AB066. From imaging to molecular diagnosis: VASARI magnetic resonance imaging features to predict isocitrate dehydrogenase mutation status in glioma.

Autor:	Setyawan NH; Department of Radiology, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia., Malueka RG; Department of Neurology, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia., Dwianingsih EK; Department of Pathological Anatomy, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia., Hartanto RA; Department of Neurosurgery, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia.
Jazyk:	angličtina
Zdroj:	Chinese clinical oncology [Chin Clin Oncol] 2024 Aug; Vol. 13 (Suppl 1), pp. AB066.
DOI:	10.21037/cco-24-ab066
Abstrakt:	Background: Glioma, the most common brain tumor, poses significant challenges in patient care and economic burden. Clinicians often struggle with management strategies, especially under the 2021 World Health Organization (WHO) central nervous system (CNS) classification emphasizing molecular diagnosis. Isocitrate dehydrogenase (IDH) mutation status is crucial in glioma management. However, many facilities lack the capability for comprehensive molecular tests, and not all patients are candidates for invasive biopsies. MRI offers a non-invasive method to evaluate glioma characteristics. The Visually Accessible Rembrandt Images (VASARI) MRI feature set provides a systematic approach to analyzing brain glioma. This study examines the association of VASARI features with IDH mutation status and their predictive capability. Methods: This study included 105 glioma patients treated between 2017 and 2022 who had not undergone surgery, chemotherapy, or radiotherapy. Brain MRIs were assessed using VASARI MRI features by two blinded radiologists. Pathological and molecular examinations were conducted per the 2021 WHO CNS tumor classification. IDH mutations were assessed using polymerase chain reaction (PCR) followed by DNA sequencing. Chi-squared analysis identified VASARI features significantly associated with IDH mutation status. A random forest model predicted IDH mutation status using these features. Results: Brain MRI assessments using VASARI terminology showed good inter-observer agreement (kappa =0.714-0.831) and excellent intra-observer agreement (kappa =0.910). Thirteen VASARI features were significantly associated with IDH mutation status. The prediction model based on VASARI MRI features achieved an area under the curve (AUC) of 0.97, with 93.75% sensitivity, 75% specificity, and 84.38% accuracy on test data. Conclusions: The VASARI MRI feature set is a reliable method for evaluating glioma patients and is feasible for routine radiological practice. Several VASARI features significantly associate with IDH mutation status, aiding glioma patient management. The IDH mutation prediction model based on VASARI features performs excellently and warrants further validation before routine implementation.
Databáze:	MEDLINE
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