Prediction of tissue outcome in acute ischemic stroke based on single-phase CT angiography at admission.

Introduction: In acute ischemic stroke, prediction of the tissue outcome after reperfusion can be used to identify patients that might benefit from mechanical thrombectomy (MT). The aim of this work was to develop a deep learning model that can predict the follow-up infarct location and extent exclusively based on acute single-phase computed tomography angiography (CTA) datasets. In comparison to CT perfusion (CTP), CTA imaging is more widely available, less prone to artifacts, and the established standard of care in acute stroke imaging protocols. Furthermore, recent RCTs have shown that also patients with large established infarctions benefit from MT, which might not have been selected for MT based on CTP core/penumbra mismatch analysis. Methods: All patients with acute large vessel occlusion of the anterior circulation treated at our institution between 12/2015 and 12/2020 were screened (N = 404) and 238 patients undergoing MT with successful reperfusion were included for final analysis. Ground truth infarct lesions were segmented on 24 h follow-up CT scans. Pre-processed CTA images were used as input for a U-Net-based convolutional neural network trained for lesion prediction, enhanced with a spatial and channel-wise squeeze-and-excitation block. Post-processing was applied to remove small predicted lesion components. The model was evaluated using a 5-fold cross-validation and a separate test set with Dice similarity coefficient (DSC) as the primary metric and average volume error as the secondary metric. Results: The mean ± standard deviation test set DSC over all folds after post-processing was 0.35 ± 0.2 and the mean test set average volume error was 11.5 mL. The performance was relatively uniform across models with the best model according to the DSC achieved a score of 0.37 ± 0.2 after post-processing and the best model in terms of average volume error yielded 3.9 mL. Conclusion: 24 h follow-up infarct prediction using acute CTA imaging exclusively is feasible with DSC measures comparable to results of CTP-based algorithms reported in other studies. The proposed method might pave the way to a wider acceptance, feasibility, and applicability of follow-up infarct prediction based on artificial intelligence.

Association Between Recanalization Attempts and Functional Outcome After Thrombectomy for Large Ischemic Stroke.

BACKGROUND: Recently, 3 randomized controlled trials provided high-level evidence that patients with large ischemic stroke achieved better functional outcomes after endovascular therapy than with medical care alone. We aimed to investigate whether the clinical benefit of endovascular therapy is associated with the number of recanalization attempts in extensive baseline infarction. METHODS: This retrospective multicenter study enrolled patients from the German Stroke Registry who underwent endovascular therapy for anterior circulation large vessel occlusion between 2015 and 2021. Large ischemic stroke was defined as an Alberta Stroke Program Early Computed Tomography Score of 3 to 5. The study cohort was divided into patients with unsuccessful reperfusion (Thrombolysis in Cerebral Infarction score, 0-2a) and successful reperfusion (Thrombolysis in Cerebral Infarction score, 2b/3) at attempts 1, 2, 3, or ≥4. The primary outcome was favorable functional outcome defined as modified Rankin Scale score of 0 to 3 at 90 days. Safety outcomes were symptomatic intracranial hemorrhage after 24 hours and death within 90 days. Multivariable logistic regression was used to identify independent determinants of primary and secondary outcomes. RESULTS: A total of 348 patients met the inclusion criteria. Successful reperfusion was observed in 83.3% and favorable functional outcomes in 36.2%. Successful reperfusion at attempts 1 (adjusted odds ratio, 5.97 [95% CI, 1.71-24.43]; P=0.008) and 2 (adjusted odds ratio, 6.32 [95% CI, 1.73-26.92]; P=0.008) increased the odds of favorable functional outcome, whereas success at attempts 3 or ≥4 did not. Patients with >2 attempts showed higher rates of symptomatic intracranial hemorrhage (12.8% versus 6.5%; P=0.046). Successful reperfusion at any attempt lowered the odds of death compared with unsuccessful reperfusion. CONCLUSIONS: In patients with large vessel occlusion and Alberta Stroke Program Early Computed Tomography Score of 3 to 5, the clinical benefit of endovascular therapy was linked to the number of recanalization attempts required for successful reperfusion. Our findings encourage to perform at least 2 recanalization attempts to seek for successful reperfusion in large ischemic strokes, while >2 attempts should follow a careful risk-benefit assessment in these highly affected patients. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT03356392.

