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

Abstract

Breast cancer is the most commonly diagnosed cancer worldwide. The therapy used and its success depend highly on the histology of the tumor. This study aimed to explore the potential of predicting the molecular subtype of breast cancer using radiomic features extracted from screening digital mammography (DM) images. A retrospective study was performed using the OPTIMAM Mammography Image Database (OMI-DB). Four binary classification tasks were performed: luminal A vs. non-luminal A, luminal B vs. non-luminal B, TNBC vs. non-TNBC, and HER2 vs. non-HER2. Feature selection was carried out by Pearson correlation and LASSO. The support vector machine (SVM) and naive Bayes (NB) ML classifiers were used, and their performance was evaluated with the accuracy and the area under the receiver operating characteristic curve (AUC). A total of 186 patients were included in the study: 58 luminal A, 35 luminal B, 52 TNBC, and 41 HER2. The SVM classifier resulted in AUCs during testing of 0.855 for luminal A, 0.812 for luminal B, 0.789 for TNBC, and 0.755 for HER2, respectively. The NB classifier showed AUCs during testing of 0.714 for luminal A, 0.746 for luminal B, 0.593 for TNBC, and 0.714 for HER2. The SVM classifier outperformed NB with statistical significance for luminal A (p = 0.0268) and TNBC (p = 0.0073). Our study showed the potential of radiomics for non-invasive breast cancer subtype classification.

Keywords: breast cancer; machine learning; mammography; molecular subtypes; naive Bayes; radiomics; support vector machine.

Figure 1

The in- and exclusion criteria flowchart used during this study.

Figure 2

The tumor segmentation process. Starting with normalization of the original DM image, where breast lesions (red) classified as ‘calcification’ underwent image enhancement. Breast lesions classified as ‘mass’ underwent segmentation using a region-growing algorithm. The segmentations were finalized with the use of the image segmenter tool from MATLAB to obtain the final tumor segmentation.

Figure 3

An example of image enhancement for a calcification region where (a) is the original DM image and (b) is the enhanced image, making the calcification more pronounced.

Figure 4

Examples of breast tumor segmentations for (a) luminal A, (b) luminal B, (c) TNBC, and (d) HER2.

Figure 5

The selected radiomic features for (a) luminal A vs. non-luminal A, (b) luminal B vs. non-luminal B, (c) TNBC vs. non-TNBC, and (d) HER2 vs. non-HER2 classification tasks.

Figure 6

The ROC curves of the SVM (blue) and NB (yellow) classifiers for (a) luminal A vs. non-luminal A, (b) luminal B vs. non-luminal B, (c) TNBC vs. non-TNBC, and (d) HER2 vs. non-HER2.