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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


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.


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.


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).


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.

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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.