From image to intervention: Innovation in cardiometabolic medicine - Thesis

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From image to intervention

Innovation in cardiometabolic medicine

Smits, L.P.

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2019

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Smits, L. P. (2019). From image to intervention: Innovation in cardiometabolic medicine.

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FROM IMAGE TO INTERVENTION:

INNOVATION IN CARDIOMETABOLIC MEDICINE

Loek Smits

FR

OM IMA

GE T

O INTER

VENTION: INNO

VA

TION IN CARDIOMET

ABOLIC MEDICINE

Loek Smits

UITNODIGING

voor het bijwonen van

de openbare verdediging

van het proefschrift

FROM IMAGE TO

INTERVENTION:

INNOVATION IN

CARDIOMETABOLIC

MEDICINE

door

Loek Smits

Donderdag 9 mei 2019 om 10.00 uur

Agnietenkapel

Oudezijds Voorburgwal 231,

Amsterdam

Receptie na de promotieplechtigheid

Café de Brakke Grond

Nes 43, Amsterdam

Feestelijke borrel vanaf 20.00 uur

Bar Bouwmeester

Rijnstraat 36, Amsterdam

De paranimfen

Ruben Smits

(rubensmits87@gmail.com)

Paulus Blommaert

(p.p.blommaert@gmail.com)

l.p.smits@amc.uva.nl

FROM IMAGE TO INTERVENTION:

INNOVATION IN CARDIOMETABOLIC MEDICINE

Copyright ©2019, Loek Smits, Amsterdam, the Netherlands

No part of this thesis may be reproduced, stored in a retrieval system or transmitted in any form or by any means, withour prior written permission of the author.

ISBN 978-94-6380-315-1

Author: Loek Smits

Layout: proefschriftmaken.nl

Photography (cover, p15, p82, p203): Marjolein van den Boogert (www.instagram.com/marjolinaaa)

Printed by: proefschriftmaken.nl

The research described in this thesis was supported by a grand of the Dutch Heart

Foundation (CVON IN-CONTROL). Financial support from the Dutch Heart Foundation for the publication of this thesis is gratefully acknowledged.

Financial support for printing this thesis was kindly provided by Stichting tot Steun Promovendi Vasculaire Geneeskunde, Servier Nederland Farma B.V., Chipsoft, Prescan Nederland, and AMSTOL stichting.

FROM IMAGE TO INTERVENTION:

INNOVATION IN CARDIOMETABOLIC MEDICINE

ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus

prof. dr. ir. K.I.J. Maex

ten overstaan van een door het College voor Promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel

op donderdag 9 mei 2019, te 10.00 uur door

Loek Pieter Smits

Copyright ©2019, Loek Smits, Amsterdam, the Netherlands

No part of this thesis may be reproduced, stored in a retrieval system or transmitted in any form or by any means, withour prior written permission of the author.

ISBN 978-94-6380-315-1

Author: Loek Smits

Layout: proefschriftmaken.nl

Photography (cover, p15, p82, p203): Marjolein van den Boogert (www.instagram.com/marjolinaaa)

Printed by: proefschriftmaken.nl

The research described in this thesis was supported by a grand of the Dutch Heart

Foundation (CVON IN-CONTROL). Financial support from the Dutch Heart Foundation for the publication of this thesis is gratefully acknowledged.

Financial support for printing this thesis was kindly provided by Stichting tot Steun Promovendi Vasculaire Geneeskunde, Servier Nederland Farma B.V., Chipsoft, Prescan Nederland, and AMSTOL stichting.

FROM IMAGE TO INTERVENTION:

INNOVATION IN CARDIOMETABOLIC MEDICINE

ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus

prof. dr. ir. K.I.J. Maex

ten overstaan van een door het College voor Promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel

op donderdag 9 mei 2019, te 10.00 uur door

Loek Pieter Smits

Promotiecommissie:

Promotores: prof. dr. M. Nieuwdorp AMC-UvA

prof. dr. ir. A.J. Nederveen AMC-UvA

Copromotor: dr. B.F. Coolen AMC-UvA

Overige leden: prof. dr. E. Lutgens AMC-UvA

prof. dr. W.J. de Jonge AMC-UvA

prof. dr. R. Shiri-Sverdlov Universiteit Maastricht prof. dr. T. Leiner Universiteit Utrecht prof. dr. ir. G.J. Strijkers AMC-UvA

prof. dr. J.J.P. Kastelein AMC-UvA Faculteit der Geneeskunde

FROM IMAGE TO INTERVENTION:

INNOVATION IN CARDIOMETABOLIC MEDICINE

Development and validation of imaging biomarkers in atherosclerosis and NAFLD

Chapter 2 Manual versus Automated Carotid Artery Plaque Component Segmentation in High and Lower Quality 3.0 Tesla MRI Scans

PloS One (2016)

Chapter 3 Three-dimensional quantitative T1 and T2 mapping of the carotid artery: Sequence design and in vivo feasibility

Magnetic Resonance Medicine (2016)

Chapter 4 Evaluation of ultrasmall superparamagnetic iron-oxide (USPIO) enhanced MRI with ferumoxytol to quantify arterial wall inflammation

Atherosclerosis (2017)

Chapter 5 Noninvasive Differentiation between Hepatic Steatosis and Steatohepatitis with MR Imaging Enhanced with USPIOs in Patients with Nonalcoholic Fatty Liver Disease: A Proof-of-Concept Study

Radiology (2016) Part II.

Application of imaging biomarkers in atherosclerosis and NAFLD

Chapter 6 Reversal of atherosclerosis with apolipoprotein A1: back to basics

Atherosclerosis (2014)

Chapter 7 HDL mimetic CER-001 targets atherosclerotic plaques in patients Atherosclerosis (2016)

Chapter 8 Effect of open-label infusion of an apoA-I-containing particle (CER-001) on RCT and artery wall thickness in patients with FHA

Journal of Lipid Research (2015)

Chapter 9 The effect of an apolipoprotein A-I-containing high-density lipoprotein-mimetic particle (CER-001) on carotid artery wall thickness in patients with homozygous familial hypercholesterolemia

Promotiecommissie:

Promotores: prof. dr. M. Nieuwdorp AMC-UvA

prof. dr. ir. A.J. Nederveen AMC-UvA

Copromotor: dr. B.F. Coolen AMC-UvA

Overige leden: prof. dr. E. Lutgens AMC-UvA

prof. dr. W.J. de Jonge AMC-UvA

prof. dr. R. Shiri-Sverdlov Universiteit Maastricht prof. dr. T. Leiner Universiteit Utrecht prof. dr. ir. G.J. Strijkers AMC-UvA

prof. dr. J.J.P. Kastelein AMC-UvA Faculteit der Geneeskunde

FROM IMAGE TO INTERVENTION:

INNOVATION IN CARDIOMETABOLIC MEDICINE

Development and validation of imaging biomarkers in atherosclerosis and NAFLD

Chapter 2 Manual versus Automated Carotid Artery Plaque Component Segmentation in High and Lower Quality 3.0 Tesla MRI Scans

PloS One (2016)

Chapter 3 Three-dimensional quantitative T1 and T2 mapping of the carotid artery: Sequence design and in vivo feasibility

Magnetic Resonance Medicine (2016)

Chapter 4 Evaluation of ultrasmall superparamagnetic iron-oxide (USPIO) enhanced MRI with ferumoxytol to quantify arterial wall inflammation

Atherosclerosis (2017)

Chapter 5 Noninvasive Differentiation between Hepatic Steatosis and Steatohepatitis with MR Imaging Enhanced with USPIOs in Patients with Nonalcoholic Fatty Liver Disease: A Proof-of-Concept Study

Radiology (2016) Part II.

