CT-based radiomics nomogram for preoperative prediction of Ki-67 in lung neuroendocrine neoplasms: a multicenter...

Lung neuroendocrine neoplasms (L-NENs) are increasingly recognized, yet reliable preoperative assessment of the Ki-67 proliferation index remains invasive and subject to sampling variability. We aimed to develop and validate a clinical-radiomics nomogram that uses routine chest CT to estimate Ki-67 status in patients with L-NENs.

In this retrospective multicenter study, 199 patients with histologically confirmed L-NENs from four hospitals between January 2014 and April 2024 were enrolled, all of whom underwent preoperative dual-phase contrast-enhanced CT. Following manual 3D tumor segmentation, a total of 1,874 radiomics features were extracted from fused non-contrast and arterial/venous phase images. Feature selection was performed using Pearson correlation analysis (removing redundant features with correlation coefficients > 0.8), followed by further variable compression via LASSO regression to identify discriminative radiomics features. Based on the selected features, five classification models were constructed, and the best-performing one was combined with clinical predictors identified through univariate and multivariate analyses to develop a radiomics-based nomogram. The model's discriminative ability, calibration, and clinical utility were evaluated in the training set (n = 116), internal test set (n = 50), and external validation set (n = 33) using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA), respectively.

The LR-based radiomics model demonstrated high discriminatory ability, achieving AUCs of 0.912 (95% CI: 0.858-0.965) in the training set and 0.943 (0.887-0.999) in the testing set, significantly outperforming other models. Consequently, it was combined with independent clinical predictors-largest tumor diameter, smoking history, and age-to build a nomogram. The final combined model exhibited excellent performance across all datasets, with AUCs of 0.958 (0.925-0.990) in training, 0.930 (0.865-0.995) in testing, and 0.911 (0.867-0.955) in external validation, accompanied by good calibration and a superior net benefit on decision curve analysis.

The CT-based clinical-radiomics nomogram provides an accurate, non-invasive tool for pre-operative Ki-67 estimation in L-NENs, potentially guiding treatment decisions. Prospective, larger-scale validation is warranted.

Not applicable.