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Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material

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Decoding Breast Cancer: Using Radiomics to Non-Invasively Unveil Molecular Subtypes Directly from Mammographic

Manon A G Bakker¹, Maria de Lurdes Ovalho², Nuno Matela^3,4

¹Faculty of Science and Engineering, University of Groningen, 9700 AS Groningen, The Netherlands.

Journal of Imaging

|September 27, 2024

Summary

This summary is machine-generated.

Radiomics analysis of digital mammography images shows promise for predicting breast cancer molecular subtypes. This non-invasive approach could aid in personalized therapy selection for breast cancer patients.

Keywords:

breast cancer machine learning mammography molecular subtypes naive Bayes radiomics support vector machine

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Area of Science:

Oncology
Radiology
Medical Imaging

Background:

Breast cancer therapy success is highly dependent on tumor histology.
Accurate identification of molecular subtypes is crucial for effective treatment planning.

Purpose of the Study:

To investigate the potential of radiomic features from digital mammography (DM) for predicting breast cancer molecular subtypes.
To compare the performance of Support Vector Machine (SVM) and Naive Bayes (NB) machine learning classifiers for this task.

Main Methods:

Retrospective study using the OPTIMAM Mammography Image Database (OMI-DB).
Extraction of radiomic features from DM images.
Binary classification for Luminal A, Luminal B, Triple-Negative Breast Cancer (TNBC), and HER2 subtypes.
Feature selection using Pearson correlation and LASSO.
Classification using SVM and NB, with performance evaluated by accuracy and Area Under the Curve (AUC).

Main Results:

The SVM classifier achieved AUCs of 0.855 (Luminal A), 0.812 (Luminal B), 0.789 (TNBC), and 0.755 (HER2).
The NB classifier achieved AUCs of 0.714 (Luminal A), 0.746 (Luminal B), 0.593 (TNBC), and 0.714 (HER2).
SVM significantly outperformed NB for Luminal A (p = 0.0268) and TNBC (p = 0.0073) subtypes.

Conclusions:

Radiomics analysis of DM images demonstrates significant potential for non-invasive prediction of breast cancer molecular subtypes.
The SVM classifier showed superior performance compared to NB, highlighting its utility in this application.
This approach may facilitate more personalized and effective breast cancer treatment strategies.