Radiomics-based machine learning atherosclerotic carotid artery disease in ultrasound: systematic review with meta-analysis of RQS.
Authors
Affiliations (20)
Affiliations (20)
- School of Medicine and Surgery, University of Cagliari, 09042, Cagliari, Italy.
- Elmezzi Graduate School of Molecular Medicine, Northwell, Manhasset, NY, USA.
- The Feinstein Institutes for Medical Research, Northwell, Manhasset, NY, USA.
- Department of Radiology, Azienda Ospedaliero-Universitaria, di Cagliari, Polo di Monserrato, 09042, Cagliari, Italy.
- AOU Cagliari, 09042, Cagliari, Italy.
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.
- Centro Cardiologico Monzino IRCCS, 20138, Milan, Italy.
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy.
- Department of Biomedical and Dental Sciences and Morphological and Functional Imaging, University of Messina, A.O.U. Policlinic 'G. Martino', Via C. Valeria 1, 98165, Messina, Italy.
- Department of Vascular Surgery, Red Cross Hospital, Athens, Greece.
- Vascular Screening and Diagnostic Centre, Nicosia, Cyprus.
- University of Nicosia Medical School, Nicosia, Cyprus.
- Department of Vascular Surgery, Imperial College, London, UK.
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.
- Department of ECE, Idaho State University, Pocatello, ID, 83209, USA.
- Department of CE, Graphic Era Deemed to Be University, 248002, Dehradun, India.
- University Center for Research and Development, Chandigarh University, Mohali, India.
- Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India.
- Department of Radiology, Azienda Ospedaliero-Universitaria, di Cagliari, Polo di Monserrato, 09042, Cagliari, Italy. [email protected].
- Department of Medical Science and Public Health, University of Cagliari, Cagliari, Italy. [email protected].
Abstract
Stroke, a leading global cause of mortality and neurological disability, is often associated with atherosclerotic carotid artery disease. Distinguishing between symptomatic and asymptomatic carotid artery disease is crucial for appropriate treatment decisions. Radiomics, a quantitative image analysis technique, and machine learning (ML) have emerged as promising tools in Ultrasound (US) imaging, potentially providing a helpful tool in the screening of such lesions. Pubmed, Web of Science and Scopus databases were searched for relevant studies published from January 2005 to May 2023. The Radiomics Quality Score (RQS) was used to assess methodological quality of studies included in the review. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) assessed the risk of bias. Sensitivity, specificity, and logarithmic diagnostic odds ratio (logDOR) meta-analyses have been conducted, alongside an influence analysis. RQS assessed methodological quality, revealing an overall low score and consistent findings with other radiology domains. QUADAS-2 indicated an overall low risk, except for two studies with high bias. The meta-analysis demonstrated that radiomics-based ML models for predicting culprit plaques on US had a satisfactory performance, with a sensitivity of 0.84 and specificity of 0.82. The logDOR analysis confirmed the positive results, yielding a pooled logDOR of 3.54. The summary ROC curve provided an AUC of 0.887. Radiomics combined with ML provide high sensitivity and low false positive rate for carotid plaque vulnerability assessment on US. However, current evidence is not definitive, given the low overall study quality and high inter-study heterogeneity. High quality, prospective studies are needed to confirm the potential of these promising techniques.