Work

Pre-Treatment and Pre-Brachytherapy MRI First-Order Radiomic Features by a Commercial Software as Survival Predictors in Radiotherapy for Cervical Cancer

Year: 2024

Type: article

Abstract: ObjectivesThis study aimed to investigate the performance of prediction models for clinical, radiomic, and both factors combined to measure treatment outcomes in patients with locally advanced cervical cancer (LACC) after concurrent chemoradiation (CCRT) and 3D image-guided adaptive brachytherapy (3D-IGABT). Materials and Methods The study included 100 patients with LACC who underwent definitive CCRT with IMRT/VMAT technique followed by 3D-IGABT. MRI-based contouring included T2WI and DWI images for primary tumor (GTVp) and lymph nodes (GTVn). The contours were imported to MIM software to extract first-order radiomic features. Radiomic values from pre-treatment (PreRx), pre-brachytherapy (PreBT), differences between PreRx and PreBT (Diff) radiomic and clinical factors were analyzed using univariate and multivariate Cox regression analysis. Predictive models of PFS, LRFS, DMFS, and OS were created along with the optimism index and calibration plot. Results The median follow-up time was 24.5 months. The 2-year of PFS, LRFS, DMFS, and OS rates were 71, 88.6, 83.1, and 83.5%, respectively. For the clinical outcomes, clinical and radiomic factors combined from PreRx and PreBT resulted in the highest Harrell’s C-index compared with the clinical factors or radiomic features alone and from Diff models. The C-index from the combined clinical and radiomics model from PreRx and PreBT for PFS, LRFS, DMFS, and OS were 0.739, 0.873, 0.830 and 0.967, respectively.ConclusionRadiomic features from the first-order statistics added values to clinical factors to predict the outcomes after CCRT. The highest prediction model performance was for the combined clinical and radiomics from PreRx and PreBT.

Source: International Journal of Radiation Oncology*Biology*Physics

DOI: 10.1016/j.ijrobp.2024.07.1573