# AI-Enhanced Hyperspectral Imaging for Intraoperative Detection of Low-Grade Glioma: A Clinical Study

arXiv: https://arxiv.org/abs/2610.17944 · HTML: https://mlsanity.com/paper/2610.17944/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 51 references that are in the map.
- First posted on arXiv 13 Oct 2026.

## Builds on (most cited first, 4 of 4)

- [Deep Learning for Hyperspectral Image Classification: An Overview](https://mlsanity.com/paper/1910.12861/) (2019)
- [Intra-operative Brain Tumor Detection with Deep Learning-Optimized Hyperspectral Imaging](https://mlsanity.com/paper/2302.02884/) (2023)
- [RGB to Hyperspectral: Spectral Reconstruction for Enhanced Surgical Imaging](https://mlsanity.com/paper/2410.13570/) (2024)
- [Towards Machine Learning-based Quantitative Hyperspectral Image Guidance for Brain Tumor Resection](https://mlsanity.com/paper/2311.10321/) (2023)

## Abstract (arXiv)

Current image-guided neuronavigation techniques, such as intraoperative MRI and ultrasound, are limited by high costs, limited sensitivity and operational complexity. There is a clear need for more efficient systems to enhance intra-operative tumor detection and improve patient outcomes for intrinsic brain tumor resection. Whereas previous intraoperative brain HSI studies have predominantly investigated high-grade gliomas, glioblastomas, or mixed-grade cohorts, this clinical study focuses specifically on low-grade gliomas (LGGs). LGGs strongly resemble normal brain under white light conditions on the operative microscope. This study characterizes spectral differences between healthy and tumor tissue and provides a paired comparison of HSI- and RGB-based classification at matched tissue locations and under identical patient-level data splits. Tumor regions showed consistently lower reflectance, which may be associated with differences in hemoglobin concentration and oxygenation. A range of classification models was evaluated. The 3D CNN, which integrates spectral and spatial features, achieved the highest median ROC-AUC of 74.5%, compared with 67.9% for the spectral-only XGBoost model and 64.0% for the RGB-based model. These results support further investigation of HSI for low-grade glioma classification and quantify its performance relative to conventional RGB imaging in this cohort.