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.
Endocr Rev. 2023 Sep 15;44(5):947-959. doi: 10.1210/endrev/bnad014.
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.
垂体的重要生理作用及其毗邻关键的神经血管结构，意味着垂体腺瘤可导致严重的发病率或死亡率。虽然在垂体腺瘤的外科治疗方面取得了巨大进展，但仍存在许多挑战，如治疗失败和复发。为了应对这些临床挑战，新型医疗技术（如内窥镜、先进成像、人工智能）得到了极大的扩展。这些创新有可能使患者治疗的每一个步骤受益，并最终提高治疗效果。早期、更准确的诊断在一定程度上解决了这个问题。对新型患者数据集的分析，如自动面部分析或医疗记录的自然语言处理，具有实现早期诊断的潜力。诊断后，治疗决策和计划将受益于放射组学和多模态机器学习模型。智能模拟方法将改变外科医生培训的安全性和有效性。下一代成像技术和增强现实将增强手术计划和术中导航。同样，未来的手术设备，包括先进的光学设备、智能仪器和手术机器人，将增强手术能力。通过机器学习分析手术视频，为手术团队成员提供术中支持，利用数据科学方法，可以改善患者安全性，并使团队成员适应共同的工作流程。术后，利用多模态数据集的神经网络将能够早期发现有并发症风险的个体，并协助预测治疗失败，从而为患者制定特定的出院和监测方案。虽然这些垂体手术的进展有望提高护理质量，但临床医生必须是这些技术转化的把关者，在临床实施前系统地评估风险和益处。这样，这些创新之间的协同作用可以为未来的患者带来更好的治疗效果。
