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.

Calcified spinal meningiomas: a systematic review of clinical characteristics, treatment strategies, and outcomes.

BACKGROUND: Calcified meningiomas involving the spine are rare but can pose significant surgical challenges. We systematically reviewed the literature on calcified spinal meningiomas. METHODS: PubMed, EMBASE, Web-of-Science, and Scopus databases were searched to include studies reporting clinical data of patients with calcified spinal meningioma. Included articles were analyzed for symptoms, imaging, spine level of the tumor, tumor location relative to the spinal cord, calcification status, treatment regimen, recurrence, progression-free survival, and outcomes. RESULTS: A total of 35 articles encompassing 94 patients were included. Most patients were female (90.4%), presenting with lower extremity weakness (44%) and/or lower extremity paresthesia (38.1%). Most calcified spinal meningiomas occurred in the thoracic spine (82%) and on the dorsal (33.3%) or ventral (27.2%) side relative to the spinal cord. Most tumors were intradural (87.2%). Histologically, most calcified spinal meningiomas were WHO grade I (97.4%) and psammomatous (50.7%). Most tumors demonstrated macroscopic calcification (48.9%). Most patients underwent gross total resection (91.5%) through a posterior approach (100%). Two patients (2.1%) received adjunctive radiotherapy. The most common treatment related complication was CSF leakage. Post-operatively, most patients demonstrated symptomatic improvement (75.5%) and 2 (2.1%) had local tumor recurrence. CONCLUSIONS: Calcified spinal meningiomas are uncommon but benign entities. These neoplasms tend to adhere to surrounding tissues and nerves and, thus, can be surgically challenging to remove. In most patients, safe gross total resection remains the standard of care, but accurate surgical planning is necessary to reduce the risks of postoperative complications.