## 3 Results and Discussion  
The sensitivity of the detection/segmentation procedure of our prototype, computed as the percentage of tumor masses correctly detected, was 75% (\(n\) = 41 out of a total of 55 samples). The Jaccard coefficient on the found masses was 0.7. As the system showed sub-optimal sensitivity, an interactive part that allowed the manual inclusion of regions missed by the automatic procedure was added for completeness. Four FPs were suggested by the ROI Hunter but excluded by the trained ANN, which thus showed an excellent specificity. With regards to the code for the discrimination between in situ and infiltrative lesions, two different configurations were explored (graphically shown in Figure 1). In the first (CONF1), the discrimination step was tested on its own--all masses visually detected and manually segmented by the radiologist (our ground truth) were used as input. In this way, discrimination quality figures (e.g., accuracy) were not influenced by the errors that the automatic detection/segmentation step introduced (in terms of false negatives, i.e., missed masses). In the second configuration (CONF2), the discrimination code was fed with only the masses found by the detection/segmentation code, giving a totally automatic standalone chain composed of detection/segmentation + discrimination. In CONF1, the evaluation of the classification performance of the trained XGBoost classifier reported a ROC curve with an AUC of 0.70. After choosing theclassifier threshold associated with the ROC curve point closest to the [0, 1] ROC space coordinates as the optimal threshold, the model correctly classified 47 subjects out of 55 (the confusion matrix was (6, 5; 3, 41); sensitivity 0.93, specificity 0.54, with an F1 score of 0.90, a balanced accuracy score of 0.74, a Matthews correlation coefficient of 0.44). In CONF2, where only the masses found by the detection/segmentation step were considered (which, as said, led to missing a non-negligible number of lesions), these values were slightly better, as most of classification errors actually came from masses not detected by the first CAD step. This suggests that the lesions missed by the detection step were also more difficult to characterize and assign to their class. Our hypothesis was that the limit in our results might be justified, at least in part, by the small size of our monocentric dataset and its imbalanced nature. This conjecture might be supported by the observation that in our dataset there is a very small number of in situ tumors (i.e., 11, far less numerous than the infiltrative lesions) and only about half of in situ were correctly classified, giving a poor specificity value. This hypothesis might also be suggested by a check of the dataset size of the three reviewed articles working in (conventional) MRI, i.e., [15; 20; 21], against the corresponding AUC found by the respective authors. While our small database consisted of only 55 patients, the number of images employed in the mentioned papers were, respectively, 248, 221 (if only the malignant cases are considered), and 131. The AUC values were 0.90, 0.83, and 0.70, which evidently correlated with sample cardinality. In particular, the last cited study of this group [21] had an AUC comparable to ours, with a dataset that was more than double in size. These considerations encouraged us to go on with our tests, increasing the dataset size in the near future. A deeper test of our approach would require a larger sample size for each class, so as to guarantee generalization and result quality, avoiding overfitting. In perspective, we point to increase the size of the dataset by involving different hospitals, thus creating a multicenter study. In this way, after solving the well-known problem of image normalization between different scanners, we might possibly build a CAD system with better quality and larger applicability.