MRI Biomarkers That Predict Brain Tumor Firmness Before Surgery Reviewed

Preoperatively predicting how firm a meningioma is could improve surgical planning, according to the review's background, by anticipating the ease of internal debulking, operative difficulty, and the feasibility of minimally invasive approaches. A new scoping review published in Acta Neurochirurgica examines which imaging biomarkers can predict intracranial meningioma consistency before surgery, evaluating everything from routine MRI measurements to radiomics and artificial intelligence models.

The review, led by Antoine Keraudy and colleagues, searched PubMed, Embase, Web of Science and the Cochrane Library from database inception to July 2026, following PRISMA-ScR guidelines. Only original human studies assessing MRI-derived biomarkers for preoperative prediction of meningioma consistency were eligible. In total, 35 publications met the criteria: 14 evaluated routine MRI biomarkers, 13 covered advanced MRI techniques, and 8 examined radiomics or artificial intelligence approaches.

Why does tumor consistency matter so much? According to the review’s background, preoperative prediction may improve surgical planning by anticipating the ease of internal debulking, operative difficulty, and the feasibility of minimally invasive approaches.

Among routine sequences, the review found that T2-weighted signal intensity ratios showed the most consistent association with softer tumors. The tumor-to-cerebellar peduncle T2-weighted intensity ratio in particular stood out, identified as the most readily deployable biomarker in routine practice. Diffusion-derived parameters provided complementary information, but the authors judged this information less reproducible.

Among advanced techniques, magnetic resonance elastography demonstrated the strongest evidence. The method directly quantifies tissue stiffness and correlated with both intraoperative consistency and histopathological features, giving it a biomechanical specificity that routine sequences lack. The review concludes that elastography offers greater biomechanical specificity, while routine T2-based biomarkers currently offer the best balance between accessibility and predictive value.

The artificial intelligence findings come with a prominent caution. Radiomics and deep-learning models reported high apparent predictive performance, frequently with areas under the curve above 0.90. However, most of these studies were retrospective, single-center, and internally validated, and in some cases were based on overlapping patient cohorts. The review therefore characterizes AI approaches as promising but insufficiently validated for routine use.

Methodological quality was assessed using QUADAS-2, the Newcastle-Ottawa Scale and the Radiomics Quality Score, as appropriate. Owing to substantial methodological heterogeneity across the included studies, the authors synthesized their findings narratively rather than through pooled quantitative analysis, a decision that reflects just how differently these studies were designed and reported.

The review’s conclusions set a clear bar for future work. Standardized reference standards, multicenter external validation, and stronger biological correlation are required before widespread clinical implementation.

Subject of Research: Imaging biomarkers for preoperative prediction of intracranial meningioma consistency

Article Title: Imaging biomarkers of intracranial meningioma consistency: a scoping review of conventional MRI, advanced MRI, radiomics and artificial intelligence techniques

Article References: Keraudy, A., Manouvriez, D., Lopes, R., Bourgeois, P., Aboukais, R., & Bretzner, M. (2026). Imaging biomarkers of intracranial meningioma consistency: a scoping review of conventional MRI, advanced MRI, radiomics and artificial intelligence techniques. Acta Neurochirurgica. https://doi.org/10.1007/s00701-026-07067-z

Image Credits: AI Generated

DOI: 10.1007/s00701-026-07067-z

Keywords: meningioma, MRI, tumor consistency, magnetic resonance elastography, T2-weighted imaging, radiomics, artificial intelligence, neurosurgery, scoping review, diffusion imaging, surgical planning, biomarkers