Predicting Model for Tumor Budding Status using Radiomics Features of 18F-PET/CT and in Cervical Cancer

ObjectiveThe aim of this study was to compare radiomics feature on 18F-FDG PET/CT and intratumoral heterogeneity according to tumor budding (TB) status and to develop predicting model for TB status using radiomics feature of 18F-FDG PET/CT in patients with cervical cancer.Materials and methodsA total of 76 cervical cancer patients who performed radical hysterectomy and preoperative 18F-FDG PET/CT were included. We assessed the status of intratumoral budding (ITP) and peritumoral budding (PTB) in all available hematoxylin and eosin-stained specimens. Three conventional metabolic parameters and a total of 59 features were extracted and analyzed. Univariate analysis was used to identify significant metabolic parameters and radiomics findings for TB status. Predicting model for TB status was built by the LASSO regularization.ResultsThe univariate analysis lead to the identification of 2 significant metabolic parameters and 12 significant radiomic features according to ITB status. Among these parameters, only compacity was remained in multivariate analysis for ITB status (odds ratio. 5.0047; 95% confidence interval, 1.1636 – 21.5253; p = 0.0305). Five radiomics features (Kurtosis, Compacity, Short-Zone Low Gray-level Emphasis, Coarseness, Low Gray-level Run Emphasis) were selected by the LASSO regularization and the predicting model for ITB status had a mean area under curve of 0.810 in training dataset and 0.794 in validation dataset.ConclusionRadiomics features on 18F-FDG PET/CT was associated with ITB status. The predicting model using radiomics features successfully predicted TB status in cervical cancer. The predicting models for ITB status may contribute to personalized medicine in the management of cervical cancer patients.