Radiomics assessment of carotid intraplaque hemorrhage: detecting the vulnerable patients

Abstract

Background Intraplaque hemorrhage (IPH), one of the key features of vulnerable plaques, has been shown to be associated with increased risk of stroke. The aim is to develop and validate a CT-based radiomics nomogram incorporating clinical factors and radiomics signature for the detection of IPH in carotid arteries. Methods This retrospective study analyzed the patients with carotid plaques on CTA from January 2013 to January 2021 at two different institutions. Radiomics features were extracted from CTA images. Demographics and CT characteristics were evaluated to build a clinical factor model. A radiomics signature was constructed by the least absolute shrinkage and selection operator method. A radiomics nomogram combining the radiomics signature and independent clinical factors was constructed. The area under curves of three models were calculated by receiver operating characteristic analysis. Results A total of 46 patients (mean age, 60.7 years ± 10.4 [standard deviation]; 36 men) with 106 carotid plaques were in the training set, and 18 patients (mean age, 61.4 years ± 10.1; 13 men) with 38 carotid plaques were in the external test sets. Stenosis was the independent clinical factor. Eight features were used to build the radiomics signature. The area under the curve (AUC) of the radiomics nomogram was significantly higher than that of the clinical factor model in both the training (p = 0.032) and external test (p = 0.039) sets. Conclusions A CT-based radiomics nomogram showed satisfactory performance in distinguishing carotid plaques with and without intraplaque hemorrhage.

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

“…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 ).…”

“…The mean overall TRIPOD adherence rate was 66.1% (SD 12.8%), with a range of 45% to 87%. Of the 19 studies, 18 were model development studies, and only 1 aimed to develop and externally validate the same model ( 42 ). Of the 31 TRIPOD items, 8 (3a, 3b, 4a, 6a, 7a, 14a, 18, 19b) were completely described in all studies, 11 (4b, 5b, 5c, 8, 10a, 10b, 11, 14, 20, 21 22) were specified in more than 60% of the studies, the remaining 9 (5a, 6b, 7b, 9, 10d, 13a, 15a, 15b, 16) were described in fewer than 50% of the studies.…”

“…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 ). The time interval between predictor assessment and outcome determination was unclear in 6 studies ( 25 , 30 , 31 , 35 , 40 , 42 ).…”

“…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 ).…”

“…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 ).…”