## 4 Discussion and Conclusions  
In this article, the methodology that was employed in the Wavelia semi-automated Quantitative Imaging Function (QIF) during the FiH clinical investigation of the device, to support morphological breast lesion detection based on persistence, lesion sizing and lesion characterization in a low-dimensional feature space, spanning shape and texture-based features, has been outlined and the critical design parameters highlighted. Semi-automated breast lesion detection using morphological post-processing of a set of parametric radar images, which are formed with Wavelia MBI under varying assumptions on the dielectric properties of the healthy background tissue of the breast was introduced in the Wavelia QIF. Automated segmentation of ROIs and association, or not, to a breast lesion is based on morphological properties (solidity, volume) of the ROI and its persistence on the set of parametric images, as evaluated based on spatial clustering. The novelty of the proposed method lies in the exploitation of the notion of persistence and its combination with the solidity feature to support validation of ROI detection in MBI images. A methodology for malignant-to-benign breast lesion discrimination, based on mapping in a low-dimensional feature space, which spans both shape-based features (solidity) and texture-based features (correlation, busyness), and training of a Naive Bayes (NB) and Quadratic Discriminant Analysis (QDA) classifier was also introduced in the Wavelia QIF. An interesting level of separability between malignant and benign breast lesions was achieved, with a classification loss of 11.5% estimated with 10-fold cross-validation for the trained QDA classifier. This is a result pending to be verified, reproduced, and validated with larger datasets in future clinical investigations of Wavelia. While extensive research work is already published on Radiomics applied to the well-established breast imaging modalities, to our knowledge, it is the first time that shape descriptors and texture-based features are computed for ROIs extracted from MBI images, to support breast lesion characterization and malignant/benign lesion labelling. In this FiH study, which was conducted in 25 patients, the Wavelia #1 prototype system demonstrated the preliminary potential to detect and discriminate between malignant and benign palpable breast lumps, the imaging procedure had no safety issues and patients reported a favorable experience of the MBI scan. Although the number of subjects included in the FiH study was small and was not intended to permit a clinically meaningful statistical analysis, the promising findings from this study provided initial data to support the valid clinical association of the technology and warranted the preparation of further clinical investigations, with an upgraded prototype version of the Wavelia system (Wavelia #2) and its semi-automated QIF, to progressively address the identified technological challenges. Larger and more diverse patient datasets are needed to validate these findings and delineate the cases where the Waelia MBI modality may offer a beneficial adjunct to current diagnostic protocols. For the first conception of the Waelia QIF and its feasibility testing for the first time in the clinical setting, simplifications were imposed to the FiH data processing for the analysis to become more straightforward. The two most important limitations of this analysis, which are intended to be loosened in future clinical investigations for the Wavelia QIF to become sufficiently realistic and clinically meaningful, are discussed in the two following paragraphs. Palpable breast lesions larger than 1 cm\({}^{3}\) were only considered in the current implementation of the Wavelia QIF. All the objects with a volumetric size inferior to 1 cm\({}^{3}\) were excluded and not morphologically validated with the Wavelia QIF.