Activation of CD4 T cells during prime immunization determines the success of a therapeutic hepatitis B vaccine in HBV-carrier mouse models.

BACKGROUND & AIMS: We recently developed a heterologous therapeutic vaccination scheme (TherVacB) comprising a particulate protein prime followed by a modified vaccinia-virus Ankara (MVA)-vector boost for the treatment of HBV. However, the key determinants required to overcome HBV-specific immune tolerance remain unclear. Herein, we aimed to study new combination adjuvants and unravel factors that are essential for the antiviral efficacy of TherVacB. METHODS: Recombinant hepatitis B surface and core antigen (HBsAg and HBcAg) particles were formulated with different liposome- or oil-in-water emulsion-based combination adjuvants containing saponin QS21 and monophosphoryl lipid A; these formulations were compared to STING-agonist c-di-AMP and conventional aluminium hydroxide formulations. Immunogenicity and the antiviral effects of protein antigen formulations and the MVA-vector boost within TherVacB were evaluated in adeno-associated virus-HBV-infected and HBV-transgenic mice. RESULTS: Combination adjuvant formulations preserved HBsAg and HBcAg integrity for ≥12 weeks, promoted human and mouse dendritic cell activation and, within TherVacB, elicited robust HBV-specific antibody and T-cell responses in wild-type and HBV-carrier mice. Combination adjuvants that prime a balanced HBV-specific type 1 and 2 T helper response induced high-titer anti-HBs antibodies, cytotoxic T-cell responses and long-term control of HBV. In the absence of an MVA-vector boost or following selective CD8 T-cell depletion, HBsAg still declined (mediated mainly by anti-HBs antibodies) but HBV replication was not controlled. Selective CD4 T-cell depletion during the priming phase of TherVacB resulted in a complete loss of vaccine-induced immune responses and its therapeutic antiviral effect in mice. CONCLUSIONS: Our results identify CD4 T-cell activation during the priming phase of TherVacB as a key determinant of HBV-specific antibody and CD8 T-cell responses. IMPACT AND IMPLICATIONS: Therapeutic vaccination is a potentially curative treatment option for chronic hepatitis B. However, it remains unclear which factors are essential for breaking immune tolerance in HBV carriers and determining successful outcomes. Our study provides the first direct evidence that efficient priming of HBV-specific CD4 T cells determines the success of therapeutic hepatitis B vaccination in two preclinical HBV-carrier mouse models. Applying an optimal formulation of HBV antigens that activates CD4 and CD8 T cells during prime immunization provided the foundation for an antiviral effect of therapeutic vaccination, while depletion of CD4 T cells led to a complete loss of vaccine-induced antiviral efficacy. Boosting CD8 T cells was important to finally control HBV in these mouse models. Our findings provide important insights into the rational design of therapeutic vaccines for the cure of chronic hepatitis B.

AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study.

OBJECTIVES: Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence. METHODS: We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence. RESULTS: The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores. CONCLUSION: This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence. KEY POINTS: • AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence. • The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.

Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning.

