心脏磁共振短轴电影成像影像组学鉴别肥厚型心肌病与健康对照

doi:10.3969/j.issn.1674-8115.2024.01.009

上海交通大学学报（医学版） ›› 2024, Vol. 44 ›› Issue (1): 79-86.doi: 10.3969/j.issn.1674-8115.2024.01.009

心脏磁共振短轴电影成像影像组学鉴别肥厚型心肌病与健康对照

刘启明(), 卢启帆, 柴烨子, 姜萌(), 卜军()

上海交通大学医学院附属仁济医院心内科，上海 200127

收稿日期:2023-03-21 接受日期:2023-09-06 出版日期:2024-01-28 发布日期:2024-02-28
通讯作者: 姜萌,卜军 E-mail:090503liu@sjtu.edu.cn;jiangmeng0919@163.com;pujun310@hotmail.com
作者简介:刘启明（1996—），男，硕士生；电子信箱：090503liu@sjtu.edu.cn。
基金资助:
国家自然科学基金(81971570);上海申康医院发展中心促进市级医院临床技能与临床创新能力三年行动计划(SHDC2020CR2025B);上海市科学技术委员会医学创新研究专项(20Y11910500);上海市科学技术委员会优秀技术带头人计划(21XD143210);上海交通大学医学院“双百人”项目(20172014);上海市浦东新区卫生和计划生育委员会联合攻关项目(PW2018D-03);上海交通大学“交大之星”计划医工交叉研究基金(YG2019ZDA13);上海理工大学医工交叉研究基金(10-20-302-425)

Short-axis cine cardiac magnetic resonance images-derived radiomics for hypertrophic cardiomyopathy and healthy control classification

LIU Qiming(), LU Qifan, CHAI Yezi, JIANG Meng(), PU Jun()

Department of Cardiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China

Received:2023-03-21 Accepted:2023-09-06 Online:2024-01-28 Published:2024-02-28
Contact: JIANG Meng,PU Jun E-mail:090503liu@sjtu.edu.cn;jiangmeng0919@163.com;pujun310@hotmail.com
Supported by:
National Natural Science Foundation of China(81971570);Shanghai Shenkang Hospital Development Center Three-Year Action Plan: Promoting Clinical Skills and Innovation in Municipal Hospital(SHDC2020CR2025B);Innovation Research Project of Shanghai Science and Technology Commission(20Y11910500);Advanced Technology Leader of Shanghai Science and Technology Commission(21XD143210);''Two-hundred Talents'' Program of Shanghai Jiao Tong University School of Medicine(20172014);Joint Research Project of Shanghai Pudong Municipal Health Commission(PW2018D-03);Medical-Engineering Cross Research of Shanghai Jiao Tong University(YG2019ZDA13);Medical-Engineering Cross Research of University of Shanghai for Science and Technology(10-20-302-425)

摘要/Abstract

摘要：

目的·分析肥厚型心肌病（hypertrophic cardiomyopathy，HCM）患者与健康对照人群的心脏磁共振（cardiac magnetic resonance，CMR）短轴电影（cine）成像影像组学特征差异，并对2类人群进行分类。方法·纳入2018年1月—2021年12月就诊于上海交通大学医学院附属仁济医院心内科的HCM患者100例，以2∶1比例随机选取同时期健康对照（healthy control，HC）50例，在放射科医师规范操作下完成CMR检查。通过CVI 42后处理软件完成对入组人员左心室心功能及形态学的测量与评估，主要包括左心室射血分数（left ventricular ejection fraction，LVEF）、舒张末期左心室容积（left ventricular end-diastolic volume，LVEDV）和舒张末期左心室心肌质量（left ventricular end-diastolic mass，LVEDM）。并从CMR短轴电影成像中获取舒张末期心肌区域3D影像组学特征。分析影像组学特征在2类人群中的分布，并构建机器学习模型对2类人群进行分类。结果·共提取3D影像组学特征107个。在排除高度一致的特征后采用最小绝对值收敛和选择算子（least absolute shrinkage and selection operator，LASSO）模型进行5折交叉验证后，仍有11个系数非0的特征；利用K-best方法选择排序靠前的8个用于后续建模分析，其中4个特征在2组人群中差异具有统计学意义（均P<0.05）。随后构建支持向量机（support vector machine，SVM）和随机森林（random forest，RF）模型用于判别2类人群。结果显示：单一特征模型（一阶：熵）最大曲线下面积（area under the curve，AUC）为0.833（95%CI 0.695~0.968）；多特征模型（SVM算法）最高准确率为83.3%，其对应的AUC为0.882（95%CI 0.705~0.980）。结论·HCM患者与HC人群在左心室功能和左心室形态上均有显著差异，同时3D心肌影像组学特征也有显著差异。尽管单一特征模型可以鉴别2类人群，但联合多特征构建的模型有更好的分类效果。