Individual glioblastoma cells harbor both proliferative and invasive capabilities during tumor progression.

BACKGROUND: Glioblastomas are characterized by aggressive and infiltrative growth, and by striking heterogeneity. The aim of this study was to investigate whether tumor cell proliferation and invasion are interrelated, or rather distinct features of different cell populations. METHODS: Tumor cell invasion and proliferation were longitudinally determined in real-time using 3D in vivo 2-photon laser scanning microscopy over weeks. Glioblastoma cells expressed fluorescent markers that permitted the identification of their mitotic history or their cycling versus non-cycling cell state. RESULTS: Live reporter systems were established that allowed us to dynamically determine the invasive behavior, and previous or actual proliferation of distinct glioblastoma cells, in different tumor regions and disease stages over time. Particularly invasive tumor cells that migrated far away from the main tumor mass, when followed over weeks, had a history of marked proliferation and maintained their proliferative capacity during brain colonization. Infiltrating cells showed fewer connections to the multicellular tumor cell network, a typical feature of gliomas. Once tumor cells colonized a new brain region, their phenotype progressively transitioned into tumor microtube-rich, interconnected, slower-cycling glioblastoma cells. Analysis of resected human glioblastomas confirmed a higher proliferative potential of tumor cells from the invasion zone. CONCLUSIONS: The detection of glioblastoma cells that harbor both particularly high proliferative and invasive capabilities during brain tumor progression provides valuable insights into the interrelatedness of proliferation and migration-2 central traits of malignancy in glioma. This contributes to our understanding of how the brain is efficiently colonized in this disease.

PSEN1/SLC20A2 double mutation causes early-onset Alzheimer's disease and primary familial brain calcification co-morbidity.

Primary familial brain calcification (PFBC; formerly Fahr's disease) and early-onset Alzheimer's disease (EOAD) may share partially overlapping pathogenic principles. Although the heterozygous loss-of-function mutation c.1523 + 1G > T in the PFBC-linked gene SLC20A2 was detected in a patient with asymmetric tremor, early-onset dementia, and brain calcifications, CSF ß-amyloid parameters and FBB-PET suggested cortical ß-amyloid pathology. Genetic re-analysis of exome sequences revealed the probably pathogenic missense mutation c.235G > A/p.A79T in PSEN1. The SLC20A2 mutation segregated with mild calcifications in two children younger than 30 years. We thus describe the stochastically extremely unlikely co-morbidity of genetic PFBC and genetic EOAD. The clinical syndromes pointed to additive rather than synergistic effects of the two mutations. MRI data revealed the formation of PFBC calcifications decades before the probable onset of the disease. Our report furthermore exemplifies the value of neuropsychology and amyloid PET for differential diagnosis.

Lightweight Visual Transformers Outperform Convolutional Neural Networks for Gram-Stained Image Classification: An Empirical Study.

We aimed to automate Gram-stain analysis to speed up the detection of bacterial strains in patients suffering from infections. We performed comparative analyses of visual transformers (VT) using various configurations including model size (small vs. large), training epochs (1 vs. 100), and quantization schemes (tensor- or channel-wise) using float32 or int8 on publicly available (DIBaS, n = 660) and locally compiled (n = 8500) datasets. Six VT models (BEiT, DeiT, MobileViT, PoolFormer, Swin and ViT) were evaluated and compared to two convolutional neural networks (CNN), ResNet and ConvNeXT. The overall overview of performances including accuracy, inference time and model size was also visualized. Frames per second (FPS) of small models consistently surpassed their large counterparts by a factor of 1-2×. DeiT small was the fastest VT in int8 configuration (6.0 FPS). In conclusion, VTs consistently outperformed CNNs for Gram-stain classification in most settings even on smaller datasets.