BACKGROUND: Precise volumetric assessment of brain tumors is relevant for treatment planning and monitoring. However, manual segmentations are time-consuming and impeded by intra- and interrater variabilities. PURPOSE: To investigate the performance of a deep-learning model (DLM) to automatically detect and segment primary central nervous system lymphoma (PCNSL) on clinical MRI. STUDY TYPE: Retrospective. POPULATION: Sixty-nine scans (at initial and/or follow-up imaging) from 43 patients with PCNSL referred for clinical MRI tumor assessment. FIELD STRENGTH/SEQUENCE: T1 -/T2 -weighted, T1 -weighted contrast-enhanced (T1 CE), and FLAIR at 1.0, 1.5, and 3.0T from different vendors and study centers. ASSESSMENT: Fully automated voxelwise segmentation of tumor components was performed using a 3D convolutional neural network (DeepMedic) trained on gliomas (n = 220). DLM segmentations were compared to manual segmentations performed in a 3D voxelwise manner by two readers (radiologist and neurosurgeon; consensus reading) from T1 CE and FLAIR, which served as the reference standard. STATISTICAL TESTS: Dice similarity coefficient (DSC) for comparison of spatial overlap with the reference standard, Pearson's correlation coefficient (r) to assess the relationship between volumetric measurements of segmentations, and Wilcoxon rank-sum test for comparison of DSCs obtained in initial and follow-up imaging. RESULTS: The DLM detected 66 of 69 PCNSL, representing a sensitivity of 95.7%. Compared to the reference standard, DLM achieved good spatial overlap for total tumor volume (TTV, union of tumor volume in T1 CE and FLAIR; average size 77.16 ± 62.4 cm3 , median DSC: 0.76) and tumor core (contrast enhancing tumor in T1 CE; average size: 11.67 ± 13.88 cm3 , median DSC: 0.73). High volumetric correlation between automated and manual segmentations was observed (TTV: r = 0.88, P < 0.0001; core: r = 0.86, P < 0.0001). Performance of automated segmentations was comparable between pretreatment and follow-up scans without significant differences (TTV: P = 0.242, core: P = 0.177). DATA CONCLUSION: In clinical MRI scans, a DLM initially trained on gliomas provides segmentation of PCNSL comparable to manual segmentation, despite its complex and multifaceted appearance. Segmentation performance was high in both initial and follow-up scans, suggesting its potential for application in longitudinal tumor imaging. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the most common tumor entity spreading to the brain and up to 50% of patients develop brain metastases (BMs). Detection of BMs on MRI is challenging with an inherent risk of missed diagnosis. PURPOSE: To train and evaluate a deep learning model (DLM) for fully automated detection and 3D segmentation of BMs in NSCLC on clinical routine MRI. STUDY TYPE: Retrospective. POPULATION: Ninety-eight NSCLC patients with 315 BMs on pretreatment MRI, divided into training (66 patients, 248 BMs) and independent test (17 patients, 67 BMs) and control (15 patients, 0 BMs) cohorts. FIELD STRENGTH/SEQUENCE: T1 -/T2 -weighted, T1 -weighted contrast-enhanced (T1 CE; gradient-echo and spin-echo sequences), and FLAIR at 1.0, 1.5, and 3.0 T from various vendors and study centers. ASSESSMENT: A 3D convolutional neural network (DeepMedic) was trained on the training cohort using 5-fold cross-validation and evaluated on the independent test and control sets. Three-dimensional voxel-wise manual segmentations of BMs by a neurosurgeon and a radiologist on T1 CE served as the reference standard. STATISTICAL TESTS: Sensitivity (recall) and false positive (FP) findings per scan, dice similarity coefficient (DSC) to compare the spatial overlap between manual and automated segmentations, Pearson's correlation coefficient (r) to evaluate the relationship between quantitative volumetric measurements of segmentations, and Wilcoxon rank-sum test to compare the volumes of BMs. A P value <0.05 was considered statistically significant. RESULTS: In the test set, the DLM detected 57 of the 67 BMs (mean volume: 0.99 ± 4.24 cm3 ), resulting in a sensitivity of 85.1%, while FP findings of 1.5 per scan were observed. Missed BMs had a significantly smaller volume (0.05 ± 0.04 cm3 ) than detected BMs (0.96 ± 2.4 cm3 ). Compared with the reference standard, automated segmentations achieved a median DSC of 0.72 and a good volumetric correlation (r = 0.95). In the control set, 1.8 FPs/scan were observed. DATA CONCLUSION: Deep learning provided a high detection sensitivity and good segmentation performance for BMs in NSCLC on heterogeneous scanner data while yielding a low number of FP findings. Level of Evidence 3 Technical Efficacy Stage 2.

Deep learning assistance increases the detection sensitivity of radiologists for secondary intracranial aneurysms in subarachnoid hemorrhage.

PURPOSE: To evaluate whether a deep learning model (DLM) could increase the detection sensitivity of radiologists for intracranial aneurysms on CT angiography (CTA) in aneurysmal subarachnoid hemorrhage (aSAH). METHODS: Three different DLMs were trained on CTA datasets of 68 aSAH patients with 79 aneurysms with their outputs being combined applying ensemble learning (DLM-Ens). The DLM-Ens was evaluated on an independent test set of 104 aSAH patients with 126 aneuryms (mean volume 129.2 ± 185.4 mm3, 13.0% at the posterior circulation), which were determined by two radiologists and one neurosurgeon in consensus using CTA and digital subtraction angiography scans. CTA scans of the test set were then presented to three blinded radiologists (reader 1: 13, reader 2: 4, and reader 3: 3 years of experience in diagnostic neuroradiology), who assessed them individually for aneurysms. Detection sensitivities for aneurysms of the readers with and without the assistance of the DLM were compared. RESULTS: In the test set, the detection sensitivity of the DLM-Ens (85.7%) was comparable to the radiologists (reader 1: 91.2%, reader 2: 86.5%, and reader 3: 86.5%; Fleiss κ of 0.502). DLM-assistance significantly increased the detection sensitivity (reader 1: 97.6%, reader 2: 97.6%,and reader 3: 96.0%; overall P=.024; Fleiss κ of 0.878), especially for secondary aneurysms (88.2% of the additional aneurysms provided by the DLM). CONCLUSION: Deep learning significantly improved the detection sensitivity of radiologists for aneurysms in aSAH, especially for secondary aneurysms. It therefore represents a valuable adjunct for physicians to establish an accurate diagnosis in order to optimize patient treatment.

Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI.

OBJECTIVES: Magnetic resonance imaging (MRI) is the method of choice for imaging meningiomas. Volumetric assessment of meningiomas is highly relevant for therapy planning and monitoring. We used a multiparametric deep-learning model (DLM) on routine MRI data including images from diverse referring institutions to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations. METHODS: We included 56 of 136 consecutive preoperative MRI datasets [T1/T2-weighted, T1-weighted contrast-enhanced (T1CE), FLAIR] of meningiomas that were treated surgically at the University Hospital Cologne and graded histologically as tumour grade I (n = 38) or grade II (n = 18). The DLM was trained on an independent dataset of 249 glioma cases and segmented different tumour classes as defined in the brain tumour image segmentation benchmark (BRATS benchmark). The DLM was based on the DeepMedic architecture. Results were compared to manual segmentations by two radiologists in a consensus reading in FLAIR and T1CE. RESULTS: The DLM detected meningiomas in 55 of 56 cases. Further, automated segmentations correlated strongly with manual segmentations: average Dice coefficients were 0.81 ± 0.10 (range, 0.46-0.93) for the total tumour volume (union of tumour volume in FLAIR and T1CE) and 0.78 ± 0.19 (range, 0.27-0.95) for contrast-enhancing tumour volume in T1CE. CONCLUSIONS: The DLM yielded accurate automated detection and segmentation of meningioma tissue despite diverse scanner data and thereby may improve and facilitate therapy planning as well as monitoring of this highly frequent tumour entity. KEY POINTS: • Deep learning allows for accurate meningioma detection and segmentation • Deep learning helps clinicians to assess patients with meningiomas • Meningioma monitoring and treatment planning can be improved.

T Cells Engineered to Express a T-Cell Receptor Specific for Glypican-3 to Recognize and Kill Hepatoma Cells In Vitro and in Mice.

BACKGROUND & AIMS: Cancer therapies are being developed based on our ability to direct T cells against tumor antigens. Glypican-3 (GPC3) is expressed by 75% of all hepatocellular carcinomas (HCC), but not in healthy liver tissue or other organs. We aimed to generate T cells with GPC3-specific receptors that recognize HCC and used them to eliminate GPC3-expressing xenograft tumors grown from human HCC cells in mice. METHODS: We used mass spectrometry to obtain a comprehensive peptidome from GPC3-expressing hepatoma cells after immune-affinity purification of human leukocyte antigen (HLA)-A2 and bioinformatics to identify immunodominant peptides. To circumvent GPC3 tolerance resulting from fetal expression, dendritic cells from HLA-A2-negative donors were cotransfected with GPC3 and HLA-A2 RNA to stimulate and expand antigen-specific T cells. RESULTS: Peptide GPC3367 was identified as a predominant peptide on HLA-A2. We used A2-GPC3367 multimers to detect, select for, and clone GPC3-specific T cells. These clones bound the A2-GPC3367 multimer and secreted interferon-γ when cultured with GPC3367, but not with control peptide-loaded cells. By genomic sequencing of these T-cell clones, we identified a gene encoding a dominant T-cell receptor. The gene was cloned and the sequence was codon optimized and expressed from a retroviral vector. Primary CD8(+) T cells that expressed the transgenic T-cell receptor specifically bound GPC3367 on HLA-A2. These T cells killed GPC3-expressing hepatoma cells in culture and slowed growth of HCC xenograft tumors in mice. CONCLUSIONS: We identified a GPC3367-specific T-cell receptor. Expression of this receptor by T cells allows them to recognize and kill GPC3-positive hepatoma cells. This finding could be used to advance development of adoptive T-cell therapy for HCC.

