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

J Neurosurg 141:1343–1351, 2024

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.

METHODS 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.

RESULTS 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.

CONCLUSIONS 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.

Ocular Optical Coherence Tomography in the Evaluation of Sellar and Parasellar Masses

Neurosurgery 92:42–67, 2023

Compression of the anterior visual pathways by sellar and parasellar masses can produce irreversible and devastating visual loss.

Optical coherence tomography (OCT) is a noninvasive high-resolution ocular imaging modality routinely used in ophthalmology clinics for qualitative and quantitative analysis of optic nerve and retinal structures, including the retinal ganglion cells.

By demonstrating structural loss of the retinal ganglion cells whose axons form the optic nerve before decussating in the optic chiasm, OCT imaging of the optic nerve and retina provides an excellent tool for detection and monitoring of compressive optic neuropathies and chiasmopathies due to sellar and parasellar masses.

Recent studies have highlighted the role of OCT imaging in the diagnosis, follow-up, and prognostication of the visual outcomes in patients with chiasmal compression. OCT parameters of optic nerve and macular scans such as peripapillary retinal nerve fiber layer thickness and macular ganglion cell thickness are correlated with the degree of visual loss; additionally, OCT can detect clinically significant optic nerve and chiasmal compression before visual field loss is revealed on automated perimetry. Preoperative values of OCT optic nerve and macular parameters represent a prognostic tool for postoperative visual outcome.

This review provides a qualitative analysis of the current applications of OCT imaging of the retina and optic nerve in patients with anterior visual pathway compression from sellar and parasellar masses. We also review the role of new technologies such as OCT-angiography, which could improve the prognostic ability of OCT to predict postoperative visual function.