The scoring system combined with radiomics and imaging features in predicting the malignant potential of incidental indeterminate small (<20 mm) solid pulmonary nodules
- Bai-Qiang Qu 1, Yun Wang 2, Yue-Peng Pan 3, Pei-Wei Cao 3, Xue-Ying Deng 4
- Bai-Qiang Qu 1, Yun Wang 2, Yue-Peng Pan 3
- 1Department of Radiology, Wenling TCM Hospital Affiliated to Zhejiang Chinese Medical University, Taizhou, Zhejiang, 317500, China.
- 2Department of Nuclear medicine, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China.
- 3Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China.
- 4Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China. a1145047838@163.com.
- 0Department of Radiology, Wenling TCM Hospital Affiliated to Zhejiang Chinese Medical University, Taizhou, Zhejiang, 317500, China.
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View abstract on PubMed
Summary
This summary is machine-generated.A new scoring system effectively predicts the malignancy of small solid pulmonary nodules using radiomics and imaging features. This tool aids in distinguishing benign from malignant nodules, improving diagnostic accuracy.
Area Of Science
- Pulmonary nodule characterization
- Radiomics and medical imaging analysis
- Oncological diagnostics
Background
- Incidental indeterminate small solid pulmonary nodules (IISSPNs) pose a diagnostic challenge due to their varied malignant potential.
- Accurate prediction of malignancy is crucial for appropriate patient management and avoiding unnecessary invasive procedures.
Purpose Of The Study
- To develop a practical scoring system for predicting the malignant potential of IISSPNs (<20 mm).
- To integrate radiomics and imaging features for enhanced diagnostic performance.
Main Methods
- Retrospective analysis of 360 patients with surgically confirmed malignant (n=213) and benign (n=147) IISSPNs.
- Feature selection using the least absolute shrinkage and selection operator (LASSO) algorithm.
- Model development via multivariate logistic analysis, with performance evaluated using ROC curves, AUC, sensitivity, and specificity.
Main Results
- A combined model incorporating Mean, age, emphysema, lobularity, and size achieved the highest AUC of 0.877 in the training group.
- The scoring system, with a cutoff >4 points, showed high diagnostic accuracy (83.3%), sensitivity (85.3%), and specificity (80.2%).
- Scores >8 points indicated a >92.7% probability of malignancy; scores >12 points achieved 100% accuracy.
Conclusions
- The developed combined model and user-friendly scoring system demonstrate significant potential for predicting IISSPN malignancy.
- This practical tool can aid clinicians in risk stratification and decision-making for patients with small pulmonary nodules.
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