Refining Endoscopic and Combined Surgical Strategies for Giant Pituitary Adenomas: A Tertiary-Center Evaluation of 49 Cases over the Past Year

English (OpenAlex)

Background/Objectives: Giant pituitary adenomas (GPAs) pose significant surgical challenges due to their large size, parasellar/suprasellar extensions, and proximity to critical neurovascular structures. Although the endoscopic endonasal approach (EEA) is preferred for pituitary tumors, achieving gross total resection (GTR) in GPAs remains difficult. Additional transcranial approaches may improve resection rates while minimizing morbidity. This study evaluates the impact of endoscopic and combined surgical approaches on resection outcomes using a classification system previously defined in GPA patients treated over the past year. Methods: Among 517 pituitary adenomas treated in our clinic between September 2023 and September 2024, 49 GPA patients underwent endoscopic endonasal, transcranial, or combined surgery. Their medical records and surgical videos were retrospectively reviewed. Data included demographics, symptoms, imaging, surgical details, and follow-up outcomes. Tumor resection rates were analyzed based on the “landmark-based classification”, considering radiological and pathological features and surgical approach. Results: The mean age was 45.5 years (female/male: 14/35). Zone distribution was 8 (Zone 1), 21 (Zone 2), and 20 (Zone 3). GTR was achieved in 34.6%, near-total resection in 36.7%, and subtotal resection in 28.5%. Endoscopic surgery was performed in 41 patients, combined surgery in 7, and a transcranial approach in 1. Complications included diabetes insipidus (9/49), cerebrospinal fluid leakage (2/49), apoplexy (2/49), hypocortisolism (3/49), epidural hematoma (1/49), and epistaxis (1/49). Conclusions: While EEA is effective for Zone 1 and 2 GPAs, Zone 3 tumors often require combined or transcranial approaches for better resection. A multimodal strategy optimizes tumor removal while minimizing morbidity. Individualized surgical planning based on tumor classification is crucial for improving outcomes.