Application of imaging biomarkers in atherosclerosis and NAFLD

Chapter 6 Reversal of atherosclerosis with apolipoprotein A1: back to basics

Atherosclerosis (2014)

Chapter 7 HDL mimetic CER-001 targets atherosclerotic plaques in patients Atherosclerosis (2016)

Chapter 8 Effect of open-label infusion of an apoA-I-containing particle (CER-001) on RCT and artery wall thickness in patients with FHA

Journal of Lipid Research (2015)

Chapter 9 The effect of an apolipoprotein A-I-containing high-density lipoprotein-mimetic particle (CER-001) on carotid artery wall thickness in patients with homozygous familial hypercholesterolemia

Chapter 10 Therapeutic potential of fecal microbiota transplantation

Gastroenterology (2013)

Chapter 11 Effect of Vegan Fecal Microbiota Transplantation on Carnitine- and Choline-Derived Trimethylamine-N-Oxide Production and Vascular Inflammation in Patients With Metabolic Syndrome

Journal of the America Heart Association (2018)

Chapter 12 The effect of vegan lean donor fecal microbiota transplantation on hepatic fat content and inflammation in subjects with non-alcoholic fatty liver disease: rationale and study design

Chapter 13 Summary, general discussion and future perspectives Appendices

Nederlandstalige samenvatting Authors and affiliations List of publications Portfolio

Dankwoord Curriculum Vitae

Chapter 10 Therapeutic potential of fecal microbiota transplantation

Gastroenterology (2013)

Chapter 11 Effect of Vegan Fecal Microbiota Transplantation on Carnitine- and Choline-Derived Trimethylamine-N-Oxide Production and Vascular Inflammation in Patients With Metabolic Syndrome

Journal of the America Heart Association (2018)

Chapter 12 The effect of vegan lean donor fecal microbiota transplantation on hepatic fat content and inflammation in subjects with non-alcoholic fatty liver disease: rationale and study design

Chapter 13 Summary, general discussion and future perspectives Appendices

Nederlandstalige samenvatting Authors and affiliations List of publications Portfolio

Dankwoord Curriculum Vitae

CHAPTER 1

General introduction and

thesis outline

CHAPTER 1

10

“Je gaat het pas zien als je het doorhebt”

(freely translated as “you can only see it, when you get it”)

This famous quote by Johan Cruijff is, like most of his quotes, hard to refute. But in medicine, more often the opposite is truth: that we only begin to understand a disease, once we have seen what it causes in the human body. In the last century, medicine upgraded from a caring (“the art of medicine consists of amusing the patient while nature cures the disease”, Voltaire, 1694 – 1778) to a curing profession 1_{. The basis of this evolution} is the increasingly detailed pathologists eye on diseased tissue, from solely macroscopic evaluation of deceased patients in the past, up to immunohistochemical microscopic evaluation of endoscopic derived biopsies nowadays. This led to insights in the pathophysiological basis of disease, which in turn led to increased ability to diagnose and treat more and more diseases.

In modern day medicine, medical imaging techniques can serve as a non-invasive eye on diseased tissue, and is therefore increasingly used for diagnostic and prognostic purposes in clinical medicine 2_{. Advances in technology enable imaging techniques not only to visualize} anatomic, but also physiologic, biochemical and functional processes. This may lead to specific imaging biomarkers, which can be used for both clinical and scientific purposes, as well as to study the mechanism of disease.

Atherosclerosis and non-alcoholic fatty liver disease (NAFLD) are two diseases in which such imaging biomarkers may play a valuable role. Both diseases have a long, mostly asymptomatic preclinical phase between disease initiation and the development of complications (i.e. arterial thrombo-embolic events for atherosclerosis, liver cirrhosis in NAFLD). Here, imaging biomarkers may aid in the detection of the disease, the identification of patients at risk for complications, and thereby the selection of patients who are eligible for early and/or aggressive treatment. Also for research purposes, imaging biomarkers can be of use in atherosclerosis and NAFLD. Because of the long subclinical phase of both diseases, therapy is aimed at the prevention of long-term complications. As a result of this, trials evaluating novel treatment strategies in atherosclerosis and NAFLD must have a high number of participants and a long follow-up, resulting in high costs. Proof-of-concept clinical trials using imaging biomarkers as surrogate outcome parameters can help to demonstrate the potential efficacy of novel therapies.

The aim of the present thesis was to developed, validate and apply novel imaging biomarkers in atherosclerosis and NAFLD for both 1) the identification of patients at risk for complications, and 2) the assessment of the potential efficacy of novel therapies.

Atherosclerosis and non-alcoholic steatohepatitis (NASH), the form of NAFLD with a higher risk of complications, share some more similarities 3_{. First, epidemiological studies report an} association between NASH and atherosclerotic cardiovascular disease (CVD) 4,5_{. Whether} this relation is solely driven by shared risk factors (caused by the metabolic syndrome), or

that NASH is an independent risk factor causing atherosclerotic CVD through low-grade inflammation, remains to be elucidated. Second, in both atherosclerosis and NASH, activation of macrophages by modified lipids are thought to play a central role in the initiation and progression of the disease. In atherosclerosis, monocyte derived macrophages in the arterial wall take up oxidized low-density lipoproteins by scavenger receptors, which will subsequently lead to lipid accumulation, foam cell formation, the initiation of a pro-inflammatory cascade and thereby progression of the disease 6_{. Likewise, activation of} hepatic resident macrophages (Kupffer cells) have been proposed to initiate the hepatic inflammatory response in NASH. Uptake of modified free-fatty acids and lipoproteins is, with gut-derived damage- and pathogen associated molecular signals (DAMPs and PAMPs) and hepatocyte injury, responsible for Kupffer cell activation 7,8_{. The shared pathophysiology} is exemplified by a NASH phenotype observed in Apolipoprotein E and LDL-R knockout mice on high-fat diet 9_{, models traditionally used as an experimental model for atherosclerosis,} and the recent finding of a protective effect on both atherosclerosis and NASH of a polymorphism which stabilized LXR 10_.

Thesis outline

Part I of this thesis focusses on the development and validation of novel imaging biomarkers in atherosclerosis and NASH. For atherosclerosis, the main goal of imaging biomarkers is the identification of characteristics related to atherosclerotic plaque vulnerability. Thrombo-embolic complications of atherosclerotic disease occur when an atherosclerotic plaque ruptures, and the subsequent thrombocyte activation causes thrombus formation, which can occlude the artery directly (i.e. in myocardial infarction) or embolize to a more distal artery (i.e. in ischemic stroke). Hence, the vulnerability (chance of rupture) of an atherosclerotic plaque determines the risk of thrombo-embolic complications for a great part. Based on findings in autopsy studies 11_{, a vulnerable plaque is characterized by 1) the} presence of certain plaque components (calcifications, lipid-rich necrotic cores, intraplaque hemorrhage), 2) the presence of inflammatory cells (mainly macrophages), 3) increased plaque permeability and 4) a thin fibrous cap. These characteristics cannot be accurately determined by imaging modalities which assess the severity of coronary artery disease (coronary angiography) and carotid artery disease (duplex ultrasonography) based on stenosis grade. Magnetic resonance imaging (MRI), due to its high spatial resolution and tissue contrast, enables the detection of lipid-rich necrotic cores (LRNC), intraplaque hemorrhage (IPH) and calcifications (calcifications), and assessment of fibrous cap thickness in carotid artery atherosclerotic plaques 12,13_{. We assessed whether identification of these} plaque components on 3.0 Tesla MRI scans of carotid artery plaques could be automated (Chapter 2), and made efforts to develop MRI protocols for quantitative T1 and T2 imaging 14 _{of the carotid arterial wall to aid in more reliable plaque component analysis in the future} (Chapter 3). 18_{F-fluorodeoxyglucose positron emission/computed tomography (}18_F-FDG PET/CT) scans of the carotid arteries and the aorta is a well validated surrogate parameter of arterial wall inflammation 15_{, and currently widely used in studies investigating novel} atherogenic risk factors 16–18 _{and to evaluate the effect of novel anti-inflammatory drugs for}

GENERAL INTRODUCTION AND THESIS OUTLINE

11

1

“Je gaat het pas zien als je het doorhebt”

(freely translated as “you can only see it, when you get it”)

This famous quote by Johan Cruijff is, like most of his quotes, hard to refute. But in medicine, more often the opposite is truth: that we only begin to understand a disease, once we have seen what it causes in the human body. In the last century, medicine upgraded from a caring (“the art of medicine consists of amusing the patient while nature cures the disease”, Voltaire, 1694 – 1778) to a curing profession 1_{. The basis of this evolution} is the increasingly detailed pathologists eye on diseased tissue, from solely macroscopic evaluation of deceased patients in the past, up to immunohistochemical microscopic evaluation of endoscopic derived biopsies nowadays. This led to insights in the pathophysiological basis of disease, which in turn led to increased ability to diagnose and treat more and more diseases.

