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Ma J, Al Moussawi K, Lou H, Chan HF, Wang Y, Chadwick J, Phetsouphanh C, Slee EA, Zhong S, Leissing TM, Roth A, Qin X, Chen S, Yin J, Ratnayaka I, Hu Y, Louphrasitthiphol P, Taylor L, Bettencourt PJG, Muers M, Greaves DR, McShane H, Goldin R, Soilleux EJ, Coleman ML, Ratcliffe PJ, Lu X. Deficiency of factor-inhibiting HIF creates a tumor-promoting immune microenvironment. Proc Natl Acad Sci U S A 2024; 121:e2309957121. [PMID: 38422022 DOI: 10.1073/pnas.2309957121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 01/03/2024] [Indexed: 03/02/2024] Open
Abstract
Hypoxia signaling influences tumor development through both cell-intrinsic and -extrinsic pathways. Inhibiting hypoxia-inducible factor (HIF) function has recently been approved as a cancer treatment strategy. Hence, it is important to understand how regulators of HIF may affect tumor growth under physiological conditions. Here we report that in aging mice factor-inhibiting HIF (FIH), one of the most studied negative regulators of HIF, is a haploinsufficient suppressor of spontaneous B cell lymphomas, particular pulmonary B cell lymphomas. FIH deficiency alters immune composition in aged mice and creates a tumor-supportive immune environment demonstrated in syngeneic mouse tumor models. Mechanistically, FIH-defective myeloid cells acquire tumor-supportive properties in response to signals secreted by cancer cells or produced in the tumor microenvironment with enhanced arginase expression and cytokine-directed migration. Together, these data demonstrate that under physiological conditions, FIH plays a key role in maintaining immune homeostasis and can suppress tumorigenesis through a cell-extrinsic pathway.
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Affiliation(s)
- Jingyi Ma
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
- Ministry of Health Holdings, Singapore 099253, Singapore
| | - Khatoun Al Moussawi
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Hantao Lou
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Hok Fung Chan
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Yihua Wang
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
- Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Joseph Chadwick
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Chansavath Phetsouphanh
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
- The Kirby Institute, University of New South Wales, Kensington, NSW 2052, Australia
| | - Elizabeth A Slee
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Shan Zhong
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Thomas M Leissing
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Andrew Roth
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
- Department of Molecular Oncology, BC Cancer, Vancouver, BC V5Z 4E6, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z7, Canada
- Department of Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Xiao Qin
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
- Department of Oncology, Faculty of Medical Sciences, University College London, London WC1E 6BT, United Kingdom
| | - Shuo Chen
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Jie Yin
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Indrika Ratnayaka
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Yang Hu
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Pakavarin Louphrasitthiphol
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Lewis Taylor
- Sir William Dunn School of Pathology, University of Oxford, Oxford OX1 3RE, United Kingdom
| | - Paulo J G Bettencourt
- The Jenner Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
- Center for Interdisciplinary Research in Health, Faculty of Medicine, Universidade Católica Portuguesa, Lisbon 1649-023, Portugal
| | - Mary Muers
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - David R Greaves
- Sir William Dunn School of Pathology, University of Oxford, Oxford OX1 3RE, United Kingdom
| | - Helen McShane
- The Jenner Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Robert Goldin
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London W2 1NY, United Kingdom
| | - Elizabeth J Soilleux
- Department of Pathology, University of Cambridge, Cambridge CB2 1QP, United Kingdom
| | - Mathew L Coleman
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Peter J Ratcliffe
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Xin Lu
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, United Kingdom
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Zhang C, Kadu S, Xiao Y, Johnson O, Kelly A, O'Connor RS, Lai M, Kong H, Srivatsa S, Tai V, Greenblatt E, Holmes M, Riley JL, June CH, Sheppard NC. Sequential Exposure to IL21 and IL15 During Human Natural Killer Cell Expansion Optimizes Yield and Function. Cancer Immunol Res 2023; 11:1524-1537. [PMID: 37649085 PMCID: PMC10618651 DOI: 10.1158/2326-6066.cir-23-0151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 06/14/2023] [Accepted: 08/28/2023] [Indexed: 09/01/2023]
Abstract
Natural killer (NK) cells are frequently expanded for the clinic using irradiated, engineered K562 feeder cells expressing a core transgene set of membrane-bound (mb) IL15 and/or mbIL21 together with 41BBL. Prior comparisons of mbIL15 to mbIL21 for NK expansion lack comparisons of key attributes of the resulting NK cells, including their high-dimensional phenotype, polyfunctionality, the breadth and potency of cytotoxicity, cellular metabolism, and activity in xenograft tumor models. Moreover, despite multiple rounds of K562 stimulation, studies of sequential use of mbIL15- and mbIL21-based feeder cells are absent. We addressed these gaps and found that using mbIL15- versus mbIL21-based feeder cells drove distinct phenotypic and functional profiles. Feeder cells expressing mbIL15 alone drove superior functionality by nearly all measures, whereas those expressing mbIL21 alone drove superior yield. In combination, most attributes resembled those imparted by mbIL21, whereas in sequence, NK yield approximated that imparted by the first cytokine, and the phenotype, transcriptome, and function resembled that driven by the second cytokine, highlighting the plasticity of NK cell differentiation. The sequence mbIL21 followed by mbIL15 was advantageous in achieving significant yields of highly functional NK cells that demonstrated equivalent in vivo activity to those expanded by mbIL15 alone in two of three xenograft models. Our findings define the impact of mbIL15 versus mbIL21 during NK expansion and reveal a previously underappreciated tradeoff between NK yield and function for which sequential use of mbIL21-based followed by mbIL15-based feeder cells may be the optimal approach in many settings.
