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Abstract #5421

Application of MRI histogram analysis based on non-Gaussian diffusion model in distinguishing recurrence from pseudoprogression

Yu Zhang1, Peipei Wang1, Eryuan Gao1, Kai Zhao1, Ting Chen1, Xiaoyue Ma1, Yong Zhang1, and Jingliang Cheng1
1The First Affiliated Hospital of Zhengzhou University, ZhengZhou, China

Synopsis

Keywords: Unmet Needs

Motivation: Glioblastoma's aggressive recurrence and conventional MRI limitations in pseudoprogression discrimination drove our assessment of non-Gaussian diffusion biomarkers for personalized surgical/adjuvant therapy planning.

Goal(s): A reliable method for distinguishing between recurrence and pseudoprogression in Glioblastoma patients (9009).

Approach: This prospective study analyzed 55 biopsy-proven glioblastoma cases. Automated segmentation of preoperative and recurrence-phase MRI delineated tumor/edema regions. Non-Gaussian diffusion-derived histogram metrics compared recurrence-associated versus non-recurrent peritumoral edema subregions

Results: Recurrence regions exhibited statistically significant differences (p<0.05) in histogram parameters compared to non-recurrence regions, highlighting the clinical utility of non-Gaussian diffusion models in identifying occult tumor infiltration

Impact: Non-Gaussian diffusion MRI detects occult tumor infiltration in peritumoral edema (invisible on conventional MRI), enhancing recurrence/pseudoprogression prediction to optimize GBM surgical and adjuvant therapy.

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