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

Abstract

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.

Keywords: CNS WHO grade; Meningioma; Prediction; Radiomics; Recurrence.

Fig. 1

The systematic approach to imaging analysis in the study.

Fig. 2

Model performance for determination of CNS WHO grade and early recurrence in intracranial meningioma.

Fig. 3

Distribution of morphological features in CNS WHO grade 1 and 2 meningiomas.

Fig. 4

Distribution of the Semantic Score in CNS WHO grade 1 and 2 meningiomas.

Fig. 5

Performance metrics of different machine learning classifiers to detect CNS WHO grade 2 and early recurrence: ROC curves for detecting CNS WHO grade 2 (all with LASSO as feature selection method) (A), ROC curves for detecting early recurrence (all with Information Gain as feature selection method) (B), Precision–Recall curve (C) and calibration plot (D) for the 4 best performing classifiers for detecting CNS WHO grade 2 (all with LASSO as feature selection method), Precision–Recall curve (E) and calibration plot (F) for the 4 best performing classifiers for detecting early relapse (all with Information Gain as feature selection method).