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      Current and Future Advances in Surgical Therapy for Pituitary Adenoma

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          Abstract

          The vital physiological role of the pituitary gland, alongside its proximity to critical neurovascular structures, means that pituitary adenomas can cause significant morbidity or mortality. While enormous advancements have been made in the surgical care of pituitary adenomas, numerous challenges remain, such as treatment failure and recurrence. To meet these clinical challenges, there has been an enormous expansion of novel medical technologies (eg, endoscopy, advanced imaging, artificial intelligence). These innovations have the potential to benefit each step of the patient’s 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, surgical abilities will be augmented by the future operative armamentarium, including advanced optical devices, smart instruments, and surgical robotics. Intraoperative support to surgical team members will benefit from a data science approach, utilizing machine learning analysis of operative videos to improve patient safety and orientate team members to a common workflow. Postoperatively, neural networks leveraging multimodal datasets will allow early detection of individuals at risk of complications and assist in the prediction of treatment failure, thus supporting patient-specific discharge and monitoring protocols.

          While these advancements in pituitary surgery hold promise to enhance the quality of care, clinicians must be the gatekeepers of the translation of such technologies, ensuring systematic assessment of risk and benefit prior to clinical implementation. In doing so, the synergy between these innovations can be leveraged to drive improved outcomes for patients of the future.

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          Most cited references138

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          High-performance medicine: the convergence of human and artificial intelligence

          Eric Topol (2019)
          The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.
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            The prevalence of pituitary adenomas: a systematic review.

            Pituitary adenomas display an array of hormonal and proliferative activity. Once primarily classified according to size (microadenomas, or = 1 cm), these tumors are now further classified according to immunohistochemistry and functional status. With these additional classifications in mind, the goals of the current study were to determine the prevalence of pituitary adenomas and to explore the clinical relevance of the findings. The authors conducted a metaanalysis of all existing English-language articles in MEDLINE. They used the search string (pituitary adenoma or pituitary tumor) and prevalence and selected relevant autopsy and imaging evaluation studies for inclusion. The authors found an overall estimated prevalence of pituitary adenomas of 16.7% (14.4% in autopsy studies and 22.5% in radiologic studies). Given the high frequency of pituitary adenomas and their potential for causing clinical pathologies, the findings of the current study suggest that early diagnosis and treatment of pituitary adenomas should have far-reaching benefits.
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              Medical progress: Acromegaly.

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                Author and article information

                Contributors
                Journal
                Endocr Rev
                Endocr Rev
                edrv
                Endocrine Reviews
                Oxford University Press (US )
                0163-769X
                1945-7189
                October 2023
                19 May 2023
                19 May 2023
                : 44
                : 5
                : 947-959
                Affiliations
                Department of Neurosurgery, National Hospital for Neurology and Neurosurgery , London WC1N 3BG, UK
                Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London , London W1W 7TY, UK
                Department of Neurosurgery, National Hospital for Neurology and Neurosurgery , London WC1N 3BG, UK
                Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London , London W1W 7TY, UK
                Department of Diabetes & Endocrinology, University College London Hospitals NHS Foundation Trust , London NW1 2BU, UK
                Centre for Obesity and Metabolism, Department of Experimental and Translational Medicine, Division of Medicine, University College London , London WC1E 6BT, UK
                Department of Neurosurgery, National Hospital for Neurology and Neurosurgery , London WC1N 3BG, UK
                Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London , London W1W 7TY, UK
                Digital Surgery Ltd, Medtronic , London WD18 8WW, UK
                Department of Neurosurgery, National Hospital for Neurology and Neurosurgery , London WC1N 3BG, UK
                Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London , London W1W 7TY, UK
                Author notes
                Correspondence: Hani J. Marcus, FRCS, PhD, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK. Email: h.marcus@ 123456ucl.ac.uk .
                Author information
                https://orcid.org/0000-0001-9213-2550
                https://orcid.org/0000-0003-4584-2298
                https://orcid.org/0000-0002-8381-1612
                https://orcid.org/0000-0001-8103-5579
                https://orcid.org/0000-0002-0980-3227
                https://orcid.org/0000-0001-8000-392X
                Article
                bnad014
                10.1210/endrev/bnad014
                10502574
                37207359
                57e21440-3132-4e21-8892-f6fc2b5f839b
                © The Author(s) 2023. Published by Oxford University Press on behalf of the Endocrine Society.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 30 September 2022
                : 17 May 2023
                : 06 July 2023
                Page count
                Pages: 13
                Funding
                Funded by: Wellcome, DOI 10.13039/100004440;
                Award ID: 203145Z/16/Z
                Award ID: NS/A000050/1
                Funded by: Centre for Interventional and Surgical Sciences, DOI 10.13039/501100020194;
                Funded by: University College London, DOI 10.13039/501100000765;
                Funded by: NIHR, DOI 10.13039/501100000272;
                Funded by: Cancer Research UK Predoctoral;
                Funded by: Wellcome Trust, DOI 10.13039/100010269;
                Categories
                Review
                AcademicSubjects/MED00250

                pituitary adenoma,transsphenoidal,artificial intelligence,robotics,augmented reality,digital health

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