Current and Future Advances in Surgical Therapy for Pituitary Adenoma

The vital physiological role of the pituitary gland, alongside its proximal critical neurovascular structures means pituitary adenomas cause significant morbidity or mortality. Whilst enormous advancements have been made in the surgical care of pituitary adenomas, treatment failure and recurrence remain challenges. To meet these clinical challenges, there has been an enormous expansion of novel medical technologies (e.g. endoscopy, advanced imaging, artificial intelligence). These innovations have the potential to benefit each step of the patient journey, and ultimately, drive improved outcomes. Earlier and more accurate diagnosis addresses this in part. Analysis of novel patient data sets, such as automated facial analysis or natural language processing of medical records holds potential in achieving an earlier diagnosis. After diagnosis, treatment decision-making and planning will benefit from radiomics and multimodal machine learning models. Surgical safety and effectiveness will be transformed by smart simulation methods for trainees. Next-generation imaging techniques and augmented reality will enhance surgical planning and intraoperative navigation. Similarly, the future armamentarium of pituitary surgeons, including advanced optical devices, smart instruments and surgical robotics, will augment the surgeon's abilities. Intraoperative support to team members will benefit from a surgical data science approach, utilising machine learning analysis of operative videos to improve patient safety and orientate team members to a common workflow. Postoperatively, early detection of individuals at risk of complications and prediction of treatment failure through neural networks of multimodal datasets will support earlier intervention, safer hospital discharge, guide follow-up and adjuvant treatment decisions. Whilst advancements in pituitary surgery hold promise to enhance the quality of care, clinicians must be the gatekeepers of technological translation, ensuring systematic assessment of risk and benefit. In doing so, the synergy between these innovations can be leveraged to drive improved outcomes for patients of the future

Endoscopic Skull Base Surgery

Endoscopic skull base surgery has undergone rapid advancement in the past decade moving from pituitary surgery to suprasellar lesions and now to a myriad of lesions extending from the cribriform plate to C2 and laterally out to the infratemporal fossa and petrous apex. Evolution of several technological advances as well as advances in understanding of endoscopic anatomy and the development of surgical techniques both in resection and reconstruction have fostered this capability. Management of benign disease via endoscopic methods is largely accepted now but more data is needed before the controversy on the role of endoscopic management of malignant disease is decided. Continued advances in surgical technique, navigation systems, endoscopic imaging technology, and robotics assure continued brisk evolution in this expanding field

Image-guidance in endoscopic pituitary surgery: an in-silico study of errors involved in tracker-based techniques

Background: Endoscopic endonasal surgery is an established minimally invasive technique for resecting pituitary adenomas. However, understanding orientation and identifying critical neurovascular structures in this anatomically dense region can be challenging. In clinical practice, commercial navigation systems use a tracked pointer for guidance. Augmented Reality (AR) is an emerging technology used for surgical guidance. It can be tracker based or vision based, but neither is widely used in pituitary surgery. Methods: This pre-clinical study aims to assess the accuracy of tracker-based navigation systems, including those that allow for AR. Two setups were used to conduct simulations: (1) the standard pointer setup, tracked by an infrared camera; and (2) the endoscope setup that allows for AR, using reflective markers on the end of the endoscope, tracked by infrared cameras. The error sources were estimated by calculating the Euclidean distance between a point’s true location and the point’s location after passing it through the noisy system. A phantom study was then conducted to verify the in-silico simulation results and show a working example of image-based navigation errors in current methodologies. Results: The errors of the tracked pointer and tracked endoscope simulations were 1.7 and 2.5 mm respectively. The phantom study showed errors of 2.14 and 3.21 mm for the tracked pointer and tracked endoscope setups respectively. Discussion: In pituitary surgery, precise neighboring structure identification is crucial for success. However, our simulations reveal that the errors of tracked approaches were too large to meet the fine error margins required for pituitary surgery. In order to achieve the required accuracy, we would need much more accurate tracking, better calibration and improved registration techniques

