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Background Brainshift can hamper the accuracy of neuronavigation systems in intra-axial tumor surgery. Correction of brainshift becomes imperative to avoid loss of accuracy and erroneous assessment of residual tumor as well as its relationship to critical eloquent substrates. Method This paper describes a case of a frontal tumor close to motor cortex. Workflow for rigid image fusion (RIF) based iUS-MR correction of brainshift is demonstrated highlighting its accuracy and clinical value in assessing tumor margins as well as functional boundaries. Conclusion iUS-MR fusion provides a cost-effective, accurate and practical solution for observation and correction of brainshift. 

Glioblastoma multiforme (GBM) are aggressive brain tumors, which lead to poor Overall Survival (OS) of patients. For precise surgical and treatment planning OS prediction of GBM patients is highly desired by the clinicians and oncologists. Radiomics research attempts at predicting disease prognosis, thus providing beneficial information for personalized treatment from a variety of imaging features extracted from multiple MR images. In this study, first order intensity based, volume and shape based and textural radiomic features are extracted from FLAIR and T1ce MRI data. The region of interest (ROI) is further decomposed with Stationary Wavelet Transform (SWT) with low pass and high pass filtering. This helped in acquiring the directional information. The efficiency of the proposed algorithm is evaluated on Brain Tumor Segmentation (BraTS) challenge training, testing and validation dataset. The proposed approach secured third position in BraTS 2018 challenge for Overall Survival prediction task.

Gliomas are the most common primary brain malignancies, with varying degrees of aggressiveness and prognosis. Understanding of tumor biology and intra-tumor heterogeneity is necessary for planning personalized therapy and predicting response to therapy. Accurate tumoral and intra-tumoral segmentation on MRI is the first step toward understanding the tumor biology through computational methods. The purpose of this study was to design a segmentation algorithm and evaluate its performance on pre-treatment brain MRIs obtained from patients with gliomas. In this study, we have designed a novel 3D U-Net architecture that segments various radiologically identifiable sub-regions like edema, enhancing tumor, and necrosis. Weighted patch extraction scheme from the tumor border regions is proposed to address the problem of class imbalance between tumor and non-tumorous patches. The architecture consists of a contracting path to capture context and the symmetric expanding path that enables precise localization. The Deep Convolutional Neural Network (DCNN) based architecture is trained on 285 patients, validated on 66 patients and tested on 191 patients with Glioma from Brain Tumor Segmentation (BraTS) 2018 challenge dataset. Three dimensional patches are extracted from multi-channel BraTS training dataset to train 3D U-Net architecture. The efficacy of the proposed approach is also tested on an independent dataset of 40 patients with High Grade Glioma from our tertiary cancer center. Segmentation results are assessed in terms of Dice Score, Sensitivity, Specificity, and Hausdorff 95 distance (ITCN intra-tumoral classification network). Our proposed architecture achieved Dice scores of 0.88, 0.83, and 0.75 for the whole tumor, tumor core and enhancing tumor, respectively, on BraTS validation dataset and 0.85, 0.77, 0.67 on test dataset. The results were similar on the independent patients' dataset from our hospital, achieving Dice scores of 0.92, 0.90, and 0.81 for the whole tumor, tumor core and enhancing tumor, respectively. The results of this study show the potential of patch-based 3D U-Net for the accurate intra-tumor segmentation. From experiments, it is observed that the weighted patch-based segmentation approach gives comparable performance with the pixel-based approach when there is a thin boundary between tumor subparts.

Background: Deep location as well as relation to major vascular structures and eloquent brain areas make insular glioma resection challenging. Transsylvian and transopercular approaches have been described for resection of these tumors. Objective: We illustrate the anatomical relations of a dominant hemisphere insular glioma and present the video demonstrating the step-wise resection of the same via frontal transopercular approach. Surgical Procedure: A 27-year-old lady with dominant hemisphere insular glioma underwent awake surgery through a transopercular approach with cortical and subcortical mapping using direct electrical stimulation for resection of the same. Result: Gross total resection of left insular glioma was achieved without any fresh postoperative deficits. Conclusion: Awake transopercular approach with intraoperative motor, language, and neuropsychological monitoring helps achieve maximum safe resection of insular glioma in the dominant cerebral hemisphere.

