arXiv：cs.AI· Siri Willems, Ishika Jain, Jens De Winne, Siri Luthman, Hamed Zivari Adab, Roeland Vandebriel, Anna Alperovich, Xiaohan Zhang, Ben Stoffelen, Tom De Schepper, Lien Solie, Steven De Vleeschouwer·· 22 小时前
AI 增强高光谱成像用于术中低级别胶质瘤检测的临床研究
AI-Enhanced Hyperspectral Imaging for Intraoperative Detection of Low-Grade Glioma: A Clinical Study
AI 导读
一项临床研究评估了 AI 增强高光谱成像（HSI）在术中检测低级别胶质瘤（LGG）的效果，并对 HSI 与 RGB 分类进行了配对比较。融合光谱与空间特征的 3D CNN 取得最高中位 ROC-AUC 74.5%，高于仅用光谱的 XGBoost 模型 67.9% 和基于 RGB 的模型 64.0%。肿瘤区域反射率持续偏低，可能与血红蛋白浓度和氧合差异有关。
正文
Authors:Siri Willems, Ishika Jain, Jens De Winne, Siri Luthman, Hamed Zivari Adab, Roeland Vandebriel, Anna Alperovich, Xiaohan Zhang, Ben Stoffelen, Tom De Schepper, Lien Solie, Steven De Vleeschouwer
Abstract: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.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.17944 [cs.CV] |
| (or arXiv:2610.17944v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17944 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Siri Willems [view email]
[v1]
Tue, 13 Oct 2026 08:27:54 UTC (8,610 KB)
来源：arXiv：cs.AI · arxiv.org