Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model.

Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been difficult to translate to clinical practice in cranioplasty, as their performance on large and complex cranial defects remains unsatisfactory. In this paper, we present a statistical shape model (SSM) built directly on the segmentation masks of the skulls represented as binary voxel occupancy grids and evaluate it on several cranial implant design datasets. Results show that, while CNN-based approaches outperform the SSM on synthetic defects, they are inferior to SSM when it comes to large, complex and real-world defects. Experienced neurosurgeons evaluate the implants generated by the SSM to be feasible for clinical use after minor manual corrections. Datasets and the SSM model are publicly available at https://github.com/Jianningli/ssm .

Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge.

Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects. These implants are usually generated offline and may require days to weeks to be available. An automated implant design process combined with onsite manufacturing facilities can guarantee immediate implant availability and avoid secondary intervention. To address this need, the AutoImplant II challenge was organized in conjunction with MICCAI 2021, catering for the unmet clinical and computational requirements of automatic cranial implant design. The first edition of AutoImplant (AutoImplant I, 2020) demonstrated the general capabilities and effectiveness of data-driven approaches, including deep learning, for a skull shape completion task on synthetic defects. The second AutoImplant challenge (i.e., AutoImplant II, 2021) built upon the first by adding real clinical craniectomy cases as well as additional synthetic imaging data. The AutoImplant II challenge consisted of three tracks. Tracks 1 and 3 used skull images with synthetic defects to evaluate the ability of submitted approaches to generate implants that recreate the original skull shape. Track 3 consisted of the data from the first challenge (i.e., 100 cases for training, and 110 for evaluation), and Track 1 provided 570 training and 100 validation cases aimed at evaluating skull shape completion algorithms at diverse defect patterns. Track 2 also made progress over the first challenge by providing 11 clinically defective skulls and evaluating the submitted implant designs on these clinical cases. The submitted designs were evaluated quantitatively against imaging data from post-craniectomy as well as by an experienced neurosurgeon. Submissions to these challenge tasks made substantial progress in addressing issues such as generalizability, computational efficiency, data augmentation, and implant refinement. This paper serves as a comprehensive summary and comparison of the submissions to the AutoImplant II challenge. Codes and models are available at https://github.com/Jianningli/Autoimplant_II.

Comparison of tumor immune environment between newly diagnosed and recurrent glioblastoma including matched patients.

PURPOSE: Glioblastoma (GBM) is the most lethal primary brain tumor in adult patients. The disease progression, response to chemotherapy and radiotherapy at initial diagnosis, and prognosis are profoundly associated with the tumor microenvironment, especially the features of tumor-infiltrating immune cells (TII). Recurrent GBM is even more challenging to manage. Differences in the immune environment between newly diagnosed and recurrent GBM and an association with tumor prognosis are not well defined. METHODS: To address this knowledge gap, we analyzed the clinical data and tissue specimens from 24 GBM patients (13 at initial diagnosis and 11 at recurrence). The expression levels of multiple immunobiological markers in patients' GBM at initial diagnosis versus at recurrence were compared, including five patients with both specimens available (paired). The distribution patterns of TII were evaluated in both the intratumoral and perivascular regions. RESULTS: We found that tumors from recurrent GBM have significantly more tumor-infiltrating lymphocytes (TILs) and macrophages and higher PD-L1 and PD-1 expression than tumors at primary diagnosis and benign brain specimens from epilepsy surgery. The pattern changes of the TILs and macrophages of the five paired specimens were consistent with the unpaired patients, while the CD8 to CD4 ratio remained constant from diagnosis to recurrence in the paired tissues. The levels of TILs, macrophages, PD-1 or PD-L1+ cells at initial diagnosis did not correlate with OS. TILs, macrophages, and PD-1+ cells were increased in recurrent tumors both in intratumoral and perivascular areas, with higher distribution levels in intratumoral than perivascular regions. Higher CD4 or CD8 infiltration at recurrence was associated with a worse prognosis, respectively. CONCLUSIONS: Our study elucidated that TIL and TAM tend to accumulate in perivascular region and are more abundant in recurrent GBM than newly diagnosed GBM.

The Association of Fractal Dimension with Vascularity and Clinical Outcomes in Glioblastoma.

