Background/Objectives: This study aimed to develop a reliable scoring system combining clinical and radiological parameters to distinguish atypical lipomatous tumours (ALTs) from lipomas, improving diagnostic accuracy and reducing expensive molecular pathology testing. Methods: A retrospective analysis of 188 patients who underwent surgery for lipomatous tumours was conducted. Patient data, including medical history, pathology, and MRI imaging results, were reviewed. Four predictive models were developed using various clinical and imaging parameters, including age, tumour size, location, and MRI characteristics (homogeneity, contrast enhancement). Statistical analysis, including ROC curve analysis and logistic regression, was performed to assess the accuracy of these models. Results: The highest predictive accuracy was achieved with Model 1, which included seven parameters, yielding an AUC of 0.952. This model achieved a sensitivity of 96.4% and a negative predictive value (NPV) of 97.2%. Reducing the number of parameters lowered the accuracy, with contrast enhancement playing a significant role in Model 1. A risk calculator based on the optimal model was developed, offering an effective tool for clinical use that can be provided. Notably, 21 out of 37 ALTs lacked atypia and would have been missed without molecular testing. Conclusions: The developed scoring system, based on clinical and imaging parameters, accurately distinguishes ALTs from lipomas, offering a practical alternative to molecular pathology testing. This multi-parameter approach significantly improves diagnostic reliability, reducing the risk of misclassification and false negatives, while also potentially lowering healthcare costs.

Background/Objectives: 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. Methods: 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. Results: 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. Conclusions: 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.