Editorial for “Improving Noninvasive Classification of Molecular Subtypes of Adult Gliomas With Diffusion‐Weighted MR Imaging: An Externally Validated Machine Learning Algorithm”
Abstract
The rapid advances in tumor biology understanding led to a substantial revision of the World Health Organization (WHO) classification of the Central Nervous System tumors guidelines (2021). In particular, molecular alterations were included in the diagnosis of a broad range of brain tumors and glioma classification was significantly re-organized. To date, advanced genetic profiling on surgical specimens is required for an integrated glioma classification and it is of utmost importance in defining its appropriate clinical treatment. Specifically, the status of isocytrate dehydrogenase (IDH) mutations and 1p/19q codelation has gained an essential relevance in categorizing adult glioma. Besides the unquestionable benefit derived from a specific molecular tumor characterization toward a more precise targeted therapy, the routine application of such innovative guidelines is still demanding and not always feasible. Molecular tests are time consuming, expensive and their widespread is not fully accomplished everywhere. Furthermore, such examinations imply the availability of a tumor sample although surgery is not always possible and not all the resected samples are suitable for genetic sequencing. The scientific community is making efforts toward the establishment of non-invasive methods to derive reliable molecular markers. In this context, artificial intelligence techniques applied to magnetic resonance imaging (MRI) were widely explored and both deep and machine learning models…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 755 participants or records.
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