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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Explainable Radiomics-Based Model for Automatic Image Quality Assessment in Breast Cancer DCE MRI Data.

Georgios S Ioannidis^1,2, Katerina Nikiforaki¹, Aikaterini Dovrou^1,3

¹Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, Greece.

Journal of Imaging

|November 26, 2025

Summary

This summary is machine-generated.

This study introduces an explainable radiomics model to automatically assess breast cancer Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) quality. The model accurately distinguishes high-quality from low-quality DCE-MRI scans, aiding clinical practice.

Keywords:

DCE MRI breast imaging explainability image quality assessment machine learning objective quality metrics radiomics

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Area of Science:

Radiomics and Medical Imaging Analysis
Machine Learning in Healthcare
Biomedical Signal Processing

Background:

Accurate assessment of medical imaging quality is crucial for reliable diagnosis.
Breast cancer Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) quality can significantly impact diagnostic accuracy.
Existing methods for image quality assessment may lack automation and explainability.

Purpose of the Study:

To develop an explainable radiomics-based model for automated image quality assessment in breast cancer DCE-MRI.
To evaluate the performance of machine learning classifiers in distinguishing high-quality from low-quality DCE-MRI scans.
To enhance the reliability and fairness of large-scale medical imaging datasets through quality control.

Main Methods:

Extraction of 819 radiomic features and 2 No-Reference image quality metrics from 280 breast cancer DCE-MRI images.
Feature extraction from the whole image and background regions of interest, considering two scenarios: 12 slices per patient and the middle slice.
Application of machine learning classifiers (including Support Vector Machine) with explainability assessed using SHapley Additive Explanations (SHAP).

Main Results:

The model achieved the best performance when using features from the middle slice (scenario ii), combining whole image and background features.
A Support Vector Machine classifier yielded high performance metrics: 85.51% sensitivity, 80.01% specificity, 82.76% accuracy, and 89.37% AUC.
The SHAP analysis provided explainability for the model's predictions, identifying key features influencing quality assessment.

Conclusions:

The developed explainable radiomics model shows significant potential for automatic image quality assessment in breast cancer DCE-MRI.
The model can be integrated into clinical workflows to ensure data quality and improve diagnostic reliability.
This approach offers a valuable tool for managing large imaging repositories and conducting fair subgroup analyses.