Author

Umid Sulaimanov

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Open access Oct 2026

MRI-based preoperative WHO grade prediction in meningioma: development and comparison of deep learning, radiomics, clinical, and combined models in multicenter data

The objective of this study was to develop and externally validate complementary magnetic resonance imaging (MRI)-based models and their stacked combinations for preoperative World Health Organization (WHO) grading of intracranial meningioma. This retrospective multicenter study included 455 histopathologically confirm...

Cagin Huryol, Umid Sulaimanov, Murat Yuce et al. · Journal of Neuro-Oncology · 0 citations

Open access Sep 2026

Multicenter machine learning model using clinical and radiomic features for prediction of postoperative residual status in meningioma.

In this multicenter study, which included external validation, clinical variables provided most of the predictive value for postoperative residual meningioma, whereas radiomic features provided only limited incremental value when added to the clinical model.

Nafiye Şanlıer, Murat Yuce, Umid Sulaimanov et al. · Neurosurgical Focus · 0 citations

Review Open access Aug 2026

How Often Do Large Language Models Agree with Each Other—And with the Truth? A Consensus- and Complexity-Stratified Analysis of Data Extraction for Neuroimaging AI

It is shown that LLM-assisted extraction in neuroimaging AI is a complexity-stratified workflow design problem: low-complexity neuroimaging variables may be selectively automated, while medium-complexity variables require rapid verification, and high-complexity methodological variables should remain human-led.

Nafiye Şanlıer, Umid Sulaimanov, Ariorad Moniri et al. · Journal of Clinical Medicine · 0 citations