Surgical Decision-Making in Anterior and Middle Skull Base Lesions: A Single-Center Experience with Comparative Outcomes and a Preoperative Predictive Model
Abstract Introduction Surgical management of anterior and middle skull base (ASB/MSB) lesions requires careful balancing of maximal resection and preservation of neurological function. Although both transcranial (TC) and endoscopic endonasal (EE) approaches are widely used, the extent to which the surgical approach independently influences outcomes remains unclear. Methods We conducted a retrospective, single-center, observational study of adult patients who underwent surgery for ASB/MSB lesions. Demographic, clinical, radiological, and surgical variables were analyzed. Outcomes included extent of resection (EOR), complications, intraoperative blood loss, operative time, and length of hospital stay. Comparative analyses between TC and EE approaches were performed and interpreted as exploratory. A preoperative predictive model for gross total resection (GTR) was developed using Least Absolute Shrinkage and Selection Operator-penalized logistic regression with internal bootstrap validation. Results A total of 84 patients were included. TC approaches were performed in 62 patients (73.8%) and EE in 22 (26.2%). GTR was achieved in 77.4% of cases, with no significant difference between approaches. The EE group showed significantly lower intraoperative blood loss, shorter operative time, and reduced length of hospital stay. Complication rates did not differ significantly between groups. The predictive model demonstrated good apparent discrimination (area under the curve 0.97). Conclusions Our findings suggest that tumor characteristics may play a substantial role in determining outcomes; however, the observational design does not allow independent assessment of the causal effect of the surgical approach. When appropriately selected, both TC and EE strategies achieve comparable effectiveness and safety. At this stage, the proposed model may serve only as an exploratory predictive tool.