Diffuse reflectance spectroscopy sensor to differentiate between glial tumor and healthy brain tissue: a proof-of-concept study

Abstract

Glial tumors grow diffusely in the brain. Survival is correlated to the extent of tumor removal, but tumor borders are often invisible. Resection beyond the borders as defined by conventional methods may further improve prognosis. In this proof-of-concept study, we evaluate diffuse reflectance spectroscopy (DRS) for discrimination between glial tumors and normal brain ex vivo. DRS spectra and histology were acquired from 22 tumor samples and nine brain tissue samples retrieved from 30 patients. The content of biological chromophores and scattering features were estimated by fitting a model derived from diffusion theory to the DRS spectra. DRS parameters differed significantly between tumor and normal brain tissue. Classification using random forest yielded a sensitivity and specificity for the detection of low-grade gliomas of 82.0% and 82.7%, respectively, and the area under curve (AUC) was 0.91. Applied in a hand-held probe or biopsy needle, DRS has the potential to provide intra-operative tissue analysis.

Fig. 1.

Study design and the DRS system. Overview of the study design and DRS system. In short, 30 patients were included in the study, from which 31 tissue samples were acquired, 22 tumor and 9 normal brain samples. Samples were examined with DRS light from a tungsten halogen broadband light source (360 to 2500 nm) coupled to an optical multimode fiber (200 micron core diameter) integrated in a handheld probe. The light exits the fiber at the tip of the probe and reflected light from the examined tissue is collected by an identical optical fiber and lead to two spectrometers, one of which resolves light in the visible and near-infrared wavelength range of 450 to 1100 nm, and the other in the infrared wavelengths from 900 to 1600 nm. The distance between the fibers at the probe tip is 0.45 mm. A previously published optical model derived from diffusion theory was used for spectral analysis (fitting) of the DRS data. Finally, random forest classification based on the fitted DRS parameters was performed to examine the ability of DRS to predict the samples’ histology.

Fig. 2.

2a Averaged DRS spectra with the standard deviation presented as dotted lines. 2b Averaged reduced scattering coefficient.

Fig. 3.

Boxplots of the DRS parameters derived from the fit model. Significantly differences in mean values are indicated with brackets where * p $\le$ 0,05, ** p $\le$ 0,01 and *** p $\le$ 0,001.

Fig. 4.

Result of random forest classification presented as ROC curves. Approximated receiver operating characteristic (ROC) curves for prediction of a) LGG and b) HGG based on the nine DRS fit parameters. The curves illustrate the trade-off between sensitivity (y-axis) and specificity, (x-axis), for every possible cut-off value. AUC was 0.91 and 0.81 respectively.