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Abstract #3391

Integrating Whole-Brain Tumor Burden MRI Features with Inflammatory Mediators to Predict High Risk of Neurocognitive Decline in Glioma Patients

Simin Zhang1, Huaiqiang Sun2, Qiyong Gong3, and Qiang Yue2
1department of radiology, west china hospital of sichuan university, Chengdu, China, 2west china hospital of sichuan university, Chengdu, China, 3west china xiamen hospital of sichuan university, Xiamen, China

Synopsis

Keywords: Diagnosis/Prediction, Tumors

Motivation: Patients with low-grade glioma (LGG) face postoperative neurocognitive decline (ND), affecting quality of life despite prolonged survival.

Goal(s): The present study aims to construct and evaluate predictive models for ND in adult LGG patients.

Approach: his study employed a 3D U-Net deep learning model for LGG segmentation, extracted radiomic features, and combined them with whole brain cortical features , pathological and inflammatory markers using automated machine learning to predict ND.

Results: The integrated model including achieved highest predictive accuracy (AUC: 0.94), demonstrating that combining whole-brain tumor burden MRI Features, pathological, and inflammatory markers significantly improves ND prediction, enabling better risk stratification and tailored interventions.

Impact: This study underscores the critical role of whole-brain tumor burden MRI features, pathological and inflammatory markers to predict ND. It encourages new research into inflammation-cognition links, promotes personalized care in glioma treatment, and could revolutionize approaches in neuro-oncology patient management.

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Keywords