A Radiomics Approach to Assess High Risk Carotid Plaques: A Non-invasive Imaging Biomarker, Retrospective Study

Abstract

ObjectiveThis study aimed to construct a radiomics-based MRI sequence from high-resolution magnetic resonance imaging (HRMRI), combined with clinical high-risk factors for non-invasive differentiation of the plaque of symptomatic patients from asyptomatic patients.MethodsA total of 115 patients were retrospectively recruited. HRMRI was performed, and patients were diagnosed with symptomatic plaques (SPs) and asymptomatic plaques (ASPs). Patients were randomly divided into training and test groups in the ratio of 7:3. T2WI was used for segmentation and extraction of the texture features. Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were employed for the optimized model. Radscore was applied to construct a diagnostic model considering the T2WI texture features and patient demography to assess the power in differentiating SPs and ASPs.ResultsSPs and ASPs were seen in 75 and 40 patients, respectively. Thirty texture features were selected by mRMR, and LASSO identified a radscore of 16 radiomics features as being related to plaque vulnerability. The radscore, consisting of eight texture features, showed a better diagnostic performance than clinical information, both in the training (area under the curve [AUC], 0.923 vs. 0.713) and test groups (AUC, 0.989 vs. 0.735). The combination model of texture and clinical information had the best performance in assessing lesion vulnerability in both the training (AUC, 0.926) and test groups (AUC, 0.898).ConclusionThis study demonstrated that HRMRI texture features provide incremental value for carotid atherosclerotic risk assessment.

Quality assessment of radiomics models in carotid plaque: a systematic review

“…Following removal of 229 duplicates, 174 studies were screened for titles and abstracts; of these, 24 inappropriate types of publication and 89 studies that were irrelevant to the purpose of this review were excluded through more detailed assessment. Finally, a full-text screening identified 19 studies that met the inclusion criteria for this review ( 25 - 43 ) ( Figure 1 ).…”

“…For predictive power, the mean AUC of the final models in training group was 0.876±0.09, ranging from 0.741 to 0.989. Internal validation was performed in 14 studies ( 25 - 27 , 29 , 32 - 38 , 40 , 41 , 43 ), and 1 study employed external validation ( 42 ), but only 12 studies ( 25 , 26 , 29 , 32 - 34 , 36 - 38 , 40 , 41 , 43 ) reported the AUCs of the validation groups, ranging from 0.73 to 0.986. Model calibration was investigated in 7 studies (36.8%) and demonstrated good calibration performance ( 25 , 30 , 32 , 37 , 40 , 42 , 43 ).…”

“…Through radiomics or texture analysis, 10 studies tended to classify unstable or risk plaques ( 25 , 27 , 29 , 32 , 33 , 39 - 43 ). Moreover, 5 studies focused on predicting unfavorable cerebral and cardiovascular events ( 28 , 35 - 38 ), 2 on evaluating stenosis or in-stent restenosis ( 26 , 30 ), 1 on assessing the robustness of radiomics features ( 34 ), and 1 on evaluating the relationship between glycated hemoglobin and ultrasound plaque textures ( 31 ).…”

“…All studies exhibited a low events per variable (EPV) value due to the large amount of candidate features and the limited carotid plaque samples. Regarding missing data, 12 studies conducted complete-case analysis and manually eliminated individuals without satisfactory images or clinical data or those who were lost to follow-up ( 25 , 26 , 28 - 30 , 32 , 36 - 41 ). In contrast, 7 studies did not describe how missing data were handled or the analytical procedures used to assess missing data ( 27 , 31 , 33 - 35 , 42 , 43 ), 4 studies lacked validation ( 28 , 30 , 31 , 39 ), and 2 studies had a high risk of bias in domain 1 for the stated inappropriate inclusion or exclusion criteria ( 33 , 39 ).…”

“…In contrast, 7 studies did not describe how missing data were handled or the analytical procedures used to assess missing data ( 27 , 31 , 33 - 35 , 42 , 43 ), 4 studies lacked validation ( 28 , 30 , 31 , 39 ), and 2 studies had a high risk of bias in domain 1 for the stated inappropriate inclusion or exclusion criteria ( 33 , 39 ). The time interval between predictor assessment and outcome determination was unclear in 6 studies ( 25 , 30 , 31 , 35 , 40 , 42 ). Of note, the overall risk of applicability was low: only 2 studies ( 33 , 39 ) had a high risk in the participant domain, and 6 studies ( 25 , 27 , 30 , 31 , 35 , 42 ) had an unclear risk in the outcome domain ( Figure 3B ).…”