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Affiliation(s)
- Caimei Zhang
- Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Siddhant Kadu
- Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Yansen Xiao
- Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Omar Johnson
- Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Andre Kelly
- Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Roddy S. O'Connor
- Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Meizan Lai
- Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Hong Kong
- Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sriram Srivatsa
- Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Victoria Tai
- Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | | | - James L. Riley
- Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Carl H. June
- Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Neil C. Sheppard
- Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
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Lee WD, Liang L, AbuSalim J, Jankowski CS, Samarah LZ, Neinast MD, Rabinowitz JD. Impact of acute stress on murine metabolomics and metabolic flux. Proc Natl Acad Sci U S A 2023; 120:e2301215120. [PMID: 37186827 PMCID: PMC10214130 DOI: 10.1073/pnas.2301215120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 04/10/2023] [Indexed: 05/17/2023] Open
Abstract
Plasma metabolite concentrations and labeling enrichments are common measures of organismal metabolism. In mice, blood is often collected by tail snip sampling. Here, we systematically examined the effect of such sampling, relative to gold-standard sampling from an in-dwelling arterial catheter, on plasma metabolomics and stable isotope tracing. We find marked differences between the arterial and tail circulating metabolome, which arise from two major factors: handling stress and sampling site, whose effects were deconvoluted by taking a second arterial sample immediately after tail snip. Pyruvate and lactate were the most stress-sensitive plasma metabolites, rising ~14 and ~5-fold. Both acute handling stress and adrenergic agonists induce extensive, immediate production of lactate, and modest production of many other circulating metabolites, and we provide a reference set of mouse circulatory turnover fluxes with noninvasive arterial sampling to avoid such artifacts. Even in the absence of stress, lactate remains the highest flux circulating metabolite on a molar basis, and most glucose flux into the TCA cycle in fasted mice flows through circulating lactate. Thus, lactate is both a central player in unstressed mammalian metabolism and strongly produced in response to acute stress.
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Affiliation(s)
- Won Dong Lee
- Department of Chemistry, Princeton University, Princeton, NJ08544
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ08544
- Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ08544
| | - Lingfan Liang
- Department of Chemistry, Princeton University, Princeton, NJ08544
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ08544
- Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ08544
| | - Jenna AbuSalim
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ08544
- Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ08544
- Department of Molecular Biology, Princeton University, Princeton, NJ08544
| | - Connor S.R. Jankowski
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ08544
- Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ08544
- Department of Molecular Biology, Princeton University, Princeton, NJ08544
| | - Laith Z. Samarah
- Department of Chemistry, Princeton University, Princeton, NJ08544
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ08544
- Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ08544
| | - Michael D. Neinast
- Department of Chemistry, Princeton University, Princeton, NJ08544
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ08544
- Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ08544
| | - Joshua D. Rabinowitz
- Department of Chemistry, Princeton University, Princeton, NJ08544
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ08544
- Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ08544
- Department of Molecular Biology, Princeton University, Princeton, NJ08544
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Lafleur VN, Halim S, Choudhry H, Ratcliffe PJ, Mole DR. Multi-level interaction between HIF and AHR transcriptional pathways in kidney carcinoma. Life Sci Alliance 2023; 6:e202201756. [PMID: 36725335 PMCID: PMC9896012 DOI: 10.26508/lsa.202201756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 02/03/2023] Open
Abstract
Hypoxia-inducible factor (HIF) and aryl hydrocarbon receptor (AHR) are members of the bHLH-PAS family of transcription factors that underpin cellular responses to oxygen and to endogenous and exogenous ligands, respectively, and have central roles in the pathogenesis of renal cancer. Composed of heterodimers, they share a common HIF-1β/ARNT subunit and similar DNA-binding motifs, raising the possibility of crosstalk between the two transcriptional pathways. Here, we identify both general and locus-specific mechanisms of interaction between HIF and AHR that act both antagonistically and cooperatively. Specifically, we observe competition for the common HIF-1β/ARNT subunit, in cis synergy for chromatin binding, and overlap in their transcriptional targets. Recently, both HIF and AHR inhibitors have been developed for the treatment of solid tumours. However, inhibition of one pathway may promote the oncogenic effects of the other. Therefore, our work raises important questions as to whether combination therapy targeting both of these pro-tumourigenic pathways might show greater efficacy than targeting each system independently.
