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Current and Future Advances in Surgical Therapy for Pituitary Adenoma.
Khan, Danyal Z; Hanrahan, John G; Baldeweg, Stephanie E; Dorward, Neil L; Stoyanov, Danail; Marcus, Hani J.
  • Khan DZ; Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK.
  • Hanrahan JG; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK.
  • Baldeweg SE; Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK.
  • Dorward NL; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK.
  • Stoyanov D; Department of Diabetes & Endocrinology, University College London Hospitals NHS Foundation Trust, London NW1 2BU, UK.
  • Marcus HJ; Centre for Obesity and Metabolism, Department of Experimental and Translational Medicine, Division of Medicine, University College London, London WC1E 6BT, UK.
Endocr Rev ; 44(5): 947-959, 2023 09 15.
Article en En | MEDLINE | ID: mdl-37207359
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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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Neoplasias Hipofisarias / Adenoma Tipo de estudio: Guideline / Prognostic_studies / Screening_studies Límite: Humans Idioma: En Año: 2023 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Neoplasias Hipofisarias / Adenoma Tipo de estudio: Guideline / Prognostic_studies / Screening_studies Límite: Humans Idioma: En Año: 2023 Tipo del documento: Article