Intraoperative fluorescence spectroscopy for IDH classification in glioma: a feasibility study.

Abstract

Background: Molecular features are fundamental to both the diagnosis and prognosis of gliomas. Specific molecular features are generally unavailable at the time of index surgery; however, upfront knowledge of molecular characteristics could meaningfully inform surgical strategy.

Aims/objectives: To determine the feasibility of a machine learning model trained on fluorescence spectral features to classify IDH mutation and MGMT promoter methylation status in glioma tissue.

Methods: Tissue samples were collected during 5-ALA-guided surgery and interrogated with a fibre probe to record fluorescence spectra. The fluorescence data were blinded and randomised and were trimmed to include only relevant wavelengths. Molecular data (IDH status and MGMT methylation) were recorded for each tumour specimen and incorporated into a machine learning algorithm. The algorithm was trained on the fluorescence data of half the patients exhibiting each label, and the model was then tested on the other half.

Results: Twenty-seven patients were recruited to the study, in whom over 8000 spectra were measured. IDH-mutant samples were identified with 87% accuracy. The algorithm was unable to reliably classify MGMT methylation.

Conclusions: In this proof-of-concept study, machine learning applied to fluorescence spectra shows promise for real-time intraoperative prediction of IDH status. Further work integrating IDH and 1p/19q status in larger cohorts is warranted.