Myelofibrosis progression grading based on type I and type III collagen and fibrillin 1 expression boosted by whole slide image analysis.

AIMS: The progression of primary myelofibrosis is characterised by ongoing extracellular matrix deposition graded based on 'reticulin' and 'collagen' fibrosis, as revealed by Gomori's silver impregnation. Here we studied the expression of the major extracellular matrix proteins of fibrosis in relation to diagnostic silver grading supported by image analysis. METHODS AND RESULTS: By using automated immunohistochemistry, in this study we demonstrate that the expression of both types I and III collagens and fibrillin 1 by bone marrow stromal cells can reveal the extracellular matrix scaffolding in line with myelofibrosis progression as classified by silver grading. 'Reticulin' fibrosis indicated by type III collagen expression and 'collagen' fibrosis featured by type I collagen expression were parallel, rather than sequential, events. This is line with the proposed role of type III collagen in regulating type I collagen fibrillogenesis. The uniformly strong fibrillin 1 immune signals offered the best inter-rater agreements and the highest statistical correlations with silver grading of the three markers, which was robustly confirmed by automated whole slide digital image analysis using a machine learning-based algorithm. The progressive up-regulation of fibrillin 1 during myelofibrosis may result from a negative feedback loop as fibrillin microfibrils sequester TGF-ß, the major promoter of fibrosis. This can also reduce TGF-ß-induced RANKL levels, which would stimulate osteoclastogenesis and thus can support osteosclerosis in advanced myelofibrosis. CONCLUSIONS: Through the in-situ detection of these extracellular matrix proteins, our results verify the molecular pathobiology of fibrosis during myelofibrosis progression. In particular, fibrillin 1 immunohistochemistry, with or without image analysis, can complement diagnostic silver grading at decent cell morphology.

Rapid Convolutional Neural Networks for Gram-Stained Image Classification at Inference Time on Mobile Devices: Empirical Study from Transfer Learning to Optimization.

Despite the emergence of mobile health and the success of deep learning (DL), deploying production-ready DL models to resource-limited devices remains challenging. Especially, during inference time, the speed of DL models becomes relevant. We aimed to accelerate inference time for Gram-stained analysis, which is a tedious and manual task involving microorganism detection on whole slide images. Three DL models were optimized in three steps: transfer learning, pruning and quantization and then evaluated on two Android smartphones. Most convolutional layers (≥80%) had to be retrained for adaptation to the Gram-stained classification task. The combination of pruning and quantization demonstrated its utility to reduce the model size and inference time without compromising model quality. Pruning mainly contributed to model size reduction by 15×, while quantization reduced inference time by 3× and decreased model size by 4×. The combination of two reduced the baseline model by an overall factor of 46×. Optimized models were smaller than 6 MB and were able to process one image in <0.6 s on a Galaxy S10. Our findings demonstrate that methods for model compression are highly relevant for the successful deployment of DL solutions to resource-limited devices.

Influence of neurovascular anatomy on perforation site in different mouse strains using the filament perforation model for induction of subarachnoid hemorrhage.