关键词: 心脏磁共振, 影像组学, 肥厚型心肌病, 电影序列, 短轴图像

Abstract:

Objective ·To analyze the differences and classify hypertrophic cardiomyopathy (HCM) patients and healthy controls (HC) using short-axis cine cardiac magnetic resonance (CMR) images-derived radiomics features. Methods ·One hundred HCM subjects were included, and fifty HC were randomly selected at 2∶1 ratio during January 2018 to December 2021 in the Department of Cardiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine. The CMR examinations were performed by experienced radiologists on these subjects. CVI 42 post-processing software was used to obtain left ventricular morphology and function measurements, including left ventricular ejection fraction (LVEF), left ventricular end-diastolic volume (LVEDV) and left ventricular end-diastolic mass (LVEDM). The 3D radiomic features of the end-diastolic myocardial region were extracted from short-axis images CMR cine. The distribution of the radiomic features in the two groups was analysed and machine learning models were constructed to classify the two groups. Results ·One hundred and seven 3D radiomic features were selected and extracted. After exclusion of highly correlated features, least absolute shrinkage and selection operator (LASSO) was used, and a 5-fold cross-validation was performed. There were still 11 characteristics with non-zero coefficients. The K-best method was used to decide the top 8 features for subsequent analysis. Among them, four features were significantly different between the two groups (all P<0.05). Support vector machine (SVM) and random forest (RF) models were constructed to discriminate the two groups. The results showed that the maximum area under the curve (AUC) for the single-feature model (first order grayscale: entropy) was 0.833 (95%CI 0.685?0.968) and the maximum accuracy for the multi-feature model was 83.3% with an AUC of 0.882 (95%CI 0.705?0.980). Conclusion ·There are significant differences in both left ventricular function and left ventricular morphology between HCM and HC. The 3D myocardial radiomic features of the two groups are also significantly different. Although single feature is able to distinguish the two groups, the combination of multi-features show better classification performance.

Key words: cardiac magnetic resonance (CMR), radiomics, hypertrophic cardiomyopathy (HCM), cine sequence, short-axis image

中图分类号:

R542.2

刘启明, 卢启帆, 柴烨子, 姜萌, 卜军. 心脏磁共振短轴电影成像影像组学鉴别肥厚型心肌病与健康对照[J]. 上海交通大学学报（医学版）, 2024, 44(1): 79-86.

LIU Qiming, LU Qifan, CHAI Yezi, JIANG Meng, PU Jun. Short-axis cine cardiac magnetic resonance images-derived radiomics for hypertrophic cardiomyopathy and healthy control classification[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2024, 44(1): 79-86.

