Prediction of non-small cell lung cancer subtypes is possible through restricted spectrum imaging

Background To evaluate the utility of restricted spectrum imaging (RSI) for predicting subtypes of non-small cell lung cancer (NSCLC). Methods A total of 97 patients with NSCLC (30 with squamous cell carcinoma (SCC) and 67 with adenocarcinoma (AC)) were included. The parameters f 1 , f 2 , f 3 , apparent diffusion coefficient (ADC), and maximum standardized uptake value (SUV max ) were measured and compared between the two subtypes. Logistic regression analysis was used to identify independent predictors, and a combined diagnostic model was developed. The performance of the model was assessed using receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA). Results Compared with the AC group, the SCC group exhibited significantly higher SUV max , f 2 , and f 3 values, and lower ADC and f 1 values (all P < 0.05). Smoking status, f 1 , SUV max , and ADC were independent predictors of NSCLC subtypes. The combined model demonstrated superior diagnostic accuracy (AUC = 0.909; sensitivity = 73.33%; specificity = 89.55%) compared with individual predictors (AUC = 0.693, 0.819, 0.767, and 0.742 for smoking status, f 1 , SUV max , and ADC, respectively; all P < 0.01). Bootstrap resampling (1000 samples) validated the robustness of the model (AUC = 0.895). Calibration curves and DCA confirmed the model’s stability and clinical utility. Conclusion RSI can effectively differentiate NSCLC subtypes.