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Background <p>Radiomics holds promise for lung cancer diagnosis. This study developed an interpretable radiomics–clinical model to predict the invasiveness of pure ground-glass nodules (pGGNs) on high-resolution computed tomography (HRCT). To address the model’s “black box” nature, we applied the SHapley Additive exPlanations (SHAP) framework.</p> Methods <p>We retrospectively analyzed 235 surgically resected, histopathologically confirmed pGGNs, classified as non-invasive (AAH/AIS/MIA) or invasive (IAC) according to the 2015 WHO classification of lung tumors. We developed three prediction models: clinical, radiomic, and combined. Feature selection for the radiomic and combined models employed LASSO regression. Model performance was assessed using AUC, calibration curves, and decision curve analysis (DCA). Additionally, SHAP was used to quantify feature importance and to generate individualized explanations.</p> Results <p>Two clinico-radiological features (mean CT value, VolumePercent₋₃₀₀) and eight radiomic features were retained. The combined model yielded AUCs of 0.923 (training) and 0.832 (testing), outperforming the clinical model (0.799/0.733) and the radiomic model (0.917/0.827). Decision curve analysis (DCA) confirmed the superior clinical utility of the combined model. SHAP analysis ranked log_sigma_2_0mm_3D_firstorder_Range as the single most important predictive feature.</p> Conclusions <p>The SHAP-augmented radiomics–clinical model offers an accurate and interpretable preoperative assessment of pGGN invasiveness. This tool can help clinicians choose the optimal surgical strategy and support individualized decision-making.</p>

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Radiomic model with SHAP-based interpretability for predicting invasiveness of pure ground-glass nodules: a retrospective study based on high-resolution computed tomography (HRCT) volumetric datasets

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  • 发表日期：

    2026年7月18日

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  • Hui Sheng,
  • Rui Wang,
  • Guowei Zhang,
  • Ning Dong,
  • Yunpeng Zhou,
  • Ping Wang,
  • Kexin Li,
  • Guojie Bai

摘要

Background

Radiomics holds promise for lung cancer diagnosis. This study developed an interpretable radiomics–clinical model to predict the invasiveness of pure ground-glass nodules (pGGNs) on high-resolution computed tomography (HRCT). To address the model’s “black box” nature, we applied the SHapley Additive exPlanations (SHAP) framework.

Methods

We retrospectively analyzed 235 surgically resected, histopathologically confirmed pGGNs, classified as non-invasive (AAH/AIS/MIA) or invasive (IAC) according to the 2015 WHO classification of lung tumors. We developed three prediction models: clinical, radiomic, and combined. Feature selection for the radiomic and combined models employed LASSO regression. Model performance was assessed using AUC, calibration curves, and decision curve analysis (DCA). Additionally, SHAP was used to quantify feature importance and to generate individualized explanations.

Results

Two clinico-radiological features (mean CT value, VolumePercent₋₃₀₀) and eight radiomic features were retained. The combined model yielded AUCs of 0.923 (training) and 0.832 (testing), outperforming the clinical model (0.799/0.733) and the radiomic model (0.917/0.827). Decision curve analysis (DCA) confirmed the superior clinical utility of the combined model. SHAP analysis ranked log_sigma_2_0mm_3D_firstorder_Range as the single most important predictive feature.

Conclusions

The SHAP-augmented radiomics–clinical model offers an accurate and interpretable preoperative assessment of pGGN invasiveness. This tool can help clinicians choose the optimal surgical strategy and support individualized decision-making.