Radiomics based on 18F-FDG PET/CT for prediction of pathological complete response to neoadjuvant therapy in non-small cell lung cancer
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View abstract on PubMed
Summary
This summary is machine-generated.This study shows that combining 18F-FDG PET/CT radiomics with clinicopathological data improves prediction of pathological complete response (pCR) to neoadjuvant therapy (NAT) for non-small cell lung cancer (NSCLC). The integrated support vector machine model achieved high accuracy, aiding clinical decisions.
Area Of Science
- Oncology
- Radiology
- Medical Imaging
Background
- Non-small cell lung cancer (NSCLC) treatment response prediction is crucial.
- Neoadjuvant therapy (NAT) is a standard treatment for NSCLC.
- Accurate prediction of pathological complete response (pCR) to NAT can guide treatment decisions.
Purpose Of The Study
- To develop and evaluate integrated models for predicting pCR to NAT in NSCLC.
- To assess the value of 18F-FDG PET/CT-based radiomics combined with clinicopathological information.
- To compare the performance of different radiomic and integrated models.
Main Methods
- 106 NSCLC patients undergoing NAT were included.
- 2,016 PET-based and 2,016 CT-based radiomic features were extracted after VOI segmentation.
- 25 machine learning models were constructed using various classifiers and feature sets (PET, CT, PET/CT radiomics, clinicopathological data, and integrated data).
- Model performance was evaluated using Area Under the Curves (AUC) from Receiver Operator Characteristic (ROC) curves.
Main Results
- The hybrid PET/CT radiomic model outperformed PET-alone and CT-alone models for pCR prediction.
- Integrating clinicopathological information further improved the predictive performance of the PET/CT radiomic model.
- The support vector machine (SVM)-based model combining PET/CT radiomics and clinicopathological data achieved the highest AUC (0.925 training, 0.863 test).
Conclusions
- Integrated 18F-FDG PET/CT radiomics and clinicopathological information provide an optimal model for non-invasively predicting pCR to NAT in NSCLC.
- The developed SVM-based model demonstrates high predictive efficacy.
- A nomogram incorporating radiomics and pathological type can facilitate clinical application.
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