BACKGROUND: Growing evidence indicates fractal analysis (FA) has potential as a computational tool to assess tumor microvasculature in glioblastoma (GBM). As fractal parameters of microvasculature have shown to be reliable quantitative biomarkers in brain tumors, there has been similar success in measuring the architecture of tumor tissue using FA in other tumor types. However, evaluating fractal parameters of tissue structure in relation to the microvasculature has not yet been implemented in GBM. We aimed to assess the utility of this methodology in quantifying structural characteristics of GBM cytoarchitecture and vascularity by correlating fractal parameters with gene expression. METHODS: Formalin-fixed paraffin-embedded specimens were retrospectively collected from 43 patients following resection of a newly diagnosed GBM; 4 normal brain specimens were obtained from epilepsy surgeries as controls. Tumor samples were processed using FA employing a software-based box-counting method algorithm and custom messenger RNA expression assays. Fractal parameters were then correlated with clinical features, outcomes, and a panel of 92 genes associated with vascularity and angiogenesis. RESULTS: Statistical analysis demonstrated that fractal-based indices were not adequate parameters for distinction of GBM cytoarchitecture compared with normal brain specimens. Correlation analysis of our gene expression findings suggested that hematoxylin and eosin-based FA may have adequate sensitivity to detect associations with vascular gene expression. CONCLUSIONS: The combination of neuropathological assessment and histology does not provide optimized data for FA in GBM. However, an association between FA and gene expression in GBM of genes pertaining to cytoarchitecture and angiogenesis warrants further investigation.

Retrospective comparison of motor and somatosensory MEG mapping-Considerations for better clinical applications.

While magnetoencephalography (MEG) has proven to be a valuable and reliable tool for presurgical functional mapping of eloquent cortices for at least two decades, widespread use of this technique by clinicians has remained elusive. This modest application may be attributable, at least in part, to misunderstandings regarding the success rate of such mapping procedures, as well as the primary sources contributing to mapping failures. To address this, we conducted a retrospective comparison of sensorimotor functional mapping success rates in 141 patients with epilepsy and 75 tumor patients from the Center for MEG in Omaha, NE. Neurosurgical candidates either completed motor mapping (i.e., finger tapping paradigm), somatosensory mapping (i.e., peripheral stimulation paradigm), or both motor and somatosensory protocols during MEG. All MEG data underwent subsequent time-domain averaging and source localization of left and right primary motor (M1) and somatosensory (S1) cortices was conducted using a single equivalent dipole model. Successful mapping was determined based on dipole goodness of fit metrics âˆ¼ 95%, as well as an accurate and conceivable spatial correspondence to precentral and postcentral gyri for M1 and S1, respectively. Our results suggest that mapping M1 in epilepsy and tumor patients was on average 94.5% successful, when patients only completed motor mapping protocols. In contrast, mapping S1 was successful 45-100% of the time in these patient groups when they only completed somatosensory mapping paradigms. Importantly, Z-tests for independent proportions revealed that the percentage of successful S1 mappings significantly increased to âˆ¼ 94% in epilepsy patients who completed both motor/somatosensory mapping protocols during MEG. Together, these data suggest that ordering more comprehensive mapping procedures (e.g., both motor and somatosensory protocols for a collective sensorimotor network) may substantially increase the accuracy of presurgical functional mapping by providing more extensive data from which to base interpretations. Moreover, clinicians and magnetoencephalographers should be considerate of the major contributors to mapping failures (i.e., low SNR, excessive motion and magnetic artifacts) in order to further increase the percentage of cases achieving successful mapping of eloquent cortices.

Structural Brain Imaging Predicts Individual-Level Task Activation Maps Using Deep Learning.

Accurate individual functional mapping of task activations is a potential tool for biomarker discovery and is critically important for clinical care. While structural imaging does not directly map task activation, we hypothesized that structural imaging contains information that can accurately predict variations in task activation between individuals. To this end, we trained a convolutional neural network to use structural imaging (T1-weighted, T2-weighted, and diffusion tensor imaging) to predict 47 different functional MRI task activation volumes across seven task domains. The U-Net model was trained on 591 subjects and then subsequently tested on 122 unrelated subjects. The predicted activation maps correlated more strongly with their actual maps than with the maps of the other test subjects. An ablation study revealed that a model using the shape of the cortex alone or the shape of the subcortical matter alone was sufficient to predict individual-level differences in task activation maps, but a model using the shape of the whole brain resulted in markedly decreased performance. The ablation study also showed that the additional information provided by the T2-weighted and diffusion tensor imaging strengthened the predictions as compared to using the T1-weighted imaging alone. These results indicate that structural imaging contains information that is predictive of inter-subject variability in task activation mapping and that cortical folding patterns, as well as microstructural features, could be a key component to linking brain structure to brain function.

AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design.

The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. The codes can be found at https://github.com/Jianningli/tmi.

Accuracy analysis of fMRI and MEG activations determined by intraoperative mapping.