Modified vaccinia virus Ankara-infected dendritic cells present CD4+ T-cell epitopes by endogenous major histocompatibility complex class II presentation pathways.

UNLABELLED: CD4(+) T lymphocytes play a central role in the immune system and mediate their function after recognition of their respective antigens presented on major histocompatibility complex II (MHCII) molecules on antigen-presenting cells (APCs). Conventionally, phagocytosed antigens are loaded on MHCII for stimulation of CD4(+) T cells. Certain epitopes, however, can be processed directly from intracellular antigens and are presented on MHCII (endogenous MHCII presentation). Here we characterized the MHCII antigen presentation pathways that are possibly involved in the immune response upon vaccination with modified vaccinia virus Ankara (MVA), a promising live viral vaccine vector. We established CD4(+) T-cell lines specific for MVA-derived epitopes as tools for in vitro analysis of MHCII antigen processing and presentation in MVA-infected APCs. We provide evidence that infected APCs are able to directly transfer endogenous viral proteins into the MHCII pathway to efficiently activate CD4(+) T cells. By using knockout mice and chemical inhibitory compounds, we further elucidated the molecular basis, showing that among the various subcellular pathways investigated, proteasomes and autophagy are key players in the endogenous MHCII presentation during MVA infection. Interestingly, although proteasomal processing plays an important role, neither TAP nor LAMP-2 was found to be involved in the peptide transport. Defining the molecular mechanism of MHCII presentation during MVA infection provides a basis for improving MVA-based vaccination strategies by aiming for enhanced CD4(+) T-cell activation by directing antigens into the responsible pathways. IMPORTANCE: This work contributes significantly to our understanding of the immunogenic properties of pathogens by deciphering antigen processing pathways contributing to efficient activation of antigen-specific CD4(+) T cells. We identified autophagosome formation, proteasomal activity, and lysosomal integrity as being crucial for endogenous CD4(+) T-cell activation. Since poxvirus vectors such as MVA are already used in clinical trials as recombinant vaccines, the data provide important information for the future design of optimized poxviral vaccines for the study of advanced immunotherapy options.

Construction and comparative evaluation of different activity detection methods in brain FDG-PET.

AIM: We constructed and evaluated reference brain FDG-PET databases for usage by three software programs (Computer-aided diagnosis for dementia (CAD4D), Statistical Parametric Mapping (SPM) and NEUROSTAT), which allow a user-independent detection of dementia-related hypometabolism in patients' brain FDG-PET. METHODS: Thirty-seven healthy volunteers were scanned in order to construct brain FDG reference databases, which reflect the normal, age-dependent glucose consumption in human brain, using either software. Databases were compared to each other to assess the impact of different stereotactic normalization algorithms used by either software package. In addition, performance of the new reference databases in the detection of altered glucose consumption in the brains of patients was evaluated by calculating statistical maps of regional hypometabolism in FDG-PET of 20 patients with confirmed Alzheimer's dementia (AD) and of 10 non-AD patients. Extent (hypometabolic volume referred to as cluster size) and magnitude (peak z-score) of detected hypometabolism was statistically analyzed. RESULTS: Differences between the reference databases built by CAD4D, SPM or NEUROSTAT were observed. Due to the different normalization methods, altered spatial FDG patterns were found. When analyzing patient data with the reference databases created using CAD4D, SPM or NEUROSTAT, similar characteristic clusters of hypometabolism in the same brain regions were found in the AD group with either software. However, larger z-scores were observed with CAD4D and NEUROSTAT than those reported by SPM. Better concordance with CAD4D and NEUROSTAT was achieved using the spatially normalized images of SPM and an independent z-score calculation. The three software packages identified the peak z-scores in the same brain region in 11 of 20 AD cases, and there was concordance between CAD4D and SPM in 16 AD subjects. CONCLUSION: The clinical evaluation of brain FDG-PET of 20 AD patients with either CAD4D-, SPM- or NEUROSTAT-generated databases from an identical reference dataset showed similar patterns of hypometabolism in the brain regions known to be involved in AD. The extent of hypometabolism and peak z-score appeared to be influenced by the calculation method used in each software package rather than by different spatial normalization parameters.

Cardiopulmonary dysfunction in the Osteogenesis imperfecta mouse model Aga2 and human patients are caused by bone-independent mechanisms.

