Clinical application of the “sellar barrier’s concept” for predicting intraoperative CSF leak in endoscopic endonasal surgery for pituitary adenomas with a machine learning analysis

Artificial Intelligence in Rhinology

Rhinology, allergy, and skull base surgery are fields primed for the integration and implementation of artificial intelligence (AI). The heterogeneity of the disease processes within these fields highlights the opportunity for AI to augment clinical care and promote personalized medicine. Numerous research studies have been published demonstrating the development and clinical potential of AI models within the field. Most describe in silico evaluation models without direct clinical implementation. The major themes of existing studies include diagnostic or clinical decisions support, clustering patients into specific phenotypes or endotypes, predicting post-treatment outcomes, and surgical planning.

Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review

PURPOSE

Pituitary adenoma surgery is a complex procedure due to critical adjacent neurovascular structures, variations in size and extensions of the lesions, and potential hormonal imbalances. The integration of artificial intelligence (AI) and machine learning (ML) has demonstrated considerable potential in assisting neurosurgeons in decision-making, optimizing surgical outcomes, and providing real-time feedback. This scoping review comprehensively summarizes the current status of AI/ML technologies in pituitary adenoma surgery, highlighting their strengths and limitations.

METHODS

PubMed, Embase, Web of Science, and Scopus were searched following the PRISMA-ScR guidelines. Studies discussing the use of AI/ML in pituitary adenoma surgery were included. Eligible studies were grouped to analyze the different outcomes of interest of current AI/ML technologies.

RESULTS

Among the 2438 identified articles, 44 studies met the inclusion criteria, with a total of seventeen different algorithms utilized across all studies. Studies were divided into two groups based on their input type: clinicopathological and imaging input. The four main outcome variables evaluated in the studies included: outcome (remission, recurrence or progression, gross-total resection, vision improvement, and hormonal recovery), complications (CSF leak, readmission, hyponatremia, and hypopituitarism), cost, and adenoma-related factors (aggressiveness, consistency, and Ki-67 labeling) prediction. Three studies focusing on workflow analysis and real-time navigation were discussed separately.

CONCLUSION

AI/ML modeling holds promise for improving pituitary adenoma surgery by enhancing preoperative planning and optimizing surgical strategies. However, addressing challenges such as algorithm selection, performance evaluation, data heterogeneity, and ethics is essential to establish robust and reliable ML models that can revolutionize neurosurgical practice and benefit patients.

Guanti bianchi technique for resection of selected pituitary adenomas

Introduction

Since the introduction of the endoscopic endonasal approach (EEA) to skull base, the nasal phase has been a true challenge as it represents the moment of definition of the corridor, thus defining the instruments maneuverability at tumor removal phase. The longstanding cooperation between ENT and neurosurgeons have provided the possibility of creating adequate corridor with maximal respect toward nasal structures and mucosa. This sparked the idea of entering the sella as thieves, so we named "Guanti Bianchi" technique a lesser invasive variation of the approach for the removal of selected pituitary adenoma.

Research Question

The purpose of this study is to present the preliminary results of "Guanti Bianchi" technique.

Material and Methods

Data from 17 patients undergoing "Guanti Bianchi" technique (out of 235 standard EEA) at our center, were retrospectively analysed. ASK Nasal-12, a quality-of-life instrument developed specifically to assess patient perception of nasal morbidity, was administered pre- and postoperatively.

Results

10 (59%) patients were men and 7 (41%) women. The mean age was 67.7 (range 35-88). The average duration of the surgical procedure was 71.17 minutes (range 45-100). GTR was achieved in all patients, no postoperative complications were observed. Baseline ASK Nasal-12 was near normal in all patients, 3/17 (17,6%) experienced transitory very mild symptoms without any worsening at 3 and 6 months.

Discussion and Conclusions

This minimally invasive technique does not require turbinectomy or carving of the nasoseptal flap, it alters the nasal mucosa as little as necessary, and it is quick and easy to perform.