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

Prevalence of haptic feedback in robot-mediated surgery : a systematic review of literature

© 2017 Springer-Verlag. This is a post-peer-review, pre-copyedit version of an article published in Journal of Robotic Surgery. The final authenticated version is available online at: https://doi.org/10.1007/s11701-017-0763-4With the successful uptake and inclusion of robotic systems in minimally invasive surgery and with the increasing application of robotic surgery (RS) in numerous surgical specialities worldwide, there is now a need to develop and enhance the technology further. One such improvement is the implementation and amalgamation of haptic feedback technology into RS which will permit the operating surgeon on the console to receive haptic information on the type of tissue being operated on. The main advantage of using this is to allow the operating surgeon to feel and control the amount of force applied to different tissues during surgery thus minimising the risk of tissue damage due to both the direct and indirect effects of excessive tissue force or tension being applied during RS. We performed a two-rater systematic review to identify the latest developments and potential avenues of improving technology in the application and implementation of haptic feedback technology to the operating surgeon on the console during RS. This review provides a summary of technological enhancements in RS, considering different stages of work, from proof of concept to cadaver tissue testing, surgery in animals, and finally real implementation in surgical practice. We identify that at the time of this review, while there is a unanimous agreement regarding need for haptic and tactile feedback, there are no solutions or products available that address this need. There is a scope and need for new developments in haptic augmentation for robot-mediated surgery with the aim of improving patient care and robotic surgical technology further.Peer reviewe

Advanced cranial navigation

Neurosurgery is performed with extremely low margins of error. Surgical inaccuracy may have disastrous consequences. The overall aim of this thesis was to improve accuracy in cranial neurosurgical procedures by the application of new technical aids. Two technical methods were evaluated: augmented reality (AR) for surgical navigation (Papers I-II) and the optical technique of diffuse reflectance spectroscopy (DRS) for real-time tissue identification (Papers III-V). Minimally invasive skull-base endoscopy has several potential benefits compared to traditional craniotomy, but approaching the skull base through this route implies that at-risk organs and surgical targets are covered by bone and out of the surgeon’s direct line of sight. In Paper I, a new application for AR-navigated endoscopic skull-base surgery, based on an augmented-reality surgical navigation (ARSN) system, was developed. The accuracy of the system, defined by mean target registration error (TRE), was evaluated and found to be 0.55±0.24 mm, the lowest value reported error in the literature. As a first step toward the development of a cranial application for AR navigation, in Paper II this ARSN system was used to enable insertions of biopsy needles and external ventricular drainages (EVDs). The technical accuracy (i.e., deviation from the target or intended path) and efficacy (i.e., insertion time) were assessed on a 3D-printed realistic, anthropomorphic skull and brain phantom; Thirty cranial biopsies and 10 EVD insertions were performed. Accuracy for biopsy was 0.8±0.43 mm with a median insertion time of 149 (87-233) seconds, and for EVD accuracy was 2.9±0.8 mm at the tip with a median angular deviation of 0.7±0.5° and a median insertion time of 188 (135-400) seconds. Glial tumors grow diffusely in the brain, and patient survival is correlated with the extent of tumor removal. Tumor borders are often invisible. Resection beyond borders as defined by conventional methods may further improve a patient’s prognosis. In Paper III, DRS was evaluated for discrimination between glioma and normal brain tissue ex vivo. DRS spectra and histology were acquired from 22 tumor samples and 9 brain tissue samples retrieved from 30 patients. Sensitivity and specificity for the detection of low-grade gliomas were 82.0% and 82.7%, respectively, with an AUC of 0.91. Acute ischemic stroke caused by large vessel occlusion is treated with endovascular thrombectomy, but treatment failure can occur when clot composition and thrombectomy technique are mismatched. Intra-procedural knowledge of clot composition could guide the choice of treatment modality. In Paper IV, DRS, in vivo, was evaluated for intravascular clot characterization. Three types of clot analogs, red blood cell (RBC)-rich, fibrin-rich and mixed clots, were injected into the external carotids of a domestic pig. An intravascular DRS probe was used for in-situ measurements of clots, blood, and vessel walls, and the spectral data were analyzed. DRS could differentiate clot types, vessel walls, and blood in vivo (p<0,001). The sensitivity and specificity for detection were 73.8% and 98.8% for RBC clots, 100% and 100% for mixed clots, and 80.6% and 97.8% for fibrin clots, respectively. Paper V evaluated DRS for characterization of human clot composition ex vivo: 45 clot units were retrieved from 29 stroke patients and examined with DRS and histopathological evaluation. DRS parameters correlated with clot RBC fraction (R=81, p<0.001) and could be used for the classification of clot type with sensitivity and specificity rates for the detection of RBC-rich clots of 0.722 and 0.846, respectively. Applied in an intravascular probe, DRS may provide intra-procedural information on clot composition to improve endovascular thrombectomy efficiency

Performance Factors in Neurosurgical Simulation and Augmented Reality Image Guidance

