Brandon P Galm1, Colleen Buckless2, Brooke Swearingen3, Martin Torriani2, Anne Klibanski4, Miriam A Bredella2, Nicholas A Tritos4. 1. Neuroendocrine Unit, Massachusetts General Hospital and Harvard Medical School, 100 Blossom Street, Suite 140, Boston, MA, 02114, USA. brandongalm@gmail.com. 2. Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. 3. Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. 4. Neuroendocrine Unit, Massachusetts General Hospital and Harvard Medical School, 100 Blossom Street, Suite 140, Boston, MA, 02114, USA.
Abstract
PURPOSE: Given the paucity of reliable predictors of tumor recurrence, progression, or response to somatostatin receptor ligand (SRL) therapy in acromegaly, we attempted to determine whether preoperative MR image texture was predictive of these clinical outcomes. We also determined whether image texture could differentiate somatotroph adenomas from non-functioning pituitary adenomas (NFPAs). METHODS: We performed a retrospective study of patients with acromegaly due to a macroadenoma who underwent transsphenoidal surgery at our institution between 2007 and 2015. Clinical data were extracted from electronic medical records. MRI texture analysis was performed on preoperative non-enhanced T1-weighted images using ImageJ (NIH). Logistic and Cox models were used to determine if image texture parameters predicted outcomes. RESULTS: Eighty-nine patients had texture parameters measured, which were compared to that of NFPAs, while 64 of these patients had follow-up and were included in the remainder of analyses. Minimum pixel intensity, skewness, and kurtosis were significantly different in somatotroph adenomas versus NFPAs (area under the receiver operating characteristic curve, 0.7771, for kurtosis). Furthermore, those with a maximum pixel intensity above the median had an increased odds of IGF-I normalization on SRL therapy (OR 5.96, 95% CI 1.33-26.66), which persisted after adjusting for several potential predictors of response. Image texture did not predict tumor recurrence or progression. CONCLUSION: Our data suggest that MRI texture analysis can distinguish NFPAs from somatotroph macroadenomas with good diagnostic accuracy and can predict normalization of IGF-I with SRL therapy.
PURPOSE: Given the paucity of reliable predictors of tumor recurrence, progression, or response to somatostatin receptor ligand (SRL) therapy in acromegaly, we attempted to determine whether preoperative MR image texture was predictive of these clinical outcomes. We also determined whether image texture could differentiate somatotroph adenomas from non-functioning pituitary adenomas (NFPAs). METHODS: We performed a retrospective study of patients with acromegaly due to a macroadenoma who underwent transsphenoidal surgery at our institution between 2007 and 2015. Clinical data were extracted from electronic medical records. MRI texture analysis was performed on preoperative non-enhanced T1-weighted images using ImageJ (NIH). Logistic and Cox models were used to determine if image texture parameters predicted outcomes. RESULTS: Eighty-nine patients had texture parameters measured, which were compared to that of NFPAs, while 64 of these patients had follow-up and were included in the remainder of analyses. Minimum pixel intensity, skewness, and kurtosis were significantly different in somatotroph adenomas versus NFPAs (area under the receiver operating characteristic curve, 0.7771, for kurtosis). Furthermore, those with a maximum pixel intensity above the median had an increased odds of IGF-I normalization on SRL therapy (OR 5.96, 95% CI 1.33-26.66), which persisted after adjusting for several potential predictors of response. Image texture did not predict tumor recurrence or progression. CONCLUSION: Our data suggest that MRI texture analysis can distinguish NFPAs from somatotroph macroadenomas with good diagnostic accuracy and can predict normalization of IGF-I with SRL therapy.
Authors: Maria Braileanu; Ranliang Hu; Michael J Hoch; Mark E Mullins; Adriana G Ioachimescu; Nelson M Oyesiku; Adlai Pappy; Amit M Saindane Journal: Clin Imaging Date: 2019-01-25 Impact factor: 1.605
Authors: Asgeir Store Jakola; Yi-Hua Zhang; Anne J Skjulsvik; Ole Solheim; Hans Kristian Bø; Erik Magnus Berntsen; Ingerid Reinertsen; Sasha Gulati; Petter Förander; Torkel B Brismar Journal: Clin Neurol Neurosurg Date: 2017-12-05 Impact factor: 1.876
Authors: Jeremy R Anthony; Ula Abed Alwahab; Naman K Kanakiya; Diana M Pontell; Emir Veledar; Nelson M Oyesiku; Adriana G Ioachimescu Journal: Endocr Pract Date: 2015-06-29 Impact factor: 3.443
Authors: Hai Sun; Jessica Brzana; Chris G Yedinak; Sakir H Gultekin; Johnny B Delashaw; Maria Fleseriu Journal: J Neurol Surg B Skull Base Date: 2013-09-09