Dataset Information

Transsulcal parafascicular brain path-assisted approach to subcortical lesions: 2-dimensional operative video.

ABSTRACT:

Background

Approaches to subcortical lesions have traditionally been limited by the morbidity of white matter dissection and fixed blade retraction required to reach these targets. Visualization of deep surgical fields with a traditional operating microscope is also poor. Coordinated use of intra-operative image guidance, a tubular retractor (BrainPath^®, Nico Corp, Indianapolis, Indiana), a high-definition exoscope (Vitom^®, Karl Storz Endoscopy America, Inc, El Segundo, California), and a low-profile resection device (Myriad^®, Nico Corp) facilitates atraumatic access to and resection of subcortical lesions including primary brain tumors, brain metastases, and intracerebral hemorrhages.[1] Use of pre-planned transsulcal and parafascicular trajectories based on magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) can further mitigate damage to white matter tracts with this technique.

Case description

We herein present details of the transsulcal parafascicular BrainPath^®-assisted approach to subcortical lesions and demonstrate the utility of this technique using two patient examples: a spontaneous deep left posterior temporal lobe hematoma in a 41-year-old male and a left hippocampal glioblastoma in a 54-year-old female. Key steps include selection of appropriate patients with non-skull base subcortical lesions, preoperative trajectory and tube depth planning based on MRI (including diffusion-weighted imaging and DTI), patient positioning and operating room setup to facilitate pre-planned trajectories and surgeon ergonomics, and use of low-profile instruments with a two-handed surgical technique.

Conclusion

Given recent data demonstrating the utility of this approach for hematoma evacuation and a likely increased future usage of this technique,[2] surgeon familiarity with the above steps will be of increasing importance.

SUBMITTER: Rennert RC

PROVIDER: S-EPMC8053449 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke survivors would be a useful aid in patient diagnosis and treatment planning. It would also greatly facilitate the study of brain-behavior relationships by eliminating the laborious step of having a human expert manually segment the lesion on each brain scan. We propose a multi-modal multi-path convolutional neural network system for automating stroke lesion segmentation. Our system has nine end-to-end UNets that take as input 2-dimensional (2D) slices and examines all three planes with three different normalizations. Outputs from these nine total paths are concatenated into a 3D volume that is then passed to a 3D convolutional neural network to output a final lesion mask. We trained and tested our method on datasets from three sources: Medical College of Wisconsin (MCW), Kessler Foundation (KF), and the publicly available Anatomical Tracings of Lesions After Stroke (ATLAS) dataset. To promote wide applicability, lesions were included from both subacute (1 to 5 weeks) and chronic ( > 3 months) phases post stroke, and were of both hemorrhagic and ischemic etiology. Cross-study validation results (with independent training and validation datasets) were obtained to compare with previous methods based on naive Bayes, random forests, and three recently published convolutional neural networks. Model performance was quantified in terms of the Dice coefficient, a measure of spatial overlap between the model-identified lesion and the human expert-identified lesion, where 0 is no overlap and 1 is complete overlap. Training on the KF and MCW images and testing on the ATLAS images yielded a mean Dice coefficient of 0.54. This was reliably better than the next best previous model, UNet, at 0.47. Reversing the train and test datasets yields a mean Dice of 0.47 on KF and MCW images, whereas the next best UNet reaches 0.45. With all three datasets combined, the current system compared to previous methods also attained a reliably higher cross-validation accuracy. It also achieved high Dice values for many smaller lesions that existing methods have difficulty identifying. Overall, our system is a clear improvement over previous methods for automating stroke lesion segmentation, bringing us an important step closer to the inter-rater accuracy level of human experts.

Project description:Background:Pulmonary segmentectomies are generally classified into simple (tri-segmentectomy or lingulectomy as well as apical or basilar segmentectomy) and complex (individual or bi-segmentectomy of the upper, middle and lower lobes). Complex segmentectomies are technically feasible by video-assisted thoracic surgery (VATS) but remain challenging, and reports on post-operative outcomes are scarce. This study analyzes the differences between simple and complex VATS segmentectomy in terms of peri- and post-operative outcomes. Methods:We retrospectively reviewed records of all patients who underwent anatomical pulmonary segmentectomy by VATS from 2014 to 2018 in two university hospitals. Results:A total of 232 patients (114 men; median age 67 years; range, 29-87 years) underwent VATS segmentectomy for primary lung cancer (n=177), metastases (n=26) and benign lesions (n=29). The overall 30-day mortality and morbidity rates were 0.8% and 29.7%, respectively. The re-operation rate was 4.7%. Complex segmentectomy was realized in 111 patients including 86 (77.5%) upper lobe segmentectomies and 44 (39.6%) bi-segmentectomies. There was no statistical difference between complex and simple segmentectomy in terms of operative time (145 vs. 143 min, respectively; P=0.79) and chest tube duration [median: 1 (range, 0-33) vs. 2 (range, 1-19) days, respectively; P=0.95]. Post-operative overall complication rates were similar for both groups (30% vs. 30%, respectively; P=0.99) and were not correlated with the type of segmentectomy. However, complex segmentectomy patients had a shorter length of hospitalization compared to simple segmentectomy patients [median: 5 (range, 1-36) vs. 7 (range, 2-31) days; P=0.026]. Interestingly, complex segmentectomies were realized most frequently 2 years after implementation of VATS segmentectomy (23% vs. 77%; P=0.01). Conclusions:In comparison with simple segmentectomy, complex segmentectomy by VATS seems to present similar post-operative complication rates. Learning curve and progressive increase in acceptance by surgeons seem to be key elements for successful implementation of complex segmentectomies and could explain the shorter length of stay we observed.