Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning
Open accessAbstract
Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence imaging technology that has potential to increase intraoperative precision, extend resection, and tailor surgery for malignant invasive brain tumors because of its subcellular dimension resolution. Despite its promising diagnostic potential, interpreting the gray tone fluorescence images can be difficult for untrained users. CLE images can be distorted by motion artifacts, fluorescence signals out of detector dynamic range, or may be obscured by red blood cells, and thus interpreted as nondiagnostic (ND). However, just a single CLE image with a detectable pathognomonic histological tissue signature can suffice for intraoperative diagnosis. Dealing with the abundance of images from CLE is not unlike sifting through a myriad of genes, proteins, or other structural or metabolic markers to find something of commonality or uniqueness in cancer that might indicate a potential treatment scheme or target. In this review, we provide a detailed description of bioinformatical analysis methodology of CLE images that begins to assist the neurosurgeon and pathologist to rapidly connect on-the-fly intraoperative imaging, pathology, and surgical observation into a conclusionary system within the concept of theranostics. We present an overview and discuss deep learning models for automatic detection of the diagnostic CLE images and discuss various training regimes and ensemble modeling effect on power of deep learning predictive models. Two major approaches reviewed in this paper include the models that can automatically classify CLE images into diagnostic/ND, glioma/nonglioma, tumor/injury/normal categories, and models that can localize histological features on the CLE images using weakly supervised methods. We also briefly review advances in the deep learning approaches used for CLE image analysis in other organs. Significant advances in speed and precision of automated diagnostic frame selection would augment the diagnostic potential of CLE, improve operative workflow, and integration into brain tumor surgery. Such technology and bioinformatics analytics lend themselves to improved precision, personalization, and theranostics in brain tumor treatment.
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Deep learning is applied for theranostics in neurosurgical imaging, combining confocal laser endomicroscopy with deep learning models for automatic detection of intraoperative CLE images on-the-fly.
“and for theranostics in neurosurgical imaging, combining confocal laser endomicroscopy with deep learning models for automatic detection of intraoperative CLE images on-the-fly [95].”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [95]
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Emerging Advanced Technologies Developed by IPR for Bio Medical Applications ‑.A Review
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- Authors
- Izadyyazdanabadi, M., Belykh, E., Mooney, M. A., Eschbacher, J. M., Nakaji, P., Yang, Y., & Preul, M. C.
- Affiliations
- Barrow Neurological Institute (US)
- Arizona State University (US)
- Corresponding author
- Preul, Mark C.
- ORCID
- Venue
- Frontiers in Oncology
- Publisher
- Frontiers Media SA
- Published
- 2018-07-03
- Volume
- 8
- Pages
- 240-240
- ISSN
- 2234-943X (ISSN-L)
- PMC
- PMC6043805
- OpenAlex
- W2798728129
- Cited by
- 45
- References
- 63
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 7 · 2023: 1 · 2022: 10 · 2021: 5 · 2020: 4 · 2019: 8 · 2018: 5
- FWCI
- 0.64
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Frontiers in Oncology (publishedVersion, cc-by)
- arXiv (Cornell University) (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Barrow Neurological Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Optical Imaging and Spectroscopy Techniques
- Keywords
- confocal laser endomicroscopy, theranostics, glioma, brain tumor surgery, intraoperative diagnosis, deep learning, image classification, weakly supervised localization, ensemble learning, motion artifacts, fluorescence imaging
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