In modern day medicine, medical imaging techniques can serve as a non-invasive eye on diseased tissue, and is therefore increasingly used for diagnostic and prognostic purposes in clinical medicine 2_{. Advances in technology enable imaging techniques not only to visualize} anatomic, but also physiologic, biochemical and functional processes. This may lead to specific imaging biomarkers, which can be used for both clinical and scientific purposes, as well as to study the mechanism of disease.

Atherosclerosis and non-alcoholic fatty liver disease (NAFLD) are two diseases in which such imaging biomarkers may play a valuable role. Both diseases have a long, mostly asymptomatic preclinical phase between disease initiation and the development of complications (i.e. arterial thrombo-embolic events for atherosclerosis, liver cirrhosis in NAFLD). Here, imaging biomarkers may aid in the detection of the disease, the identification of patients at risk for complications, and thereby the selection of patients who are eligible for early and/or aggressive treatment. Also for research purposes, imaging biomarkers can be of use in atherosclerosis and NAFLD. Because of the long subclinical phase of both diseases, therapy is aimed at the prevention of long-term complications. As a result of this, trials evaluating novel treatment strategies in atherosclerosis and NAFLD must have a high number of participants and a long follow-up, resulting in high costs. Proof-of-concept clinical trials using imaging biomarkers as surrogate outcome parameters can help to demonstrate the potential efficacy of novel therapies.

The aim of the present thesis was to developed, validate and apply novel imaging biomarkers in atherosclerosis and NAFLD for both 1) the identification of patients at risk for complications, and 2) the assessment of the potential efficacy of novel therapies.

Atherosclerosis and non-alcoholic steatohepatitis (NASH), the form of NAFLD with a higher risk of complications, share some more similarities 3_{. First, epidemiological studies report an} association between NASH and atherosclerotic cardiovascular disease (CVD) 4,5_{. Whether} this relation is solely driven by shared risk factors (caused by the metabolic syndrome), or

that NASH is an independent risk factor causing atherosclerotic CVD through low-grade inflammation, remains to be elucidated. Second, in both atherosclerosis and NASH, activation of macrophages by modified lipids are thought to play a central role in the initiation and progression of the disease. In atherosclerosis, monocyte derived macrophages in the arterial wall take up oxidized low-density lipoproteins by scavenger receptors, which will subsequently lead to lipid accumulation, foam cell formation, the initiation of a pro-inflammatory cascade and thereby progression of the disease 6_{. Likewise, activation of} hepatic resident macrophages (Kupffer cells) have been proposed to initiate the hepatic inflammatory response in NASH. Uptake of modified free-fatty acids and lipoproteins is, with gut-derived damage- and pathogen associated molecular signals (DAMPs and PAMPs) and hepatocyte injury, responsible for Kupffer cell activation 7,8_{. The shared pathophysiology} is exemplified by a NASH phenotype observed in Apolipoprotein E and LDL-R knockout mice on high-fat diet 9_{, models traditionally used as an experimental model for atherosclerosis,} and the recent finding of a protective effect on both atherosclerosis and NASH of a polymorphism which stabilized LXR 10_.

Thesis outline

Part I of this thesis focusses on the development and validation of novel imaging biomarkers in atherosclerosis and NASH. For atherosclerosis, the main goal of imaging biomarkers is the identification of characteristics related to atherosclerotic plaque vulnerability. Thrombo-embolic complications of atherosclerotic disease occur when an atherosclerotic plaque ruptures, and the subsequent thrombocyte activation causes thrombus formation, which can occlude the artery directly (i.e. in myocardial infarction) or embolize to a more distal artery (i.e. in ischemic stroke). Hence, the vulnerability (chance of rupture) of an atherosclerotic plaque determines the risk of thrombo-embolic complications for a great part. Based on findings in autopsy studies 11_{, a vulnerable plaque is characterized by 1) the} presence of certain plaque components (calcifications, lipid-rich necrotic cores, intraplaque hemorrhage), 2) the presence of inflammatory cells (mainly macrophages), 3) increased plaque permeability and 4) a thin fibrous cap. These characteristics cannot be accurately determined by imaging modalities which assess the severity of coronary artery disease (coronary angiography) and carotid artery disease (duplex ultrasonography) based on stenosis grade. Magnetic resonance imaging (MRI), due to its high spatial resolution and tissue contrast, enables the detection of lipid-rich necrotic cores (LRNC), intraplaque hemorrhage (IPH) and calcifications (calcifications), and assessment of fibrous cap thickness in carotid artery atherosclerotic plaques 12,13_{. We assessed whether identification of these} plaque components on 3.0 Tesla MRI scans of carotid artery plaques could be automated (Chapter 2), and made efforts to develop MRI protocols for quantitative T1 and T2 imaging 14 _{of the carotid arterial wall to aid in more reliable plaque component analysis in the future} (Chapter 3). 18_{F-fluorodeoxyglucose positron emission/computed tomography (}18_F-FDG PET/CT) scans of the carotid arteries and the aorta is a well validated surrogate parameter of arterial wall inflammation 15_{, and currently widely used in studies investigating novel} atherogenic risk factors 16–18 _{and to evaluate the effect of novel anti-inflammatory drugs for}

CHAPTER 1

12

the treatment of atherosclerotic CVD 19_{. Ultrasmall superparamagnetic iron-oxide enhanced} MRI (USPIO-MRI) of the arteriall wall 20 _{is an alternative to measure plaque inflammation,} not hampered by low spatial resolution or radiation exposure. In Chapter 4, we developed a novel, quantitative protocol for USPIO-MRI, using a clinically available USPIO, and studied the correlation between USPIO and 18_{F-FDG uptake in carotid atherosclerotic plaques.} In NAFLD, the distinction between NASH and simple steatosis is of clinical importance mainly for prognostic reasons, as patients with NASH have a higher risk of hepatic (liver cirrhosis, hepatocellular carcinoma) and extrahepatic (mainly CVD) complications 21_. Furthermore, as in recent years advances have been made in the development of drugs for the treatment of NASH, differentiation between these disease states will become important to select patients eligible for treatment. At the moment, histopathological evaluation of a liver biopsy sample remains the gold standard, and only reliable method to differentiate NASH from simple steatosis. Imaging biomarkers can reliably assess steatosis grade and fibrosis grade 22_{, but fail on the assessment of (necro-) inflammatory grade, the key} distinction between NASH and simple steatosis. In Chapter 5 we developed a protocol to measure USPIO uptake in the liver using MRI, and studied whether USPIO-MRI of the liver was able to non-invasively asses hepatic inflammation and discriminate between NASH and simple steatosis.

In Part II we evaluated the efficacy of novel treatment strategies for atherosclerosis and NASH in proof-of-concept studies using imaging biomarkers as primary (Chapter 8 and 9) or secondary outcome parameter (Chapter 11, 12). The gut had a central role in both treatment strategies evaluated in this thesis. In Chapter 7, 8 and 9 we aimed to improve reversed cholesterol transport to the gut by recombinant HDL infusions, and assessed the effect on atherosclerosis severity. Also, we studied the effect of fecal microbiota

transplantation, aiming to modulate gut microbiota composition and thereby host

metabolism, in patients with at risk for atherosclerotic CVD (Chapter 11) and NASH (Chapter 12). For an extensive introduction to, and rationale on recombinant HDL infusions (Chapter 6) and fecal microbiota transplantation (Chapter 10) we refer to the published review articles included in this thesis.