Background Surgical resection of insular gliomas is a challenge. TO resection is considered more versatile and has lower risk of vascular damage. In this study, we aimed to understand the factors that affect resection rates, ischemic changes and neurological outcomes and studied the utility of IONM in patients who underwent TO resection for IGs. Methods Retrospective analysis of 66 patients with IG who underwent TO resection was performed. Results Radical resection was possible in 39% patients. Involvement of zone II and the absence of contrast enhancement predicted lower resection rate. Persistent deficit rate was 10.9%. Although dominant lobe tumors increased immediate deficit and fronto-orbital operculum involvement reduced prolonged deficit rate, no tumor related factor showed significant association with persistent deficits. 45% of patients developed a postoperative infarct, 53% of whom developed deficits. Most affected vascular territory was lenticulostriate (39%). MEP changes were observed in 9/57 patients. 67% of stable TcMEPs and 74.5% of stable strip MEPs did not develop any postoperative motor deficits. Long-term deficits were seen in 3 and 6% patients with stable TcMEP and strip MEPs respectively. In contrast, 25% and 50% of patients with reversible strip MEP and Tc MEP changes respectively had persistent motor deficits. DWI changes were clinically more relevant when accompanied by MEP changes intraoperatively, with persistent deficit rates three times greater when MEP changes occurred than when MEPs were stable. Conclusion Radical resection can be achieved in large, multizone IGs, with reasonable outcomes using TO approach and multimodal intraoperative strategy with IONM.

BackgroundMaximizing resection is an oft-sought-after albeit challenging goal in diffuse gliomas. Microsurgical technique remains the mainstay.MethodBy virtue of their pattern of growth and spread, gliomas respect anatomical boundaries like the pia. Using subpial dissection, en bloc resections provide the most optimal surgical technique. This paper revisits this technique and describes the rationale and basic principles integrating it in the modern multimodal glioma surgery workflow.ConclusionSubpial resection is a very useful and “anatomical” technique for en bloc resection of diffuse gliomas which is easy to master and execute and optimizes the extent of resection and minimizes complications effectively.

Navigable ultrasound (NUS) is a useful adjunct for controlling resection in intra-axial brain tumors. We investigated its role in predicting residual disease and thereby in influencing the intraoperative decision regarding additional resection as also its influence on survival in glioblastoma patients. A prospectively maintained database was accessed to retrieve the data regarding consecutive histologically verified gliomas operated using the NUS. We documented the number of times US images were obtained, the surgeon's impression of each scan and the subsequent decision regarding further resection. Survival (progression-free and overall) was calculated for patients with a glioblastoma, and univariate and multivariate analyses performed. The NUS was used for resection control in 88 gliomas. In 66 cases, intermediate scans were performed resulting in further resection in 60 of them. Radiological gross total resection was obtained in 46 cases (44%). The US correctly predicted postoperative residue in 83% cases (sensitivity and specificity of 87 and 78% respectively; positive and negative predictive values of 82 and 84%). There were 9 false positives and 6 false negative cases. When the US was false positive, the resolution was more often good (7 of 9 cases); whereas when there were false negatives, it was more likely to be less than optimal (4 of 6). Morbidity was 17% and this was not related to the additional resections. In the subset of glioblastoma patients (n = 28) use of NUS was associated with significantly better progression-free as well as overall survival rates. NUS is a useful intraoperative adjunct in controlling resections. It positively and decisively influences the intraoperative course of the surgery. Understanding its correct technique and limitations, along with experience in image interpretation can help in maximizing its accuracy without compromising functional outcomes. Optimally utilized, it can improve survival.