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Affiliation(s)
| | - Silvia Halim
- NDM Research Building, University of Oxford, Old Road Campus, Oxford, UK
| | - Hani Choudhry
- Department of Biochemistry, Faculty of Science, Center of Innovation in Personalized Medicine, King Fahd Center for Medical Research, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Peter J Ratcliffe
- Ludwig Institute for Cancer Research, University of Oxford, Old Road Campus, Oxford, UK
- The Francis Crick Institute, London, UK
| | - David R Mole
- NDM Research Building, University of Oxford, Old Road Campus, Oxford, UK
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5
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Gong YY, Shao H, Li Y, Brafford P, Stine ZE, Sun J, Felsher DW, Orange JS, Albelda SM, Dang CV. Na +/H +-exchanger 1 enhances antitumor activity of engineered NK-92 natural killer cells. Cancer Res Commun 2022; 2:842-856. [PMID: 36380966 PMCID: PMC9648415 DOI: 10.1158/2767-9764.crc-22-0270] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 07/27/2022] [Accepted: 07/28/2022] [Indexed: 06/16/2023]
Abstract
Adoptive cell transfer (ACT) immunotherapy has remarkable efficacy against some hematological malignancies. However, its efficacy in solid tumors is limited by the adverse tumor microenvironment (TME) conditions, most notably that acidity inhibits T and natural killer (NK) cell mTOR complex 1 (mTORC1) activity and impairs cytotoxicity. In several reported studies, systemic buffering of tumor acidity enhanced the efficacy of immune checkpoint inhibitors. Paradoxically, we found in a c-Myc-driven hepatocellular carcinoma model that systemic buffering increased tumor mTORC1 activity, negating inhibition of tumor growth by anti-PD1 treatment. Therefore, in this proof-of-concept study, we tested the metabolic engineering of immune effector cells to mitigate the inhibitory effect of tumor acidity while avoiding side effects associated with systemic buffering. We first overexpressed an activated RHEB in the human NK cell line NK-92, thereby rescuing acid-blunted mTORC1 activity and enhancing cytolytic activity. Then, to directly mitigate the effect of acidity, we ectopically expressed acid extruder proteins. Whereas ectopic expression of carbonic anhydrase IX (CA9) moderately increased mTORC1 activity, it did not enhance effector function. In contrast, overexpressing a constitutively active Na+/H+-exchanger 1 (NHE1; SLC9A1) in NK-92 did not elevate mTORC1 but enhanced degranulation, target engagement, in vitro cytotoxicity, and in vivo antitumor activity. Our findings suggest the feasibility of overcoming the inhibitory effect of the TME by metabolically engineering immune effector cells, which can enhance ACT for better efficacy against solid tumors.
