Surgical management of tumors of the cervical spine and craniovertebral junction involving the vertebral artery: A narrative review.

BACKGROUND: The vertebral artery (VA) is in close proximity to bony structures, nerves and nerve sheaths of the cervical spine and craniovertebral junction (CVJ). These structures can be sources of tumors that are responsible for displacement, encasement and sometimes invasion of the VA. Removing these tumors while minimizing the risk of vascular injury requires thorough knowledge of the vascular anatomy, risk factors of vascular injury, the relationships of each tumor type with the VA, and the different surgical approaches and techniques that result in the best outcomes in terms of vascular control, tumoral exposure and resection. OBJECTIVE: To present an overview of preoperative and anatomical considerations, differential diagnoses and various approaches to consider in cases of tumors in close relationship with the VA. METHOD: A review of recent literature was conducted to examine the anatomy of the VA, the tumors most frequently affecting it, surgical approaches, and the necessary pre-operative preparations for ensuring safe and maximal tumor resection. This review aims to underscore the principles of treatment. CONCLUSION: Tumors located at the CVJ and the cervical spine intimately involved with the VA, pose a surgical challenge and increase the risk of incomplete removal of the lesion. Detailed knowledge of the patient-specific anatomy and a targeted pre-operative work-up enable optimal planning of surgical approach and management of the VA, thereby reducing surgical risks and improving extent of resection.

eICAB: A novel deep learning pipeline for Circle of Willis multiclass segmentation and analysis.

BACKGROUND: The accurate segmentation, labeling and quantification of cerebral blood vessels on MR imaging is important for basic and clinical research, yet results are not generalizable, and often require user intervention. New methods are needed to automate this process. PURPOSE: To automatically segment, label and quantify Circle of Willis (CW) arteries on Magnetic Resonance Angiography images using deep convolutional neural networks. MATERIALS AND METHODS: MRA images were pooled from three public and private databases. A total of 116 subjects (mean age 56 years ± 21 [standard deviation]; 72 women) were used to make up the training set (N=101) and the testing set (N=15). In each image, fourteen arterial segments making up or surrounding the CW were manually annotated and validated by a clinical expert. Convolutional neural network (CNN) models were trained on a training set to be finally combined in an ensemble to develop eICAB. Model performances were evaluated using (1) quantitative analysis (dice score on test set) and (2) qualitative analysis (external datasets, N=121). The reliability was assessed using multiple MRAs of healthy participants (ICC of vessel diameters and volumes on test-retest). RESULTS: Qualitative analysis showed that eICAB correctly predicted the large, medium and small arteries in 99±0.4%, 97±1% and 88±7% of all images, respectively. For quantitative assessment, the average dice score coefficients for the large (ICAs, BA), medium (ACAs, MCAs, PCAs-P2), and small (AComm, PComm, PCAs-P1) vessels were 0.76±0.07, 0.76±0.08 and 0.41±0.27, respectively. These results were similar and, in some cases, statistically better (p<0.05) than inter-expert annotation variability and robust to image SNR. Finally, test-retest analysis showed that the model yielded high diameter and volume reliability (ICC=0.99). CONCLUSION: We have developed a quick and reliable open-source CNN-based method capable of accurately segmenting and labeling the CW in MRA images. This method is largely independent of image quality. In the future, we foresee this approach as a critical step towards fully automated analysis of MRA databases in basic and clinical research.

Superior Longitudinal Fasciculus: A Review of the Anatomical Descriptions With Functional Correlates.

The superior longitudinal fasciculus (SLF) is part of the longitudinal association fiber system, which lays connections between the frontal lobe and other areas of the ipsilateral hemisphere. As a dominant association fiber bundle, it should correspond to a well-defined structure with a clear anatomical definition. However, this is not the case, and a lot of confusion and overlap surrounds this entity. In this review/opinion study, we survey relevant current literature on the topic and try to clarify the definition of SLF in each hemisphere. After a comparison of postmortem dissections and data obtained from diffusion MRI studies, we discuss the specifics of this bundle regarding its anatomical landmarks, differences in lateralization, as well as individual variability. We also discuss the confusion regarding the arcuate fasciculus in relation to the SLF. Finally, we recommend a nomenclature based on the findings exposed in this review and finalize with a discussion on relevant functional correlates of the structure.