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Published on: August 9, 2024
Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for
Jung Youn Kim1, Ji Eun Park1, Youngheun Jo1
1Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
Background:
Pseudoprogression is a diagnostic challenge in early posttreatment glioblastoma. We therefore developed and validated a radiomics model using multiparametric MRI to differentiate pseudoprogression from early tumor progression in patients with glioblastoma.
Methods:
The model was developed from the enlarging contrast-enhancing portions of 61 glioblastomas within 3 months after standard treatment with 6472 radiomic features being obtained from contrast-enhanced T1-weighted imaging, fluid-attenuated inversion recovery imaging, and apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps. Imaging features were selected using a LASSO (least absolute shrinkage and selection operator) logistic regression model with 10-fold cross-validation. Diagnostic performance for pseudoprogression was compared with that for single parameters (mean and minimum ADC and mean and maximum CBV) and single imaging radiomics models using the area under the receiver operating characteristics curve (AUC). The model was validated with an external cohort (n = 34) imaged on a different scanner and internal prospective registry data (n = 23).
Results:
Twelve significant radiomic features (3 from conventional, 2 from diffusion, and 7 from perfusion MRI) were selected for model construction. The multiparametric radiomics model (AUC, 0.90) showed significantly better performance than any single ADC or CBV parameter (AUC, 0.57-0.79, P < 0.05), and better than a single radiomics model using conventional MRI (AUC, 0.76, P = 0.012), ADC (AUC, 0.78, P = 0.014), or CBV (AUC, 0.80, P = 0.43). The multiparametric radiomics showed higher performance in the external validation (AUC, 0.85) and internal validation (AUC, 0.96) than any single approach, thus demonstrating robustness.
Conclusions:
Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improved diagnostic performance for identifying pseudoprogression and showed robustness in a multicenter setting.
Insights
A new multiparametric MRI radiomics model accurately differentiates pseudoprogression from tumor progression in glioblastoma patients. This approach shows robust performance, improving diagnostic accuracy for early post-treatment assessment.
Area of Science:
- Radiology
- Oncology
- Medical Imaging Analysis
Background:
- Pseudoprogression poses a diagnostic challenge in early post-treatment glioblastoma.
- Accurate differentiation from tumor progression is crucial for appropriate patient management.
Purpose of the Study:
- To develop and validate a multiparametric MRI radiomics model for distinguishing pseudoprogression from early glioblastoma progression.
- To assess the diagnostic performance and robustness of the developed model.
Main Methods:
- A radiomics model was developed using multiparametric MRI data (contrast-enhanced T1, FLAIR, ADC, CBV) from 61 glioblastomas.
- Feature selection employed LASSO regression, and model performance was evaluated using AUC.
- Validation was performed on an external cohort (n=34) and internal registry data (n=23).
Main Results:
- The multiparametric radiomics model achieved an AUC of 0.90, significantly outperforming single parameters and conventional MRI radiomics models.
- The model demonstrated strong performance in external (AUC, 0.85) and internal (AUC, 0.96) validation cohorts.
- Twelve significant radiomic features from conventional, diffusion, and perfusion MRI were selected for model construction.
Conclusions:
- Integrating diffusion- and perfusion-weighted MRI into a radiomics model significantly enhances diagnostic performance for pseudoprogression detection.
- The multiparametric radiomics model is robust and reliable, even in a multicenter setting.
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