BACKGROUND: Filament perforation is a widely-used method to induce subarachnoid hemorrhage (SAH) in mice. Whereas the perforation site has been assumed to be in the branching of middle cerebral artery (MCA) and anterior cerebral artery (ACA), we recently observed more proximal perforations. METHODS: Filament perforation was performed in CD1- (n = 10) and C57Bl/6N-mice (n = 9) ex vivo. The filament was left in place and the perforation site was microscopically assessed. Digital subtraction angiography (DSA) was performed in CD1- (n = 9) and C57Bl/6J-mice (n = 29) and anatomical differences of the internal carotid artery (ICA) were determined. RESULTS: Whereas in C57Bl/6N-mice perforation occurred in the proximal intracranial ICA in 89% (n = 8), in CD1-mice the perforation site was in the proximal ICA in 50% (n = 5), in the branching between MCA and ACA in 40% (n = 4), and in the proximal ACA in 10% (n = 1). DSA revealed a stronger angulation (p<0.001) of the ICA in CD1-mice (163.5±2.81°) compared to C57Bl/6J-mice (124.5±5.49°). Body weight and ICA-angle showed no significant correlation in C57Bl/6J- (r = -0.06, pweight/angle = 0.757) and CD1-mice (r = -0.468, pweight/angle = 0.242). CONCLUSION: Filament perforation in mice occurs not only at the hitherto presumed branching between MCA and ACA, but seems to depend on mouse strain and anatomy as the proximal intracranial ICA may also be perforated frequently.

Transfer learning for medical image classification: a literature review.

BACKGROUND: Transfer learning (TL) with convolutional neural networks aims to improve performances on a new task by leveraging the knowledge of similar tasks learned in advance. It has made a major contribution to medical image analysis as it overcomes the data scarcity problem as well as it saves time and hardware resources. However, transfer learning has been arbitrarily configured in the majority of studies. This review paper attempts to provide guidance for selecting a model and TL approaches for the medical image classification task. METHODS: 425 peer-reviewed articles were retrieved from two databases, PubMed and Web of Science, published in English, up until December 31, 2020. Articles were assessed by two independent reviewers, with the aid of a third reviewer in the case of discrepancies. We followed the PRISMA guidelines for the paper selection and 121 studies were regarded as eligible for the scope of this review. We investigated articles focused on selecting backbone models and TL approaches including feature extractor, feature extractor hybrid, fine-tuning and fine-tuning from scratch. RESULTS: The majority of studies (n = 57) empirically evaluated multiple models followed by deep models (n = 33) and shallow (n = 24) models. Inception, one of the deep models, was the most employed in literature (n = 26). With respect to the TL, the majority of studies (n = 46) empirically benchmarked multiple approaches to identify the optimal configuration. The rest of the studies applied only a single approach for which feature extractor (n = 38) and fine-tuning from scratch (n = 27) were the two most favored approaches. Only a few studies applied feature extractor hybrid (n = 7) and fine-tuning (n = 3) with pretrained models. CONCLUSION: The investigated studies demonstrated the efficacy of transfer learning despite the data scarcity. We encourage data scientists and practitioners to use deep models (e.g. ResNet or Inception) as feature extractors, which can save computational costs and time without degrading the predictive power.

Cell Cycle Regulatory Protein Expression in Multinucleated Giant Cells of Giant Cell Tumor of Bone: do They Proliferate?

Cells of the monocyte macrophage lineage form multinucleated giant cells (GCs) by fusion, which may express some cell cycle markers. By using a comprehensive marker set, here we looked for potential replication activities in GCs, and investigated whether these have diagnostic or clinical relevance in giant cell tumor of bone (GCTB). GC rich regions of 10 primary and 10 first recurrence GCTB cases were tested using immunohistochemistry in tissue microarrays. The nuclear positivity rate of the general proliferation marker, replication licensing, G1/S-phase, S/G2/M-phase, mitosis promoter, and cyclin dependent kinase (CDK) inhibitor reactions was analyzed in GCs. Concerning Ki67, moderate SP6 reaction was seen in many GC nuclei, while B56 and Mib1 positivity was rare, but the latter could be linked to more aggressive (p = 0.012) phenotype. Regular MCM6 reaction, as opposed to uncommon MCM2, suggested an initial DNA unwinding. Early replication course in GCs was also supported by widely detecting CDK4 and cyclin E, for the first time, and confirming cyclin D1 upregulation. However, post-G1-phase markers CDK2, cyclin A, geminin, topoisomerase-2a, aurora kinase A, and phospho-histone H3 were rare or missing. These were likely silenced by upregulated CDK inhibitors p15INK4b, p16INK4a, p27KIP1, p53 through its effector p21WAF1 and possibly cyclin G1, consistent with the prevention of DNA replication. In conclusion, the upregulation of known and several novel cell cycle progression markers detected here clearly verify early replication activities in GCs, which are controlled by cell cycle arresting CDK inhibitors at G1 phase, and support the functional maturation of GCs in GCTB.

