Pre-Treatment and Pre-Brachytherapy MRI first-order Radiomic Features by a Commercial software as survival predictors in radiotherapy for cervical cancer Objectives
Issued Date
2025-07-01
Resource Type
eISSN
24056308
Scopus ID
2-s2.0-105002907891
Journal Title
Clinical and Translational Radiation Oncology
Volume
53
Rights Holder(s)
SCOPUS
Bibliographic Citation
Clinical and Translational Radiation Oncology Vol.53 (2025)
Suggested Citation
Sittiwong W., Dankulchai P., Wongsuwan P., Prasartseree T., Thaweerat W., Thornsri N., Tuntapakul P. Pre-Treatment and Pre-Brachytherapy MRI first-order Radiomic Features by a Commercial software as survival predictors in radiotherapy for cervical cancer Objectives. Clinical and Translational Radiation Oncology Vol.53 (2025). doi:10.1016/j.ctro.2025.100965 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/109786
Title
Pre-Treatment and Pre-Brachytherapy MRI first-order Radiomic Features by a Commercial software as survival predictors in radiotherapy for cervical cancer Objectives
Author's Affiliation
Corresponding Author(s)
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Abstract
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 all clinical outcomes, CF + RF combined from PreRx and PreBT resulted in the highest Harrell's C-index compared with the CF or RF alone. Compare with Diff models, models from PreRx and PreBT resulted in higher Harrell's C-index. The C-indexes from the CF + RF model from PreRx and PreBT for PFS, LRFS, DMFS, and OS were 0.739, 0.873, 0.830 and 0.967 with the optimism indexes of 0.312, 0.381, 0.316, and 0.242, respectively. Conclusion: Radiomic 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.