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Imaging features (radiomics) have potential for predicting Triple Negative Breast Cancer and other subtypes using magnetic resonance images (MRI). This work uses 244 images from the Duke-Breast-Cancer-MRI dataset to investigate the complex interplay between radiomics feature stability, with respect to segmentation variability, and prediction results of machine learning models. Our analysis reveals that features demonstrating high stability across different segmentations tend to enhance model performance, whereas unstable features sensitive to small segmentation changes degrade predictive accuracy. This exploration underscores the importance of feature stability in the development of reliable models for breast cancer subtype classification.