References

1. Underwood, J. C. E. More than meets the eye: the changing face of histopathology. Histopathology 70, 4–9 (2017).

2. Prescott, J. W. Quantitative imaging biomarkers: the application of advanced image processing and analysis to clinical and preclinical decision making. J. Digit. Imaging 26, 97–108 (2013).

3. Bieghs, V., Rensen, P. C. N., Hofker, M. H. & Shiri-Sverdlov, R. NASH and atherosclerosis are two aspects of a shared disease: central role for macrophages. Atherosclerosis 220, 287–93 (2012).

4. Pisto, P., Santaniemi, M., Bloigu, R., Ukkola, O. & Kesäniemi, Y. A. Fatty liver predicts the risk for cardiovascular events in middle-aged population: a population-based cohort study. BMJ Open 4, e004973 (2014).

5. Bhatia, L. S., Curzen, N. P., Calder, P. C. & Byrne, C. D. Non-alcoholic fatty liver disease: a new and important cardiovascular risk factor? Eur. Heart J. 33, 1190–200 (2012).

6. Libby, P., Ridker, P. M. & Hansson, G. K. Inflammation in atherosclerosis: from pathophysiology to practice. J. Am. Coll. Cardiol. 54, 2129–38 (2009).

7. Zhu, L. et al. Characterization of gut microbiomes in nonalcoholic steatohepatitis (NASH) patients: A connection between endogenous alcohol and NASH. Hepatology 57, 601–609 (2013).

8. Liu, W., Baker, R. D., Bhatia, T., Zhu, L. & Baker, S. S. Pathogenesis of nonalcoholic steatohepatitis. Cell. Mol. Life Sci. 73, 1969–87 (2016).

9. Tous, M., Ferré, N., Camps, J., Riu, F. & Joven, J. Feeding apolipoprotein E-knockout mice with cholesterol and fat enriched diets may be a model of non-alcoholic steatohepatitis. Mol. Cell. Biochem. 268, 53–8 (2005).

10. Hsieh, J. et al. TTC39B deficiency stabilizes LXR reducing both atherosclerosis and steatohepatitis. Nature 535, 303–7 (2016). 11. Virmani, R., Burke, A. P., Farb, A. & Kolodgie, F. D. Pathology of the vulnerable plaque. J. Am. Coll. Cardiol. 47, C13-8 (2006). 12. Balu, N. et al. Carotid plaque assessment using fast 3D isotropic resolution black-blood MRI. Magn. Reson. Med. 65, 627–37

(2011).

13. Balu, N. et al. Current techniques for MR imaging of atherosclerosis. Top. Magn. Reson. Imaging 20, 203–15 (2009). 14. Coolen, B. F. et al. Vessel wall characterization using quantitative MRI: what’s in a number? MAGMA 31, 201–222 (2018). 15. Rudd, J. H. F. Imaging Atherosclerotic Plaque Inflammation With [18F]-Fluorodeoxyglucose Positron Emission Tomography.

Circulation 105, 2708–2711 (2002).

16. van der Valk, F. M. et al. Oxidized Phospholipids on Lipoprotein(a) Elicit Arterial Wall Inflammation and an Inflammatory Monocyte Response in Humans. Circulation 134, 611–24 (2016).

17. Bernelot Moens, S. J. et al. Arterial and Cellular Inflammation in Patients with CKD. J. Am. Soc. Nephrol. 28, 1278–1285 (2017). 18. Lawal, I. O., Ankrah, A. O., Popoola, G. O., Lengana, T. & Sathekge, M. M. Arterial inflammation in young patients with human

immunodeficiency virus infection: A cross-sectional study using F-18 FDG PET/CT. J. Nucl. Cardiol. (2018). doi:10.1007/s12350-018-1207-x

19. Fayad, Z. a et al. Safety and efficacy of dalcetrapib on atherosclerotic disease using novel non-invasive multimodality imaging (dal-PLAQUE): a randomised clinical trial. Lancet 378, 1547–59 (2011).

20. Trivedi, R. a et al. Identifying inflamed carotid plaques using in vivo USPIO-enhanced MR imaging to label plaque macrophages.

Arterioscler. Thromb. Vasc. Biol. 26, 1601–6 (2006).

21. Bedossa, P. Diagnosis of non-alcoholic fatty liver disease/non-alcoholic steatohepatitis: Why liver biopsy is essential. Liver Int. 38 Suppl 1, 64–66 (2018).

GENERAL INTRODUCTION AND THESIS OUTLINE

13

1

the treatment of atherosclerotic CVD 19_{. Ultrasmall superparamagnetic iron-oxide enhanced} MRI (USPIO-MRI) of the arteriall wall 20 _{is an alternative to measure plaque inflammation,} not hampered by low spatial resolution or radiation exposure. In Chapter 4, we developed a novel, quantitative protocol for USPIO-MRI, using a clinically available USPIO, and studied the correlation between USPIO and 18_{F-FDG uptake in carotid atherosclerotic plaques.} In NAFLD, the distinction between NASH and simple steatosis is of clinical importance mainly for prognostic reasons, as patients with NASH have a higher risk of hepatic (liver cirrhosis, hepatocellular carcinoma) and extrahepatic (mainly CVD) complications 21_. Furthermore, as in recent years advances have been made in the development of drugs for the treatment of NASH, differentiation between these disease states will become important to select patients eligible for treatment. At the moment, histopathological evaluation of a liver biopsy sample remains the gold standard, and only reliable method to differentiate NASH from simple steatosis. Imaging biomarkers can reliably assess steatosis grade and fibrosis grade 22_{, but fail on the assessment of (necro-) inflammatory grade, the key} distinction between NASH and simple steatosis. In Chapter 5 we developed a protocol to measure USPIO uptake in the liver using MRI, and studied whether USPIO-MRI of the liver was able to non-invasively asses hepatic inflammation and discriminate between NASH and simple steatosis.

In Part II we evaluated the efficacy of novel treatment strategies for atherosclerosis and NASH in proof-of-concept studies using imaging biomarkers as primary (Chapter 8 and 9) or secondary outcome parameter (Chapter 11, 12). The gut had a central role in both treatment strategies evaluated in this thesis. In Chapter 7, 8 and 9 we aimed to improve reversed cholesterol transport to the gut by recombinant HDL infusions, and assessed the effect on atherosclerosis severity. Also, we studied the effect of fecal microbiota

transplantation, aiming to modulate gut microbiota composition and thereby host

metabolism, in patients with at risk for atherosclerotic CVD (Chapter 11) and NASH (Chapter 12). For an extensive introduction to, and rationale on recombinant HDL infusions (Chapter 6) and fecal microbiota transplantation (Chapter 10) we refer to the published review articles included in this thesis.

References

1. Underwood, J. C. E. More than meets the eye: the changing face of histopathology. Histopathology 70, 4–9 (2017).

2. Prescott, J. W. Quantitative imaging biomarkers: the application of advanced image processing and analysis to clinical and preclinical decision making. J. Digit. Imaging 26, 97–108 (2013).

3. Bieghs, V., Rensen, P. C. N., Hofker, M. H. & Shiri-Sverdlov, R. NASH and atherosclerosis are two aspects of a shared disease: central role for macrophages. Atherosclerosis 220, 287–93 (2012).

4. Pisto, P., Santaniemi, M., Bloigu, R., Ukkola, O. & Kesäniemi, Y. A. Fatty liver predicts the risk for cardiovascular events in middle-aged population: a population-based cohort study. BMJ Open 4, e004973 (2014).

