Campbell, B. C.V. et al. (2019) Penumbral imaging and functional outcome in patients with anterior circulation ischaemic stroke treated with endovascular thrombectomy versus medical therapy: a meta-analysis of individual patient-level data.ABSTRACT Background: CT-perfusion (CTP) and MRI may assist patient selection for endovascular thrombectomy. We aimed to establish whether imaging assessments of ischaemic core and penumbra volumes were associated with functional outcomes and treatment effect.

Campbell, B. C. V. et al. (2018) Effect of general anaesthesia on functional outcome in patients with anterior circulation ischaemic stroke having endovascular thrombectomy versus standard care: a meta-analysis of individual patient data. Lancet Neurology, 17(1), pp. 47-53. (doi:10.1016/S1474-4422(17)30407-6) This is the author's final accepted version.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.http://eprints.gla.ac.uk/149670/ variables. An alternative approach using propensity-score stratification was also used. To account for between-trial variance we used mixed-effects modeling with a random effect for trial incorporated in all models. Bias was assessed using the Cochrane tool.Findings: Of 1764 patients in 7 trials, 871 were allocated to endovascular thrombectomy. After exclusion of 74 patients (72 who did not undergo the procedure and 2 with missing data on anaesthetic strategy), 236/797 (30%) of endovascular patients were treated under GA. At baseline, GA patients were younger and had shorter time to randomisation but similar pre-treatment clinical severity compared to non-GA. Endovascular thrombectomy improved functional outcome at 3 months versus standard care in both GA (adjusted common odds ratio (cOR) 1·52, 95%CI 1·09-2·11, p=0·014) and non-GA (adjusted cOR 2·33, 95%CI 1·75-3·10, p<0·001) patients. However, outcomes were significantly better for those treated under non-GA versus GA (covariate-adjusted cOR 1·53, 95%CI 1·14-2·04, p=0·004; propensitystratified cOR 1·44 95%CI 1·08-1·92, p=0·012). The risk of bias and variability among studies was assessed to be low.Interpretation: Worse outcomes after endovascular thrombectomy were associated with GA, after adjustment for baseline prognostic variables. These data support avoidance of GA whenever possible. The procedure did, however, remain effective versus standard care in patients treated under GA, indicating that treatment should not be withheld in those who require anaesthesia for medical reasons. Funding:The HERMES collaboration was funded by an unrestricted grant from Medtronic to the University of Calgary. Research in contextEvidence before this study between abolition of the thrombectomy treatment effect in MR CLEAN and no effect in THRACE. Three single-centre randomised trials of general anaesthesia versus conscious sedation found either no difference in functional outcome between groups or a slight benefit of general anaesthesia. Added value of this studyThese data from contemporary, high quality randomised trials form the largest study to date of the association between general anesthesia and the benefit of endovascular thrombectomy versus standard care. We used two different approaches to adjust for baseline imbalances (multivariable logistic regression and propensity-score stratification). We found that GA for endovascular thrombectomy, as practiced in contemporary clinical care across a wide range of expert centres during the rand...

Histopathologic grading of prostate cancer using Gleason patterns (GPs) is subject to a large inter-observer variability, which may result in suboptimal treatment of patients. With the introduction of digitization and whole-slide images of prostate biopsies, computer-aided grading becomes feasible. Computer-aided grading has the potential to improve histopathological grading and treatment selection for prostate cancer. Automated detection of GPs and determination of the grade groups (GG) using a convolutional neural network. In total, 96 prostate biopsies from 38 patients are annotated on pixel-level. Automated detection of GP 3 and GP ≥ 4 in digitized prostate biopsies is performed by re-training the Inception-v3 convolutional neural network (CNN). The outcome of the CNN is subsequently converted into probability maps of GP ≥ 3 and GP ≥ 4, and the GG of the whole biopsy is obtained according to these probability maps. Differentiation between non-atypical and malignant (GP ≥ 3) areas resulted in an accuracy of 92% with a sensitivity and specificity of 90 and 93%, respectively. The differentiation between GP ≥ 4 and GP ≤ 3 was accurate for 90%, with a sensitivity and specificity of 77 and 94%, respectively . Concordance of our automated GG determination method with a genitourinary pathologist was obtained in 65% ( κ = 0.70), indicating substantial agreement. A CNN allows for accurate differentiation between non-atypical and malignant areas as defined by GPs, leading to a substantial agreement with the pathologist in defining the GG. Electronic supplementary material The online version of this article (10.1007/s00428-019-02577-x) contains supplementary material, which is available to authorized users.

