Longitudinal FDG-PET Radiomics for Early Prediction of Treatment Response to Chemoradiation in Locally Advanced Cervical Cancer: A Pilot Study.

Simple Summary: Locally advanced cervical cancer (LACC) poses a significant challenge in oncology due to its heterogeneous nature and inconsistent response to standard chemoradiation therapy. The ability to predict response to treatment early in LACC has profound implications for enhancing patient care through tailored and adaptive therapies. Sequential FDG-PET imaging is crucial in managing LACC, offering a non-invasive method to assess tumor progression and response to chemoradiation over the course of treatment. The current study had recourse to longitudinal FDG-PET radiomics for early prediction of treatment response to chemoradiation in LACC. The implications of this study aid in demonstrating the potentially pivotal role of longitudinal radiomics in personalized medicine and improving therapeutic outcomes for LACC. Objectives: This study aimed to assess the capacity of longitudinal FDG-PET radiomics for early distinguishing between locally advanced cervical cancer (LACC) patients who responded to treatment and those who did not. Methods: FDG-PET scans were obtained before and midway through concurrent chemoradiation for a study cohort of patients with LACC. Radiomics features related to image textures were extracted from the primary tumor volumes and stratified for relevance to treatment response status with the aid of random forest recursive feature elimination. Predictive models based on the k-nearest neighbors time series classifier were developed using the top-selected features to differentiate between responders and non-responders. The performance of the developed models was evaluated using receiver operating characteristic (ROC) curve analysis and n-fold cross-validation. Results: The top radiomics features extracted from scans taken midway through treatment showed significant differences between the two responder groups (p-values < 0.0005). In contrast, those from pretreatment scans did not exhibit significant differences. The AUC of the mean ROC curve for the predictive model based on the top features from pretreatment scans was 0.8529, while it reached 0.9420 for those derived midway through treatment scans. Conclusions: The study highlights the potential of longitudinal FDG-PET radiomics extracted midway through treatment for predicting response to chemoradiation in LACC patients and emphasizes that interim PET scans could be crucial in personalized medicine, ultimately enhancing therapeutic outcomes for LACC. [ABSTRACT FROM AUTHOR]