OBJECTIVE: By looking at how the accuracy of preoperative brain mapping methods vary according to differences in the distance from the activation clusters used for the analysis, the present study aimed to elucidate how preoperative functional neuroimaging may be used in such a way that maximizes the mapping accuracy. METHODS: The eloquent function of 19 patients with a brain tumor or cavernoma was mapped prior to resection with both functional MRI (fMRI) and magnetoencephalography (MEG). The mapping results were then validated using direct cortical stimulation mapping performed immediately after craniotomy and prior to resection. The subset of patients with equivalent MEG and fMRI tasks performed for motor (n = 14) and language (n = 12) were evaluated as both individual and combined predictions. Furthermore, the distance resulting in the maximum accuracy, as evaluated by the J statistic, was determined by plotting the sensitivities and specificities against a linearly increasing distance threshold. RESULTS: fMRI showed a maximum mapping accuracy at 5 mm for both motor and language mapping. MEG showed a maximum mapping accuracy at 40 mm for motor and 15 mm for language mapping. At the standard 10-mm distance used in the literature, MEG showed a greater specificity than fMRI for both motor and language mapping but a lower sensitivity for motor mapping. Combining MEG and fMRI showed a maximum accuracy at 15 mm and 5 mm-MEG and fMRI distances, respectively-for motor mapping and at a 10-mm distance for both MEG and fMRI for language mapping. For motor mapping, combining MEG and fMRI at the optimal distances resulted in a greater accuracy than the maximum accuracy of the individual predictions. CONCLUSIONS: This study demonstrates that the accuracy of language and motor mapping for both fMRI and MEG is heavily dependent on the distance threshold used in the analysis. Furthermore, combining MEG and fMRI showed the potential for increased motor mapping accuracy compared to when using the modalities separately.Clinical trial registration no.: NCT01535430 (clinicaltrials.gov).

Post-operative perfusion and diffusion MR imaging and tumor progression in high-grade gliomas.

PURPOSE: Perfusion and diffusion magnetic resonance imaging (MRI) provide important biomarkers for brain tumor analysis. Our aim was to investigate if regions of increased perfusion or tumor with restricted diffusion on the immediate post-operative MRI examination would be predictive of time to tumor progression in patients with high-grade gliomas. MATERIALS AND METHODS: Twenty-three patients with high-grade gliomas were retrospectively analyzed. We measured the perfusion at the resection area and evaluated the presence or absence of the restricted diffusion in residual tumor masses. The associations of the perfusion, diffusion and contrast enhancement (delayed static enhancement (DSE)) characteristics with time to tumor progression were statistically calculated. We also evaluated if the location of the tumor progression was concordant to the areas of the elevated perfusion, tumor type restricted diffusion and enhancement. RESULTS: Patients with >200 days to progression are more likely to have no elevated relative cerebral blood volume (rCBV) ratio (p = 0.0004), no tumor restriction (p = 0.024), and no DSE (p = 0.052). The elevated mean rCBV ratio (p<0.001) and tumor type restricted diffusion (p = 0.002) were significantly associated with a higher risk of progression. All cases with rCBV ratio of >1.5 progressed in 275 days or earlier. Tumors tended to progress at the area where patients with post-operative MRIs showed elevated perfusion (p = 0.006), tumor-type restricted diffusion (p = 0.005) and DSE (p = 0.008). CONCLUSIONS: Post-operative analysis of rCBV, tumor type restricted diffusion and enhancement characteristics are predictive of time to progression, risk of progression and where tumor progression is likely to occur.

Comprehensive genetic alteration profiling in primary and recurrent glioblastoma.

INTRODUCTION: Glioblastoma (GBM) is heterogeneous and underlying genomic profiles influence evolution, resistance, and therapeutic responses. While extensive knowledge regarding genomic profiling of primary GBM exists, there remains a lack of understanding of genomic differences in recurrent GBM. METHODS: We used the FoundationOne® comprehensive genomic profiling assay (CGP) to analyze ten matched primary and recurrent GBM. Genomic alterations (GA) were compared to the cancer database Catalogue of Somatic Mutations in Cancer (COSMIC). RESULTS: All matched tumor pairs demonstrated differences in GA between the primary and recurrence including one resected without any intervening therapy. This suggests that time and/or therapeutic intervention contribute to GA. Although mutations were common to both the primary and recurrence, the percent reads varied substantially suggesting clonal expansions and contractions. For example, EGFR mutations were significantly expanded in three patients, and CNAs were increased in two patients at recurrence. Four genes that were commonly altered in both primary and recurrent GBM were more prevalent in our cohort than reported in COSMIC: CDKN2A (86% vs. 53%) and CDKN2B (86% vs. 54%) deletions, EGFR activating mutation (52% vs. 10%) or amplification (81% vs. 45%), and TERT mutation (95% vs. 51%). Lastly, PI3K pathway activating mutations were also commonly seen in our cohort (67%). CONCLUSIONS: CGP revealed that GA identified in GBM changed over time and with treatment. Mutations in TERT, CDKN2A/CDKN2B, EGFR, and PI3K pathway were commonly observed in both primary and recurrent GBM revealing their prognostic and therapeutic potential. This may have important implications for individualized therapies and needs further evaluation.