Accurate grading of nonemuscle-invasive urothelial cell carcinoma is of major importance; however, high interobserver variability exists. A fully automated detection and grading network based on deep learning is proposed to enhance reproducibility. A total of 328 transurethral resection specimens from 232 patients were included, and a consensus reading by three specialized pathologists was used. The slides were digitized, and the urothelium was annotated by expert observers. The U-Netebased segmentation network was trained to automatically detect urothelium. This detection was used as input for the classification network. The classification network aimed to grade the tumors according to the World Health Organization grading system adopted in 2004. The automated grading was compared with the consensus and individual grading. The segmentation network resulted in an accurate detection of urothelium. The automated grading shows moderate agreement (k Z 0.48 AE 0.14 SEM) with the consensus reading. The agreement among pathologists ranges between fair (k Z 0.35 AE 0.13 SEM and k Z 0.38 AE 0.11 SEM) and moderate (k Z 0.52 AE 0.13 SEM). The automated classification correctly graded 76% of the low-grade cancers and 71% of the high-grade cancers according to the consensus reading. These results indicate that deep learning can be used for the fully automated detection and grading of urothelial cell carcinoma.

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BackgroundCT perfusion (CTP) is used to estimate the extent of ischemic core and penumbra in patients with acute ischemic stroke. CTP reliability, however, is limited. This study aims to identify regions misclassified as ischemic core on CTP, using infarct on follow-up noncontrast CT. We aim to assess differences in volumetric and perfusion characteristics in these regions compared to areas that ended up as infarct on follow-up.Materials and MethodsThis study included 35 patients with >100 mm brain coverage CTP. CTP processing was performed using Philips software (IntelliSpace 7.0). Final infarct was automatically segmented on follow-up noncontrast CT and used as reference. CTP and follow-up noncontrast CT image data were registered. This allowed classification of ischemic lesion agreement (core on CTP: rMTT≥145%, aCBV<2.0 ml/100g and infarct on follow-up noncontrast CT) and misclassified ischemic core (core on CTP, not identified on follow-up noncontrast CT) regions. False discovery ratio (FDR), defined as misclassified ischemic core volume divided by total CTP ischemic core volume, was calculated. Absolute and relative CTP parameters (CBV, CBF, and MTT) were calculated for both misclassified CTP ischemic core and ischemic lesion agreement regions and compared using paired rank-sum tests.ResultsMedian total CTP ischemic core volume was 49.7ml (IQR:29.9ml-132ml); median misclassified ischemic core volume was 30.4ml (IQR:20.9ml-77.0ml). Median FDR between patients was 62% (IQR:49%-80%). Median relative mean transit time was 243% (IQR:198%-289%) and 342% (IQR:249%-432%) for misclassified and ischemic lesion agreement regions, respectively. Median absolute cerebral blood volume was 1.59 (IQR:1.43–1.79) ml/100g (P<0.01) and 1.38 (IQR:1.15–1.49) ml/100g (P<0.01) for misclassified ischemic core and ischemic lesion agreement, respectively. All CTP parameter values differed significantly.ConclusionFor all patients a considerable region of the CTP ischemic core is misclassified. CTP parameters significantly differed between ischemic lesion agreement and misclassified CTP ischemic core, suggesting that CTP analysis may benefit from revisions.

Background Histopathological analysis is the cornerstone in bladder cancer (BCa) diagnosis. These analysis suffer from a moderate observer agreement in the staging of bladder cancer. Three-dimensional reconstructions have the potential to support the pathologists in visualizing spatial arrangements of structures, which may improve the interpretation of specimen. The aim of this study is to present three-dimensional (3D) reconstructions of histology images. Methods En-bloc specimens of transurethral bladder tumour resections were formalin fixed and paraffin embedded. Specimens were cut into sections of 4 μm and stained with Hematoxylin and Eosin (H&E). With a Phillips IntelliSite UltraFast scanner, glass slides were digitized at 20x magnification. The digital images were aligned by performing rigid and affine image alignment. The tumour and the muscularis propria (MP) were manually delineated to create 3D segmentations. In conjunction with a 3D display, the results were visualized with the Vesalius3D interactive visualization application for a 3D workstation. Results En-bloc resection was performed in 21 BCa patients. Per case, 26–30 sections were included for the reconstruction into a 3D volume. Five cases were excluded due to export problems, size of the dataset or condition of the tissue block. Qualitative evaluation suggested an accurate registration for 13 out of 16 cases. The segmentations allowed full 3D visualization and evaluation of the spatial relationship of the BCa tumour and the MP. Conclusion Digital scanning of en-bloc resected specimens allows a full-fledged 3D reconstruction and analysis and has a potential role to support pathologists in the staging of BCa.