## 4 Conclusions  
The automatic pre-operative, non-invasive distinction between infiltrative and in situ breast cancer represents an important challenge in the biomedical field.  
In this work, a two-step CAD system was developed and tested on DCE-MRI scans, with the aim of discriminating infiltrating from in situ breast tumors. The first step initially performed an ROI Hunting procedure to automatically extract 2D ROIs exploiting intensity values. This level consisted of a dynamical threshold algorithm that allowed us to select suspicious regions that were likely to contain a tumor mass. From the candidate ROIs, 1000 features were extracted through a deep learning method (starting from a pre-trained GoogleNet), followed by a classical machine-learning classifier (ANN) in the task of excluding FP regions. The second step performed the classification of in situ vs. invasive breast cancer of the previously detected ROIs (merged into 3D regions), through a Radiomics-based analysis. The results showed that the ROI Hunter segmentation procedure correctly identified 75% of tumor volumes, but the software contained an interactive part that allowed manual inclusion of the regions missed by the automatic detection/segmentation procedure. The infiltrative vs. in situ classification task achieved a final F1 score of 0.90 on all masses and a slightly better score on the masses automatically identified by the detection/segmentation step.  
Our preliminary results on tumor type classification were still worse than those reported in the few specific studies existing in the literature, which could be partly explained by the small size of the dataset we used, which was moreover quite imbalanced.  
Our future efforts would be towards the enrichment of the database employed, considering a multicentric development of the research. From the algorithmic point of view, we shall pursue an increase in sensitivity in the detection/segmentation step, with alternative approaches, and an increment of the accuracy of the classification step. In particular, the latter would also explore the effectiveness of including the peritumoral area in the ROIs. The aim would be the complete automatization of our CAD system in detecting the tumors and then distinguishing the two classes, so that in perspective this could be used as a valuable support to radiologists for detection and characterization of breast cancers in DCE-MRI images. As a long-term project, we plan to later propose the building of an ensemble classification system merging the classifiers developed by the few groups currently active on the specific subject of in situ vs. infiltrative BC discrimination.  
Conceptualization, M.P. and G.D.N.; Data curation, L.C., B.T., A.G., and E.M.; Formal analysis, L.C., B.T., and M.P.; Funding acquisition, G.D.N.; Investigation, L.C., B.T., M.P., A.G., and G.D.N.; Methodology, G.D.N.; Project administration, G.D.N.; Resources, M.P. and A.G.; Software, L.C., B.T., and G.D.N.; Supervision, G.D.N.; Validation, M.P., A.G., E.M., and G.D.N.; Visualization, L.C. and B.T.; Writing-original draft, L.C.; Writing--review and editing, B.T. and G.D.N. All authors have read and agreed to the published version of the manuscript. This research was partially funded by the Trans Adriatic Pipeline project within the TAP Start funding program. The authors report no conflict of interest. The authors alone are responsible for the content and writing of this article.