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Carlo Serra, Kevin Akeret, Victor E. Staartjes, Georgia Ramantani, Thomas Grunwald, Hennric Jokeit, Julia Bauer and Niklaus Krayenbühl

OBJECTIVE

The goal of this study was to assess the reproducibility and safety of the recently introduced paramedian supracerebellar–transtentorial (PST) approach for selective amygdalohippocampectomy (SA).

METHODS

The authors performed a retrospective analysis of prospectively collected data originating from their surgical register of patients undergoing SA via a PST approach for lesional medial temporal lobe epilepsy. All patients received thorough pre- and postoperative clinical (neurological, neuropsychological, psychiatric) and instrumental (ictal and long-term EEG, invasive EEG if needed, MRI) workup. Surgery-induced complications were assessed at discharge and at every follow-up thereafter and were classified according to Clavien-Dindo grade (CDG). Epilepsy outcome was defined according to Engel classification. Data were reported according to common descriptive statistical methods.

RESULTS

Between May 2015 and May 2018, 17 patients underwent SA via a PST approach at the authors’ institution (hippocampal sclerosis in 13 cases, WHO grade II glioma in 2 cases, and reactive gliosis in 2 cases). The median postoperative follow-up was 7 months (mean 9 months, range 3–19 months). There was no surgery-related mortality and no complication (CDG ≥ 2) in the whole series. Transitory CDG 1 surgical complications occurred in 4 patients and had resolved in all of them by the first postoperative follow-up. One patient showed a deterioration of neuropsychological performance with new slight mnestic deficits. No patient experienced a clinically relevant postoperative visual field defect. No morbidity due to semisitting position was recorded. At last follow-up 13/17 (76.4%) patients were in Engel class I (9/17 [52.9%] were in class IA).

CONCLUSIONS

The PST approach is a reproducible and safe surgical route for SA. The achievable complication rate is in line with the best results in the literature. Visual function outcome particularly benefits from this highly selective, neocortex-sparing approach. A larger patient sample and longer follow-up will show in the future if the seizure control rate and neuropsychological outcome also compare better than those achieved with current common surgical techniques.

Free access

Kevin Akeret, David Bellut, Hans-Jürgen Huppertz, Georgia Ramantani, Kristina König, Carlo Serra, Luca Regli and Niklaus Krayenbühl

OBJECTIVE

Surgery has proven to be the best therapeutic option for drug-refractory cases of focal cortical dysplasia (FCD)–associated epilepsy. Seizure outcome primarily depends on the completeness of resection, rendering the intraoperative FCD identification and delineation particularly important. This study aims to assess the diagnostic yield of intraoperative ultrasound (IOUS) in surgery for FCD-associated drug-refractory epilepsy.

METHODS

The authors prospectively enrolled 15 consecutive patients with drug-refractory epilepsy who underwent an IOUS-assisted microsurgical resection of a radiologically suspected FCD between January 2013 and July 2016. The findings of IOUS were compared with those of presurgical MRI postprocessing and the sonographic characteristics were analyzed in relation to the histopathological findings. The authors investigated the added value of IOUS in achieving completeness of resection and improving postsurgical seizure outcome.

RESULTS

The neurosurgeon was able to identify the dysplastic tissue by IOUS in all cases. The visualization of FCD type I was more challenging compared to FCD II and the demarcation of its borders was less clear. Postsurgical MRI showed residual dysplasia in 2 of the 3 patients with FCD type I. In all FCD type II cases, IOUS allowed for a clear intraoperative visualization and demarcation, strongly correlating with presurgical MRI postprocessing. Postsurgical MRI confirmed complete resection in all FCD type II cases. Sonographic features correlated with the histopathological classification of dysplasia (sonographic abnormalities increase continuously in the following order: FCD IA/IB, FCD IC, FCD IIA, FCD IIB). In 1 patient with IOUS features atypical for FCD, histopathological investigation showed nonspecific gliosis.

CONCLUSIONS

Morphological features of FCD, as identified by IOUS, correlate well with advanced presurgical imaging. The resolution of IOUS was superior to MRI in all FCD types. The appreciation of distinct sonographic features on IOUS allows the intraoperative differentiation between FCD and non-FCD lesions as well as the discrimination of different histological subtypes of FCD. Sonographic demarcation depends on the underlying degree of dysplasia. IOUS allows for more tailored resections by facilitating the delineation of the dysplastic tissue.

Free access

Julia Velz, Flavio Vasella, Kevin Akeret, Sandra F. Dias, Elisabeth Jehli, Oliver Bozinov, Luca Regli, Menno R. Germans and Martin N. Stienen

OBJECTIVE

Skin depressions may appear as undesired effects after burr-hole trepanation for the evacuation of chronic subdural hematomas (cSDH). Placement of burr-hole covers to reconstruct skull defects can prevent skin depressions, with the potential to improve the aesthetic result and patient satisfaction. The perception of the relevance of this practice, however, appears to vary substantially among neurosurgeons. The authors aimed to identify current practice variations with regard to the application of burr-hole covers after trepanation for cSDH.

METHODS

An electronic survey containing 12 questions was sent to resident and faculty neurosurgeons practicing in different parts of the world, as identified by an Internet search. All responses completed between September 2018 and December 2018 were considered. Descriptive statistics and logistic regression were used to analyze the data.

RESULTS

A total of 604 responses were obtained, of which 576 (95.4%) provided complete data. The respondents’ mean age was 42.4 years (SD 10.5), and 86.5% were male. The sample consisted of residents, fellows, junior/senior consultants, and department chairs from 79 countries (77.4% Europe, 11.8% Asia, 5.4% America, 3.5% Africa, and 1.9% Australasia). Skin depressions were considered a relevant issue by 31.6%, and 76.0% indicated that patients complain about skin depressions more or less frequently. Burr-hole covers are placed by 28.1% in the context of cSDH evacuation more or less frequently. The most frequent reasons for not placing a burr-hole cover were the lack of proven benefit (34.8%), followed by additional costs (21.9%), technical difficulty (19.9%), and fear of increased complications (4.9%). Most respondents (77.5%) stated that they would consider placing burr-hole covers in the future if there was evidence for superiority of the practice. The use of burr-hole covers varied substantially across countries, but a country’s gross domestic product per capita was not associated with their placement.

CONCLUSIONS

Only a minority of neurosurgeons place burr-hole covers after trepanation for cSDH on a regular basis, even though the majority of participants reported complaints from patients regarding postoperative skin depressions. There are significant differences in the patterns of care among countries. Class I evidence with regard to patient satisfaction and safety of burr-hole cover placement is likely to have an impact on future cSDH management.

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Victor E. Staartjes, Costanza M. Zattra, Kevin Akeret, Nicolai Maldaner, Giovanni Muscas, Christiaan Hendrik Bas van Niftrik, Jorn Fierstra, Luca Regli and Carlo Serra

OBJECTIVE

Although 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.

METHODS

From 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.

RESULTS

Intraoperative 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.

CONCLUSIONS

The 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.

Free access

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.