Osteogenesis imperfecta (OI) is an inherited connective tissue disorder with skeletal dysplasia of varying severity, predominantly caused by mutations in the collagen I genes (COL1A1/COL1A2). Extraskeletal findings such as cardiac and pulmonary complications are generally considered to be significant secondary features. Aga2, a murine model for human OI, was systemically analyzed in the German Mouse Clinic by means of in vivo and in vitro examinations of the cardiopulmonary system, to identify novel mechanisms accounting for perinatal lethality. Pulmonary and, especially, cardiac fibroblast of perinatal lethal Aga2/+ animals display a strong down-regulation of Col1a1 transcripts in vivo and in vitro, resulting in a loss of extracellular matrix integrity. In addition, dysregulated gene expression of Nppa, different types of collagen and Agt in heart and lung tissue support a bone-independent vicious cycle of heart dysfunction, including hypertrophy, loss of myocardial matrix integrity, pulmonary hypertension, pneumonia and hypoxia leading to death in Aga2. These murine findings are corroborated by a pediatric OI cohort study, displaying significant progressive decline in pulmonary function and restrictive pulmonary disease independent of scoliosis. Most participants show mild cardiac valvular regurgitation, independent of pulmonary and skeletal findings. Data obtained from human OI patients and the mouse model Aga2 provide novel evidence for primary effects of type I collagen mutations on the heart and lung. The findings will have potential benefits of anticipatory clinical exams and early intervention in OI patients.

Motion Artifact Detection for T1-Weighted Brain MR Images Using Convolutional Neural Networks.

Quality assessment (QA) of magnetic resonance imaging (MRI) encompasses several factors such as noise, contrast, homogeneity, and imaging artifacts. Quality evaluation is often not standardized and relies on the expertise, and vigilance of the personnel, posing limitations especially with large datasets. Machine learning based on convolutional neural networks (CNNs) is a promising approach to address these challenges by performing automated inspection of MR images. In this study, a CNN for the detection of random head motion artifacts (RHM) in T1-weighted MRI as one aspect of image quality is proposed. A two-step approach aimed to first identify images exhibiting pronounced motion artifacts, and second to evaluate the feasibility of a more detailed three-class classification. The utilized dataset consisted of 420 T1-weighted whole-brain image volumes with isotropic resolution. Human experts assigned each volume to one of three classes of artifact prominence. Results demonstrate an accuracy of 95% for the identification of images with pronounced artifact load. The addition of an intermediate class retained an accuracy of 76%. The findings highlight the potential of CNN-based approaches to increase the efficiency of post-hoc QAs in large datasets by flagging images with potentially relevant artifact loads for closer inspection.

Voxel-based classification of FDG PET in dementia using inter-scanner normalization.

Statistical mapping of FDG PET brain images has become a common tool in differential diagnosis of patients with dementia. We present a voxel-based classification system of neurodegenerative dementias based on partial least squares (PLS). Such a classifier relies on image databases of normal controls and dementia cases as training data. Variations in PET image characteristics can be expected between databases, for example due to differences in instrumentation, patient preparation, and image reconstruction. This study evaluates (i) the impact of databases from different scanners on classification accuracy and (ii) a method to improve inter-scanner classification. Brain FDG PET databases from three scanners (A, B, C) at two clinical sites were evaluated. Diagnostic categories included normal controls (NC, nA=26, nB=20, nC=24 for each scanner respectively), Alzheimer's disease (AD, nA=44, nB=11, nC=16), and frontotemporal dementia (FTD, nA=13, nB=13, nC=5). Spatially normalized images were classified as NC, AD, or FTD using partial least squares. Supervised learning was employed to determine classifier parameters, whereby available data is sub-divided into training and test sets. Four different database setups were evaluated: (i) "in-scanner": training and test data from the same scanner, (ii) "x-scanner": training and test data from different scanners, (iii) "train other": train on both x-scanners, and (iv) "train all": train on all scanners. In order to moderate the impact of inter-scanner variations on image evaluation, voxel-by-voxel scaling was applied based on "ratio images". Good classification accuracy of on average 94% was achieved for the in-scanner setups. Accuracy deteriorated for setups with mismatched scanners (79-91%). Ratio-image normalization improved all results with mismatched scanners (85-92%). In conclusion, automatic classification of individual FDG PET in differential diagnosis of dementia is feasible. Accuracy can vary with respect to scanner or acquisition characteristics of the training image data. The adopted approach of ratio-image normalization has been demonstrated to effectively moderate these effects.