Comparative analysis of machine learning algorithms for computer-assisted reporting based on fully automated cross-lingual RadLex mappings.

Computer-assisted reporting (CAR) tools were suggested to improve radiology report quality by context-sensitively recommending key imaging biomarkers. However, studies evaluating machine learning (ML) algorithms on cross-lingual ontological (RadLex) mappings for developing embedded CAR algorithms are lacking. Therefore, we compared ML algorithms developed on human expert-annotated features against those developed on fully automated cross-lingual (German to English) RadLex mappings using 206 CT reports of suspected stroke. Target label was whether the Alberta Stroke Programme Early CT Score (ASPECTS) should have been provided (yes/no:154/52). We focused on probabilistic outputs of ML-algorithms including tree-based methods, elastic net, support vector machines (SVMs) and fastText (linear classifier), which were evaluated in the same 5 × fivefold nested cross-validation framework. This allowed for model stacking and classifier rankings. Performance was evaluated using calibration metrics (AUC, brier score, log loss) and -plots. Contextual ML-based assistance recommending ASPECTS was feasible. SVMs showed the highest accuracies both on human-extracted- (87%) and RadLex features (findings:82.5%; impressions:85.4%). FastText achieved the highest accuracy (89.3%) and AUC (92%) on impressions. Boosted trees fitted on findings had the best calibration profile. Our approach provides guidance for choosing ML classifiers for CAR tools in fully automated and language-agnostic fashion using bag-of-RadLex terms on limited expert-labelled training data.

Good Clinical Outcome Decreases With Number of Retrieval Attempts in Stroke Thrombectomy: Beyond the First-Pass Effect.

BACKGROUND AND PURPOSE: Endovascular therapy is the standard of care in the treatment of acute ischemic stroke due to large-vessel occlusion. Often, more than one retrieval attempt is needed to achieve reperfusion. We aimed to quantify the influence of endovascular therapy on clinical outcome depending on the number of retrievals needed for successful reperfusion in a large multi-center cohort. METHODS: For this observational cohort study, 2611 patients from the prospective German Stroke Registry included between June 2015 and April 2018 were analyzed. Patients who received endovascular therapy for acute anterior circulation stroke with known admission National Institutes of Health Stroke Scale score and Alberta Stroke Program Early CT Score, final Thrombolysis in Cerebral Infarction score, and number of retrievals were included. Successful reperfusion was defined as a Thrombolysis in Cerebral Infarction score of 2b or 3. The primary outcome was defined as functional independence (modified Rankin Scale score of 0-2) at day 90. Multivariate mixed-effects models were used to adjust for cluster effects of the participating centers and confounders. RESULTS: The inclusion criteria were met by 1225 patients. The odds of good clinical outcome decreased with every retrieval attempt required for successful reperfusion: the first retrieval had the highest odds of good clinical outcome (adjusted odds ratio, 6.45 [95% CI, 4.0-10.4]), followed by the second attempt (adjusted odds ratio, 4.56 [95% CI, 2.7-7.7]), and finally the third (adjusted odds ratio, 3.16 [95% CI, 1.8-5.6]). CONCLUSIONS: Successful reperfusion within the first 3 retrieval attempts is associated with improved clinical outcome compared with patients without reperfusion. We conclude that at least 3 retrieval attempts should be performed in endovascular therapy of anterior circulation strokes. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT03356392.

Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data.

