Background: Unstable carotid atherosclerosis causes stroke, but methods to identify patients and
lesions at risk are lacking. Currently, this risk estimation is based on measurements of stenosis and
neurological symptoms, which determines the therapy of either medical treatment with or without
carotid endarterectomy. The efficacy of this therapy is low and higher accuracy of diagnosis and
therapy is warranted. Imaging of carotid plaque morphology using software for visualisation of
plaque components may improve assessment of plaque phenotype and stroke risk. These studies
aimed firstly to investigate if, and if yes, how, the carotid plaque morphology with image analysis of
CTA associated with on-going biology in the corresponding specimen. Secondly, if risk
stratification in clinical risk scores can be linked to the aforementioned associations. Finally, if the
on-going biological processes can be specifically predicted out of the CTA imaging analysis.
Methods: Plaque features were analysed in pre-operative CTA with dedicated software. In study
I and II, the plaques were stratified according to quantified high and low of each feature, profiled
with microarrays, followed by bioinformatic analyses. Immunohistochemistry was performed to
evaluate the findings in plaques. In study III, patient phenotype, according to clinical stroke risk
scores of CAR and ABCD2 stratified the cohorts of high vs low scores which were subsequently
profiled with microarrays, followed by bioinformatic analyses and correlation analyses of plaque
morphology in CTA. In study IV, the microarray transcriptomes were individually coupled to
morphological data from the CTA analysis, developing models with machine intelligence to predict
the gene expression from a CTA image. The models were then tested in unseen patients.
Results: In study I, stabilising markers and processes related to SMCs and ECM organisation were
associated with highly calcified plaques, while inflammatory and lipid related processes were
repressed. PRG4, a novel marker for atherosclerosis, was identified as the most up-regulated gene
in highly calcified plaques. Study II showed that carotid lesions with large lipid rich necrotic core,
intraplaque haemorrhage or plaque burden were characterized by molecular signatures coupled
with inflammation and extracellular matrix degradation, typically linked with instability.
Symptomatology associated with large lipid rich necrotic core and plaque burden. Cross-validated
prediction model for symptoms, showed that plaque morphology by CTA alone was superior to
stenosis degree. Study III revealed that a high clinical risk score in CAR and ABCD2, reflect a
plaque phenotype linked to immune response and coagulation, where the novel ABCB5, was one
of the most up-regulated genes. The high risk scores correlated with the plaque components matrix
and calcification but no positive association with stenosis degree. Study IV resulted in 414 robustly
predicted transcripts from the CTA image analysis, of which pathway analysis showed biological
processes associated with typical pathophysiology of atherosclerosis and plaque instability. The
model testing demonstrated a good correlation between predicted and observed transcript
expression levels and pathway analysis revealed a unique dominant mechanism for each individual.
Conclusions: Biological processes in carotid plaques associated to vulnerability, can be linked to
plaque morphology analysed with CTA image analysis. Patient phenotype classified with clinical
risk scores associates to plaque phenotype and morphology in CTA. The biological processes in
the atherosclerotic plaque can be predicted with plaque morphology CTA analysis in this small
pilot study, providing a possibility to precision medicine after validation in larger scale studie
