Foundation Los Angeles California USA

3. Society for Brain Mapping and Therapeutics Los Angeles California USA

4. Intelligent Quantitative Bio‐Medical imaging (IQBMI) Tehran Iran

5. Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC) Lugano Switzerland

Abstract

Level of Evidence5Technical Efficacy Stage2

Publisher

Wiley

Subject

Radiology, Nuclear Medicine and imaging

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmri.28908

Reference9 articles.

1. Breast cancer molecular subtype classifier that incorporates MRI features

2. Diffusion‐weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping as a quantitative imaging biomarker for prediction of immunohistochemical receptor status, proliferation rate, and molecular subtypes of breast cancer

3. Correlations between diffusion-weighted imaging and breast cancer biomarkers

4. Discordance in pathology report after central pathology review: Implications for breast cancer adjuvant treatment

5. Do the histologic features and results of breast cancer biomarker studies differ between core biopsy and surgical excision specimens?