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Affiliation(s)
- Yao-Yu Gong
- Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | - Yu Li
- Department of Pediatrics, Columbia University Medical Center, New York, New York
| | | | | | - Jing Sun
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Dean W. Felsher
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - Jordan S. Orange
- Department of Pediatrics, Columbia University Medical Center, New York, New York
| | - Steven M. Albelda
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Chi V. Dang
- The Wistar Institute, Philadelphia, Pennsylvania
- Ludwig Institute for Cancer Research, New York, New York
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6
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Ali S, Zhou F, Braden B, Bailey A, Yang S, Cheng G, Zhang P, Li X, Kayser M, Soberanis-Mukul RD, Albarqouni S, Wang X, Wang C, Watanabe S, Oksuz I, Ning Q, Yang S, Khan MA, Gao XW, Realdon S, Loshchenov M, Schnabel JA, East JE, Wagnieres G, Loschenov VB, Grisan E, Daul C, Blondel W, Rittscher J. An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy. Sci Rep 2020; 10:2748. [PMID: 32066744 PMCID: PMC7026422 DOI: 10.1038/s41598-020-59413-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 01/28/2020] [Indexed: 02/07/2023] Open
Abstract
We present a comprehensive analysis of the submissions to the first edition of the Endoscopy Artefact Detection challenge (EAD). Using crowd-sourcing, this initiative is a step towards understanding the limitations of existing state-of-the-art computer vision methods applied to endoscopy and promoting the development of new approaches suitable for clinical translation. Endoscopy is a routine imaging technique for the detection, diagnosis and treatment of diseases in hollow-organs; the esophagus, stomach, colon, uterus and the bladder. However the nature of these organs prevent imaged tissues to be free of imaging artefacts such as bubbles, pixel saturation, organ specularity and debris, all of which pose substantial challenges for any quantitative analysis. Consequently, the potential for improved clinical outcomes through quantitative assessment of abnormal mucosal surface observed in endoscopy videos is presently not realized accurately. The EAD challenge promotes awareness of and addresses this key bottleneck problem by investigating methods that can accurately classify, localize and segment artefacts in endoscopy frames as critical prerequisite tasks. Using a diverse curated multi-institutional, multi-modality, multi-organ dataset of video frames, the accuracy and performance of 23 algorithms were objectively ranked for artefact detection and segmentation. The ability of methods to generalize to unseen datasets was also evaluated. The best performing methods (top 15%) propose deep learning strategies to reconcile variabilities in artefact appearance with respect to size, modality, occurrence and organ type. However, no single method outperformed across all tasks. Detailed analyses reveal the shortcomings of current training strategies and highlight the need for developing new optimal metrics to accurately quantify the clinical applicability of methods.
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Affiliation(s)
- Sharib Ali
- Institute of Biomedical Engineering, Big Data Institute, Department of Engineering Science, University of Oxford, Oxford, UK.
| | - Felix Zhou
- Ludwig Institute for Cancer Research, University of Oxford, Oxford, UK
| | - Barbara Braden
- Translational Gastroenterology Unit, Nuffield Department of Medicine, Experimental Medicine Div., John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Adam Bailey
- Translational Gastroenterology Unit, Nuffield Department of Medicine, Experimental Medicine Div., John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Suhui Yang
- Ping An Technology (Shenzhen) Co. Ltd, Shenzhen, China
| | - Guanju Cheng
- Ping An Technology (Shenzhen) Co. Ltd, Shenzhen, China
| | | | | | | | | | | | - Xiaokang Wang
- Department of Biomedical Engineering, University of California, Davis, USA
| | - Chunqing Wang
- Department of Ultrasound Imaging, Tiantan Hospital, Beijing, China
| | - Seiryo Watanabe
- Department of Bioinformatic Engineering, Osaka University, Suita, Osaka, Japan
| | - Ilkay Oksuz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey
| | - Qingtian Ning
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Shufan Yang
- School of Engineering, University of Glasgow, Glasgow, UK
| | - Mohammad Azam Khan
- Department of Computer Science and Engineering, Korea University, Seoul, South Korea
| | - Xiaohong W Gao
- Department of Computer Science, Middlesex University, London, UK
| | | | - Maxim Loshchenov
- A.M. Prokhorov General Physics Institute, Russian Academy of Science, Moscow, Russia
| | - Julia A Schnabel
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - James E East
- Translational Gastroenterology Unit, Nuffield Department of Medicine, Experimental Medicine Div., John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Georges Wagnieres
- Swiss Federal Institute of Technology in Lausanne (EPFL), Lausanne, Switzerland
| | - Victor B Loschenov
- A.M. Prokhorov General Physics Institute, Russian Academy of Science, Moscow, Russia
| | - Enrico Grisan
- Department of Information Engineering, University of Padova, Padova, Italy
- School of Engineering, London South Bank University, London, UK
| | - Christian Daul
- CRAN UMR 7039, University of Lorraine, CNRS, Nancy, France
| | - Walter Blondel
- CRAN UMR 7039, University of Lorraine, CNRS, Nancy, France
| | - Jens Rittscher
- Institute of Biomedical Engineering, Big Data Institute, Department of Engineering Science, University of Oxford, Oxford, UK
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