Radiomics Based on Dual-Layer Detector Spectral Computed Tomography for Predicting Histologic Subtypes and Ki-67...

To develop a combined radiomics nomogram model and a clinical model based on dual-layer detector spectral computed tomography (DLCT) for predicting pathologic subtypes and Ki-67 index status in patients with non-small cell lung cancer (NSCLC).

A total of 405 patients with NSCLC who underwent DLCT scans and Ki-67 testing from 2 medical centers were enrolled in the study. Patients from center 1 were divided into training (n=233) and internal validation (n=83) sets, while center 2 (n=89) formed the external validation dataset. The most valuable radiomics features from dual-phase enhanced CT images and their corresponding iodine images were extracted to construct radiomics models. Clinical factors were screened by univariate and multivariate logistic regression analysis for the construction of clinical models. Subsequently, 2 nomograms that incorporated the radiomics score and clinical factors were established and tested for the 2 tasks, respectively.

For type classification, the clinical model, radiomics model, and nomogram had AUCs of 0.863, 0.853, and 0.931, respectively, in the internal validation datasets and 0.862, 0.832, and 0.910, respectively, in the external validation datasets. For Ki-67 classification, the clinical model, radiomics model, and nomogram were available in the internal validation datasets at 0.718, 0.724, and 0.808, respectively, and the AUCs in the external validation dataset were 0.734, 0.721, and 0.791, respectively. Decision curve analysis demonstrated that the nomogram had greater net benefits than did the clinical model and the radiomics model in both terms of type classification and Ki-67 classification.

The nomograms integrating clinical models and radiomics models all showed good performance not only for type classification but also for Ki-67 classification in NSCLC patients, which could facilitate clinical decision-making.