5. Bhatia, L. S., Curzen, N. P., Calder, P. C. & Byrne, C. D. Non-alcoholic fatty liver disease: a new and important cardiovascular risk factor? Eur. Heart J. 33, 1190–200 (2012).

6. Libby, P., Ridker, P. M. & Hansson, G. K. Inflammation in atherosclerosis: from pathophysiology to practice. J. Am. Coll. Cardiol. 54, 2129–38 (2009).

7. Zhu, L. et al. Characterization of gut microbiomes in nonalcoholic steatohepatitis (NASH) patients: A connection between endogenous alcohol and NASH. Hepatology 57, 601–609 (2013).

8. Liu, W., Baker, R. D., Bhatia, T., Zhu, L. & Baker, S. S. Pathogenesis of nonalcoholic steatohepatitis. Cell. Mol. Life Sci. 73, 1969–87 (2016).

9. Tous, M., Ferré, N., Camps, J., Riu, F. & Joven, J. Feeding apolipoprotein E-knockout mice with cholesterol and fat enriched diets may be a model of non-alcoholic steatohepatitis. Mol. Cell. Biochem. 268, 53–8 (2005).

10. Hsieh, J. et al. TTC39B deficiency stabilizes LXR reducing both atherosclerosis and steatohepatitis. Nature 535, 303–7 (2016). 11. Virmani, R., Burke, A. P., Farb, A. & Kolodgie, F. D. Pathology of the vulnerable plaque. J. Am. Coll. Cardiol. 47, C13-8 (2006). 12. Balu, N. et al. Carotid plaque assessment using fast 3D isotropic resolution black-blood MRI. Magn. Reson. Med. 65, 627–37

(2011).

13. Balu, N. et al. Current techniques for MR imaging of atherosclerosis. Top. Magn. Reson. Imaging 20, 203–15 (2009). 14. Coolen, B. F. et al. Vessel wall characterization using quantitative MRI: what’s in a number? MAGMA 31, 201–222 (2018). 15. Rudd, J. H. F. Imaging Atherosclerotic Plaque Inflammation With [18F]-Fluorodeoxyglucose Positron Emission Tomography.

Circulation 105, 2708–2711 (2002).

16. van der Valk, F. M. et al. Oxidized Phospholipids on Lipoprotein(a) Elicit Arterial Wall Inflammation and an Inflammatory Monocyte Response in Humans. Circulation 134, 611–24 (2016).

17. Bernelot Moens, S. J. et al. Arterial and Cellular Inflammation in Patients with CKD. J. Am. Soc. Nephrol. 28, 1278–1285 (2017). 18. Lawal, I. O., Ankrah, A. O., Popoola, G. O., Lengana, T. & Sathekge, M. M. Arterial inflammation in young patients with human

immunodeficiency virus infection: A cross-sectional study using F-18 FDG PET/CT. J. Nucl. Cardiol. (2018). doi:10.1007/s12350-018-1207-x

19. Fayad, Z. a et al. Safety and efficacy of dalcetrapib on atherosclerotic disease using novel non-invasive multimodality imaging (dal-PLAQUE): a randomised clinical trial. Lancet 378, 1547–59 (2011).

20. Trivedi, R. a et al. Identifying inflamed carotid plaques using in vivo USPIO-enhanced MR imaging to label plaque macrophages.

Arterioscler. Thromb. Vasc. Biol. 26, 1601–6 (2006).

21. Bedossa, P. Diagnosis of non-alcoholic fatty liver disease/non-alcoholic steatohepatitis: Why liver biopsy is essential. Liver Int. 38 Suppl 1, 64–66 (2018).

PART I

Development and validation of imaging

biomarkers in atherosclerosis and NAFLD

Loek P. Smits; Diederik F. van Wijk; Raphael Duivenvoorden; Dongxiang Xu; Chun Yuan; Erik S. Stroes; Aart J. Nederveen.

PloS One (2016)

CHAPTER 2

Manual versus Automated Carotid Artery Plaque

Component Segmentation in High and Lower

CHAPTER 2

Purpose: To study the interscan reproducibility of manual versus automated

segmentation of carotid artery plaque components, and the agreement between both methods, in high and lower quality MRI scans.

Methods: 24 patients with 30-70% carotid artery stenosis were planned for 3T

carotid MRI, followed by a rescan within 1 month. A multicontrast protocol (T1w,T2w, PDw and TOF sequences) was used. After co-registration and delineation of the lumen and outer wall, segmentation of plaque components (lipid-rich necrotic cores (LRNC) and calcifications) was performed both manually and automated. Scan quality was assessed using a visual quality scale.

Results: Agreement for the detection of LRNC (cohen’s kappa (k) is 0.04) and

calcification (k=0.32) between both manual and automated segmentation methods was poor. In the high quality scans (visual quality score ≥ 3), the agreement between manual and automated segmentation increased to k=0.55 and k=0.58 for respectively the detection of LRNC and calcification larger than 1 mm2_{. Both manual} and automated analysis showed good interscan reproducibility for the quantification of LRNC (intraclass correlation coefficient (ICC) of 0.94 and 0.80 respectively) and calcified plaque area (ICC of 0.95 and 0.77 respectively).

Conclusion: Agreement between manual and automated segmentation of LRNC and

calcifications was poor, despite a good interscan reproducibility of both methods. The agreement between both methods increased to moderate in high quality scans. These findings indicate that image quality is a critical determinant of the performance of both manual and automated segmentation of carotid artery plaque components.

Introduction

Based on randomized controlled clinical trials 1,2_{, current guidelines recommend surgical} treatment (carotid endarterectomy) for symptomatic severe carotid artery stenosis (70%-99%) 3_{. Due to the relatively high risk of complications, surgical therapy is mainly beneficial} in patients at high risk for recurrent stroke. For patients with a moderate (<70%) symptomatic carotid artery stenosis, guidelines therefore advise medical treatment, consisting of lipid-lowering, antihypertensive and antiplatelet medication 3_{. Despite optimal} medical treatment, patients with moderate carotid artery stenosis are still at risk for recurrent stroke.

Insights in the individual patient risk for recurrent stroke can aid in the clinical decision for surgical or medical treatment. Besides luminal stenosis grade, measurement of other plaque specific characteristics (i.e. plaque composition, fibrous cap thickness, inflammatory activity 4_{) may help in identification of high risk patients. Multicontrast carotid Magnetic Resonance} Imaging (MRI) allows non-invasively assessment of plaque composition 5,6_{. The identification} of certain carotid artery plaque components by MRI (specifically intraplaque hemorrhage (IPH), lipid-rich necrotic core (LRNC) and calcifications), detected by MRI, were able to predict future ischemic stroke in several studies 7-11_{. Currently, larger prospective} multicenter studies are running to investigate the role of MRI-based plaque characterization in clinical risk-stratification models to predict (recurrent) ipsilateral stroke (PARISK 12_{), and to} aid in the choice for surgical or medical treatment in symptomatic carotid artery stenosis <70% (ECST-2, ISRCTN# 97744893).

Clinical implementation of carotid MRI for risk stratification in patients with carotid artery stenosis requires accurate, reproducible and high-throughput evaluation of MR-images of arterial wall plaques. The variability in neck size and location of the vessels relative to the skin may however in practice lead to a broad spectrum in image quality. To date, analysis of plaque components is predominantly performed manually 13_{. For widespread} implementation of carotid plaque component analysis (for example as an outcome parameter in large multicenter studies, or for the clinical decision whether or not to perform carotid endarterectomy), rapid and reliable analysis is essential. Automation of the analysis may aid in meeting these requirements. The findings in recent studies suggesting that fully automated plaque component analysis software (PlaqueView) may be as accurate and reproducible as the aforementioned manual analysis 14,15 _{are thus} encouraging. We, however, hypothesize that image quality is a critical determinant of the accuracy and reproducibility of automated segmentation of plaque components.

In the present paper, we therefore studied the agreement between manual versus automated plaque component segmentation and compared the reproducibility of both methods in patients with moderate (30-70%) carotid artery stenosis. In addition, we explored the impact of MR image quality on both the reproducibility of, and the agreement between, both methods.

