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Updated: Jul 27, 2025
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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
Published on: September 19, 2012
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Value of [
Ewa Burchardt1,2, Agnieszka Bos-Liedke3, Kamila Serkowska4
1Department of Radiotherapy and Oncological Gynecology, Greater Poland Cancer Center, 61-866, Poznan, Poland.
Scientific Reports
|June 5, 2023
Summary
Radiomic features from [18F] FDG PET/CT imaging, including statistical and texture analysis, can predict cervical cancer recurrence and chemotherapy response. These parameters offer valuable insights into treatment effectiveness and patient outcomes.
Area of Science:
- Oncology
- Radiology
- Medical Imaging
Background:
- Cervical cancer staging (IIIC1-IVB) requires accurate assessment of disease progression and treatment response.
- Radiomics, extracting quantitative features from medical images, shows promise in non-invasively characterizing tumors.
Purpose of the Study:
- To evaluate the utility of first-order statistics (FOS) and Gray-Level Co-occurrence Matrix (GLCM) texture analysis of [18F] FDG PET/CT scans in assessing cervical cancer.
- To correlate radiomic parameters with chemotherapy response, recurrence, and patient age.
Main Methods:
- Retrospective analysis of 83 patients with stage IIIC1-IVB cervical cancer.
- Acquisition of [18F] FDG PET/CT scans before and after chemotherapy.
- Calculation of FOS and GLCM texture parameters, including SUVmax, SUVmean, TLG, MTV, ASP, E, COR, En, and H.
Main Results:
- Significant differences in metabolic and textural parameters were observed between pre- and post-therapy scans.
- Pre-treatment coefficient of variation (COV) moderately correlated with recurrence (R=0.34, p=0.001).
- Post-treatment contrast (C) showed moderate correlation with patient age (R=0.3, p=0.0038), while Energy (En) and Homogeneity (H) strongly correlated with chemotherapy response (R=0.54 and R=0.46, respectively).
Conclusions:
- Pre- and post-treatment [18F] FDG PET/CT radiomic parameters, encompassing statistical and textural features, are significant predictors of cervical cancer recurrence and chemotherapy response.
- These findings highlight the potential of radiomics to guide treatment decisions and improve patient management in cervical cancer.
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