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Updated: Jun 1, 2025
Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
Published on: May 19, 2023
Development of a clinical prediction model for benign and malignant pulmonary nodules with a CTR ≥ 50% utilizing
Wensong Shi1,2, Yuzhui Hu3, Guotao Chang1
1Department of Thoracic Surgery, The Fifth Clinical Medical College of Henan, University of Chinese Medicine (Zhengzhou People's Hospital), Zhengzhou, 450003, China.
Objective:
In clinical practice, diagnosing the benignity and malignancy of solid-component-predominant pulmonary nodules is challenging, especially when 3D consolidation-to-tumor ratio (CTR) ≥ 50%, as malignant ones are more invasive. This study aims to develop and validate an AI-driven radiomics prediction model for such nodules to enhance diagnostic accuracy.
Methods:
Data of 2,591 pulmonary nodules from five medical centers (Zhengzhou People's Hospital, etc.) were collected. Applying exclusion criteria, 370 nodules (78 benign, 292 malignant) with 3D CTR ≥ 50% were selected and randomly split 7:3 into training and validation cohorts. Using R programming, Lasso regression with 10-fold cross-validation filtered features, followed by univariate and multivariate logistic regression to construct the model. Its efficacy was evaluated by ROC, DCA curves and calibration plots.
Results:
Lasso regression picked 18 non-zero coefficients from 108 features. Three significant factors-patient age, solid component volume and mean CT value-were identified. The logistic regression equation was formulated. In the training set, the ROC AUC was 0.721 (95%CI: 0.642-0.801); in the validation set, AUC was 0.757 (95%CI: 0.632-0.881), showing the model's stability and predictive ability.
Conclusion:
The model has moderate accuracy in differentiating benign from malignant 3D CTR ≥ 50% nodules, holding clinical potential. Future efforts could explore more to improve its precision and value.
Clinical Trial Number:
Not applicable.
