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Jorn Fierstra, Stephanie Spieth, Leanne Tran, John Conklin, Michael Tymianski, Karel G. ter Brugge, Joseph A. Fisher, David J. Mikulis and Timo Krings

Object

Cerebral proliferative angiopathy (CPA) has been morphologically distinguished from classically appearing brain arteriovenous malformations (AVMs) by exhibition of functional brain parenchyma that is intermingled with abnormal vascular channels. The presence of oligemia in this intralesional brain tissue may suggest ischemia, which is not detected in classic brain AVMs. The authors hypothesized that patients with CPA would exhibit a greater impairment of cerebrovascular reserve in neuronal tissue surrounding the true nidus compared with those with brain AVMs.

Methods

Four patients with CPA, 10 patients with brain AVMs and seizures, and 12 young healthy individuals were studied. The 4 patients with CPA underwent blood oxygen level–dependent MR imaging examinations while applying normoxic step changes in end-tidal CO2 to obtain quantitative cerebrovascular reactivity measurements.

Results

Patients with a CPA lesion exhibited severely impaired perilesional cerebrovascular reserve in comparison with patients with brain AVMs and seizures (0.10 ± 0.03 vs 0.16 ± 0.03, respectively; p < 0.05), and young healthy individuals (0.10 ± 0.03 vs 0.21 ± 0.06, respectively; p < 0.01)

Conclusions

This study demonstrated severely impaired cerebrovascular reserve in the perilesional brain tissue surrounding the abnormal vessels of patients with CPA. This finding may provide an additional means to distinguish CPA from classic brain AVMs.

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Victor E. Staartjes, Carlo Serra, Giovanni Muscas, Nicolai Maldaner, Kevin Akeret, Christiaan H. B. van Niftrik, Jorn Fierstra, David Holzmann and Luca Regli

OBJECTIVE

Gross-total resection (GTR) is often the primary surgical goal in transsphenoidal surgery for pituitary adenoma. Existing classifications are effective at predicting GTR but are often hampered by limited discriminatory ability in moderate cases and by poor interrater agreement. Deep learning, a subset of machine learning, has recently established itself as highly effective in forecasting medical outcomes. In this pilot study, the authors aimed to evaluate the utility of using deep learning to predict GTR after transsphenoidal surgery for pituitary adenoma.

METHODS

Data from a prospective registry were used. The authors trained a deep neural network to predict GTR from 16 preoperatively available radiological and procedural variables. Class imbalance adjustment, cross-validation, and random dropout were applied to prevent overfitting and ensure robustness of the predictive model. The authors subsequently compared the deep learning model to a conventional logistic regression model and to the Knosp classification as a gold standard.

RESULTS

Overall, 140 patients who underwent endoscopic transsphenoidal surgery were included. GTR was achieved in 95 patients (68%), with a mean extent of resection of 96.8% ± 10.6%. Intraoperative high-field MRI was used in 116 (83%) procedures. The deep learning model achieved excellent area under the curve (AUC; 0.96), accuracy (91%), sensitivity (94%), and specificity (89%). This represents an improvement in comparison with the Knosp classification (AUC: 0.87, accuracy: 81%, sensitivity: 92%, specificity: 70%) and a statistically significant improvement in comparison with logistic regression (AUC: 0.86, accuracy: 82%, sensitivity: 81%, specificity: 83%) (all p < 0.001).

CONCLUSIONS

In this pilot study, the authors demonstrated the utility of applying deep learning to preoperatively predict the likelihood of GTR with excellent performance. Further training and validation in a prospective multicentric cohort will enable the development of an easy-to-use interface for use in clinical practice.

Free access

Giovanni Muscas, Christiaan Hendrik Bas van Niftrik, Jorn Fierstra, Marco Piccirelli, Martina Sebök, Jan-Karl Burkhardt, Antonios Valavanis, Athina Pangalu, Luca Regli and Oliver Bozinov

Blood oxygenation level–dependent functional MRI cerebrovascular reactivity (BOLD-CVR) is a contemporary technique to assess brain tissue hemodynamic changes after extracranial- intracranial (EC-IC) bypass flow augmentation surgery. The authors conducted a preliminary study to investigate the feasibility and safety of intraoperative 3-T MRI BOLD-CVR after EC-IC bypass flow augmentation surgery. Five consecutive patients selected for EC-IC bypass revascularization underwent an intraoperative BOLD-CVR examination to assess early hemodynamic changes after revascularization and to confirm the safety of this technique. All patients had a normal postoperative course, and none of the patients exhibited complications or radiological alterations related to prolonged anesthesia time. In addition to intraoperative flow measurements of the bypass graft, BOLD-CVR maps added information on the hemodynamic status and changes at the brain tissue level. Intraoperative BOLD-CVR is feasible and safe in patients undergoing EC-IC bypass revascularization. This technique can offer immediate hemodynamic feedback on brain tissue revascularization after bypass flow augmentation surgery.