Exploring the Predictive Value of Grading in Regions Beyond Peritumoral Edema in Gliomas Based on Radiomics

Jie Pan; Jun Lu; Shaohua Peng; Minhai Wang

doi:10.2174/0115734056387494250823132119

Exploring the Predictive Value of Grading in Regions Beyond Peritumoral Edema in Gliomas Based on Radiomics

Curr Med Imaging. 2025 Aug 28. doi: 10.2174/0115734056387494250823132119. Online ahead of print.

Authors

Jie Pan¹, Jun Lu¹, Shaohua Peng¹, Minhai Wang¹

Affiliation

¹ Department of Medical Imaging Center, The First Affiliated Hospital of Medical College, Shihezi University, Medical Imaging Center, Shihezi, China.

PMID: 40910233
DOI: 10.2174/0115734056387494250823132119

Abstract

Introduction: Accurate preoperative grading of adult-type diffuse gliomas is crucial for personalized treatment. Emerging evidence suggests tumor cell infiltration extends beyond peritumoral edema, but the predictive value of radiomics features in these regions remains underexplored.

Method: A retrospective analysis was conducted on 180 patients from the UCSF-PDGM dataset, split into training (70%) and validation (30%) cohorts. Intratumoral volumes (VOI_I, including tumor body and edema) and peritumoral volumes (VOI_P) at 7 expansion distances (1-5, 10, 15 mm) were analyzed. Feature selection involved Levene's test, t-test, mRMR, and LASSO regression. Radiomics models (VOI_I, VOI_P, and combined intratumoral-peritumoral models) were evaluated using AUC, accuracy, sensitivity, specificity, and F1 score, with Delong tests for comparisons.

Results: The combined radiomics models established for the intratumoral and peritumoral 1-5mm ranges (VOI_1-5mm) showed better predictive performance than the VOI_I model (AUC=0.815/0.672), among which the VOI_1 model performed the best: in the training cohort, the AUC was 0.903 (accuracy=0.880, sensitivity=0.905, specificity=0.855, F1=0.884); in the validation cohort, the AUC was 0.904 (accuracy=0.852, sensitivity=0.778, specificity=0.926, F1=0.840). This model significantly outperformed the VOI_I model (p<0.05) and the 10/15mm combined models (p<0.05).

Discussion: The peritumoral regions within 5 mm beyond the edematous area contain critical grading information, likely reflecting subtle tumor infiltration. Model performance declined with larger peritumoral distances, possibly due to increased normal tissue dilution.

Conclusion: The radiomics features of the intratumoral region and the peritumoral region within 5 mm can optimize the preoperative grading of gliomas, providing support for surgical planning and prognostic evaluation.

Keywords: Glioma grading; MRI; Peritumoral regions.; Radiomics.