This study looked at whether a new computer model could better tell the difference between pulmonary tuberculosis and other solid lung lesions using contrast-enhanced CT scans. The team analyzed data from 900 patients who were enrolled before October 2017. They compared a combined model that used both radiomics and clinical-semantic features against a model that used only clinical-semantic features.
In the training set, the combined model achieved an average precision of 0.91, while the clinical-semantic model scored 0.64. In the internal validation set, the combined model scored 0.85 compared to 0.61 for the clinical-semantic model. However, in the temporal validation set, the combined model dropped to 0.62, and the clinical-semantic model fell to 0.41.
No safety concerns were reported because the study focused on diagnostic accuracy rather than treatment or patient outcomes. Readers should note that this was an observational study using historical data, which limits how much these results can be applied to current practice. The drop in performance over time suggests the model may need careful testing in real-world settings before it can be trusted for routine use.