Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning

Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence imaging technology that has the 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. 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 the power of deep learning predictive models. Two major approaches reviewed in this paper include the models that can automatically classify CLE images into diagnostic/nondiagnostic, 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.Comment: See the final version published in Frontiers in Oncology here: https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful

Improving utility of brain tumor confocal laser endomicroscopy: objective value assessment and diagnostic frame detection with convolutional neural networks

Confocal laser endomicroscopy (CLE), although capable of obtaining images at cellular resolution during surgery of brain tumors in real time, creates as many non-diagnostic as diagnostic images. Non-useful images are often distorted due to relative motion between probe and brain or blood artifacts. Many images, however, simply lack diagnostic features immediately informative to the physician. Examining all the hundreds or thousands of images from a single case to discriminate diagnostic images from nondiagnostic ones can be tedious. Providing a real-time diagnostic value assessment of images (fast enough to be used during the surgical acquisition process and accurate enough for the pathologist to rely on) to automatically detect diagnostic frames would streamline the analysis of images and filter useful images for the pathologist/surgeon. We sought to automatically classify images as diagnostic or non-diagnostic. AlexNet, a deep-learning architecture, was used in a 4-fold cross validation manner. Our dataset includes 16,795 images (8572 nondiagnostic and 8223 diagnostic) from 74 CLE-aided brain tumor surgery patients. The ground truth for all the images is provided by the pathologist. Average model accuracy on test data was 91% overall (90.79 % accuracy, 90.94 % sensitivity and 90.87 % specificity). To evaluate the model reliability we also performed receiver operating characteristic (ROC) analysis yielding 0.958 average for the area under ROC curve (AUC). These results demonstrate that a deeply trained AlexNet network can achieve a model that reliably and quickly recognizes diagnostic CLE images.Comment: SPIE Medical Imaging: Computer-Aided Diagnosis 201

Intraoperative fluorescence imaging for personalized brain tumor resection: Current state and future directions

Introduction: Fluorescence-guided surgery is one of the rapidly emerging methods of surgical theranostics. In this review, we summarize current fluorescence techniques used in neurosurgical practice for brain tumor patients, as well as future applications of recent laboratory and translational studies.Methods: Review of the literature.Results: A wide spectrum of fluorophores that have been tested for brain surgery is reviewed. Beginning with a fluorescein sodium application in 1948 by Moore, fluorescence guided brain tumor surgery is either routinely applied in some centers or is under active study in clinical trials. Besides the trinity of commonly used drugs (fluorescein sodium, 5-ALA and ICG), less studied fluorescent stains, such as tetracyclines, cancer-selective alkylphosphocholine analogs, cresyl violet, acridine orange, and acriflavine can be used for rapid tumor detection and pathological tissue examination. Other emerging agents such as activity-based probes and targeted molecular probes that can provide biomolecular specificity for surgical visualization and treatment are reviewed. Furthermore, we review available engineering and optical solutions for fluorescent surgical visualization. Instruments for fluorescent-guided surgery are divided into wide-field imaging systems and hand-held probes. Recent advancements in quantitative fluorescence-guided surgery are discussed.Conclusion: We are standing on the doorstep of the era of marker-assisted tumor management. Innovations in the fields of surgical optics, computer image analysis, and molecular bioengineering are advancing fluorescence-guided tumor resection paradigms, leading to cell-level approaches to visualization and resection of brain tumors

Laser application in neurosurgery

BACKGROUND: Technological innovations based on light amplification created by stimulated emission of radiation (LASER) have been used extensively in the field of neurosurgery. METHODS: We reviewed the medical literature to identify current laser-based technological applications for surgical, diagnostic, and therapeutic uses in neurosurgery. RESULTS: Surgical applications of laser technology reported in the literature include percutaneous laser ablation of brain tissue, the use of surgical lasers in open and endoscopic cranial surgeries, laser-assisted microanastomosis, and photodynamic therapy for brain tumors. Laser systems are also used for intervertebral disk degeneration treatment, therapeutic applications of laser energy for transcranial laser therapy and nerve regeneration, and novel diagnostic laser-based technologies (e.g., laser scanning endomicroscopy and Raman spectroscopy) that are used for interrogation of pathological tissue. CONCLUSION: Despite controversy over the use of lasers for treatment, the surgical application of lasers for minimally invasive procedures shows promising results and merits further investigation. Laser-based microscopy imaging devices have been developed and miniaturized to be used intraoperatively for rapid pathological diagnosis. The multitude of ways that lasers are used in neurosurgery and in related neuroclinical situations is a testament to the technological advancements and practicality of laser science