Ab

st

ra

AUTOMATED VERSUS MANUAL PLAQUE COMPONENT ANALYSIS

19

2

Purpose: To study the interscan reproducibility of manual versus automated

segmentation of carotid artery plaque components, and the agreement between both methods, in high and lower quality MRI scans.

Methods: 24 patients with 30-70% carotid artery stenosis were planned for 3T

carotid MRI, followed by a rescan within 1 month. A multicontrast protocol (T1w,T2w, PDw and TOF sequences) was used. After co-registration and delineation of the lumen and outer wall, segmentation of plaque components (lipid-rich necrotic cores (LRNC) and calcifications) was performed both manually and automated. Scan quality was assessed using a visual quality scale.

Results: Agreement for the detection of LRNC (cohen’s kappa (k) is 0.04) and

calcification (k=0.32) between both manual and automated segmentation methods was poor. In the high quality scans (visual quality score ≥ 3), the agreement between manual and automated segmentation increased to k=0.55 and k=0.58 for respectively the detection of LRNC and calcification larger than 1 mm2_{. Both manual} and automated analysis showed good interscan reproducibility for the quantification of LRNC (intraclass correlation coefficient (ICC) of 0.94 and 0.80 respectively) and calcified plaque area (ICC of 0.95 and 0.77 respectively).

Conclusion: Agreement between manual and automated segmentation of LRNC and

calcifications was poor, despite a good interscan reproducibility of both methods. The agreement between both methods increased to moderate in high quality scans. These findings indicate that image quality is a critical determinant of the performance of both manual and automated segmentation of carotid artery plaque components.

Introduction

Based on randomized controlled clinical trials 1,2_{, current guidelines recommend surgical} treatment (carotid endarterectomy) for symptomatic severe carotid artery stenosis (70%-99%) 3_{. Due to the relatively high risk of complications, surgical therapy is mainly beneficial} in patients at high risk for recurrent stroke. For patients with a moderate (<70%) symptomatic carotid artery stenosis, guidelines therefore advise medical treatment, consisting of lipid-lowering, antihypertensive and antiplatelet medication 3_{. Despite optimal} medical treatment, patients with moderate carotid artery stenosis are still at risk for recurrent stroke.

Insights in the individual patient risk for recurrent stroke can aid in the clinical decision for surgical or medical treatment. Besides luminal stenosis grade, measurement of other plaque specific characteristics (i.e. plaque composition, fibrous cap thickness, inflammatory activity 4_{) may help in identification of high risk patients. Multicontrast carotid Magnetic Resonance} Imaging (MRI) allows non-invasively assessment of plaque composition 5,6_{. The identification} of certain carotid artery plaque components by MRI (specifically intraplaque hemorrhage (IPH), lipid-rich necrotic core (LRNC) and calcifications), detected by MRI, were able to predict future ischemic stroke in several studies 7-11_{. Currently, larger prospective} multicenter studies are running to investigate the role of MRI-based plaque characterization in clinical risk-stratification models to predict (recurrent) ipsilateral stroke (PARISK 12_{), and to} aid in the choice for surgical or medical treatment in symptomatic carotid artery stenosis <70% (ECST-2, ISRCTN# 97744893).

Clinical implementation of carotid MRI for risk stratification in patients with carotid artery stenosis requires accurate, reproducible and high-throughput evaluation of MR-images of arterial wall plaques. The variability in neck size and location of the vessels relative to the skin may however in practice lead to a broad spectrum in image quality. To date, analysis of plaque components is predominantly performed manually 13_{. For widespread} implementation of carotid plaque component analysis (for example as an outcome parameter in large multicenter studies, or for the clinical decision whether or not to perform carotid endarterectomy), rapid and reliable analysis is essential. Automation of the analysis may aid in meeting these requirements. The findings in recent studies suggesting that fully automated plaque component analysis software (PlaqueView) may be as accurate and reproducible as the aforementioned manual analysis 14,15 _{are thus} encouraging. We, however, hypothesize that image quality is a critical determinant of the accuracy and reproducibility of automated segmentation of plaque components.

In the present paper, we therefore studied the agreement between manual versus automated plaque component segmentation and compared the reproducibility of both methods in patients with moderate (30-70%) carotid artery stenosis. In addition, we explored the impact of MR image quality on both the reproducibility of, and the agreement between, both methods.

CHAPTER 2

20

Methods

This observational single center (Academic Medical Center Amsterdam) study was conducted in concordance with Good Clinical Practice guidelines. The study protocol was approved by the local investigational review board and written informed consent was obtained from all participants. As the current study used patient and MRI data from a previous study, patient selection and most study procedures are described in detail in previous publications 16,17_{. In short, patients with a 30-70% carotid artery stenosis on} ultrasound were included for a 3T-MRI scan of the carotid artery, followed by a rescan within 1 month. For the MRI scans, a 3T whole-body MRI scan (Intera, Philips Medical Systems, Best, The Netherlands) combined with a 8 channel dedicated bilateral carotid artery coil (Shanghai Chenguang Medical Technologies, Shanghai, China) was used. High resolution (0.25 by 0.25 mm) T1w, T2w, PDw and TOF images were acquired centered around the area with the most profound plaque burden, using ECG-gated unilateral axial sequences (imaging parameters in supplemental table 1 17_{). Overview images of the} planning scans were used to plan the repeat scan. Analysis of plaque composition was performed manually and automated for all included patients.

Image analysis

Before any quantitative analysis was performed, one reader (RD, 3 years of experience with carotid MRI) manually corrected all scan and rescan images for possible Z-axis displacement using T1w, PDw and TOF images in VesselMass. All scans and rescans were checked for co-registration using the carotid bifurcation as a localizer.

For manual analysis, vessel wall dimensions and components (areas with lipid rich necrotic core (LRNC) and calcifications) were analysed by one reader (DFvW, 3 years of experience with carotid MRI) using dedicated software (VesselMass, Leiden) 18_{. First, the lumen and} outer wall were delineated. For the analysis of plaque components, all four weightings (T1w, T2w, PDw and TOF) were used to identify areas of LRNC and calcifications. Iso-intense to hyperintense areas on T1w and PDw images with varying intensities on T2w and TOF images were considered to correspond with the LRNC. Calcification was defined by a hypo-intense signal on all four weightings(5). LRNC and calcification dimensions were displayed as mean wall area (MWA) (mm2) per slice.

Automatic analysis of vessel wall components was performed by one reader (LPS) using the PlaqueView software (VP diagnostics, Seattle, USA), an automated program for segmentation of vessel wall components using the MEPPS algorithm 15_{. Four weightings} (T1w, T2w, PDw and TOF) were used for plaque components analysis. First, lumen and outer wall contours were delineated automatically, with the possibility of the reader to manually correct, followed by co-registration of the different contrast weightings. The delineation of areas with LRNC or calcification was then performed fully automated using the MEPPS algorithm 19_{). Correction of automated segmented areas of LRNC or calcification was not}

allowed. LRNC and calcification dimensions were displayed as MWA per slice (mm2). Figure 1 shows representative images of manual and automated segmentation of a LRNC area and a calcified plaque.

Manual analysis of plaque components was performed using both open (during analysis, the reader was able to see both the scan and rescan) and closed segmentation (during analysis, the reader was blinded for the baseline or follow-up scan). Closed segmentation was used to calculate the interscan reproducibility of manual segmentation, whereas the manual open segmentation was used to study the agreement between manual and automated analysis.

Figure 1. Representative images of manual and automated segmentation of LRNC and calcification.

