From Validation to Refinement: Optimising a Diagnostic Score for Atypical Lipomatous Tumours and Lipomas

<b>Background/Objectives</b>: Differentiating atypical lipomatous tumours (ALT) from lipomas remains challenging, as both share similar clinical and radiological features but require different forms of management. We previously proposed a clinical-radiological score integrating routine parameters to improve preoperative discrimination. This study aimed to externally validate the score in an independent cohort and refine it for enhanced robustness. <b>Methods</b>: We retrospectively analysed 119 patients with lipomatous tumours treated between 2022 and 2024 at an external university hospital. Diagnostic performance of the original models was assessed using receiver operating characteristic analysis. Data were then combined with the initial development cohort (n = 106) to recalibrate the models and define new cut-offs. <b>Results</b>: In the external validation cohort, predictive accuracy decreased compared to the derivation cohort, especially in extremity tumours assessed without contrast (AUC 0.830 vs. 0.942). Across four recalibrated models in the combined dataset (n = 225), diagnostic accuracy remained high (AUCs 0.918-0.954). Models combining clinical and imaging parameters consistently outperformed single-parameter approaches, with contrast enhancement providing the greatest incremental value. Accuracy was lower in trunk-localised tumours, highlighting the need for molecular confirmation in selected subgroups. <b>Conclusions</b>: The re-modelled score demonstrated robust diagnostic accuracy and practicality for routine use, offering a resource-efficient tool to support preoperative risk stratification. While molecular testing remains essential in high-risk cases, the refined score may reduce unnecessary testing and facilitate tailored diagnostic strategies. To support clinical adoption, the score is available as a web application that automatically selects the appropriate model and presents results in a colour-coded format.