Assessment of vascularity in glioblastoma and its implications on patient outcomes.

There is little data on why glioblastomas (GBM) hemorrhage and how it may affect patient outcomes. The aim of this study was to investigate the mechanisms of hemorrhage in glioblastoma by examining molecular and genetic features by immunohistochemistry (IHC) and mRNA expression profiles in association with imaging and clinical outcomes. An observational retrospective cohort analysis was performed on 43 FFPE GBM tissue samples. MR images were assessed for the presence of hemorrhage and extent of resection. Specimens were examined for CD34 and CD105 expression using IHC. Tumor mRNA expression profiles were analyzed for 92 genes related to angiogenesis and vascularity. Forty-three specimens were analyzed, and 20 showed signs of hemorrhage, 23 did not. The average OS for patients with GBM with hemorrhage was 19.12 months (95% CI 10.39-27.84), versus 13.85 months (95% CI 8.85-18.85) in those without hemorrhage (p > 0.05). Tumors that hemorrhaged had higher IHC staining for CD34 and CD105. mRNA expression analysis revealed tumor hemorrhage was associated with increased expression of HIF1α and MDK, and decreased expression of F3. Hemorrhage in GBM was not associated with worsened OS. Increased expression of angiogenic factors and increased CD34 and CD105 IHC staining in tumors with hemorrhage suggests that increased hypoxia-induced angiogenesis and vessel density may play a role in glioblastoma hemorrhage. Characterizing tumors that are prone to hemorrhage and mechanisms behind the development of these hemorrhages may provide insights that can lead to the development of targeted, individualized therapies for glioblastoma.

Multifocal histologically malignant Epstein-Barr virus-associated smooth muscle tumor in a pediatric transplant patient with an indolent course.

Epstein-Barr virus-associated smooth muscle tumors (EBV-SMTs) are rare lesions that occur in immunocompromised patients. Dural involvement appears to be less common in organ transplant recipients than in HIV patients. Due to the paucity of reported cases following organ transplantation, the natural history of these lesions is unclear. We describe an 8-year-old female who presented with adrenal, small bowel, and intracranial tumors 6 years following renal transplantation. Histopathological analysis revealed a highly cellular, mitotically active, smooth muscle neoplasm without necrosis. The tumor stained diffusely for smooth muscle actin and myosin. In situ hybridization for EBV-encoded RNA was diffusely positive. Following gross total resection, antiviral therapy, and a reduction in immunosuppression, the patient is tumor-free at 3 years follow-up. In patients with compromised immune systems, it is important to recognize this unique form of SMT because, even when there are multiple lesions, the prognosis may be excellent.

Atypical presentation of primary central nervous system non-Hodgkin lymphoma in immunocompetent young adults.

OBJECTIVE: Primary central nervous system non-Hodgkin lymphoma (PCNSL) is a malignant lymphoma limited to the cranial-spinal axis in the absence of systemic lymphoma. Historically, PCNSL accounts for fewer than 5% of all cases of primary intracranial neoplasms. PCNSL is rare in immunocompetent young adults. Although the prognosis for PCNSL is poor, approximately 20%-30% percent of cases achieve a cure. METHODS: We report two cases of PCNSL originating in the ventricle in otherwise healthy immunocompetent young adults. RESULTS: A 27-year-old man presented with 10 days of nausea, vomiting, and headache and was found to have a large intraventricular mass emanating from the choroid plexus with resultant hydrocephalus. He underwent placement of external ventricular drain and systemic and intrathecal chemotherapy for cytologically proven PCNSL. A 31-year-old pregnant woman presented with headaches, vision difficulties, and ataxia and was found to have a septum pellucidum mass. She underwent craniotomy and subtotal resection of the mass with subsequent systemic therapy and whole brain radiation for treatment of PCNSL. CONCLUSIONS: To our knowledge, this is the first report of primary CNS lymphoma of the choroid plexus and septum pellucidum in otherwise healthy, immunocompetent young adults.

