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Clin Cancer Res. 2019 Nov 1;25(21):6329-6338. doi: 10.1158/1078-0432.CCR-19-0854. Epub 2019 Jul 17.

AI-Assisted In Situ Detection of Human Glioma Infiltration Using a Novel Computational Method for Optical Coherence Tomography.

Author information

1
Department of Biomedical Engineering, Texas A&M University, College Station, Texas.
2
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland.
3
Department of Neurologic Surgery, Mayo Clinic, Jacksonville, Florida.
4
Facultad de Ciencias, Universidad Autónoma de San Luis de Potosí, San Luis de Potosí, Mexico.
5
Division of Neuropathology, Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland.
6
School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma. javierjo@ou.edu.

Abstract

PURPOSE:

In glioma surgery, it is critical to maximize tumor resection without compromising adjacent noncancerous brain tissue. Optical coherence tomography (OCT) is a noninvasive, label-free, real-time, high-resolution imaging modality that has been explored for glioma infiltration detection. Here, we report a novel artificial intelligence (AI)-assisted method for automated, real-time, in situ detection of glioma infiltration at high spatial resolution.Experimental Design: Volumetric OCT datasets were intraoperatively obtained from resected brain tissue specimens of 21 patients with glioma tumors of different stages and labeled as either noncancerous or glioma-infiltrated on the basis of histopathology evaluation of the tissue specimens (gold standard). Labeled OCT images from 12 patients were used as the training dataset to develop the AI-assisted OCT-based method for automated detection of glioma-infiltrated brain tissue. Unlabeled OCT images from the other 9 patients were used as the validation dataset to quantify the method detection performance.

RESULTS:

Our method achieved excellent levels of sensitivity (∼100%) and specificity (∼85%) for detecting glioma-infiltrated tissue with high spatial resolution (16 μm laterally) and processing speed (∼100,020 OCT A-lines/second).

CONCLUSIONS:

Previous methods for OCT-based detection of glioma-infiltrated brain tissue rely on estimating the tissue optical attenuation coefficient from the OCT signal, which requires sacrificing spatial resolution to increase signal quality, and performing systematic calibration procedures using tissue phantoms. By overcoming these major challenges, our AI-assisted method will enable implementing practical OCT-guided surgical tools for continuous, real-time, and accurate intraoperative detection of glioma-infiltrated brain tissue, facilitating maximal glioma resection and superior surgical outcomes for patients with glioma.

PMID:
31315883
PMCID:
PMC6825537
[Available on 2020-05-01]
DOI:
10.1158/1078-0432.CCR-19-0854

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