Radiomics in Advancing and Explainable Liposarcoma Classification with MR Imaging

Abstract

Background: Soft tissue sarcomas are rare and highly heterogeneous malignant tumors, often asymptomatic in the early stages. Accurate diagnosis and reliable assessment of the risk of metastasis, classified as low, intermediate, or high, are therefore essential for effective clinical decision-making. However, the application of Artificial Intelligence (AI) approaches to these diseases is often limited by the small size and quality of available datasets, which can compromise model robustness and reliability. Methods: The use of formal methods, based on mathematical modeling and logical verification, can be an alternative to AI techniques. When integrated with radiomics, formal methods provide a structured and interpretable approach to support disease diagnosis. Results: The proposed methodology yielded encouraging results, in line with those reported in the literature. A process was conducted to extract several first- and second-order radiomic classes, which were then selected based on their significance. The resulting models were evaluated using standard performance metrics and obtained 80% accuracy, 83% precision, and 83% recall. Conclusion: The transparency of formal methods improves the interpretability of models and radiomic features, allowing new links with clinical practice to be discovered. The proposed approach is presented as a feasibility and proof-of-concept framework aimed at improving interpretability. Given the very small cohort size, performance metrics should be considered preliminary and descriptive, as they require validation on larger external datasets before any clinical applicability can be claimed.

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