Predicting the Invasiveness of Mixed Ground-Glass Nodules Based on Spectral Computed Tomography-Derived Parameters and Tumor Abnormal Protein Levels: Development and Validation of a Model

Rationale and objectives: Mixed ground-glass nodules (mGGNs) are highly malignant and common nonspecific lung imaging findings. This study aimed to explore whether combining quantitative and qualitative spectral dual-layer detector-based computed tomography (SDCT)-derived parameters with serological tumor abnormal proteins (TAPs) and thymidine kinase 1 (TK1) expression enhances invasive mGGN diagnostic efficacy and to develop a joint diagnostic model.

Materials and methods: This prospective study included patients with mGGNs undergoing preoperative triple-phase contrast-enhanced SDCT with TAP and TK1 tests. Based on pathologic invasiveness, mGGNs were classified as noninvasive or invasive adenocarcinomas. To establish the predictive model, 397 patients were divided into training and internal validation cohorts. Another 144 patients comprised the external validation set. A nomogram predicting invasive mGGNs was generated and assessed using receiver operating characteristic curves.

Results: CT100keV_a, Zeff_a, ED_a, TAP, Dsolid, and Internal_bronchial_morphology were identified as independent risk factors for mGGN invasiveness. The SDCT parameter-TAP nomogram combining these six predictors demonstrated satisfactory discrimination capabilities in all three datasets (areas under the curves 0.840-0.911). The optimal training set cutoff was 0.566, yielding an 88.2% sensitivity and 80.4% specificity. Decision curve analysis showed the highest net benefit across a breadth of threshold probabilities, and clinical impact curve analysis confirmed the model's clinical validity. The nomogram had significantly higher discriminative accuracy than any variable alone.

Conclusion: Multiple SDCT-derived parameters predict mGGN invasiveness, with Zeff_a playing a prominent role. The developed SDCT parameter-TAP nomogram has excellent diagnostic performance and high calibration accuracy, facilitating individual noninvasive risk prediction of malignant mGGNs.

Critical relevance statement: Multiple quantitative and functional parameters derived from SDCT can predict the pathological invasiveness of mGGNs, with Zeff_a playing a prominent role. A SDCT parameters-TAP nomogram has excellent diagnostic performance and high calibration accuracy, facilitating noninvasive prediction of individual risks of malignant mGGNs.