Accurate preoperative distinction between in situ and invasive Breast Cancer (BC) is critical for clinical decision-making and treatment planning. Radiomics and Machine Learning (ML) have shown promise in enhancing diagnostic performance from breast MRI, yet their application to this specific task remains underexplored. The aim of this study was to evaluate the performance of several ML classifiers, trained on radiomic features extracted from DCE–MRI and supported by basic clinical information, for the classification of in situ versus invasive BC lesions. In this study, we retrospectively analysed 71 post-contrast DCE–MRI scans (24 in situ, 47 invasive cases). Radiomic features were extracted from manually segmented tumour regions using the PyRadiomics library, and a limited set of basic clinical variables was also included. Several ML classifiers were evaluated in a Leave-One-Out Cross-Validation (LOOCV) scheme. Feature selection was performed using two different strategies: Minimum Redundancy Maximum Relevance (MRMR), mutual information. Axial 3D rotation was used for data augmentation. Support Vector Machine (SVM), K Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were the best-performing models, with an Area Under the Curve (AUC) ranging from 0.77 to 0.81. Notably, KNN achieved the best balance between sensitivity and specificity without the need for data augmentation. Our findings confirm that radiomic features extracted from DCE–MRI, combined with well-validated ML models, can effectively support the differentiation of in situ vs. invasive breast cancer. This approach is quite robust even in small datasets and may aid in improving preoperative planning. Further validation on larger cohorts and integration with additional imaging or clinical data are recommended.

Cutaneous melanoma is one of the most aggressive skin cancers, and early diagnosis remains essential to reduce mortality. Reflectance Confocal Microscopy (RCM) provides non-invasive, quasi-histological images of the epidermis, dermoepidermal junction (DEJ), and dermis, enabling real-time assessment of melanocytic lesions. However, interpretation still relies on expert visual evaluation, which is time-consuming and subjective. In this context, Artificial Intelligence (AI) and Computer-Assisted Detection (CAD) systems are emerging as valuable tools to improve diagnostic accuracy and reproducibility. This review summarizes research on AI applications in RCM imaging for melanoma, focusing on three major areas: delineation of skin strata, segmentation of tissues and morphological patterns, and classification of benign versus malignant lesions. Early approaches included Bayesian classifiers, wavelet-based decision trees, and logistic regression, while recent studies have employed support vector machines, random forests, and increasingly deep learning architectures such as convolutional and recurrent neural networks. The results demonstrate encouraging accuracy in DEJ localization, the segmentation of diagnostically relevant patterns, and the discrimination of melanoma from benign nevi. We distinguish the maturity of dermoscopy-based AI (AUC (ROC) > 0.80 on large multicenter cohorts) from the still-exploratory evidence for RCM-based AI. Nonetheless, current studies are often limited by small datasets, heterogeneous protocols, and a lack of multicenter validation. Overall, progress in AI applied to RCM supports the development of CAD systems that could assist clinicians during acquisition and diagnosis, reducing unnecessary biopsies and improving early melanoma detection. Future work should address standardization, dataset expansion, and the integration of advanced AI methods to move closer to clinical implementation.