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

Purpose: The prediction of immunotherapy efficacy in cervical cancer patients remains a critical clinical challenge. This study aims to develop and validate a deep learning-based automatic tumor segmentation method on PET/CT images, extract texture features from the tumor regions in cervical cancer patients, and investigate their correlation with PD-L1 expression. Furthermore, a predictive model for immunotherapy efficacy will be constructed.
Methods: We retrospectively collected data from 283 pathologically confirmed cervical cancer patients who underwent 18F-FDG PET/CT examinations, divided into three subsets. Subset-I (n = 97) was used to develop a deep learning-based segmentation model using Attention-UNet and region-growing methods on co-registered PET/CT images. Subset-II (n = 101) was used to explore correlations between radiomic features and PD-L1 expression. Subset-III (n = 85) was used to construct and validate a radiomic model for predicting immunotherapy response.
Results: Using Subset-I, a segmentation model was developed. The segmentation model achieved optimal performance at the 94th epoch with an IoU of 0.746 in the validation set. Manual evaluation confirmed accurate tumor localization. Sixteen features demonstrated excellent reproducibility (ICC > 0.75). Using Subset-II, PD-L1–correlated features were extracted and identified. In Subset-II, 183 features showed significant correlations with PD-L1 expression (P < 0.05).Using these features in Subset-III, a predictive model for immunotherapy efficacy was constructed and evaluated. In Subset-III, the SVM-based radiomic model achieved the best predictive performance with an AUC of 0.935.
Conclusion: We validated, respectively in Subset-I, Subset-II, and Subset-III, that deep learning models incorporating medical prior knowledge can accurately and automatically segment cervical cancer lesions, that texture features extracted from 18F-FDG PET/CT are significantly associated with PD-L1 expression, and that predictive models based on these features can effectively predict the efficacy of PD-L1 immunotherapy. This approach offers a non-invasive, efficient, and cost-effective tool for guiding individualized immunotherapy in cervical cancer patients and may help reduce patient burden, accelerate treatment planning.