Tag: diagnostic biomarkers

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Exploring the Diagnostic Test Accuracy of MicroRNAs as Potential Biomarkers for Glioblastoma

Neurosurgery 98:1221–1230, 2026

This systematic review and meta-analysis evaluates microRNA (miRNA)–based liquid biopsies for diagnosing glioblastoma (GBM), synthesizing data from 15 studies and 28 biomarker evaluations across 868 samples. Key diagnostic metrics—pooled sensitivity 0.84 and specificity 0.89—indicate strong potential, with miR-21 showing the highest accuracy among single-miRNA assays.

The report details search strategy, inclusion criteria, statistical methods, subgroup analyses (miRNA type, biofluid source, control type), and study quality assessment. Limitations include methodological heterogeneity, high risk of bias in patient selection, inconsistent reporting (CSF source, IDH status), and small cohort sizes, underscoring need for standardized clinical validation.

Aim Evaluate the diagnostic accuracy of microRNA (miRNA) liquid biopsies (blood/serum/plasma/CSF) for diagnosing glioblastoma (GBM) via systematic review and meta-analysis.

Methods PRISMA-guided searches of Ovid Medline and Embase (updated through Oct 16, 2024); included studies had histologic GBM confirmation and extractable 2×2 diagnostic data; pooled estimates generated using a random-effects bivariate model.

Evidence base 15 included articles (published 2011–2022) provided 28 miRNA evaluations, totaling 868 samples from 551 GBM patients and 811 samples from 578 controls.

Overall accuracy Pooled sensitivity 0.84 and specificity 0.89, with heterogeneity of 66% (sensitivity) and 39% (specificity); pooled AUC 0.89.

Likelihood ratios Pooled PLR 7.26, NLR 0.19, and DOR 40.17, indicating strong overall discriminatory performance.

Key biomarker miR-21 showed the highest pooled performance among assessed groupings (sensitivity 0.90, specificity 0.95).

Subgroups Single miRNAs had higher specificity than multi-miRNA panels, while diagnostic capability did not differ clearly by biofluid source (CSF vs blood) in this dataset.

Limitations All included studies had high risk of bias in patient selection, and many had bias in index test interpretation/flow-timing; limited CSF comparisons and inconsistent qPCR thresholds/normalization contributed to heterogeneity and constrain clinical translation without standardization and validation.

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Comprehensive Morphometric Analysis to Identify Key Neuroimaging Biomarkers for the Diagnosis of Adult Hydrocephalus Using Artificial Intelligence

Neurosurgery 96:1386–1396, 2025

This study used AI and SHAP analysis to identify five key, easily measurable 1-D neuroimaging biomarkers—FTHR, MEI, MCMI, SMLH, and CPCA—for accurately diagnosing adult non-normal pressure hydrocephalus, offering a practical, standardized, and interpretable approach to improve early detection and clinical decision-making.

• Hydrocephalus diagnosis is often inconsistent due to reliance on clinical and qualitative radiological assessments.

• This study used artificial intelligence (AI) and SHAP analysis to identify key, easily measurable neuroimaging biomarkers for adult non-normal pressure hydrocephalus (non-NPH).

• A comprehensive set of 21 morphometric features was analyzed from MRI images of 42 adult non-NPH patients and 40 healthy controls.

• Gradient Boosting was the best-performing AI classifier, achieving 0.94 accuracy and 0.97 AUC.

• Ventricular volume is the most important biomarker, but measurement is complex for clinicians.

• Five key 1-D biomarkers identified are: frontal-temporal horn ratio (FTHR), modified Evans index (MEI), modified cella media index (MCMI), sagittal maximum lateral ventricle height (SMLH), and coronal posterior callosal angle (CPCA).

• These five markers are easily measurable and provide high diagnostic accuracy, supporting practical clinical use.

• The approach addresses multicollinearity and improves diagnostic standardization, but further validation with larger datasets is needed.

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Use of circulating tumor cells and microemboli to predict diagnosis and prognosis in diffuse glioma

J Neurosurg 141:673–683, 2024

Circulating tumor cell (CTC) detection is a promising noninvasive technique that can be used to diagnose cancer, monitor progression, and predict prognosis. In this study, the authors aimed to investigate the clinical utility of CTCs in the management of diffuse glioma.

METHODS Sixty-three patients with newly diagnosed diffuse glioma were included in this multicenter clinical cohort. The authors used a platform based on isolation by size of epithelial tumor cells (ISET) to detect and analyze CTCs and circulating tumor microemboli (CTMs) in the peripheral blood of patients both before and after surgery. Least absolute shrinkage and selector operation (LASSO) and Cox regression analyses were used to verify whether CTCs and CTMs are independent prognostic factors for diffuse glioma.

RESULTS CTC levels were closely related to the degree of malignancy, WHO grade, and pathological subtypes. Receiver operating characteristic curve analysis revealed that a high CTC level was a predictor for glioblastoma. The results also showed that CTMs originate from the parental tumor rather than from the circulation and are an independent prognostic factor for diffuse glioma. The postoperative CTC level is related to the peripheral immune system and patient survival. Cox regression analysis showed that postoperative CTC levels and CTM status are independent prognostic factors for diffuse glioma, and CTC- and CTM-based survival models had high accuracy in internal validation.

CONCLUSIONS The authors revealed a correlation between CTCs and clinical characteristics and demonstrated that CTCs and CTMs are independent predictors for the diagnosis and prognosis of diffuse glioma. Their CTC- and CTMbased survival models can enable clinicians to evaluate patients’ response to surgery as well as their outcomes.

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