Predicting resection success in giant pituitary adenomas: morphologic determinants and a preoperative multivariate model.

Introduction: Giant pituitary adenomas present significant therapeutic challenges due to their size, complex morphology, and frequent extension into critical neuroanatomical regions. Endoscopic transsphenoidal surgery (ETSS) has become the preferred first-line treatment, yet factors determining resectability remain incompletely defined. This study retrospectively analyzed 60 patients with giant pituitary adenomas treated via ETSS at a tertiary pituitary center to evaluate clinical characteristics, early surgical outcomes, and preoperative determinants of resection. Visual impairment was the most common presenting symptom and improved in 83.7% of patients. Based on early postoperative imaging, total or gross-total resection was achieved in 76.7% of the cohort, and permanent diabetes insipidus occurred in only 3.3%, with no major vascular or neurological complications observed. Larger tumor volume and maximum diameter, multilobulated morphology, cranial extension pattern, and higher Hardy grades were independently associated with limited resection. A multivariate model integrating volumetric, anatomical, and hormonal variables demonstrated moderate predictive accuracy for identifying patients at risk for subtotal resection (AUC = 0.745). These findings highlight the critical role of tumor geometry and extension patterns in determining the feasibility of complete removal and provide a practical framework for preoperative risk stratification. ETSS provides effective early tumor debulking in patients with giant pituitary adenomas; however, longer-term follow-up is required to fully assess the durability of surgical outcomes and disease control. Methods: We retrospectively analyzed 60 consecutive patients with giant pituitary adenomas who underwent endoscopic transsphenoidal surgery (ETSS) at a tertiary pituitary center between 2015 and 2023. Clinical, radiological, hormonal, and surgical variables were collected from medical records. Tumor volume, cranial extension pattern, Hardy and Knosp classifications, and multilobulated morphology were evaluated using preoperative MRI. Surgical outcomes, complications, and postoperative endocrine and visual results were recorded. Predictors of resection extent were assessed using multivariate logistic regression, and a preoperative prediction model was developed and evaluated with ROC analysis.