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Objective <p>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.</p> Methods <p>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 &gt; 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 (<i>n</i> = 116), internal test set (<i>n</i> = 50), and external validation set (<i>n</i> = 33) using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA), respectively.</p> Results <p>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.</p> Conclusion <p>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.</p> Clinical trial number <p>Not applicable.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月24日

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  • Xiao Pan,
  • Yanni Zou,
  • Xiaoxiao Huang,
  • Tao Li,
  • Quan Zhang,
  • Jing Hu,
  • Wenhua Zhao,
  • Peng Peng

摘要

Objective

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.

Methods

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.

Results

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.

Conclusion

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.

Clinical trial number

Not applicable.