DNA methylation data-based precision cancer diagnostics is emerging as the state of the art for molecular tumor classification. Standards for choosing statistical methods with regard to well-calibrated probability estimates for these typically highly multiclass classification tasks are still lacking. To support this choice, we evaluated well-established machine learning (ML) classifiers including random forests (RFs), elastic net (ELNET), support vector machines (SVMs) and boosted trees in combination with post-processing algorithms and developed ML workflows that allow for unbiased class probability (CP) estimation. Calibrators included ridge-penalized multinomial logistic regression (MR) and Platt scaling by fitting logistic regression (LR) and Firth's penalized LR. We compared these workflows on a recently published brain tumor 450k DNA methylation cohort of 2,801 samples with 91 diagnostic categories using a 5 × 5-fold nested cross-validation scheme and demonstrated their generalizability on external data from The Cancer Genome Atlas. ELNET was the top stand-alone classifier with the best calibration profiles. The best overall two-stage workflow was MR-calibrated SVM with linear kernels closely followed by ridge-calibrated tuned RF. For calibration, MR was the most effective regardless of the primary classifier. The protocols developed as a result of these comparisons provide valuable guidance on choosing ML workflows and their tuning to generate well-calibrated CP estimates for precision diagnostics using DNA methylation data. Computation times vary depending on the ML algorithm from <15 min to 5 d using multi-core desktop PCs. Detailed scripts in the open-source R language are freely available on GitHub, targeting users with intermediate experience in bioinformatics and statistics and using R with Bioconductor extensions.

Longitudinal MRI findings in patients with newly diagnosed glioblastoma after intraoperative radiotherapy.

BACKGROUND AND PURPOSE: Post-radiation treatment effects (pseudoprogression/radionecrosis) may bias MRI-based tumor response evaluation. To understand these changes specifically after high doses of radiotherapy, we analyzed MRIs of patients enrolled in the INTRAGO study (NCT02104882), a phase I/II dose-escalation trial of intraoperative radiotherapy (20-40 Gy) in glioblastoma. METHODS: INTRAGO patients were evaluated and compared to control patients who received standard therapy with focus on contrast enhancement patterns/volume, T2 lesion volume, and mean rCBV. RESULTS: Overall, 11/15 (73.3%) INTRAGO patients (median age 60 years) were included. Distant failure was observed in 7/11 (63.6%) patients, local tumor recurrence in one patient (9.1%). On the first follow-up MRI all but one patient demonstrated enhancement of varying patterns around the resection cavity which were: in 2/11 (18.2%) patients thin and linear, in 7/11 (63.6%) combined linear and nodular, and in 1/11 (9.1%) voluminous, indistinct, and mesh-like. In the course of treatment, most patients developed the latter two patterns (8/11 [72.7%]). INTRAGO patients demonstrated more often combined linear and nodular and/or voluminous, indistinct, mesh-like components (8/11 [72.7%]) in comparison to control patients (3/12 [25%], P = 0.02). INTRAGO patients demonstrated significantly increasing enhancing lesion (P = 0.001) and T2 lesion volumes (P < 0.001) in the longitudinal non-parametric analysis in comparison to the control group. rCBV showed no significant differences between both groups. CONCLUSIONS: High doses of radiotherapy to the tumor cavity result in more pronounced enhancement patterns/volumes and T2 lesion volumes. These results will be useful for the response evaluation of patients exposed to high doses of radiotherapy in future studies.

Longitudinal imaging and evaluation of SAH-associated cerebral large artery vasospasm in mice using micro-CT and angiography.

