Figure 1.

This scheme summarizes the entire study pipeline and results. Patients with MRI among the patients operated on for breast cancer in our hospital were included. After normalization and resampling, three observers independently segmented the lesions and obtained a radiomics feature. The agreements of the segmentations were tested with the Dice coefficient. Interobserver agreement for radiomics features was tested using intra-class correlation coefficient. Patient data were divided into three experimental groups based on the lesion size. Following the European Society of Radiology guideline, the pipeline coefficient variance and variance inflation factor analyses are also performed. As lesion size increased, lesion stability and the success of automated artificial neural networks also increase.

MRI: Magnetic resonance imaging, LASSO: Least absolute shrinkage and selection operator, ANN: Artificial neural networks, AUC: Area under the curve, ACC: Accuracy, HER2: Human epidermal growth factor receptor 2, TN: Triple-negative