Endoscopy in Neurosurgery

Introduction of endoscope was undoubtedly a great advancement in neurosurgery. It minimises trauma to the brain tissue and maximises the vision around the remote areas. The access to the ventricle and cisterns has become much easier. Development in optics, lenses, long and angled instruments made the endoscopy in neurosurgery very versatile. In this chapter, the introduction of the endoscopy in neurosurgery has been described along with its use in different branches of neurosurgery like neuro-oncology, hydrocephalus, skullbase, aneurysms, craniosynostosis etc. The details of some common and important operation have been described. Some relevant anatomy, which can be encountered in endoscopic approach, has been described which will be helpful to the readers. This chapter will also act as an eye-opener to the vast use of neuroendoscopy and will help broaden the horizon of trainee neurosurgeons, following which the further details can be sought in relevant books and literature. In brief, this chapter will tell us about the evolution to revolution of the neuroendoscopy

Endoscopy

Endoscopy is a fast moving field, and new techniques are continuously emerging. In recent decades, endoscopy has evolved and branched out from a diagnostic modality to enhanced video and computer assisting imaging with impressive interventional capabilities. The modern endoscopy has seen advances not only in types of endoscopes available, but also in types of interventions amenable to the endoscopic approach. To date, there are a lot more developments that are being trialed. Modern endoscopic equipment provides physicians with the benefit of many technical advances. Endoscopy is an effective and safe procedure even in special populations including pediatric patients and renal transplant patients. It serves as the tool for diagnosis and therapeutic interventions of many organs including gastrointestinal tract, head and neck, urinary tract and others

ICAR: endoscopic skull‐base surgery

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Computational ultrasound tissue characterisation for brain tumour resection

In brain tumour resection, it is vital to know where critical neurovascular structuresand tumours are located to minimise surgical injuries and cancer recurrence. Theaim of this thesis was to improve intraoperative guidance during brain tumourresection by integrating both ultrasound standard imaging and elastography in thesurgical workflow. Brain tumour resection requires surgeons to identify the tumourboundaries to preserve healthy brain tissue and prevent cancer recurrence. Thisthesis proposes to use ultrasound elastography in combination with conventionalultrasound B-mode imaging to better characterise tumour tissue during surgery.Ultrasound elastography comprises a set of techniques that measure tissue stiffness,which is a known biomarker of brain tumours. The objectives of the researchreported in this thesis are to implement novel learning-based methods for ultrasoundelastography and to integrate them in an image-guided intervention framework.Accurate and real-time intraoperative estimation of tissue elasticity can guide towardsbetter delineation of brain tumours and improve the outcome of neurosurgery. We firstinvestigated current challenges in quasi-static elastography, which evaluates tissuedeformation (strain) by estimating the displacement between successive ultrasoundframes, acquired before and after applying manual compression. Recent approachesin ultrasound elastography have demonstrated that convolutional neural networkscan capture ultrasound high-frequency content and produce accurate strain estimates.We proposed a new unsupervised deep learning method for strain prediction, wherethe training of the network is driven by a regularised cost function, composed of asimilarity metric and a regularisation term that preserves displacement continuityby directly optimising the strain smoothness. We further improved the accuracy of our method by proposing a recurrent network architecture with convolutional long-short-term memory decoder blocks to improve displacement estimation and spatio-temporal continuity between time series ultrasound frames. We then demonstrateinitial results towards extending our ultrasound displacement estimation method toshear wave elastography, which provides a quantitative estimation of tissue stiffness.Furthermore, this thesis describes the development of an open-source image-guidedintervention platform, specifically designed to combine intra-operative ultrasoundimaging with a neuronavigation system and perform real-time ultrasound tissuecharacterisation. The integration was conducted using commercial hardware andvalidated on an anatomical phantom. Finally, preliminary results on the feasibilityand safety of the use of a novel intraoperative ultrasound probe designed for pituitarysurgery are presented. Prior to the clinical assessment of our image-guided platform,the ability of the ultrasound probe to be used alongside standard surgical equipmentwas demonstrated in 5 pituitary cases

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