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

in Scientific reports by Darius Kalasauskas, Michael Kosterhon, Elena Kurz, Leon Schmidt, Sebastian Altmann, Nils F Grauhan, Clemens Sommer, Ahmed Othman, Marc A Brockmann, Florian Ringel, Naureen Keric

TLDR

  • Radiomic analysis is a new way of looking at medical images that can help doctors identify certain types of tumors.
  • Radiomic analysis is a way of using computer algorithms to analyze medical images and find patterns that can help doctors make better decisions about treatment.
  • Radiomic analysis is a promising tool that could help doctors identify aggressive and atypical tumors before they become a problem.

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.

Overview

  • The study aims to compare the predictive value of conventional, semantic based and radiomic analyses to determine CNS WHO grade and early tumour relapse in intracranial meningiomas.
  • The study used a single-centre retrospective analysis of intracranial meningiomas operated between 2007 and 2018.
  • The study found that radiomic analysis 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).

Comparative Analysis & Findings

  • The radiomic model 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.
  • The study found that radiomic analysis is a promising tool for preoperative identification of aggressive and atypical intracranial meningiomas and could become a useful tool in the future.

Implications and Future Directions

  • The study highlights the potential of radiomic analysis as a powerful diagnostic tool with potential applications in patient risk stratification.
  • The study identifies the need for further research to validate the results and improve the performance of the radiomic model.
  • The study suggests that radiomic analysis could become a useful tool in the future for preoperative identification of aggressive and atypical intracranial meningiomas.
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