Vulnerable carotid plaque: robustness and classification capabilities of mri radiomic features

Objectives To assess how radiomic features may be combined with plaque morphological and compositional features identified by multi-contrast magnetic resonance imaging (MRI) to improve upon conventional risk assessment models in determining culprit lesions. Methods Fifty-five patients (mean age: 62.6; 35 males) with bilateral carotid stenosis who experienced transient ischaemic attack (TIA) or stroke were included from the CARE-II multi-centre carotid imaging trial ([ClinicalTrials.gov][1] Identifier: [NCT02017756][2]). They underwent MRI within 2 weeks of the event. Classification capability in distinguishing culprit lesions was assessed by machine learning. Repeatability and reproducibility of the results were investigated by assessing the robustness of the radiomic features. Results Radiomics combined with a relatively conventional plaque morphological and compositional metric-based model provided incremental value over a conventional model alone [area under curve (AUC), 0.819 ± 0.002 vs. 0.689 ± 0.019 respectively, p = 0.014]. The radiomic model alone also provided value over the conventional model [AUC, 0.805 ± 0.003 vs. 0.689 ± 0.019 respectively, p = 0.031]. T2-weighted imaging-based radiomic features had consistently higher robustness and classification capabilities compared with T1-weighted images. Higher-dimensional radiomic features outperformed first-order features. Grey Level Co-occurrence Matrix (GLCM), Grey Level Dependence Matrix (GLDM) and Grey Level Size Zone Matrix (GLSZM) sub-types were particularly useful in identifying textures which could detect vulnerable lesions. Conclusions The combination of MRI-based radiomic features and lesion morphological and compositional parameters provided added value to the reference-standard risk assessment for carotid atherosclerosis. This may improve future risk stratification for individuals at risk of major adverse ischemic cerebrovascular events. Clinical Relevance The clinical relevance of this work is that it addresses the need for a more comprehensive method of risk assessment for patients at risk of ischemic stroke, beyond conventional stenosis measurement. Radiomics provides a non-invasive means of assessing plaque vulnerability. Key points ### Competing Interest Statement Dr. Zhongzhao Teng is the chief scientist of Nanjing Jingsan Medical Science and Technology, Ltd., Jiangsu, China, and Tenoke, Ltd., Cambridge, UK. Other authors do not have any conflict of interests to declare. ### Funding Statement MJG receives funding from the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014) and ZM receives funding from the Cambridge Trust (10468740) ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Institutional Review Board (IRB) approval was obtained for the CARE-II study (ClinicalTrials.gov Identifier: [NCT02017756][2]) and for each participating institution. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors * ANOVA : analysis of variance AUC : area under the curve CARE : Chinese atherosclerotic risk evaluation CAS : carotid artery stenting CEA : carotid endarterectomy CVD : cardiovascular disease FC : fibrous cap GLCM : grey level co-occurrence matrix GLRLM : grey level run length matrix GLSZM : grey level size zone matrix GLDM : grey level dependence matrix NGTDM : neighbouring grey tone difference matrix MLA : minimum lumen area MMAL : minimum minor axis length MP-RAGE : magnetisation prepared rapid gradient echo MRI : multi-contrast magnetic resonance imaging ICC : intra-class correlation co-efficient IPH : intra-plaque haemorrhage IRI : inward remodelling index LASSO : least absolute shrinkage and selection operator LRNC : lipid-rich necrotic core MDIR : multi-slice double inversion recovery MLA : minimum lumen area MMAL : minimum minor axis length NPV : negative predictive value OR : odds ratio ORI : outward remodelling index PPV : positive predictive value QIR : quadruple inversion recovery ROC : receiver operating characteristic ROI : region of interest TIA : transient ischemic attack TOF : time of flight [1]: http://ClinicalTrials.gov [2]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT02017756&atom=%2Fmedrxiv%2Fearly%2F2023%2F06%2F27%2F2023.06.19.23291556.atom