Keywords: Preclinical Image Analysis, AI/ML Image Reconstruction, Glioma; Recurrence; Imaging omics; Column chart; prediction model
Motivation: Glioma’s high recurrence rate and short survival period make early preoperative prediction challenging. Integrating multiple data dimensions, such as imaging and pathological parameters, offers potential for more accurate prognostic predictions.
Goal(s): To investigate the value of a radiomics model combined with a pathological parameters model in predicting early recurrence of glioma.
Approach: Establish preoperative radiomics models, pathological parameter models, and combined models for 115 glioma patients, and analyze the predictive efficacy of these models in forecasting recurrence.
Results: Both the radiomics model and the pathological parameters model can effectively predict glioma recurrence, with the combined model demonstrating superior predictive performance.
Impact: The established radiomics and pathological parameter model demonstrate superior performance, encompassing dimensions from cellular to imaging levels, thereby providing more comprehensive information. These models hold substantial potential as pivotal tools for accurately predicting glioma recurrence, thereby enhancing prognostic accuracy.
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