In vitro analysis of bone phenotypes in Col1a1 and Jagged1 mutant mice using a standardized osteoblast cell culture system.

The mouse is a valuable model organism for studying bone biology and for unravelling pathological processes in skeletal disorders. In vivo methods like X-ray analysis, DXA measurements, pQCT and µCT are available to investigate the bone phenotype of mutant mice. However, the descriptive nature of such methods does not provide insights into the cellular and molecular bases of the observed bone alterations. Thus, first-line investigations might be complemented by cell culture-based methods to characterize the pathological processes at the cellular level independent from systemic influences. By combining well-established assays, we designed a comprehensive test system to investigate the cellular and molecular phenotype of primary calvarial osteoblasts in mutant mice compared to wild-type controls as a first-line phenotyping method. The compilation of 9 different quantifiable assays allows assessment of general properties of cell growth and investigation of bone-specific parameters at the functional, protein and RNA level in a kinetic fashion throughout a 3-week culture period, thus maximizing the chance to discover and explain new phenotypes in mutant mice. By analyzing mutant mouse lines for Col1a1 and Jag1 (Delta-Notch pathway) that both showed clear alterations in several bone-related parameters we could demonstrate the usefulness of our cell culture system to discriminate between primary (Col1a1) and secondary effects (Jag1) in osteoblasts.

Accuracy of Four Different CT Perfusion Thresholds for Ischemic Core Volume and Location Estimation Using IntelliSpace Portal.

Computed tomography perfusion (CTP) is frequently used in the triage of ischemic stroke patients for endovascular thrombectomy (EVT). We aimed to quantify the volumetric and spatial agreement of the CTP ischemic core estimated with different thresholds and follow-up MRI infarct volume on diffusion-weighted imaging (DWI). Patients treated with EVT between November 2017 and September 2020 with available baseline CTP and follow-up DWI were included. Data were processed with Philips IntelliSpace Portal using four different thresholds. Follow-up infarct volume was segmented on DWI. In 55 patients, the median DWI volume was 10 mL, and median estimated CTP ischemic core volumes ranged from 10-42 mL. In patients with complete reperfusion, the intraclass correlation coefficient (ICC) showed moderate-good volumetric agreement (range 0.55-0.76). A poor agreement was found for all methods in patients with successful reperfusion (ICC range 0.36-0.45). Spatial agreement (median Dice) was low for all four methods (range 0.17-0.19). Severe core overestimation was most frequently (27%) seen in Method 3 and patients with carotid-T occlusion. Our study shows moderate-good volumetric agreement between ischemic core estimates for four different thresholds and subsequent infarct volume on DWI in EVT-treated patients with complete reperfusion. The spatial agreement was similar to other commercially available software packages.

ER stress-mediated apoptosis in a new mouse model of osteogenesis imperfecta.

Osteogenesis imperfecta is an inherited disorder characterized by increased bone fragility, fractures, and osteoporosis, and most cases are caused by mutations affecting the type I collagen genes. Here, we describe a new mouse model for Osteogenesis imperfecta termed Aga2 (abnormal gait 2) that was isolated from the Munich N-ethyl-N-nitrosourea mutagenesis program and exhibited phenotypic variability, including reduced bone mass, multiple fractures, and early lethality. The causal gene was mapped to Chromosome 11 by linkage analysis, and a C-terminal frameshift mutation was identified in the Col1a1 (procollagen type I, alpha 1) gene as the cause of the disorder. Aga2 heterozygous animals had markedly increased bone turnover and a disrupted native collagen network. Further studies showed that abnormal proalpha1(I) chains accumulated intracellularly in Aga2/+ dermal fibroblasts and were poorly secreted extracellularly. This was associated with the induction of an endoplasmic reticulum stress-specific unfolded protein response involving upregulation of BiP, Hsp47, and Gadd153 with caspases-12 and -3 activation and apoptosis of osteoblasts both in vitro and in vivo. These studies resulted in the identification of a new model for Osteogenesis imperfecta, and identified a role for intracellular modulation of the endoplasmic reticulum stress-associated unfolded protein response machinery toward osteoblast apoptosis during the pathogenesis of disease.

Bone-Preserving Arthroplasty With Abductor Pollicis Longus Tenodesis for Trapeziometacarpal Osteoarthritis.

