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Objectives Chordomas are rare, slow-growing, and osteo-destructive tumors of the primitive notochord. There is still contention in the literature as to the optimal management of chordoma. We conducted a systematic review of the surgical management of chordoma along with our 10-year institutional experience. Design A systematic search of the literature was performed in October 2020 by using MEDLINE and EMBASE for articles relating to the surgical management of clival chordomas. We also searched for all adult patients surgically treated for primary clival chordomas at our institute between 2009 and 2019. Participants Only articles describing chordomas arising from the clivus were included in the analysis. For our institution experience, only adult primary clival chordoma cases were included. Main Outcome Measures Patients were divided into endoscopic or open surgery. Rate of gross total resection (GTR), recurrence, and complications were measured. Results Our literature search yielded 24 articles to include in the study. Mean GTR rate among endoscopic cases was 51.9% versus 41.7% for open surgery. Among the eight cases in our institutional experience, we found similar GTR rates between endoscopic and open surgery. Conclusion Although there is clear evidence in the literature that endoscopic approaches provide better rates of GTR with fewer overall complications compared to open surgery. However, there are still situations where endoscopy is not viable, and thus, open surgery should still be considered if required.

ObjectiveThe Koos grading scale is a frequently used classification system for vestibular schwannoma (VS) that accounts for extrameatal tumor dimension and compression of the brain stem. We propose an artificial intelligence (AI) pipeline to fully automate the segmentation and Koos classification of VS from MRI to improve clinical workflow and facilitate patient management.MethodsWe propose a method for Koos classification that does not only rely on available images but also on automatically generated segmentations. Artificial neural networks were trained and tested based on manual tumor segmentations and ground truth Koos grades of contrast-enhanced T1-weighted (ceT1) and high-resolution T2-weighted (hrT2) MR images from subjects with a single sporadic VS, acquired on a single scanner and with a standardized protocol. The first stage of the pipeline comprises a convolutional neural network (CNN) which can segment the VS and 7 adjacent structures. For the second stage, we propose two complementary approaches that are combined in an ensemble. The first approach applies a second CNN to the segmentation output to predict the Koos grade, the other approach extracts handcrafted features which are passed to a Random Forest classifier. The pipeline results were compared to those achieved by two neurosurgeons.ResultsEligible patients (n = 308) were pseudo-randomly split into 5 groups to evaluate the model performance with 5-fold cross-validation. The weighted macro-averaged mean absolute error (MA-MAE), weighted macro-averaged F1 score (F1), and accuracy score of the ensemble model were assessed on the testing sets as follows: MA-MAE = 0.11 ± 0.05, F1 = 89.3 ± 3.0%, accuracy = 89.3 ± 2.9%, which was comparable to the average performance of two neurosurgeons: MA-MAE = 0.11 ± 0.08, F1 = 89.1 ± 5.2, accuracy = 88.6 ± 5.8%. Inter-rater reliability was assessed by calculating Fleiss' generalized kappa (k = 0.68) based on all 308 cases, and intra-rater reliabilities of annotator 1 (k = 0.95) and annotator 2 (k = 0.82) were calculated according to the weighted kappa metric with quadratic (Fleiss-Cohen) weights based on 15 randomly selected cases.ConclusionsWe developed the first AI framework to automatically classify VS according to the Koos scale. The excellent results show that the accuracy of the framework is comparable to that of neurosurgeons and may therefore facilitate management of patients with VS. The models, code, and ground truth Koos grades for a subset of publicly available images (n = 188) will be released upon publication.

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OBJECTIVE The growth characteristics of vestibular schwannomas (VSs) under surveillance can be studied using a Bayesian method of growth risk stratification by time after surveillance onset, allowing dynamic evaluations of growth risks. There is no consensus on the optimum surveillance strategy in terms of frequency and duration, particularly for long-term growth risks. In this study, the long-term conditional probability of new VS growth was reported for patients after 5 years of demonstrated nongrowth. This allowed modeling of long-term VS growth risks, the creation of an evidence-based surveillance protocol, and the proposal of a cost-benefit analysis decision aid. METHODS The authors performed an international multicenter retrospective analysis of prospectively collected databases from five tertiary care referral skull base units. Patients diagnosed with sporadic unilateral VS between 1990 and 2010 who had a minimum of 10 years of surveillance MRI showing VS nongrowth in the first 5 years of follow-up were included in the analysis. Conditional probabilities of growth were calculated according to Bayes’ theorem, and nonlinear regression analyses allowed modeling of growth. A cost-benefit analysis was also performed. RESULTS A total of 354 patients were included in the study. Across the surveillance period from 6 to 10 years postdiagnosis, a total of 12 tumors were seen to grow (3.4%). There was no significant difference in long-term growth risk for intracanalicular versus extracanalicular VSs (p = 0.41). At 6 years, the residual conditional probability of growth from this point onward was seen to be 2.28% (95% CI 0.70%–5.44%); at 7 years, 1.35% (95% CI 0.25%–4.10%); at 8 years, 0.80% (95% CI 0.07%–3.25%); at 9 years, 0.47% (95% CI 0.01%–2.71%); and at 10 years, 0.28% (95% CI 0.00%–2.37%). Modeling determined that the remaining lifetime risk of growth would be less than 1% at 7 years 7 months, less than 0.5% at 8 years 11 months, and less than 0.25% at 10 years 4 months. CONCLUSIONS This multicenter study evaluates the conditional probability of VS growth in patients with long-term VS surveillance (6–10 years). On the basis of these growth risks, the authors posited a surveillance protocol with imaging at 6 months (t = 0.5), annually for 3 years (t = 1.5, 2.5, 3.5), twice at 2-year intervals (t = 5.5, 7.5), and a final scan after 3 years (t = 10.5). This can be used to better inform patients of their risk of growth at particular points along their surveillance timeline, balancing the risk of missing late growth with the costs of repeated imaging. A cost-benefit analysis decision aid was also proposed to allow units to make their own decisions regarding the cessation of surveillance.