Predicting meningioma recurrence using spectrochemical analysis of tissues and subsequent predictive computational algorithms

Abstract

Abstract Introduction Meningioma recurrence remains a clinical dilemma. This has a significant clinical and huge financial implication. Hence, the search for predictors for meningioma recurrence has become an increasingly urgent research topic in recent years. Objective Using spectrochemical analytical methods such as attenuated total reflection Fourier-transform infrared (ATR-FTIR) and Raman spectroscopy, our primary objective is to compare the spectral fingerprint signature of WHO grade I meningioma vs. WHO grade I meningioma that recurred. Secondary objectives compare WHO grade I meningioma vs. WHO grade II meningioma and WHO grade II meningioma vs. WHO grade I meningioma recurrence. Materials and Methods Our selection criteria included convexity meningioma only restricted to Simpson grade I & II only and WHO grade I & grade II only with a minimum 5 years follow up. We obtained tissue from tumour blocks retrieved from the tissue bank. These were sectioned onto slides and de-waxed prior to ATR-FTIR or Raman spectrochemical analysis. Derived spectral datasets were then explored for discriminating features using computational algorithms in the IRootLab toolbox within MATLAB; this allowed for classification and feature extraction. Results After analysing the data using various classification algorithms with cross-validation to avoid over-fitting of the spectral data, we can readily and blindly segregate those meningioma samples that recurred from those that did not recur in the follow-up timeframe. The forward feature extraction classification algorithms generated results that exhibited excellent sensitivity and specificity, especially with spectra obtained following ATR-FTIR spectroscopy. Our secondary objectives remain to be fully developed.