Preoperative prediction of CNS WHO grade and tumour aggressiveness in intracranial meningioma based on radiomics and structured semantics.

Preoperative identification of intracranial meningiomas with aggressive behaviour may help in choosing the optimal treatment strategy. Radiomics is emerging as a powerful diagnostic tool with potential applications in patient risk stratification. In this study, we aimed to compare the predictive value of conventional, semantic based and radiomic analyses to determine CNS WHO grade and early tumour relapse in intracranial meningiomas. We performed a single-centre retrospective analysis of intracranial meningiomas operated between 2007 and 2018. Recurrence within 5 years after Simpson Grade I-III resection was considered as early. Preoperative T1 CE MRI sequences were analysed conventionally by two radiologists. Additionally a semantic feature score based on systematic analysis of morphological characteristics was developed and a radiomic analysis were performed. For the radiomic model, tumour volume was extracted manually, 791 radiomic features were extracted. Eight feature selection algorithms and eight machine learning methods were used. Models were analysed using test and training datasets. In total, 226 patients were included. There were 21% CNS WHO grade 2 tumours, no CNS WHO grade 3 tumour, and 25 (11%) tumour recurrences were detected in total. In ROC analysis the best radiomic models demonstrated superior performance for determination of CNS WHO grade (AUC 0.930) and early recurrence (AUC 0.892) in comparison to the semantic feature score (AUC 0.74 and AUC 0.65) and conventional radiological analysis (AUC 0.65 and 0.54). The combination of human classifiers, semantic score and radiomic analysis did not markedly increase the model performance. Radiomic analysis is a promising tool for preoperative identification of aggressive and atypical intracranial meningiomas and could become a useful tool in the future.