Longitudinal in vivo imaging studies characterizing subarachnoid hemorrhage (SAH)-induced large artery vasospasm (LAV) in mice are lacking. We developed a SAH-scoring system to assess SAH severity in mice using micro CT and longitudinally analysed LAV by intravenous digital subtraction angiography (i.v. DSA). Thirty female C57Bl/6J-mice (7 sham, 23 SAH) were implanted with central venous ports for repetitive contrast agent administration. SAH was induced by filament perforation. LAV was assessed up to 14 days after induction of SAH by i.v. DSA. SAH-score and neuroscore showed a highly significant positive correlation (rsp = 0.803, p < 0.001). SAH-score and survival showed a negative significant correlation (rsp = -0.71, p < 0.001). LAV peaked between days 3-5 and normalized on days 7-15. Most severe LAV was observed in the internal carotid (Δmax = 30.5%, p < 0.001), anterior cerebral (Δmax = 21.2%, p = 0.014), middle cerebral (Δmax = 28.16%, p < 0.001) and basilar artery (Δmax = 23.49%, p < 0.001). Cerebral perfusion on day 5 correlated negatively with survival time (rPe = -0.54, p = 0.04). Arterial diameter of the left MCA correlated negatively with cerebral perfusion on day 3 (rPe = -0.72, p = 0.005). In addition, pseudoaneurysms arising from the filament perforation site were visualized in three mice using i.v. DSA. Thus, micro-CT and DSA are valuable tools to assess SAH severity and to longitudinally monitor LAV in living mice.

In situ cell cycle analysis in giant cell tumor of bone reveals patients with elevated risk of reduced progression-free survival.

OBJECTIVE: Giant cell tumor of bone (GCTB) is a frequently recurring locally aggressive osteolytic lesion, where pathological osteoclastogenesis and bone destruction are driven by neoplastic stromal cells. Here, we studied if cell cycle fractions within the mononuclear cell compartment of GCTB can predict its progression-free survival (PFS). METHODS: 154 cases (100 primaries and 54 recurrent) from 139 patients of 40 progression events, was studied using tissue microarrays. Ploidy and in situ cell cycle progression related proteins including Ki67 and those linked with replication licensing (mcm2), G1-phase (cyclin D1, Cdk4), and S-G2-M-phase (cyclin A; Cdk2) fractions; cell cycle control (p21waf1) and repression (geminin), were tested. The Prentice-Williams-Peterson (PWP) gap-time models with the Akaike information criterion (AIC) were used for PFS analysis. RESULTS: Cluster analysis showed good correlation between functionally related marker positive cell fractions indicating no major cell cycle arrested cell populations in GCTB. Increasing hazard of progression was statistically associated with the elevated post-G1/S-phase cell fractions. Univariate analysis revealed significant negative association of poly-/aneuploidy (p < 0.0001), and elevated cyclin A (p < 0.001), geminin (p = 0.015), mcm2 (p = 0.016), cyclin D1 (p = 0.022) and Ki67 (B56: p = 0.0543; and Mib1: p = 0.0564 -strong trend) positive cell fractions with PFS. The highest-ranked multivariate interaction model (AIC = 269.5) also included ploidy (HR 5.68, 95%CI: 2.62-12.31, p < 0.0001), mcm2 (p = 0.609), cyclin D1 (HR 1.89, 95%CI: 0.88-4.09, p = 0.105) and cyclin A (p < 0.0001). The first and second best prognostic models without interaction (AIC = 271.6) and the sensitivity analysis (AIC = 265.7) further confirmed the prognostic relevance of combining these markers. CONCLUSION: Ploidy and elevated replication licensing (mcm2), G1-phase (cyclin D1) and post-G1 phase (cyclin A) marker positive cell fractions, indicating enhanced cell cycle progression, can assist in identifying GCTB patients with increased risk for a reduced PFS.

Objective Comparison Using Guideline-based Query of Conventional Radiological Reports and Structured Reports.

