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• Journal article
  Weiner M, Goh SM, Mohammad AJ, Hrušková Z, Tanna A, Sharp P, Kang A, Bruchfeld A, Selga D, Chocová Z, Westman K, Eriksson P, Harper L, Pusey CD, Tesaŕ V, Salama AD, Segelmark Met al., 2020,

  Impact of treatment on damage and hospitalization in elderly patients with microscopic polyangiitis and granulomatosis with polyangiitis

  , J Rheumatol, Vol: 47, Pages: 580-588, ISSN: 0315-162X

  OBJECTIVE: Age is a risk factor for organ damage, adverse events, and mortality in microscopic polyangiitis (MPA) and granulomatosis with polyangiitis (GPA). However, the relationship between treatment and damage, hospitalizations, and causes of death in elderly patients is largely unknown. METHODS: Consecutive patients from Sweden, England, and the Czech Republic diagnosed between 1997 and 2013 were included. Inclusion criteria were a diagnosis of MPA or GPA and age 75 years or more at diagnosis. Treatment with cyclophosphamide, rituximab, and corticosteroids the first three months was registered. Outcomes up to two years from diagnosis included vasculitis damage index (VDI), hospitalization, and cause of death. RESULTS: Treatment data was available for 167 of 202 patients. At two years, 4% had no items of damage. There was a positive association between VDI score at two years and Birmingham Vasculitis Activity Score at onset, and a negative association with treatment using cyclophosphamide or rituximab. Intravenous methylprednisolone dose was associated with treatment-related damage. During the first year, 69% of patients were readmitted to hospital. MPO-ANCA positivity and lower creatinine levels decreased the odds for readmission. The most common cause of death was infection, and this was associated with cumulative oral prednisolone dose. CONCLUSION: Immunosuppressive treatment with cyclophosphamide or rituximab in elderly patients with MPA and GPA was associated with development of less permanent organ damage and was not associated with hospitalization. However, higher doses of corticosteroids during the first three months was associated with treatment-related damage and fatal infections.

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• Journal article
  Dufton NP, Peghaire CR, Osuna-Almagro L, Raimondi C, Kalna V, Chauhan A, Webb G, Yang Y, Birdsey GM, Lalor P, Mason JC, Adams DH, Randi AMet al., 2020,

  Dynamic regulation of canonical TGF beta signalling by endothelial transcription factor ERG protects from liver fibrogenesis (vol 31, pg 450, 2017)

  , Nature Communications, Vol: 11, Pages: 1-1, ISSN: 2041-1723
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• Journal article
  Gao Z, Zhang H, Dong S, Sun S, Wang X, Yang G, Wu W, Li S, de Albuquerque VHCet al., 2020,

  Salient Object Detection in the Distributed Cloud-Edge Intelligent Network

  , IEEE NETWORK, Vol: 34, Pages: 216-224, ISSN: 0890-8044
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  • Citations: 60
• Journal article
  Walsh M, Merkel PA, Peh C-A, Szpirt WM, Puechal X, Fujimoto S, Hawley CM, Khalidi N, Flossmann O, Wald R, Girard LP, Levin A, Gregorini G, Harper L, Clark WF, Pagnoux C, Specks U, Smyth L, Tesar V, Ito-Ihara T, de Zoysa JR, Szczeklik W, Flores-Suarez LF, Carette S, Guillevin L, Pusey CD, Casian AL, Brezina B, Mazzetti A, McAlear CA, Broadhurst E, Reidlinger D, Mehta S, Ives N, Jayne DRWet al., 2020,

  Plasma Exchange and Glucocorticoids in Severe ANCA-Associated Vasculitis

  , NEW ENGLAND JOURNAL OF MEDICINE, Vol: 382, Pages: 622-631, ISSN: 0028-4793
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  • Citations: 578
• Journal article
  Tarkin JM, Cole GD, Gopalan D, Flora R, McAdoo SP, Mason JC, Peters NS, Pusey CD, Varnava Aet al., 2020,

  Multimodal imaging of granulomatosis with polyangiitis aortitis complicated by severe aortic regurgitation and complete heart block

  , Circulation: Cardiovascular Imaging, Vol: 13, Pages: 1-3, ISSN: 1941-9651
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• Journal article
  Li L, Wu F, Yang G, Xu L, Wong T, Mohiaddin R, Firmin D, Keegan J, Zhuang Xet al., 2020,

  Atrial scar quantification via multi-scale CNN in the graph-cuts framework

  , Medical Image Analysis, Vol: 60, ISSN: 1361-8415

  Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternative for scarassessment in patients with atrial fibrillation (AF). Automating the quantification and analysis of atrial scars can bechallenging due to the low image quality. In this work, we propose a fully automated method based on the graph-cutsframework, where the potentials of the graph are learned on a surface mesh of the left atrium (LA) using a multi-scaleconvolutional neural network (MS-CNN). For validation, we have included fifty-eight images with manual delineations.MS-CNN, which can efficiently incorporate both the local and global texture information of the images, has been shownto evidently improve the segmentation accuracy of the proposed graph-cuts based method. The segmentation could befurther improved when the contribution between the t-link and n-link weights of the graph is balanced. The proposedmethod achieves a mean accuracy of 0.856 ± 0.033 and mean Dice score of 0.702 ± 0.071 for LA scar quantification.Compared to the conventional methods, which are based on the manual delineation of LA for initialization, our methodis fully automatic and has demonstrated significantly better Dice score and accuracy (p < 0.01). The method is promisingand can be potentially useful in diagnosis and prognosis of AF.

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• Journal article
  Wang Y, Yue W, Li X, Liu S, Guo L, Xu H, Zhang H, Yang Get al., 2020,

  Comparison Study of Radiomics and Deep Learning-Based Methods for Thyroid Nodules Classification Using Ultrasound Images

  , IEEE ACCESS, Vol: 8, Pages: 52010-52017, ISSN: 2169-3536
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  • Citations: 37
• Journal article
  Liu Y, Yang G, Hosseiny M, Azadikhah A, Mirak SA, Miao Q, Raman SS, Sung Ket al., 2020,

  Exploring Uncertainty Measures in Bayesian Deep Attentive Neural Networks for Prostate Zonal Segmentation

  , IEEE ACCESS, Vol: 8, Pages: 151817-151828, ISSN: 2169-3536
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  • Citations: 68
• Journal article
  Ali A-RH, Li J, Yang G, 2020,

  Automating the ABCD Rule for Melanoma Detection: A Survey

  , IEEE ACCESS, Vol: 8, Pages: 83333-83346, ISSN: 2169-3536
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  • Citations: 27
• Journal article
  Zhuang X, Li L, Payer C, Stern D, Urschler M, Heinrich M, Oster J, Wang C, Smedby O, Bian C, Yang X, Heng P-A, Mortazi A, Bagci U, Yang G, Sun C, Galisot G, Ramel J-Y, Brouard T, Tong Q, Si W, Liao X, Zeng G, Shi Z, Zheng G, Wang C, MacGillivray T, Newby D, Rhode K, Ourselin S, Mohiaddin R, Keegan J, Firmin D, Yang Get al., 2019,

  Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge

  , Medical Image Analysis, Vol: 58, ISSN: 1361-8415

  Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS),which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functionsof the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape,and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally neededfor constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods,largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologiesand evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensionalcardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environmentswith manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelvegroups, have been evaluated. The results showed that the performance of CT WHS was generally better than thatof MRI WHS. The segmentation of the substructures for different categories of patients could present different levelsof challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methodsdemonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms,mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computationalefficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, conti

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