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Free access

Anthony V. Nguyen, Elizabeth E. Blears, Evan Ross, Rishi R. Lall and Juan Ortega-Barnett

OBJECTIVE

Glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common intracranial pathologies encountered by neurosurgeons. They often may have similar radiological findings, making diagnosis difficult without surgical biopsy; however, management is quite different between these two entities. Recently, predictive analytics, including machine learning (ML), have garnered attention for their potential to aid in the diagnostic assessment of a variety of pathologies. Several ML algorithms have recently been designed to differentiate GBM from PCNSL radiologically with a high sensitivity and specificity. The objective of this systematic review and meta-analysis was to evaluate the implementation of ML algorithms in differentiating GBM and PCNSL.

METHODS

The authors performed a systematic review of the literature using PubMed in accordance with PRISMA guidelines to select and evaluate studies that included themes of ML and brain tumors. These studies were further narrowed down to focus on works published between January 2008 and May 2018 addressing the use of ML in training models to distinguish between GBM and PCNSL on radiological imaging. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

RESULTS

Eight studies were identified addressing use of ML in training classifiers to distinguish between GBM and PCNSL on radiological imaging. ML performed well with the lowest reported AUC being 0.878. In studies in which ML was directly compared with radiologists, ML performed better than or as well as the radiologists. However, when ML was applied to an external data set, it performed more poorly.

CONCLUSIONS

Few studies have applied ML to solve the problem of differentiating GBM from PCNSL using imaging alone. Of the currently published studies, ML algorithms have demonstrated promising results and certainly have the potential to aid radiologists with difficult cases, which could expedite the neurosurgical decision-making process. It is likely that ML algorithms will help to optimize neurosurgical patient outcomes as well as the cost-effectiveness of neurosurgical care if the problem of overfitting can be overcome.

Free access

Juan Antonio Ponce-Gómez, Luis Alberto Ortega-Porcayo, Hector Enrique Soriano-Barón, Arturo Sotomayor-González, Nicasio Arriada-Mendicoa, Juan Luis Gómez-Amador, Marité Palma-Díaz and Juan Barges-Coll

Object

The goal of this study was to compare the indications, benefits, and complications between the endoscopic endonasal approach (EEA) and the microscopic transoral approach to perform an odontoidectomy. Transoral approaches have been standard for odontoidectomy procedures; however, the potential benefits of the EEA might be demonstrated to be a more innocuous technique. The authors present their experience with 12 consecutive cases that required odontoidectomy and posterior instrumentation.

Methods

Twelve consecutive cases of craniovertebral junction instability with or without basilar invagination were diagnosed at the National Institute of Neurology and Neurosurgery in Mexico City, Mexico, between January 2009 and January 2013. The EEA was used for 5 cases in which the odontoid process was above the nasopalatine line, and was compared with 7 cases in which the odontoid process was beneath the nasopalatine line; these were treated using the transoral microscopic approach (TMA). Odontoidectomy was performed after occipital-cervical or cervical posterior augmentation with lateral mass and translaminar screws. One case was previously fused (Oc–C4 fusion). The senior author performed all surgeries. American Spinal Injury Association scores were documented before surgical treatment and after at least 6 months of follow-up.

Results

Neurological improvement after odontoidectomy was similar for both groups. From the transoral group, 2 patients had postoperative dysphonia, 1 patient presented with dysphagia, and 1 patient had intraoperative CSF leakage. The endoscopic procedure required longer surgical time, less time to extubation and oral feeding, a shorter hospital stay, and no complications in this series.

Conclusions

Endoscopic endonasal odontoidectomy is a feasible, safe, and well-tolerated procedure. In this small series there was no difference in the outcome between the EEA and the TMA; however, fewer complications were documented with the endonasal technique.

Restricted access

Juan Luis Gómez-Amador, Luis Alberto Ortega-Porcayo, Isaac Jair Palacios-Ortíz, Alexander Perdomo-Pantoja, Felipe Eduardo Nares-López and Alfredo Vega-Alarcón

Brainstem cavernous malformations are challenging due to the critical anatomy and potential surgical risks. Anterolateral, lateral, and dorsal surgical approaches provide limited ventral exposure of the brainstem. The authors present a case of a midline ventral pontine cavernous malformation resected through an endoscopic endonasal transclival approach based on minimal brainstem transection, negligible cranial nerve manipulation, and a straightforward trajectory. Technical and reconstruction technique advances in endoscopic endonasal skull base surgery provide a direct, safe, and effective corridor to the brainstem.