图/表 7

参考文献 26

1	MARON B J, MARON M S. Hypertrophic cardiomyopathy[J]. Lancet, 2013, 381(9862): 242-255.
2	WANG J, BRAVO L, ZHANG J Q, et al. Radiomics analysis derived from LGE-MRI predict sudden cardiac death in participants with hypertrophic cardiomyopathy[J]. Front Cardiovasc Med, 2021, 8: 766287.
3	GEORGIOPOULOS G, FIGLIOZZI S, PATERAS K, et al. Comparison of demographic, clinical, biochemical, and imaging findings in hypertrophic cardiomyopathy prognosis: a network meta-analysis[J]. JACC Heart Fail, 2023, 11(1): 30-41.
4	LOCKIE T, ISHIDA M, PERERA D, et al. High-resolution magnetic resonance myocardial perfusion imaging at 3.0-Tesla to detect hemodynamically significant coronary stenoses as determined by fractional flow reserve[J]. J Am Coll Cardiol, 2011, 57(1): 70-75.
5	CHIRIBIRI A, HAUTVAST G L T F, LOCKIE T, et al. Assessment of coronary artery stenosis severity and location[J]. JACC Cardiovasc Imaging, 2013, 6(5): 600-609.
6	NEISIUS U, MYERSON L, FAHMY A S, et al. Cardiovascular magnetic resonance feature tracking strain analysis for discrimination between hypertensive heart disease and hypertrophic cardiomyopathy[J]. PLoS One, 2019, 14(8): e0221061.
7	NEISIUS U, EL-REWAIDY H, NAKAMORI S, et al. Radiomic analysis of myocardial native T1 imaging discriminates between hypertensive heart disease and hypertrophic cardiomyopathy[J]. JACC Cardiovasc Imaging, 2019, 12(10): 1946-1954.
8	LIU J, ZHAO S H, YU S Q, et al. Patterns of replacement fibrosis in hypertrophic cardiomyopathy[J]. Radiology, 2022, 302(2): 298-306.
9	GERMAIN P, VARDAZARYAN A, PADOY N, et al. Classification of cardiomyopathies from MR cine images using convolutional neural network with transfer learning[J]. Diagnostics (Basel), 2021, 11(9): 1554.
10	JIANG S, ZHANG L L, WANG J, et al. Differentiating between cardiac amyloidosis and hypertrophic cardiomyopathy on non-contrast cine-magnetic resonance images using machine learning-based radiomics[J]. Front Cardiovasc Med, 2022, 9: 1001269.
11	CHENG S N, FANG M J, CUI C, et al. LGE-CMR-derived texture features reflect poor prognosis in hypertrophic cardiomyopathy patients with systolic dysfunction: preliminary results[J]. Eur Radiol, 2018, 28(11): 4615-4624.
12	AVARD E, SHIRI I, HAJIANFAR G, et al. Non-contrast cine cardiac magnetic resonance image radiomics features and machine learning algorithms for myocardial infarction detection[J]. Comput Biol Med, 2022, 141: 105145.
13	SHI R Y, WU R, AN D A L, et al. Texture analysis applied in T1 maps and extracellular volume obtained using cardiac MRI in the diagnosis of hypertrophic cardiomyopathy and hypertensive heart disease compared with normal controls[J]. Clin Radiol, 2021, 76(3): 236.e9-236.e19.
14	ANTONOPOULOS A S, BOUTSIKOU M, SIMANTIRIS S, et al. Machine learning of native T1 mapping radiomics for classification of hypertrophic cardiomyopathy phenotypes[J]. Sci Rep, 2021, 11(1): 23596.
15	FAHMY A S, ROWIN E J, ARAFATI A, et al. Radiomics and deep learning for myocardial scar screening in hypertrophic cardiomyopathy[J]. J Cardiovasc Magn Reson, 2022, 24(1): 40.
16	ZHOU D, XU J, ZHAO S H, et al. CMR publications from China of the last more than 30 years[J]. Int J Cardiovasc Imaging, 2020, 36(9): 1737-1747.
17	LIU Q M, LU Q F, CHAI Y Z, et al. Papillary-muscle-derived radiomic features for hypertrophic cardiomyopathy versus hypertensive heart disease classification[J]. Diagnostics (Basel), 2023, 13(9): 1544.
18	VAN GRIETHUYSEN J J M, FEDOROV A, PARMAR C, et al. Computational radiomics system to decode the radiographic phenotype[J]. Cancer Res, 2017, 77(21): e104-e107.
19	BIAU G. Analysis of a random forests model[EB/OL]. arXiv: 1005.0208v3(2012-05-26)[2023-03-20]. https://doi.org/10.48550/arXiv.1005.0208.
20	CORTES C, VAPNIK V. Support-vector networks[J]. Mach Lang, 1995, 20(3): 273-297.
21	PEDREGOSA F, VAROQUAUX G, GRAMFORT A, et al. Scikit-learn: machine learning in python[EB/OL]. arXiv: 1201.0490v4(2018-07-05)[2023-03-20]. https://arxiv.org/abs/1201.0490.
22	YU F, HUANG H B, YU Q H, et al. Artificial intelligence-based myocardial texture analysis in etiological differentiation of left ventricular hypertrophy[J]. Ann Transl Med, 2021, 9(2): 108.
23	GILLIES R J, KINAHAN P E, HRICAK H. Radiomics: images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563-577.
24	LAMBIN P, LEIJENAAR R T H, DEIST T M, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
25	RAISI-ESTABRAGH Z, IZQUIERDO C, CAMPELLO V M, et al. Cardiac magnetic resonance radiomics: basic principles and clinical perspectives[J]. Eur Heart J Cardiovasc Imaging, 2020, 21(4): 349-356.
26	YING X. An overview of overfitting and its solutions[J]. J Phys: Conf Ser, 2019, 1168: 022022.

Indicator	Total (n=150)	HCM (n=100)	HC (n=50)	P value
Age/year	51 (39, 60)	52 (43, 60)	46 (36, 61)	0.408
Male/n(%)	110 (73)	71 (71)	39 (78)	0.362
Weight/kg	71.2±13.1	70.5±12.8	72.7±13.7	0.671
Height/cm	170 (162, 175)	170 (160, 173)	173 (166, 180)	0.002
BMI/(kg·m^‒2)	24.2 (22.4, 26.6)	24.5 (22.5, 26.8)	23.9 (22.3, 25.3)	0.253
BSA/m²	1.79±0.21	1.77±0.20	1.83±0.22	0.419
CMR parameter
LVEF/%	66.3±7.1	67.1±7.8	64.6±5.0	0.019
LVEDV/mL	132.4±32.3	132.3±29.0	132.7±38.4	0.035
LVEDV index/(mL·m^‒2)	72.3±31.7	75.0±15.4	71.8±15.6	0.876
LVEDM/g	114.0 (90.9, 158.4)	137.1 (104.8, 178.0)	90.4 (68.7, 103.5)	0.000
LVEDM index/(g·m^‒2)	62.7 (49.9, 88.1)	77.8 (59.3, 99.0)	47.4 (42.4, 55.1)	0.000

