NIMG-55. PITUITARY ADENOMAS TREATED WITH FRACTIONATED RADIOTHERAPY: A VOLUMETRIC MRI ANALYSIS. (6th November 2017) - Chandra, Vyshak; Zhao, Sherry; Workman, Kathryn; Harris, Timothy; Broaddus, William
Abstract: BACKGROUND: Treatment refractory and recurrent pituitary adenomas often require radiation therapy or stereotactic radiosurgery. Long term control with radiation treatment is well known, but the pattern of volume response after radiation has not been characterized in detail. We sought to characterize tumor response to radiation therapy in order to improve clinical decision-making. METHODS: Patients with biopsy-proven pituitary adenomas were identified from pathology diagnoses at one institution between 2004 and 2014. Inclusion criteria were patients who had received radiation therapy for a pituitary adenoma, who had been followed for a minimum of 3 or more MRI scans, and were over 18 years in age. T1-weighted 3-plane post-contrast MR images were used; tumor dimensions were measured with orthogonal diameters and volumes using the ABC/2 method (simplified ellipsoid volume). In this series, all patients were treated with fractionated radiation therapy. RESULTS: Sixty-one MRI scans for 7 males (44%) and 9 females (66%) were evaluated. Tumor diameters ranged from 0.7cm to 4.22cm. Tumor volumes ranged from 0.12cc to 26.8cc. The median and mean volumes were 3.08cc and 5.25cc, respectively. Follow-up ranged from 8 to 70 months, with a median follow-up of 23 months. Among the 16 tumors that were followed, volumetric analysis showed slopes ranging from -1.75 mm/year to +5.46 mm/year, with a median slope of -0.29 mm/year. 3 tumors showed positives slopes after radiation treatment (18%). Overall survival of this cohort of patients was 100%. Treatment was well tolerated with minimal toxicity. CONCLUSIONS: Analysis of scans showed that the majority of tumors respond to fractionated radiation treatment, and our study concludes that this treatment is effective for pituitary adenomas. Future analysis will focus on accurately modeling and predicting tumor volume decay following radiation therapy to aid in clinical decision-making.