Background: Oral cavity tumors are often diagnosed at advanced stages due to non-specific early symptoms and limitations in imaging sensitivity. This study proposes a novel methodology combining Radiomics and Model Checking to improve diagnosis and prognosis. Methods: A retrospective dataset of 18 patients (12 with oral squamous cell carcinoma and 6 healthy controls) who underwent contrast-enhanced MRI was analyzed. Radiomic features were extracted and selected, then encoded into formal models. A two-tier Model Checking approach was applied: (i) classification of healthy vs. pathological patients and (ii) prediction of treatment response. Results: The proposed method achieved a diagnostic accuracy of 93% and a prognostic accuracy of 75%. The approach demonstrated robustness even with a limited dataset, outperforming traditional data-driven methods in small-sample settings. Conclusions: The integration of Radiomics with Model Checking provides an explainable and effective tool for early detection and prognosis of oral cavity tumors. This approach may support clinicians as a decision-making aid, particularly in data-scarce scenarios.

Objective: This study introduces an explainable, radiomics-based machine learning framework for the automated classification of sarcoma tumors using MRI. The approach aims to empower clinicians, reducing dependence on subjective image interpretation. Methods: A total of 186 MRI scans from 86 patients diagnosed with bone and soft tissue sarcoma were manually segmented to isolate tumor regions and corresponding healthy tissue. From these segmentations, 851 handcrafted radiomic features were extracted, including wavelet-transformed descriptors. A Random Forest classifier was trained to distinguish between tumor and healthy tissue, with hyperparameter tuning performed through nested cross-validation. To ensure transparency and interpretability, model behavior was explored through Feature Importance analysis and Local Interpretable Model-agnostic Explanations (LIME). Results: The model achieved an F1-score of 0.742, with an accuracy of 0.724 on the test set. LIME analysis revealed that texture and wavelet-based features were the most influential in driving the model’s predictions. Conclusions: By enabling accurate and interpretable classification of sarcomas in MRI, the proposed method provides a non-invasive approach to tumor classification, supporting an earlier, more personalized and precision-driven diagnosis. This study highlights the potential of explainable AI to assist in more secure clinical decision-making.