INTRODUCTION: Automated delineation of gliomas in intraoperative ultrasound (iUS) could provide timely information to surgeons. While artificial intelligence (AI) frameworks have shown promising results for delineating gliomas in preoperative MRI, automated delineation in iUS remains a challenging and open problem. METHODS: A deep learning algorithm was developed to automatically delineate gliomas in intraoperative ultrasound. Instead of using time-consuming manual delineations from experts, the algorithm was developed using MR-based automated delineations. First, a MR nnUnet was trained for delineating gliomas in 3D preoperative MRI using an annotated public dataset (N=1000; RSNA-ASNR-MICCAI-BraTS 2021). Then, this algorithm delineated gliomas in preoperative MRI of patients (N = 84) in whom pre-dural opening 3D iUS reconstructed from a tracked handheld 2D probe was also acquired. To obtain the MR-based delineations in iUS, preoperative MRI and iUS were aligned using NiftyReg. Finally, a 3D iUS nnUnet was trained using a random subset (N = 78) of this pseudo-annotated iUS dataset. The method was assessed on the remaining test set (N = 943 slices from 6 volumes), with a treating neurosurgeon delineating gliomas in 2D iUS only and then in 2D iUS and aligned MR slices to obtain gold-standard delineations. RESULTS: The median Dice scores of the nnUnet and the neurosurgeon were respectively 78.6% (IQ:15.5%) and 84.3% (IQ: 21.1%) against the gold-standard delineations. In this preliminary study, the network's performance was comparable to the neurosurgeon's (two paired one-sided tests, equivalence margin:5%, p<1e-5, N = 943), with rapid delineation (<10 second) of 3D iUS (95-216 slices) compared to neurogeons (32-93 minutes), making automated delineation practical during surgery. CONCLUSIONS: The results demonstrate the feasibility of using AI-based methods for automated delineation of brain tumors in iUS, which could improve surgical outcomes by providing real-time information to surgeons.
Abstract The aim of the study was to map the disruption of functional connectome in patients with gliomas using resting-state functional MRI (rs-fMRI) acquired before surgery (3T scanner). 54 patients with gliomas were included (M = 34, mean age = 50.8 y). Manual segmentation of lesions was performed on T1w and T2w MRI scans. Whole-brain measures of network centrality, modularity, integration and segregation were computed for a set of Regions of Interest (ROIs) including the Harvard Oxford anatomical atlas and tumor-related ROIs (i.e., edema, solid tumor, necrotic core). The analysis was performed on adjacency matrices obtained with a cost of 0.15, i.e. considering the 15% of the edges with the largest correlation coefficient values for each subject. A p< 0.05, two-sided, FDR corrected threshold was used for statistical analysis. In newly diagnosed patients (n = 18), we found a decrease of Integration (Degree, Global Efficiency) and Centrality (Betweenness Centrality) and an increase of Segregation (Local Efficiency) in the tumor ROIs compared to healthy brain regions. A similar integration/segregation imbalance pattern was present across the edema, solid tumor and necrotic core, with the edema being the most similar to healthy grey matter and the necrosis being the more altered, possibly reflecting tumor cells’ density and neuronal disruption. In patients at recurrence (n = 36), the same pattern was found, though with a less clear separation among tissue classes, possibly due to the altered anatomical/functional environment caused by functional plastic rearrangement and the effects the treatment(s). Finally, a significant correlation with overall survival was found for local efficiency values of the edema (p< 0.05, r 0,53=, R2 = 28%). We found significant alterations of functional connectome measures in brain regions affected by the tumor, with a decrease of network integration and an increase of network segregation measures characterizing different levels of tumor infiltration and potentially correlating with clinical outcomes.
