BOLD CVR corresponded well to CBF perfusion reserve measurements obtained with (O-)HO-PET, especially for detecting hemodynamic failure in the affected hemisphere and middle cerebral artery territory and for identifying hemodynamic failure stage II. BOLD CVR may, therefore, be considered for prospective studies assessing stroke risk in patients with chronic cerebrovascular steno-occlusive disease, in particular because it can potentially be implemented in routine clinical imaging.

This study provides Class II evidence that BOLD-CVR identifies CCD in patients with symptomatic unilateral cerebrovascular steno-occlusive disease.

OBJECTIVEAlthough rates of postoperative morbidity and mortality have become relatively low in patients undergoing transnasal transsphenoidal surgery (TSS) for pituitary adenoma, cerebrospinal fluid (CSF) fistulas remain a major driver of postoperative morbidity. Persistent CSF fistulas harbor the potential for headache and meningitis. The aim of this study was to investigate whether neural network–based models can reliably identify patients at high risk for intraoperative CSF leakage.METHODSFrom a prospective registry, patients who underwent endoscopic TSS for pituitary adenoma were identified. Risk factors for intraoperative CSF leaks were identified using conventional statistical methods. Subsequently, the authors built a prediction model for intraoperative CSF leaks based on deep learning.RESULTSIntraoperative CSF leaks occurred in 45 (29%) of 154 patients. No risk factors for CSF leaks were identified using conventional statistical methods. The deep neural network–based prediction model classified 88% of patients in the test set correctly, with an area under the curve of 0.84. Sensitivity (83%) and specificity (89%) were high. The positive predictive value was 71%, negative predictive value was 94%, and F1 score was 0.77. High suprasellar Hardy grade, prior surgery, and older age contributed most to the predictions.CONCLUSIONSThe authors trained and internally validated a robust deep neural network–based prediction model that identifies patients at high risk for intraoperative CSF. Machine learning algorithms may predict outcomes and adverse events that were previously nearly unpredictable, thus enabling safer and improved patient care and better patient counseling.