BACKGROUND: This feasibility study of text-mining-based scoring algorithm provides an objective comparison of structured reports (SR) and conventional free-text reports (cFTR) by means of guideline-based key terms. Furthermore, an open-source online version of this ranking algorithm was provided with multilingual text-retrieval pipeline, customizable query and real-time-scoring. MATERIALS AND METHODS: Twenty-five patients with suspected stroke and magnetic resonance imaging were re-assessed by two independent/blinded readers [inexperienced: 3 years; experienced >6 years/Board-certified). SR and cFTR were compared with guideline-query using the cosine similarity score (CSS) and Wilcoxon signed-rank test. RESULTS: All pathological findings (18/18) were identified by SR and cFTR. The impressions section of the SRs of the inexperienced reader had the highest median (0.145) and maximal (0.214) CSS and were rated significantly higher (p=2.21×10-5 and p=1.4×10-4, respectively) than cFTR (median=0.102). CSS was robust to variations of query. CONCLUSION: Objective guideline-based comparison of SRs and cFTRs using the CSS is feasible and provides a scalable quality measure that can facilitate the adoption of structured reports in all fields of radiology.

Intra-individual diagnostic image quality and organ-specific-radiation dose comparison between spiral cCT with iterative image reconstruction and z-axis automated tube current modulation and sequential cCT.

OBJECTIVES: To prospectively evaluate image quality and organ-specific-radiation dose of spiral cranial CT (cCT) combined with automated tube current modulation (ATCM) and iterative image reconstruction (IR) in comparison to sequential tilted cCT reconstructed with filtered back projection (FBP) without ATCM. METHODS: 31 patients with a previous performed tilted non-contrast enhanced sequential cCT aquisition on a 4-slice CT system with only FBP reconstruction and no ATCM were prospectively enrolled in this study for a clinical indicated cCT scan. All spiral cCT examinations were performed on a 3rd generation dual-source CT system using ATCM in z-axis direction. Images were reconstructed using both, FBP and IR (level 1-5). A Monte-Carlo-simulation-based analysis was used to compare organ-specific-radiation dose. Subjective image quality for various anatomic structures was evaluated using a 4-point Likert-scale and objective image quality was evaluated by comparing signal-to-noise ratios (SNR). RESULTS: Spiral cCT led to a significantly lower (p < 0.05) organ-specific-radiation dose in all targets including eye lense. Subjective image quality of spiral cCT datasets with an IR reconstruction level 5 was rated significantly higher compared to the sequential cCT acquisitions (p < 0.0001). Consecutive mean SNR was significantly higher in all spiral datasets (FBP, IR 1-5) when compared to sequential cCT with a mean SNR improvement of 44.77% (p < 0.0001). CONCLUSIONS: Spiral cCT combined with ATCM and IR allows for significant-radiation dose reduction including a reduce eye lens organ-dose when compared to a tilted sequential cCT while improving subjective and objective image quality.

The neglected need for psychological intervention in patients suffering from incidentally discovered intracranial aneurysms.

OBJECTIVES: Previous studies demonstrated a conspicuously elevated rate of psychiatric disorders in patients with incidental intracranial aneurysms. This study was designed to analyze the impact of this observation on the post-interventional rates of PTSD, depressions and anxiety disorders in this collective. METHODS: Physically unaffected iA patients with an unremarkable medical history were included in this two center study. Pre-interventional psychiatric histories, rates of post-interventional depressions, subjective trauma, PTSD, and pre-interventional fears were determined by questionnaires (Beck Depression Inventory (BDI), Impact of Event Scale (IES), civilian Post-traumatic-Stress-Disorder (PTSD) Check List (PCL-C)). Benign meningioma (M) patients served as controls. RESULTS: 58 M and 45 iA patients were enrolled. Significantly higher rates of PTSD, elevated trauma scores, and moderate/severe depressions (PTSD: p=0.0017; IES: p=0.0038; BDI: p=0.0301) were demonstrated in the iA collective. After excluding patients with a positive pre-interventional psychiatric history those differences were not reproducible. 70% of the iA patients reported an improvement of their unspecific pre-interventional symptoms, while 30% would have rated a psychological consultation as helpful. CONCLUSION: The data identifies the early psychological consultation as a relevant and by affected patients accepted treatment modification when trying to improve the outcome after treatment of incidental aneurysms.