Representative images of the manual and automated segmentation of a calcified plaque area and a lipid-rich necrotic core (LRNC) using a multicontrast MRI protocol of the carotid artery. Shown are all the individual MRI sequences (T1w,PDw,T2w,TOF), as well as the manual and automated analysis. Lumen contours were delineated in red for both methods, and outer wall contours were delineated in green for manual segmentation, and light blue for automated segmentation. Calcified plaque areas were coloured orange in manual segmentation, and delineated white in automated segmentation. LRNCs were delineated yellow in both manual and automated segmentation. In these examples, both methods agree on the identification of a large calcified plaque area (left example) and large LRNC (right example). Please also note the identification of three small LRNC areas using automated segmentation (*), which are not detected by manual segmentation.

AUTOMATED VERSUS MANUAL PLAQUE COMPONENT ANALYSIS

21

2

Methods

This observational single center (Academic Medical Center Amsterdam) study was conducted in concordance with Good Clinical Practice guidelines. The study protocol was approved by the local investigational review board and written informed consent was obtained from all participants. As the current study used patient and MRI data from a previous study, patient selection and most study procedures are described in detail in previous publications 16,17_{. In short, patients with a 30-70% carotid artery stenosis on}

ultrasound were included for a 3T-MRI scan of the carotid artery, followed by a rescan within 1 month. For the MRI scans, a 3T whole-body MRI scan (Intera, Philips Medical

Systems, Best, The Netherlands) combined with a 8 channel dedicated bilateral carotid artery coil (Shanghai Chenguang Medical Technologies, Shanghai, China) was used. High resolution (0.25 by 0.25 mm) T1w, T2w, PDw and TOF images were acquired centered around the area with the most profound plaque burden, using ECG-gated unilateral axial sequences (imaging parameters in supplemental table 1 17_{). Overview images of the}

planning scans were used to plan the repeat scan. Analysis of plaque composition was performed manually and automated for all included patients.

Image analysis

Before any quantitative analysis was performed, one reader (RD, 3 years of experience with carotid MRI) manually corrected all scan and rescan images for possible Z-axis displacement using T1w, PDw and TOF images in VesselMass. All scans and rescans were checked for co-registration using the carotid bifurcation as a localizer.

For manual analysis, vessel wall dimensions and components (areas with lipid rich necrotic core (LRNC) and calcifications) were analysed by one reader (DFvW, 3 years of experience with carotid MRI) using dedicated software (VesselMass, Leiden) 18_{. First, the lumen and}

outer wall were delineated. For the analysis of plaque components, all four weightings (T1w, T2w, PDw and TOF) were used to identify areas of LRNC and calcifications. Iso-intense to hyperintense areas on T1w and PDw images with varying intensities on T2w and TOF images were considered to correspond with the LRNC. Calcification was defined by a hypo-intense signal on all four weightings(5). LRNC and calcification dimensions were displayed as mean wall area (MWA) (mm2) per slice.

Automatic analysis of vessel wall components was performed by one reader (LPS) using the PlaqueView software (VP diagnostics, Seattle, USA), an automated program for segmentation of vessel wall components using the MEPPS algorithm 15_{. Four weightings}

(T1w, T2w, PDw and TOF) were used for plaque components analysis. First, lumen and outer wall contours were delineated automatically, with the possibility of the reader to manually correct, followed by co-registration of the different contrast weightings. The delineation of areas with LRNC or calcification was then performed fully automated using the MEPPS algorithm 19_{). Correction of automated segmented areas of LRNC or calcification was not}

allowed. LRNC and calcification dimensions were displayed as MWA per slice (mm2). Figure 1 shows representative images of manual and automated segmentation of a LRNC area and a calcified plaque.

Manual analysis of plaque components was performed using both open (during analysis, the reader was able to see both the scan and rescan) and closed segmentation (during analysis, the reader was blinded for the baseline or follow-up scan). Closed segmentation was used to calculate the interscan reproducibility of manual segmentation, whereas the manual open segmentation was used to study the agreement between manual and automated analysis.

Figure 1. Representative images of manual and automated segmentation of LRNC and calcification.

Representative images of the manual and automated segmentation of a calcified plaque area and a lipid-rich necrotic core (LRNC) using a multicontrast MRI protocol of the carotid artery. Shown are all the individual MRI sequences (T1w,PDw,T2w,TOF), as well as the manual and automated analysis. Lumen contours were delineated in red for both methods, and outer wall contours were delineated in green for manual segmentation, and light blue for automated segmentation. Calcified plaque areas were coloured orange in manual segmentation, and delineated white in automated segmentation. LRNCs were delineated yellow in both manual and automated segmentation. In these examples, both methods agree on the identification of a large calcified plaque area (left example) and large LRNC (right example). Please also note the identification of three small LRNC areas using automated segmentation (*), which are not detected by manual segmentation.

CHAPTER 2

22

Scan quality

To assess scan quality as a parameter influencing reproducibility, all images were scored according to a visual quality score from 1 (poor) to 4 (excellent), based on the ability to delineate the outer wall and lumen (1, arterial wall margins unidentifiable; 2, arterial wall is visible, but lumen and outer boundaries are indistinct; 3, arterial wall structures are identifiable, but lumen and outer boundaries are not totally clear; 4, arterial wall and lumen are well defined). The mean visual quality score from the scan and rescan was calculated. A mean score of ≥ 3 was defined as a high quality scan, a score < 3 was defined as a low quality scan. The between reader intraclass correlation coefficient for the visual quality score in the present study was 0.77 (0.46 – 0.90), reflecting a good reproducibility.

Statistical analysis

Continuous variables are expressed as mean ± SD. Quantitative agreement between the successive MRI measurement of LRNC and calcification plaque area was assessed using intra-class correlation coefficients (ICC). Only MRI scans from subjects containing the specific plaque component in the scan and/or rescan in automated and/or manual analysis were included in this analysis. An ICC of <0.40 indicated poor, one between 0.40 and 0.75 indicated fair to good, and one of >0.75 indicated excellent reproducibility. The agreement between manual and automated detection of a LRNC-containing and calcified plaque was assessed with using Cohen’s kappa (k, with 0.00 – 0.20 = slight agreement, 0.21-0.40 = fair agreement, 0.41-0.6 = moderate agreement, 0.61-0.80 = substantial agreement, 0.81-1.00 = near-perfect agreement) 20_{. This analysis was repeated for large plaque components (only} LRNC or calcifications with a MWA of > 1mm2 _{included). Both the reproducibility of each} method, as well as the agreement between both methods, were stratified for scan quality (high quality scans versus low quality scans). All statistical analyses were performed using PASW statistics 18.0 for Windows (SPSS Inc., Chicago, IL, USA).

Results

Participants

Fifty-one individuals with one or more atherosclerotic events were screened for the presence of atherosclerotic carotid artery disease using ultrasound. Thirty-one individuals with a 30 to 70% stenosis of the carotid artery were included in the study protocol. Of those, seven subjects were excluded due to absence of a rescan (n=4), or because automated plaque component analysis could not be performed (n=3), resulting in a total of twenty-four subjects for our analysis (48% female, mean age 68 years).

Using manual segmentation, LRNC were detected in carotid artery plaques of in 6/24 (25%) of the included subjects. A substantially higher number was found using automated segmentation: 23/24 (96%) of plaques contained a LRNC. Also after exclusion of small areas of LRNC (< 1 mm2 _{plaque area per slice), automated segmentation still identified higher} numbers of LRNC containing plaques compared to the manual analysis (14/24 in automated analysis versus 5/24 in manual analysis). Calcified plaques were found in 19/24 subjects

using manual segmentation, compared to 22/24 using automated segmentation. After exclusion of small areas of calcification (MWA <1mm2 _{per slice), 17/24 plaques contained} calcifications in the manual analysis, and 12/24 in automated analysis. In neither manual nor automated analysis, IPH containing plaques were found.