Indicator	Total (n=150)	HCM (n=100)	HC (n=50)	P value
Age/year	51 (39, 60)	52 (43, 60)	46 (36, 61)	0.408
Male/n(%)	110 (73)	71 (71)	39 (78)	0.362
Weight/kg	71.2±13.1	70.5±12.8	72.7±13.7	0.671
Height/cm	170 (162, 175)	170 (160, 173)	173 (166, 180)	0.002
BMI/(kg·m^‒2)	24.2 (22.4, 26.6)	24.5 (22.5, 26.8)	23.9 (22.3, 25.3)	0.253
BSA/m²	1.79±0.21	1.77±0.20	1.83±0.22	0.419
CMR parameter
LVEF/%	66.3±7.1	67.1±7.8	64.6±5.0	0.019
LVEDV/mL	132.4±32.3	132.3±29.0	132.7±38.4	0.035
LVEDV index/(mL·m^‒2)	72.3±31.7	75.0±15.4	71.8±15.6	0.876
LVEDM/g	114.0 (90.9, 158.4)	137.1 (104.8, 178.0)	90.4 (68.7, 103.5)	0.000
LVEDM index/(g·m^‒2)	62.7 (49.9, 88.1)	77.8 (59.3, 99.0)	47.4 (42.4, 55.1)	0.000

Feature class and name	HCM	HC	P value
GLDM: large dependence high gray level emphasis	34 779.60	23 528.60	0.047
First-order grayscale: kurtosis	5.92	5.05	0.116
First-order grayscale: entropy	4.03	4.38	0.004
GLSZM: zone entropy	7.76	7.67	0.037
GLCM: correlation	0.86	0.84	0.077
First-order grayscale: 90th percentile	129.70	174.60	0.054
Shape: major axis length	94.00	88.20	0.037
Shape: least axis length	75.10	72.50	0.322

Feature class and name	HCM	HC	P value
GLDM: large dependence high gray level emphasis	34 779.60	23 528.60	0.047
First-order grayscale: kurtosis	5.92	5.05	0.116
First-order grayscale: entropy	4.03	4.38	0.004
GLSZM: zone entropy	7.76	7.67	0.037
GLCM: correlation	0.86	0.84	0.077
First-order grayscale: 90th percentile	129.70	174.60	0.054
Shape: major axis length	94.00	88.20	0.037
Shape: least axis length	75.10	72.50	0.322

Feature class and name	AUC (95%CI)
Feature class and name	SVM	RF
GLDM: large dependence high gray level emphasis	0.758 (0.520‒0.863)	0.668 (0.486‒0.825)
First-order grayscale: kurtosis	0.726 (0.517‒0.866)	0.623 (0.445‒0.803)
First-order grayscale: entropy	0.833 (0.695‒0.968)	0.679 (0.435‒0.782)
GLSZM: zone entropy	0.712 (0.365‒0.801)	0.648 (0.449‒0.836)
GLCM: correlation	0.707 (0.507‒0.857)	0.667 (0.511‒0.845)
First-order grayscale: 90th percentile	0.752 (0.544‒0.910)	0.697 (0.446‒0.801)
Shape: major axis length	0.673 (0.282‒0.735)	0.554 (0.323‒0.677)
Shape: least axis length	0.680 (0.318‒0.740)	0.810 (0.625‒0.912)

心脏磁共振短轴电影成像影像组学鉴别肥厚型心肌病与健康对照

Short-axis cine cardiac magnetic resonance images-derived radiomics for hypertrophic cardiomyopathy and healthy control classification

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图/表 7

参考文献 26

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Metrics

Feature number	Model	Accuracy/%	AUC	P for accuracy	P for AUC
2	SVM	66.7±0	0.756±0	0.000	0.000
	RF	60.7±4.1	0.729±0.20
4	SVM	70.0±0	0.776±0	0.000	0.000
	RF	61.7±2.4	0.740±0.22
6	SVM	80.0±0	0.871±0.03	0.080	0.043
	RF	76.0±6.8	0.847±0.35
8	SVM	83.3±0	0.882±0.05	0.005	0.022
	RF	77.7±5.7	0.868±0.18

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