Purpose: The objective of this study was to describe an original method of bone-preserving arthroplasty with abductor pollicis longus (APL) tenodesis and determine its safety and effectiveness as a treatment for early-stage osteoarthritis of the trapeziometacarpal joint. Methods: Eleven patients underwent a trapezium-preserving arthroplasty with APL tenodesis for stage 1 and 2 osteoarthritis were retrospectively reviewed. This arthroplasty consisted of a distally-based APL tendon being passed through the trapeziometacarpal joint. The proximal end of the tendon was then pulled and passed through a drill hole made at the neck of the second metacarpal and sutured to itself. Thus, distraction of the first metacarpal and interposition of the tendon were created. Postoperative radiologic and clinical follow-up visits were performed at 4, 8, and 12 weeks. Range of motion and strength were assessed after surgery. Patient satisfaction and outcome were assessed, and the disabilities of the arm, shoulder, and hand (DASH) score was used. Results: After a mean follow-up of 29.5 months (range, 16-43 months), the mean patient visual analog scale pain score improved from 7.1 to 2.3. The average DASH score of all patients at the follow-up examination was 18.3 ± 19.8. Patients' mean grip strength was 25.3 kg, which represented 102% of the value on the contralateral side. The key-pinch strength was 6.2 kg on the operated hand compared with 6.5 kg on the contralateral side. The mean thumb opposition Kapandji index was 9.4, which was similar to that of the contralateral side. Three patients were very satisfied with the postoperative outcome and 3 patients were satisfied. Two patients were lost to follow-up, 1 patient did not consent to share her data, and 2 patients had to undergo trapeziectomy. Conclusions: Although a larger study population and a longer follow-up period are needed to draw conclusions, bone-preserving arthroplasty with APL tenodesis showed satisfying results in patients presenting with early-stage osteoarthritis. This method is technically simple and time-efficient, does not reduce the range of motion, and leaves open all other surgical options. Type of study/level of evidence: Therapeutic IV, Case Series.

Automated Meningioma Segmentation in Multiparametric MRI : Comparable Effectiveness of a Deep Learning Model and Manual Segmentation.

PURPOSE: Volumetric assessment of meningiomas represents a valuable tool for treatment planning and evaluation of tumor growth as it enables a more precise assessment of tumor size than conventional diameter methods. This study established a dedicated meningioma deep learning model based on routine magnetic resonance imaging (MRI) data and evaluated its performance for automated tumor segmentation. METHODS: The MRI datasets included T1-weighted/T2-weighted, T1-weighted contrast-enhanced (T1CE) and FLAIR of 126 patients with intracranial meningiomas (grade I: 97, grade II: 29). For automated segmentation, an established deep learning model architecture (3D deep convolutional neural network, DeepMedic, BioMedIA) operating on all four MR sequences was used. Segmentation included the following two components: (i) contrast-enhancing tumor volume in T1CE and (ii) total lesion volume (union of lesion volume in T1CE and FLAIR, including solid tumor parts and surrounding edema). Preprocessing of imaging data included registration, skull stripping, resampling, and normalization. After training of the deep learning model using manual segmentations by 2 independent readers from 70 patients (training group), the algorithm was evaluated on 56 patients (validation group) by comparing automated to ground truth manual segmentations, which were performed by 2 experienced readers in consensus. RESULTS: Of the 56 meningiomas in the validation group 55 were detected by the deep learning model. In these patients the comparison of the deep learning model and manual segmentations revealed average dice coefficients of 0.91 ± 0.08 for contrast-enhancing tumor volume and 0.82 ± 0.12 for total lesion volume. In the training group, interreader variabilities of the 2 manual readers were 0.92 ± 0.07 for contrast-enhancing tumor and 0.88 ± 0.05 for total lesion volume. CONCLUSION: Deep learning-based automated segmentation yielded high segmentation accuracy, comparable to manual interreader variability.

Advanced Deep Learning Approach to Automatically Segment Malignant Tumors and Ablation Zone in the Liver With Contrast-Enhanced CT.

