MRI Radiomics and Predictive Models in Assessing Ischemic Stroke Outcome-A Systematic Review

Hanna Maria Dragoș; Adina Stan; Roxana Pintican; Diana Feier; Andrei Lebovici; Paul-Ștefan Panaitescu; Constantin Dina; Stefan Strilciuc; Dafin F Muresanu

doi:10.3390/diagnostics13050857

MRI Radiomics and Predictive Models in Assessing Ischemic Stroke Outcome-A Systematic Review

Diagnostics (Basel). 2023 Feb 23;13(5):857. doi: 10.3390/diagnostics13050857.

Authors

Hanna Maria Dragoș^{1

2

3}, Adina Stan^{1

2

3}, Roxana Pintican⁴, Diana Feier⁴, Andrei Lebovici⁴, Paul-Ștefan Panaitescu⁵, Constantin Dina⁶, Stefan Strilciuc^{1

2}, Dafin F Muresanu^{1

2

3}

Affiliations

¹ Department of Neurosciences, Iuliu Hațieganu University of Medicine and Pharmacy, No. 8 Victor Babeș Street, 400012 Cluj-Napoca, Romania.
² RoNeuro Institute for Neurological Research and Diagnostic, No. 37 Mircea Eliade Street, 400364 Cluj-Napoca, Romania.
³ Neurology Department, Emergency County Hospital, No. 43 Victor Babes Street, 400347 Cluj-Napoca, Romania.
⁴ Department of Radiology, Iuliu Haţieganu University of Medicine and Pharmacy, No. 3-5, Clinicilor Street, 400006 Cluj-Napoca, Romania.
⁵ Department of Microbiology, Iuliu Hatieganu University of Medicine and Pharmacy, No. 8 Victor Babes Street, 400012 Cluj-Napoca, Romania.
⁶ Department of Radiology, Faculty of Medicine, Ovidius University, 900527 Constanta, Romania.

Abstract

Stroke is a leading cause of disability and mortality, resulting in substantial socio-economic burden for healthcare systems. With advances in artificial intelligence, visual image information can be processed into numerous quantitative features in an objective, repeatable and high-throughput fashion, in a process known as radiomics analysis (RA). Recently, investigators have attempted to apply RA to stroke neuroimaging in the hope of promoting personalized precision medicine. This review aimed to evaluate the role of RA as an adjuvant tool in the prognosis of disability after stroke. We conducted a systematic review following the PRISMA guidelines, searching PubMed and Embase using the keywords: 'magnetic resonance imaging (MRI)', 'radiomics', and 'stroke'. The PROBAST tool was used to assess the risk of bias. Radiomics quality score (RQS) was also applied to evaluate the methodological quality of radiomics studies. Of the 150 abstracts returned by electronic literature research, 6 studies fulfilled the inclusion criteria. Five studies evaluated predictive value for different predictive models (PMs). In all studies, the combined PMs consisting of clinical and radiomics features have achieved the best predictive performance compared to PMs based only on clinical or radiomics features, the results varying from an area under the ROC curve (AUC) of 0.80 (95% CI, 0.75-0.86) to an AUC of 0.92 (95% CI, 0.87-0.97). The median RQS of the included studies was 15, reflecting a moderate methodological quality. Assessing the risk of bias using PROBAST, potential high risk of bias in participants selection was identified. Our findings suggest that combined models integrating both clinical and advanced imaging variables seem to better predict the patients' disability outcome group (favorable outcome: modified Rankin scale (mRS) ≤ 2 and unfavorable outcome: mRS > 2) at three and six months after stroke. Although radiomics studies' findings are significant in research field, these results should be validated in multiple clinical settings in order to help clinicians to provide individual patients with optimal tailor-made treatment.

Keywords: ischemic stroke; predictive model; radiomics.

Publication types

Review

Grants and funding

This research received no external funding.