Multimodal radiomics fusion for predicting postoperative recurrence in NSCLC patients

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Fusion of PET, CT, and clinical data improved prediction of postoperative recurrence in NSCLC patients.
The best performing model using late fusion with Elastic Net achieved a cross-validated AUC of 0.811.
Nested cross-validation and SMOTE were used to address class imbalance during model training.
These findings indicate a shift towards incorporating multimodal frameworks for better risk stratification.

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Analysis shows improved prediction of postoperative recurrence in NSCLC using multimodal radiomics, indicating better clinical decision-making.

Mehri-Kakavand et al. (2025) studied this question.

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Analysis shows improved prediction of postoperative recurrence in NSCLC using multimodal radiomics, indicating better clinical decision-making.