OBJECTIVE: Liver cancer is one of the most commonly diagnosed cancer, and energy-based tumor ablation is a widely accepted treatment. Automatic and robust segmentation of liver tumors and ablation zones would facilitate the evaluation of treatment success. The purpose of this study was to develop and evaluate an automatic deep learning based method for (1) segmentation of liver and liver tumors in both arterial and portal venous phase for pre-treatment CT, and (2) segmentation of liver and ablation zones in both arterial and portal venous phase for after ablation treatment. MATERIALS AND METHODS: 252 CT images from 63 patients undergoing liver tumor ablation at a large University Hospital were retrospectively included; each patient had pre-treatment and post-treatment multi-phase CT images. 3D voxel-wise manual segmentation of the liver, tumors and ablation region by the radiologist provided reference standard. Deep learning models for liver and lesion segmentation were initially trained on the public Liver Tumor Segmentation Challenge (LiTS) dataset to obtain base models. Then, transfer learning was applied to adapt the base models on the clinical training-set, to obtain tumor and ablation segmentation models both for arterial and portal venous phase images. For modeling, 2D residual-attention Unet (RA-Unet) was employed for liver segmentation and a multi-scale patch-based 3D RA-Unet for tumor and ablation segmentation. RESULTS: On the independent test-set, the proposed method achieved a dice similarity coefficient (DSC) of 0.96 and 0.95 for liver segmentation on arterial and portal venous phase, respectively. For liver tumors, the model on arterial phase achieved detection sensitivity of 71%, DSC of 0.64, and on portal venous phase sensitivity of 82%, DSC of 0.73. For liver tumors >0.5cm3 performance improved to sensitivity 79%, DSC 0.65 on arterial phase and, sensitivity 86%, DSC 0.72 on portal venous phase. For ablation zone, the model on arterial phase achieved detection sensitivity of 90%, DSC of 0.83, and on portal venous phase sensitivity of 90%, DSC of 0.89. CONCLUSION: The proposed deep learning approach can provide automated segmentation of liver tumors and ablation zones on multi-phase (arterial and portal venous) and multi-time-point (before and after treatment) CT enabling quantitative evaluation of treatment success.

B-spline-based stereotactical normalization of brain FDG PET scans in suspected neurodegenerative disease: impact on voxel-based statistical single-subject analysis.

A b-spline-based method 'Lobster', originally designed as a general technique for non-linear image registration, was tailored for stereotactical normalization of brain FDG PET scans. Lobster was compared with the normalization methods of SPM2 and Neurostat with respect to the impact on the accuracy of voxel-based statistical analysis. (i) Computer simulation: Seven representative patterns of cortical hypometabolism served as artificial ground truth. They were inserted into 26 normal control scans with different simulated severity levels. After stereotactical normalization and voxel-based testing, statistical maps were compared voxel-by-voxel with the ground truth. This was done at different levels of statistical significance. There was a highly significant effect of the stereotactical normalization method on the area under the resulting ROC curve. Lobster showed the best average performance and was most stable with respect to variation of the severity level. (ii) Clinical evaluation: Statistical maps were obtained for the normal controls as well as patients with Alzheimer's disease (AD, n=44), Lewy-Body disease (LBD, 9), fronto-temporal dementia (FTD, 13), and cortico-basal dementia (CBD, 4). These maps were classified as normal, AD, LBD, FTD, or CBD by two experienced readers. The stereotactical normalization method had no significant effect on classification by of each of the experts, but it appeared to affect agreement between the experts. In conclusion, Lobster is appropriate for use in single-subject analysis of brain FDG PET scans in suspected dementia, both in early diagnosis (mild hypometabolism) and in differential diagnosis in advanced disease stages (moderate to severe hypometabolism). The computer simulation framework developed in the present study appears appropriate for quantitative evaluation of the impact of the different processing steps and their interaction on the performance of voxel-based single-subject analysis.

Towards subject-level cerebral infarction classification of CT scans using convolutional networks.

Automatic evaluation of 3D volumes is a topic of importance in order to speed up clinical decision making. We describe a method to classify computed tomography scans on volume level for the presence of non-acute cerebral infarction. This is not a trivial task, as the lesions are often similar to other areas in the brain regarding shape and intensity. A three stage architecture is used for classification: 1) A cranial cavity segmentation network is developed, trained and applied. 2) Region proposals are generated 3) Connected regions are classified using a multi-resolution, densely connected 3D convolutional network. Mean area under curve values for subject level classification are 0.95 for the unstratified test set, 0.88 for stratification by patient age and 0.93 for stratification by CT scanner model. We use a partly segmented dataset of 555 scans of which 186 scans are used in the unstratified test set. Furthermore we examine possible dataset bias for scanner model and patient age parameters. We show a successful application of the proposed three-stage model for full volume classification. In contrast to black-box approaches, the convolutional network's decision can be further assessed by examination of intermediate segmentation results.