A Clinical-Radiomics Nomogram for the Preoperative Prediction of Aggressive Micropapillary and a Solid Pattern in Lung Adenocarcinoma
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View abstract on PubMed
Summary
This summary is machine-generated.Accurately identifying aggressive lung adenocarcinoma patterns like micropapillary and solid patterns preoperatively is crucial. Combining clinical data with radiomics analysis significantly improves diagnostic accuracy, aiding treatment decisions.
Area Of Science
- Oncology
- Radiology
- Medical Imaging
Background
- Micropapillary pattern (MP) and solid pattern (SP) in lung adenocarcinoma (LUAD) are linked to poor prognosis.
- Accurate preoperative identification of these aggressive patterns is essential for patient management.
Purpose Of The Study
- To develop and validate a predictive model for differentiating high-risk MP/SP in LUAD.
- To combine clinical and radiomics features for improved diagnostic accuracy.
Main Methods
- Retrospective analysis of 180 surgically confirmed non-small-cell lung cancer (NSCLC) patients.
- Development of three models: clinical, radiomics, and a comprehensive integrated model.
- Feature selection using LASSO and extraction via 3D Slicer.
Main Results
- The comprehensive model integrating clinical and radiomics data achieved the highest diagnostic accuracy (AUCs of 0.9186 training, 0.9396 validation).
- The radiomics model (AUCs 0.8896 training, 0.8901 validation) outperformed the clinical model (AUCs 0.7975 training, 0.8462 validation).
- Decision curve analysis confirmed the enhanced clinical utility of the combined approach.
Conclusions
- Integrating clinical and radiomics features significantly enhances preoperative identification of aggressive NSCLC patterns.
- The comprehensive model serves as a valuable tool for guiding surgical and adjuvant therapy decisions in LUAD.
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