Department of Neurosurgery, Harvard Medical School/Brigham and Women's Hospital, Hale Building for Transformative Medicine, Boston, Massachusetts Department of Radiology, Harvard Medical School/Brigham and Women's Hospital, Hale Building for Transformative Medicine, Boston, Massachusetts Correspondence: Alexandra J. Golby, MD, Department of Neurosurgery, Harvard Medical School/Brigham and Women's Hospital, Hale Building for Transformative Medicine, 60 Fenwood Road, Boston, MA 02115. E-mail: [email protected]
The trigeminal nerve (TGN) is the largest cranial nerve and can be involved in multiple inflammatory, compressive, ischemic or other pathologies. Currently, imaging-based approaches to identify the TGN mostly rely on T2-weighted magnetic resonance imaging (MRI), which provides localization of the cisternal portion of the TGN where the contrast between nerve and cerebrospinal fluid (CSF) is high enough to allow differentiation. The course of the TGN within the brainstem as well as anterior to the cisternal portion, however, is more difficult to display on traditional imaging sequences. An advanced imaging technique, diffusion MRI (dMRI), enables tracking of the trajectory of TGN fibers and has the potential to visualize anatomical regions of the TGN not seen on T2-weighted imaging. This may allow a more comprehensive assessment of the nerve in the context of pathology. To date, most work in TGN tracking has used clinical dMRI acquisitions with a b-value of 1000 s/mm2 and conventional diffusion tensor MRI (DTI) tractography methods. Though higher b-value acquisitions and multi-tensor tractography methods are known to be beneficial for tracking brain white matter fiber tracts, there have been no studies conducted to evaluate the performance of these advanced approaches on nerve tracking of the TGN, in particular on tracking different anatomical regions of the TGN. We compare TGN tracking performance using dMRI data with different b-values, in combination with both single- and multi-tensor tractography methods. Our goal is to assess the advantages and limitations of these different strategies for identifying the anatomical regions of the TGN. We proposed seven anatomical rating criteria including true and false positive structures, and we performed an expert rating study of over 1000 TGN visualizations, as follows. We tracked the TGN using high-quality dMRI data from 100 healthy adult subjects from the Human Connectome Project (HCP). TGN tracking performance was compared across dMRI acquisitions with b = 1000 s/mm2, b = 2000 s/mm2 and b = 3000 s/mm2, using single-tensor (1T) and two-tensor (2T) unscented Kalman filter (UKF) tractography. This resulted in a total of six tracking strategies. The TGN was identified using an anatomical region-of-interest (ROI) selection approach. First, in a subset of the dataset we identified ROIs that provided good TGN tracking performance across all tracking strategies. Using these ROIs, the TGN was then tracked in all subjects using the six tracking strategies. An expert rater (GX) visually assessed and scored each TGN based on seven anatomical judgment criteria. These criteria included the presence of multiple expected anatomical segments of the TGN (true positive structures), specifically branch-like structures, cisternal portion, mesencephalic trigeminal tract, and spinal cord tract of the TGN. False positive criteria included the presence of any fibers entering the temporal lobe, the inferior cerebellar peduncle, or the middle cerebellar peduncle. Expert rating scores were analyzed to compare TGN tracking performance across the six tracking strategies. Intra- and inter-rater validation was performed to assess the reliability of the expert TGN rating result. The TGN was selected using two anatomical ROIs (Meckel's Cave and cisternal portion of the TGN). The two-tensor tractography method had significantly better performance on identifying true positive structures, while generating more false positive streamlines in comparison to the single-tensor tractography method. TGN tracking performance was significantly different across the three b-values for almost all structures studied. Tracking performance was reported in terms of the percentage of subjects achieving each anatomical rating criterion. Tracking of the cisternal portion and branching structure of the TGN was generally successful, with the highest performance of over 98% using two-tensor tractography and b = 1000 or b = 2000. However, tracking the smaller mesencephalic and spinal cord tracts of the TGN was quite challenging (highest performance of 37.5% and 57.07%, using two-tensor tractography with b = 1000 and b = 2000, respectively). False positive connections to the temporal lobe (over 38% of subjects for all strategies) and cerebellar peduncles (100% of subjects for all strategies) were prevalent. High joint probability of agreement was obtained in the inter-rater (on average 83%) and intra-rater validation (on average 90%), showing a highly reliable expert rating result. Overall, the results of the study suggest that researchers and clinicians may benefit from tailoring their acquisition and tracking methodology to the specific anatomical portion of the TGN that is of the greatest interest. For example, tracking of branching structures and TGN-T2 overlap can be best achieved with a two-tensor model and an acquisition using b = 1000 or b = 2000. In general, b = 1000 and b = 2000 acquisitions provided the best-rated tracking results. Further research is needed to improve both sensitivity and specificity of the depiction of the TGN anatomy using dMRI.