Virtual reality surgical simulators have seen widespread adoption in an effort to provide safe, cost-effective and realistic practice of surgical skills. However, the majority of these simulators focus on training low-level technical skills, providing only prototypical surgical cases. For many complex procedures, this approach is deficient in representing anatomical variations that present clinically, failing to challenge users’ higher-level cognitive skills important for navigation and targeting. Surgical simulators offer the means to not only simulate any case conceivable, but to test novel approaches and examine factors that influence performance. Unfortunately, there is a void in the literature surrounding these questions. This thesis was motivated by the need to expand the role of surgical simulators to provide users with clinically relevant scenarios and evaluate human performance in relation to image guidance technologies, patient-specific anatomy, and cognitive abilities. To this end, various tools and methodologies were developed to examine cognitive abilities and knowledge, simulate procedures, and guide complex interventions all within a neurosurgical context. The first chapter provides an introduction to the material. The second chapter describes the development and evaluation of a virtual anatomical training and examination tool. The results suggest that learning occurs and that spatial reasoning ability is an important performance predictor, but subordinate to anatomical knowledge. The third chapter outlines development of automation tools to enable efficient simulation studies and data management. In the fourth chapter, subjects perform abstract targeting tasks on ellipsoid targets with and without augmented reality guidance. While the guidance tool improved accuracy, performance with the tool was strongly tied to target depth estimation – an important consideration for implementation and training with similar guidance tools. In the fifth chapter, neurosurgically experienced subjects were recruited to perform simulated ventriculostomies. Results showed anatomical variations influence performance and could impact outcome. Augmented reality guidance showed no marked improvement in performance, but exhibited a mild learning curve, indicating that additional training may be warranted. The final chapter summarizes the work presented. Our results and novel evaluative methodologies lay the groundwork for further investigation into simulators as versatile research tools to explore performance factors in simulated surgical procedures

Integrated care pathways in neurosurgery: A systematic review.

IntroductionIntegrated care pathways (ICPs) are a pre-defined framework of evidence based, multidisciplinary practice for specific patients. They have the potential to enhance continuity of care, patient safety, patient satisfaction, efficiency gains, teamwork and staff education. In order to inform the development of neurosurgical ICPs in the future, we performed a systematic review to aggregate examples of neurosurgical ICP, to consider their impact and design features that may be associated with their success.MethodsElectronic databases MEDLINE, EMBASE, and CENTRAL were searched for relevant literature published from date of inception to July 2020. Primary studies reporting details of neurosurgical ICPs, across all pathologies and age groups were eligible for inclusion. Patient outcomes in each case were also recorded.ResultsTwenty-four studies were included in our final dataset, from the United States, United Kingdom, Italy, China, Korea, France, Netherlands and Switzerland, and a number of sub-specialties. 3 for cerebrospinal fluid diversion, 1 functional, 2 neurovascular, 1 neuro-oncology, 2 paediatric, 2 skull base, 10 spine, 1 for trauma, 2 miscellaneous (other craniotomies). All were single centre studies with no regional or national examples. Thirteen were cohort studies while 11 were case series which lacked a control group. Effectiveness was typically evaluated using hospital or professional performance metrics, such as length of stay (n = 11, 45.8%) or adverse events (n = 17, 70.8%) including readmission, surgical complications and mortality. Patient reported outcomes, including satisfaction, were evaluated infrequently (n = 3, 12.5%). All studies reported a positive impact. No study reported how the design of the ICP was informed by published literature or other methods.ConclusionsICPs have been successfully developed across numerous neurosurgical sub-specialities. However, there is often a lack of clarity over their design and weaknesses in their evaluation, including an underrepresentation of the patient's perspective

Intraoperative Image Guidance in Neurosurgery: Development, Current Indications, and Future Trends

Introduction. As minimally invasive surgery becomes the standard of care in neurosurgery, it is imperative that surgeons become skilled in the use of image-guided techniques. The development of image-guided neurosurgery represents a substantial improvement in the microsurgical treatment of tumors, vascular malformations, and other intracranial lesions. Objective. There have been numerous advances in neurosurgery which have aided the neurosurgeon to achieve accurate removal of pathological tissue with minimal disruption of surrounding healthy neuronal matter including the development of microsurgical, endoscopic, and endovascular techniques. Neuronavigation systems and intraoperative imaging should improve success in cranial neurosurgery. Additional functional imaging modalities such as PET, SPECT, DTI (for fiber tracking), and fMRI can now be used in order to reduce neurological deficits resulting from surgery; however the positive long-term effect remains questionable for many indications. Method. PubMed database search using the search term “image guided neurosurgery.” More than 1400 articles were published during the last 25 years. The abstracts were scanned for prospective comparative trials. Results and Conclusion. 14 comparative trials are published. To date significant data amount show advantages in intraoperative accuracy influencing the perioperative morbidity and long-term outcome only for cerebral glioma surgery

Image guidance in neurosurgical procedures, the "Visages" point of view.

This paper gives an overview of the evolution of clinical neuroinformatics in the domain of neurosurgery. It shows how image guided neurosurgery (IGNS) is evolving according to the integration of new imaging modalities before, during and after the surgical procedure and how this acts as the premise of the Operative Room of the future. These different issues, as addressed by the VisAGeS INRIA/INSERM U746 research team (http://www.irisa.fr/visages), are presented and discussed in order to exhibit the benefits of an integrated work between physicians (radiologists, neurologists and neurosurgeons) and computer scientists to give adequate answers toward a more effective use of images in IGNS

Evaluating Human Performance for Image-Guided Surgical Tasks

The following work focuses on the objective evaluation of human performance for two different interventional tasks; targeted prostate biopsy tasks using a tracked biopsy device, and external ventricular drain placement tasks using a mobile-based augmented reality device for visualization and guidance. In both tasks, a human performance methodology was utilized which respects the trade-off between speed and accuracy for users conducting a series of targeting tasks using each device. This work outlines the development and application of performance evaluation methods using these devices, as well as details regarding the implementation of the mobile AR application. It was determined that the Fitts’ Law methodology can be applied for evaluation of tasks performed in each surgical scenario, and was sensitive to differentiate performance across a range which spanned experienced and novice users. This methodology is valuable for future development of training modules for these and other medical devices, and can provide details about the underlying characteristics of the devices, and how they can be optimized with respect to human performance