Microscope-integrated optical coherence tomography for in vivo human brain tumor detection with artificial intelligence

• Published in: Journal of neurosurgery Vol. 141; no. 5; pp. 1 - 9
• Main Authors: Kuppler, Patrick, Strenge, Paul, Lange, Birgit, Spahr-Hess, Sonja, Draxinger, Wolfgang, Hagel, Christian, Theisen-Kunde, Dirk, Brinkmann, Ralf, Huber, Robert, Tronnier, Volker, Bonsanto, Matteo Mario
• Format: Journal Article
• Language: English
• Published: United States 01-11-2024
• Subjects: in vivo imaging; brain tumor imaging; automated tissue characterization; optical coherence tomography; residual tumor detection; tissue classification; tumor border detection; artificial intelligence; oncology
• ISSN: 0022-3085; 1933-0693; 1933-0693
• DOI: 10.3171/2024.1.JNS231511

It has been shown that optical coherence tomography (OCT) can identify brain tumor tissue and potentially be used for intraoperative margin diagnostics. However, there is limited evidence on its use in human in vivo settings, particularly in terms of its applicability and accuracy of residual brain tumor detection (RTD). For this reason, a microscope-integrated OCT system was examined to determine in vivo feasibility of RTD after resection with automated scan analysis.
Healthy and diseased brain was 3D scanned at the resection edge in 18 brain tumor patients and investigated for its informative value in regard to intraoperative tissue classification. Biopsies were taken at these locations and labeled by a neuropathologist for further analysis as ground truth. Optical OCT properties were obtained, compared, and used for separation with machine learning. In addition, two artificial intelligence-assisted methods were utilized for scan classification, and all approaches were examined for RTD accuracy and compared to standard techniques.
In vivo OCT tissue scanning was feasible and easily integrable into the surgical workflow. Measured backscattered light signal intensity, signal attenuation, and signal homogeneity were significantly distinctive in the comparison of scanned white matter to increasing levels of scanned tumor infiltration (p < 0.001) and achieved high values of accuracy (85%) for the detection of diseased brain in the tumor margin with support vector machine separation. A neuronal network approach achieved 82% accuracy and an autoencoder approach 85% accuracy in the detection of diseased brain in the tumor margin. Differentiating cortical gray matter from tumor tissue was not technically feasible in vivo.
In vivo OCT scanning of the human brain has been shown to contain significant value for intraoperative RTD, supporting what has previously been discussed for ex vivo OCT brain tumor scanning, with the perspective of complementing current intraoperative methods for this purpose, especially when deciding to withdraw from further resection toward the end of the surgery.