Potential role for magnetoencephalography in distinguishing low- and high-grade gliomas: a preliminary study with histopathological confirmation.

Gliomas are the most common form of tumor in the CNS and are exceptionally heterogeneous. Accurately characterizing gliomas, in terms of grade and type, is essential for predicting the rate of tumor progression. Histopathological grading and analysis based on biopsied tissue remains the gold standard, but non- and semi-invasive neuroimaging also plays a key role. Neuroimaging has been used to guide and optimize biopsies for several decades, but more recently molecular imaging and variants of MRI have shown promise in independently predicting glioma grade. Here we evaluated whether magnetoencephalographic (MEG) measurements of population-level physiology within the glioma space were predictive of the inherent grade of the tissue, based on definitive histopathological analyses. High-density MEG data were recorded from 11 patients who were undergoing functional mapping in preparation for resective surgery. The primary results indicated that glioma grade was positively correlated with the local amplitude of activity within the glioma space in the theta (4-7 Hz), alpha (8-14 Hz), and beta bands (14-30 Hz). Additionally, activity within the glioma was significantly elevated relative to the nonaffected homologue area in the same frequency bands. These results indicate that pathological levels of synchronization exist within the tumor space and that MEG may be a viable tool for noninvasively differentiating gliomas by their grade. Although these results should be considered preliminary and are only correlative in nature, these data suggest that MEG can potentially detect neurophysiological signatures or markers that predict the inherent grade of a glial tumor.

Surgical management of unknown primary tumors metastatic to the spine.

OBJECT: Patients presenting with spinal metastases from unknown primary tumors (UPTs) are rare. The authors reviewed their surgical experience to evaluate outcomes and identify predictors of survival in these patients. METHODS: This study is a retrospective analysis of patients undergoing surgery for metastatic spine disease from UPTs between June 1993 and February 2007 at The University of Texas M. D. Anderson Cancer Center. RESULTS: Fifty-one patients undergoing 52 surgical procedures were identified. The median age at spine surgery was 60 years. The median survival from time of diagnosis was 15.8 months (95% CI 8.1-23.6) and it was 8.1 months (95% CI 1.6-14.7) from time of spine surgery. Postoperative neurological function (Frankel score) was the same or improved in 94% of patients. At presentation, 77% had extraspinal disease, which was associated with poorer survival (6.4 vs 18.1 months; p = 0.041). Multiple sites (vs a single site) of spine disease did not impact survival (12.7 vs 8.7 months; p = 0.50). Patients with noncervical spinal disease survived longer than those with cervical disease (11.8 vs 6.4 months, respectively; p = 0.029). Complete versus incomplete resection at index surgery had no impact on survival duration (p > 0.5) or local recurrence (p = 1.0). Identification of a primary cancer was achieved in 31% of patients. CONCLUSIONS: This is the first reported surgical series of patients with an unknown source of spinal metastases. The authors found that multiple sites of spinal disease did not influence survival; however, the presence of extraspinal disease had a negative impact. The extent of resection had no effect on survival duration or local recurrence. With an overall median survival of 8.1 months following surgery, aggressive evaluation and treatment of patients with metastatic disease of the spine from an unknown primary source is warranted.

Convection perfusion of glucocerebrosidase for neuronopathic Gaucher's disease.

Systemic enzyme replacement for Gaucher's disease has not prevented premature death or severe morbidity in patients with a neuronopathic phenotype, because the enzyme does not cross the blood-brain barrier. We used convection-enhanced delivery for regional distribution of glucocerebrosidase in rat and primate brains and examined its safety and feasibility for neuronopathic Gaucher's disease. Rats underwent intrastriatal infusion and were observed and then sacrificed at 14 hours, 4 days, or 6 weeks. Primates underwent serial magnetic resonance imaging during enzyme perfusion of the right frontal lobe or brainstem, were observed and then sacrificed after infusion completion. Animals underwent histologic and enzymatic tissue analyses. Magnetic resonance imaging revealed perfusion of the primate right frontal lobe or pons with infusate. Enzyme activity was substantially and significantly (p < 0.05) increased in cortex and white matter of the infused frontal lobe and pons compared to control. Immunohistochemistry demonstrated intraneuronal glucocerebrosidase. There was no toxicity. Convection-enhanced delivery can be used to safely perfuse large regions of the brain and brainstem with therapeutic levels of glucocerebrosidase. Patients with neuronopathic Gaucher's disease and similar central nervous system disorders may benefit from this treatment.