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PET/CT radiomics for non-invasive prediction of immunotherapy efficacy in cervical cancer

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Retrospective analysis assesses the correlation between radiomic features and PD-L1, suggesting predictive models enhance immunotherapy strategies.

The SVM-based radiomic model achieved a predictive performance with an AUC of 0.935.
In examining correlation, 183 features exhibited significant links with PD-L1 expression at P < 0.05.
A segmentation model developed using deep learning achieved an IoU of 0.746 for tumor localization.
The findings suggest that this non-invasive approach can guide individualized immunotherapy treatment.

Du et al. (2025) studied this question.

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Retrospective analysis assesses the correlation between radiomic features and PD-L1, suggesting predictive models enhance immunotherapy strategies.