Machine learning-based radiomics model for risk stratification of severe asymptomatic carotid stenosis_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

LIU Zhan ¹ , LIU Xiaopeng ¹ , LIU Min ² , ZHEN Yanan ¹ , ZHENG Xia ¹ , WEN Jianyan ¹ , YE Zhidong ¹ ,  LIU Peng ¹

1. Department of Cardiovascular Surgery, China-Japan Friendship Hospital, Peking University China-Japan Friendship School of Medicine, Beijing, 100029, P. R. China;
2. Department of Radiology, China-Japan Friendship Hospital, Beijing, 100029, P. R. China;

Corresponding?author：

LIU Peng, Email: pengliu5417@163.com

Keywords：

Severe asymptomatic carotid stenosis; machine learning; radiomics; prediction model; artificial intelligence

DOI：

10.7507/1007-4848.202205046

Video：

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Objective To explore the utility of machine learning-based radiomics models for risk stratification of severe asymptomatic carotid stenosis (ACS). Methods The clinical data and head and neck CT angiography images of 188 patients with severe carotid artery stenosis at the Department of Cardiovascular Surgery, China-Japan Friendship Hospital from 2017 to 2021 were retrospectively collected. The patients were randomly divided into a training set (n=131, including 107 males and 24 females aged 68±8 years), and a validation set (n=57, including 50 males and 7 females aged 67±8 years). The volume of interest was manually outlined layer by layer along the edge of the carotid plaque on cross-section. Radiomics features were extracted using the Pyradiomics package of Python software. Intraclass and interclass correlation coefficient analysis, redundancy analysis, and least absolute shrinkage and selection operator regression analysis were used for feature selection. The selected radiomics features were constructed into a predictive model using 6 different supervised machine learning algorithms: logistic regression, decision tree, random forest, support vector machine, naive Bayes, and K nearest neighbor. The diagnostic efficacy of each prediction model was compared using the receiver operating characteristic (ROC) curve and the area under the curve (AUC), which were validated in the validation set. Calibration and clinical usefulness of the prediction model were evaluated using calibration curve and decision curve analysis (DCA). Results Four radiomics features were finally selected based on the training set for the construction of a predictive model. Among the 6 machine learning models, the logistic regression model exhibited higher and more stable diagnostic efficacy, with an AUC of 0.872, a sensitivity of 100.0%, and a specificity of 66.2% in the training set; the AUC, sensitivity and specificity in the validation set were 0.867, 83.3% and 78.8%, respectively. The calibration curve and DCA showed that the logistic regression model had good calibration and clinical usefulness. Conclusion The machine learning-based radiomics model shows application value in the risk stratification of patients with severe ACS.

Citation： LIU Zhan, LIU Xiaopeng, LIU Min, ZHEN Yanan, ZHENG Xia, WEN Jianyan, YE Zhidong, LIU Peng. Machine learning-based radiomics model for risk stratification of severe asymptomatic carotid stenosis. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(10): 1270-1276. doi: 10.7507/1007-4848.202205046 Copy

1.	GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet, 2020, 396(10258): 1204-1222.
2.	Zhou M, Wang H, Zeng X, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet, 2019, 394(10204): 1145-1158.
3.	Paraskevas KI, Veith FJ, Spence JD. How to identify which patients with asymptomatic carotid stenosis could benefit from endarterectomy or stenting. Stroke Vasc Neurol, 2018, 3(2): 92-100.
4.	Naylor AR, Ricco JB, de Borst GJ, et al. Editor's Choice—Management of atherosclerotic carotid and vertebral artery disease: 2017 clinical practice guidelines of the European Society for Vascular Surgery (ESVS). Eur J Vasc Endovasc Surg, 2018, 55(1): 3-81.
5.	李洪, 韓路, 李響, 等. 影像組學輔助磨玻璃結節診斷的研究進展. 中國胸心血管外科臨床雜志, 2019, 26(8): 805-809.
6.	毛咪咪, 李海明, 石健, 等. 基于多序列MRI影像組學列線圖預測上皮性卵巢癌患者對鉑類藥物化療的敏感性. 中華醫學雜志, 2022, 102(3): 201-208.
7.	吳曉璐, 徐秋貞, 陳文達, 等. 基于影像組學的肺亞實性結節侵襲性預測模型建立及分析. 中華醫學雜志, 2022, 102(3): 209-215.
8.	Zhang R, Zhang Q, Ji A, et al. Identification of high-risk carotid plaque with MRI-based radiomics and machine learning. Eur Radiol, 2021, 31(5): 3116-3126.
9.	Chen S, Liu C, Chen X, et al. A radiomics approach to assess high risk carotid plaques: A non-invasive imaging biomarker, retrospective study. Front Neurol, 2022, 13: 788652.
10.	Huang Z, Cheng XQ, Liu HY, et al. Relation of carotid plaque features detected with ultrasonography-based radiomics to clinical symptoms. Transl Stroke Res, 2021: 10.1007/s12975-021-00963-9.
11.	Acharya UR, Sree SV, Mookiah MR, et al. Computed tomography carotid wall plaque characterization using a combination of discrete wavelet transform and texture features: A pilot study. Proc Inst Mech Eng H, 2013, 227(6): 643-654.
12.	Zaccagna F, Ganeshan B, Arca M, et al. CT texture-based radiomics analysis of carotid arteries identifies vulnerable patients: A preliminary outcome study. Neuroradiology, 2021, 63(7): 1043-1052.
13.	夏冰清, 李翠萍, 錢朝霞, 等. 基于機器學習的影像組學模型預測三陰性乳腺癌新輔助化療遠期預后的應用價值. 中華放射學雜志, 2021, 55(10): 1059-1064.
14.	North American Symptomatic Carotid Endarterectomy Trial Collaborators, Barnett HJM, Taylor DW, et al. Beneficial effect of carotid endarterectomy in symptomatic patients with high-grade carotid stenosis. N Engl J Med, 1991, 325(7): 445-453.
15.	中華醫學會外科學分會血管外科學組. 頸動脈狹窄診治指南. 中華血管外科雜志, 2017, 2(2): 78-84.
16.	Baradaran H, Gupta A. Carotid vessel wall imaging on CTA. AJNR Am J Neuroradiol, 2020, 41(3): 380-386.
17.	Baessler B, Luecke C, Lurz J, et al. Cardiac MRI and texture analysis of myocardial T1 and T2 maps in myocarditis with acute versus chronic symptoms of heart failure. Radiology, 2019, 292(3): 608-617.
18.	Ekert K, Hinterleitner C, Baumgartner K, et al. Extended texture analysis of non-enhanced whole-body MRI image data for response assessment in multiple myeloma patients undergoing systemic therapy. Cancers (Basel), 2020, 12(3): 761.
19.	Heo J, Yoon JG, Park H, et al. Machine learning-based model for prediction of outcomes in acute stroke. Stroke, 2019, 50(5): 1263-1265.
20.	Christodoulou E, Ma J, Collins GS, et al. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol, 2019, 110: 12-22.

1. GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet, 2020, 396(10258): 1204-1222.
2. Zhou M, Wang H, Zeng X, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet, 2019, 394(10204): 1145-1158.
3. Paraskevas KI, Veith FJ, Spence JD. How to identify which patients with asymptomatic carotid stenosis could benefit from endarterectomy or stenting. Stroke Vasc Neurol, 2018, 3(2): 92-100.
4. Naylor AR, Ricco JB, de Borst GJ, et al. Editor's Choice—Management of atherosclerotic carotid and vertebral artery disease: 2017 clinical practice guidelines of the European Society for Vascular Surgery (ESVS). Eur J Vasc Endovasc Surg, 2018, 55(1): 3-81.
5. 李洪, 韓路, 李響, 等. 影像組學輔助磨玻璃結節診斷的研究進展. 中國胸心血管外科臨床雜志, 2019, 26(8): 805-809.
6. 毛咪咪, 李海明, 石健, 等. 基于多序列MRI影像組學列線圖預測上皮性卵巢癌患者對鉑類藥物化療的敏感性. 中華醫學雜志, 2022, 102(3): 201-208.
7. 吳曉璐, 徐秋貞, 陳文達, 等. 基于影像組學的肺亞實性結節侵襲性預測模型建立及分析. 中華醫學雜志, 2022, 102(3): 209-215.
8. Zhang R, Zhang Q, Ji A, et al. Identification of high-risk carotid plaque with MRI-based radiomics and machine learning. Eur Radiol, 2021, 31(5): 3116-3126.
9. Chen S, Liu C, Chen X, et al. A radiomics approach to assess high risk carotid plaques: A non-invasive imaging biomarker, retrospective study. Front Neurol, 2022, 13: 788652.
10. Huang Z, Cheng XQ, Liu HY, et al. Relation of carotid plaque features detected with ultrasonography-based radiomics to clinical symptoms. Transl Stroke Res, 2021: 10.1007/s12975-021-00963-9.
11. Acharya UR, Sree SV, Mookiah MR, et al. Computed tomography carotid wall plaque characterization using a combination of discrete wavelet transform and texture features: A pilot study. Proc Inst Mech Eng H, 2013, 227(6): 643-654.
12. Zaccagna F, Ganeshan B, Arca M, et al. CT texture-based radiomics analysis of carotid arteries identifies vulnerable patients: A preliminary outcome study. Neuroradiology, 2021, 63(7): 1043-1052.
13. 夏冰清, 李翠萍, 錢朝霞, 等. 基于機器學習的影像組學模型預測三陰性乳腺癌新輔助化療遠期預后的應用價值. 中華放射學雜志, 2021, 55(10): 1059-1064.
14. North American Symptomatic Carotid Endarterectomy Trial Collaborators, Barnett HJM, Taylor DW, et al. Beneficial effect of carotid endarterectomy in symptomatic patients with high-grade carotid stenosis. N Engl J Med, 1991, 325(7): 445-453.
15. 中華醫學會外科學分會血管外科學組. 頸動脈狹窄診治指南. 中華血管外科雜志, 2017, 2(2): 78-84.
16. Baradaran H, Gupta A. Carotid vessel wall imaging on CTA. AJNR Am J Neuroradiol, 2020, 41(3): 380-386.
17. Baessler B, Luecke C, Lurz J, et al. Cardiac MRI and texture analysis of myocardial T1 and T2 maps in myocarditis with acute versus chronic symptoms of heart failure. Radiology, 2019, 292(3): 608-617.
18. Ekert K, Hinterleitner C, Baumgartner K, et al. Extended texture analysis of non-enhanced whole-body MRI image data for response assessment in multiple myeloma patients undergoing systemic therapy. Cancers (Basel), 2020, 12(3): 761.
19. Heo J, Yoon JG, Park H, et al. Machine learning-based model for prediction of outcomes in acute stroke. Stroke, 2019, 50(5): 1263-1265.
20. Christodoulou E, Ma J, Collins GS, et al. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol, 2019, 110: 12-22.

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