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Aim <p>To explore the added value of the combination of radiomics and visual features based on contrasted enhanced computed tomography (CECT) images for predicting the invasiveness of pure ground-glass nodules (pGGNs).</p> Materials and methods <p>The clinical and imaging data of 123 patients with 143 pGGNs confirmed by surgical pathology were retrospectively analyzed. The lesions-based dataset was randomly divided with a ratio of 7:3 into training set and test set. Radiomics models and visual features model were constructed independently using logistic regression. Two combined model of 2D + and 3D + were also established. The performance of the five models was evaluated via receiver operating characteristic (ROC) curve analysis and the clinical validity was assessed by using the model’s integrated discrimination improvement (IDI) indices.</p> Results <p>The 3D + model and 2D + model performed better with higher AUC (training: 0.839/0.793; test: 0.829/0.794) than three independent models alone (all <i>P</i> &lt; 0.05) and the DCA showed the IDI of 3D + model had a significant improvement in the test set than 2D + model, 3D radiomics model and visual features model (9.96%, 8.61% and 13.79%, <i>P</i> &lt; 0.05). In the training and test set, no statistically significant difference in AUC of the three independent models. DCA showed the IDI of 3D radiomics model had no significant improvement than 2D or visual features model in the training and test set (all <i>P</i> &gt; 0.05).</p> Conclusion <p>2D radiomic model was comparable to 3D for predicting the invasiveness of pGGNs. The incorporation of visual features into 2D and 3D radiomics models further improved the predictive performance.</p>

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Added value of the combination of radiomics and visual features based on CECT images for predicting the invasiveness of pGGNs

  • 期刊论文

  • 发表日期：

    2025年4月4日

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  • Liuqing Kang,
  • Jing Li,
  • Xiaoxian Zhang,
  • Xuejun Chen,
  • Jinrong Qu

摘要

Aim

To explore the added value of the combination of radiomics and visual features based on contrasted enhanced computed tomography (CECT) images for predicting the invasiveness of pure ground-glass nodules (pGGNs).

Materials and methods

The clinical and imaging data of 123 patients with 143 pGGNs confirmed by surgical pathology were retrospectively analyzed. The lesions-based dataset was randomly divided with a ratio of 7:3 into training set and test set. Radiomics models and visual features model were constructed independently using logistic regression. Two combined model of 2D + and 3D + were also established. The performance of the five models was evaluated via receiver operating characteristic (ROC) curve analysis and the clinical validity was assessed by using the model’s integrated discrimination improvement (IDI) indices.

Results

The 3D + model and 2D + model performed better with higher AUC (training: 0.839/0.793; test: 0.829/0.794) than three independent models alone (all P < 0.05) and the DCA showed the IDI of 3D + model had a significant improvement in the test set than 2D + model, 3D radiomics model and visual features model (9.96%, 8.61% and 13.79%, P < 0.05). In the training and test set, no statistically significant difference in AUC of the three independent models. DCA showed the IDI of 3D radiomics model had no significant improvement than 2D or visual features model in the training and test set (all P > 0.05).

Conclusion

2D radiomic model was comparable to 3D for predicting the invasiveness of pGGNs. The incorporation of visual features into 2D and 3D radiomics models further improved the predictive performance.