## 3 Results
### Breast Lesion Sizing: Correlation with Conventional Imaging and Post-Surgery Histology
Ultrasound images of the four patient test cases are included in Figure 12b, for a straightforward comparison with the MBI findings, both in terms of the morphology of each lesion and its localization in the breast. Mapping of the four breast lesions (1 IDC, 1 ILC, 1 fibroadenoma and 1 cyst) on the 3-d feature space of Wavelia MBI, together with the QDA decision surface, are shown in Figure 12c. The probability of malignancy, as predicted by the trained QDA classifier, is also annotated for each of the four lesions. The values of the three features and the associated probabilities of malignancy are reported in Table 2. The morphological detection (W1 or W2 pc_fib search range) which was associated with the highest probability of malignancy, has been used to represent each patient test case in Figure 12, and in Table 2.  
Figure 12: Breast Lesion Characterization in the Wavelia 3-d feature space—Four illustrative patient test cases: (**a**) morphologically validated persistent ROI detections with Wavelia MBI, (**b**) Ultrasound images, (**c**) mapping in the Wavelia 3-d feature space, probability of malignancy and QDA decision surface. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Patient** & **Lesion** & \multicolumn{3}{c}{**Features**} & \multicolumn{1}{c}{**Probability of**} \\ \cline{2-6}
**ID** & **Histological Type** & **Solidity** & **Correlation** & **Busyness** & **Malignancy [\%]** \\ \hline P010 & Invasive Ductal & 0.529 & 0.496 & 0.041 & 99.95\% \\  & Carcinoma & 0.661 & 0.556 & 0.074 & 100\% \\ P032 & Invasive Lobular & 0.643 & 0.403 & 0.018 & 34.03\% \\ P040 & Simple Cyst & 0.816 & 0.422 & 0.02 & 7.55\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Wavelia MBI lesion feature values and derived probability of malignancy. It is interesting to observe the following:  
* the clear differentiability of the simple cyst (Patient 040) in terms of higher solidity,
* the fibroadenoma and the two cancerous lesions had similar solidity levels, however clear distinction was achieved between the benign and the two malignant lesions in terms of texture features,
* both the correlation and the busyness feature values were slightly increased in the case of the malignant lesions, with the increase being more notable on the busyness feature,
* substantially increased busyness value was associated with more heterogeneous lesion patterns, which may be interpretable as being indicative of distributed non-mass like ILC's, such as in the Patient 032 case. This illustration highlights the physical reasoning behind the selection of the three specific features, for benign-to-malignant MBI lesion classification, based on shape and texture in the Wavelia QIF. It also serves to justify the achievable level of separability with this small proof-of-concept FiH dataset, while working with continuous subspace manifolds and very simple classifier models. The potential generalization of the above findings is intended to be confirmed with future clinical investigations, involving larger and more diverse patient datasets. Expansion of the feature space to include additional dimensions (features), supporting the generalization of the above findings on larger patient cohorts, will be also evaluated further during the development of Wavelia.