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The differential computed tomography features between small benign and malignant solid solitary pulmonary nodules
Xiao-Qun He1, Xing-Tao Huang2, Tian-You Luo1
1Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Quantitative Imaging in Medicine and Surgery
|February 28, 2024
Summary
Computed tomography (CT) features for small solid solitary pulmonary nodules (SSPNs) differ by size. Identifying these varied CT signs helps distinguish benign from malignant SSPNs, improving diagnostic accuracy.
Area of Science:
- Radiology
- Pulmonology
- Oncology
Background:
- Computed tomography (CT) is the primary diagnostic tool for solid solitary pulmonary nodules (SSPNs).
- Distinguishing benign from malignant SSPNs is challenging, especially for smaller nodules due to limited differential CT signs.
- This study focuses on small SSPNs (≤15 mm) to identify size-dependent CT features.
Purpose of the Study:
- To investigate and compare the differential CT features of small (≤15 mm) benign and malignant SSPNs across different size categories.
- To identify independent predictors for benign SSPNs based on CT characteristics within specific size cohorts.
- To enhance the diagnostic accuracy of CT in differentiating benign from malignant small pulmonary nodules.
Main Methods:
- Retrospective analysis of CT data from 794 patients with small SSPNs (≤15 mm) between May 2018 and November 2021.
- SSPNs were categorized into benign and malignant groups and further divided into three size cohorts: ≤6 mm, 6-8 mm, and 8-15 mm.
- Multivariable logistic regression and receiver operating characteristic (ROC) curve analysis were used to identify significant CT features and assess diagnostic performance.
Main Results:
- In the smallest cohort (≤6 mm), polygonal shape was the most effective predictor of benign SSPNs (AUC=0.747).
- For nodules between 6-8 mm, polygonal shape and absence of pleural retraction were key predictors (AUC=0.778).
- In the largest cohort (8-15 mm), multiple features including polygonal shape, calcification, halo sign, and absence of lobulation/pleural retraction/air bronchogram were significant predictors (AUC=0.869).
Conclusions:
- CT features differentiating benign from malignant small pulmonary nodules are size-dependent.
- Analyzing specific CT characteristics within defined diameter ranges can improve the discrimination between benign and malignant SSPNs.
- This size-stratified approach aids in minimizing diagnostic ambiguity for small pulmonary nodules.
