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

To evaluate the utility of restricted spectrum imaging (RSI) for predicting subtypes of non-small cell lung cancer (NSCLC).

A total of 97 patients with NSCLC (30 with squamous cell carcinoma (SCC) and 67 with adenocarcinoma (AC)) were included. The parameters f₁, f₂, f₃, 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).

Compared with the AC group, the SCC group exhibited significantly higher SUV_max, f₂, and f₃ values, and lower ADC and f₁ values (all P < 0.05). Smoking status, f₁, 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, f₁, 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.

RSI can effectively differentiate NSCLC subtypes.