Table 1_A logistic paradigm to predict molecular glioblastoma on clinical MRI.docx

Objective<p>This study aims to externally validate a logistic strategy for predicting the integrated molecular diagnosis of glioblastoma (mGBM) as defined by the World Health Organization (WHO) 2021 Classification of Tumors of the Central Nervous System (CNS).</p>Methods<p>Ethical approval was obtained for a retrospective study of diffuse gliomas (CNS WHO grades 2–4) with available isocitrate dehydrogenase (IDH) and 1p19q status and whole genome profiling for mGBM, where appropriate.

Age and MRI characteristics (apparent diffusion coefficient (ADC), tumor location, contrast enhancement pattern, cyst(s), and calcification) were scored by two observers blinded to the tissue results and to each other’s results. Observer concordance was assessed using intraclass correlation coefficient (ICC) and Cohen’s kappa. Diagnostic performance, comparisons using McNemar and DeLong tests, and model calibration were obtained in SPSS 27 and Python (v3.12).</p>Results<p>A total of 108 consecutively enrolled cases were eligible from January to December 2023 (mean age 56.9 ± 15.5 years; range 20–82), comprising 85 mGBM/IDH wild-type (IDH^wt) and 23 IDH mutant (IDH^mut) gliomas.