Subsequently, we explored the agreement (using Cohen’s k) between the manual and automated detection of plaques containing LRNC and/or calcifications (figure 2). We found a poor agreement for the detection of LRNC (k=0.04) and a fair agreement for the detection of a calcified plaque (k= 0.41). When plaque components < 1 mm2 _{were excluded from} analysis, the kappa increased slightly for LRNC detection (to k=0.30), but decreased for calcified plaque detection(to k=0.29). When only high quality scans (10 scans with a visual quality score of 3 or more) were included, agreement between manual and automated detection of large plaque components increased to k=0.55 for LRNC and k=0.58 for calcified plaques, reflecting moderate agreement.

Reproducibility of manual and automated plaque component analysis

Both the manual and automated analysis showed good interscan reproducibility for the

quantification of LRNC plaque area (ICC of 0.80 and 0.94 respectively) and calcified plaque

area (ICC of 0.77 and 0.95 respectively) (table 1). Overall, interscan reproducibility was higher for the automated segmentation. In the lower quality scans (visual quality score < 3) the interscan reproducibility for the quantification of LRNC and calcification remained good in automated segmentation (0.92 for LRNC, 0.95 for calcification), whereas this was markedly reduced in the manual segmentation (0.60 for LRNC and 0.69 for calcification). Table 1. Interscan reproducibility of quantification of plaque components using manual and automated segmentation

Interscan ICC All scans

(n=24)

Interscan ICC High quality scans

(n=10)

Interscan ICC Lower quality scans

(n=14) LRNC Automated segmentation 0.94 (0.86 – 0.94) 0.98 (0.94 – 1.00) 0.92 (0.73 – 1.0) Manual segmentation 0.80 (0.52 – 0.91) 0.90 (0.61 – 0.98) 0.60 (0.00 – 0.88) Calcifications Automated segmentation 0.95 (0.89 – 0.98) 0.98 (0.90 – 0.99) 0.90 (0.70 – 0.97) Manual segmentation 0.77 (0.48 – 0.90) 0.82 (0.27 – 0.96) 0.69 (0.02 – 0.90)

Abbreviations: ICC, intraclass correlation coefficient; LRNC, lipid-rich necrotic core Post-hoc analysis

To evaluate the disagreement in plaque component detection between manual and automated segmentation, despite the high interscan reproducibility for both methods, we performed a post-hoc analysis in all MRI scans with a mismatch between both methods. All scans in which a large plaque component (MWA > 1 mm2_{) was detected in manual analysis} (in both the scan and rescan), but not in automated analysis, as well as all large plaque

AUTOMATED VERSUS MANUAL PLAQUE COMPONENT ANALYSIS

23

2

Scan quality

To assess scan quality as a parameter influencing reproducibility, all images were scored according to a visual quality score from 1 (poor) to 4 (excellent), based on the ability to delineate the outer wall and lumen (1, arterial wall margins unidentifiable; 2, arterial wall is visible, but lumen and outer boundaries are indistinct; 3, arterial wall structures are identifiable, but lumen and outer boundaries are not totally clear; 4, arterial wall and lumen are well defined). The mean visual quality score from the scan and rescan was calculated. A mean score of ≥ 3 was defined as a high quality scan, a score < 3 was defined as a low quality scan. The between reader intraclass correlation coefficient for the visual quality score in the present study was 0.77 (0.46 – 0.90), reflecting a good reproducibility.

Statistical analysis

Continuous variables are expressed as mean ± SD. Quantitative agreement between the successive MRI measurement of LRNC and calcification plaque area was assessed using intra-class correlation coefficients (ICC). Only MRI scans from subjects containing the specific plaque component in the scan and/or rescan in automated and/or manual analysis were included in this analysis. An ICC of <0.40 indicated poor, one between 0.40 and 0.75 indicated fair to good, and one of >0.75 indicated excellent reproducibility. The agreement between manual and automated detection of a LRNC-containing and calcified plaque was assessed with using Cohen’s kappa (k, with 0.00 – 0.20 = slight agreement, 0.21-0.40 = fair agreement, 0.41-0.6 = moderate agreement, 0.61-0.80 = substantial agreement, 0.81-1.00 = near-perfect agreement) 20_{. This analysis was repeated for large plaque components (only} LRNC or calcifications with a MWA of > 1mm2 _{included). Both the reproducibility of each} method, as well as the agreement between both methods, were stratified for scan quality (high quality scans versus low quality scans). All statistical analyses were performed using PASW statistics 18.0 for Windows (SPSS Inc., Chicago, IL, USA).

Results

Participants

Fifty-one individuals with one or more atherosclerotic events were screened for the presence of atherosclerotic carotid artery disease using ultrasound. Thirty-one individuals with a 30 to 70% stenosis of the carotid artery were included in the study protocol. Of those, seven subjects were excluded due to absence of a rescan (n=4), or because automated plaque component analysis could not be performed (n=3), resulting in a total of twenty-four subjects for our analysis (48% female, mean age 68 years).

Using manual segmentation, LRNC were detected in carotid artery plaques of in 6/24 (25%) of the included subjects. A substantially higher number was found using automated segmentation: 23/24 (96%) of plaques contained a LRNC. Also after exclusion of small areas of LRNC (< 1 mm2 _{plaque area per slice), automated segmentation still identified higher} numbers of LRNC containing plaques compared to the manual analysis (14/24 in automated analysis versus 5/24 in manual analysis). Calcified plaques were found in 19/24 subjects

using manual segmentation, compared to 22/24 using automated segmentation. After exclusion of small areas of calcification (MWA <1mm2 _{per slice), 17/24 plaques contained} calcifications in the manual analysis, and 12/24 in automated analysis. In neither manual nor automated analysis, IPH containing plaques were found.

Subsequently, we explored the agreement (using Cohen’s k) between the manual and automated detection of plaques containing LRNC and/or calcifications (figure 2). We found a poor agreement for the detection of LRNC (k=0.04) and a fair agreement for the detection of a calcified plaque (k= 0.41). When plaque components < 1 mm2 _{were excluded from} analysis, the kappa increased slightly for LRNC detection (to k=0.30), but decreased for calcified plaque detection(to k=0.29). When only high quality scans (10 scans with a visual quality score of 3 or more) were included, agreement between manual and automated detection of large plaque components increased to k=0.55 for LRNC and k=0.58 for calcified plaques, reflecting moderate agreement.

Reproducibility of manual and automated plaque component analysis

Both the manual and automated analysis showed good interscan reproducibility for the

quantification of LRNC plaque area (ICC of 0.80 and 0.94 respectively) and calcified plaque

area (ICC of 0.77 and 0.95 respectively) (table 1). Overall, interscan reproducibility was higher for the automated segmentation. In the lower quality scans (visual quality score < 3) the interscan reproducibility for the quantification of LRNC and calcification remained good in automated segmentation (0.92 for LRNC, 0.95 for calcification), whereas this was markedly reduced in the manual segmentation (0.60 for LRNC and 0.69 for calcification). Table 1. Interscan reproducibility of quantification of plaque components using manual and automated segmentation

Interscan ICC All scans

(n=24)

Interscan ICC High quality scans

(n=10)

Interscan ICC Lower quality scans

(n=14) LRNC Automated segmentation 0.94 (0.86 – 0.94) 0.98 (0.94 – 1.00) 0.92 (0.73 – 1.0) Manual segmentation 0.80 (0.52 – 0.91) 0.90 (0.61 – 0.98) 0.60 (0.00 – 0.88) Calcifications Automated segmentation 0.95 (0.89 – 0.98) 0.98 (0.90 – 0.99) 0.90 (0.70 – 0.97) Manual segmentation 0.77 (0.48 – 0.90) 0.82 (0.27 – 0.96) 0.69 (0.02 – 0.90)

Abbreviations: ICC, intraclass correlation coefficient; LRNC, lipid-rich necrotic core Post-hoc analysis

To evaluate the disagreement in plaque component detection between manual and automated segmentation, despite the high interscan reproducibility for both methods, we performed a post-hoc analysis in all MRI scans with a mismatch between both methods. All scans in which a large plaque component (MWA > 1 mm2_{) was detected in manual analysis} (in both the scan and rescan), but not in automated analysis, as well as all large plaque