Journal of Shanghai Jiao Tong University (Medical Science) ›› 2024, Vol. 44 ›› Issue (1): 79-86.doi: 10.3969/j.issn.1674-8115.2024.01.009
• Clinical research • Previous Articles Next Articles
LIU Qiming(), LU Qifan, CHAI Yezi, JIANG Meng(), PU Jun()
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:
CLC Number:
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.
Add to citation manager EndNote|Ris|BibTeX
URL: https://xuebao.shsmu.edu.cn/EN/10.3969/j.issn.1674-8115.2024.01.009
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 |
Tab 1 Comparison of demographic data and CMR parameters between the HCM group and HC group
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 |
Tab 2 Comparison of 3D radiomic features between the HCM group and HC group in testing dataset
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) | |
---|---|---|
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) |
Tab 3 Performance of single 3D radiomic feature-based classification models in testing dataset
Feature class and name | AUC (95%CI) | |
---|---|---|
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) |
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 |
Tab 4 Performance of multi-3D radiomic features-based classification models in testing dataset
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 |
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. |
[1] | LIU Qiming, LU Qifan, CHAI Yezi, JIANG Meng, PU Jun. Radiomics-based left ventricular ejection fraction prediction: a feasibility study [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2023, 43(9): 1162-1168. |
[2] | MA Ben, ZHAO Cheng, SHU Yijun, DONG Ping. Application progress of CT radiomics in gastrointestinal stromal tumor [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2023, 43(7): 923-930. |
[3] | CHENG Ran, HU Jiajia, LI Biao. Advances in the application of 18F-FDG PET/CT radiomics for diagnosis, treatment and prognosis prediction of lymphoma [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2023, 43(6): 781-787. |
[4] | Jun-lin HE, Qing LU, Xin XU, Shu-dong HU. Value of CT radiomic features in preoperative prediction of cervical lymph node metastasis in patients with papillary thyroid carcinoma [J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2021, 41(9): 1233-1239. |
[5] | Pei-kun HU, Jie HE, Lian-ming WU, Heng GE, Jian-rong XU, Jun PU. Effect of microvascular obstruction on left ventricle function and prognosis in patients with ST-segment elevation myocardial infarction [J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2021, 41(2): 173-179. |
[6] | Ze-hao FENG, Ye-zi CHAI, Xuan SU, Bao-hang-xing SUN, Qi-ming LIU, Meng JIANG, Jun PU. Association between body mass index and myocardial involvements in patients with systemic lupus erythematosus [J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2021, 41(2): 180-186. |
[7] | Ye-zi CHAI, Meng JIANG, Jun PU. Relation between body mass index and left ventricular structure and function in patients with hypertrophic cardiomyopathy: a cardiovascular magnetic resonance imaging study [J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2021, 41(12): 1636-1641. |
[8] | Jian-xun DONG, Lai WEI, Jie HE, Ling-cong KONG, Heng GE, Jun PU. Progress of cardiac magnetic resonance in assessment of left ventricular mechanical dyssynchrony [J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2021, 41(12): 1698-1702. |
[9] | Ya-jie GAO, Wen-kun MA, Cheng-jie GAO, Yi ZHOU, Jing-wei PAN. Exploration of the predictive value of myocardial strain on ventricular remodeling after acute ST-segment elevation myocardial infarction [J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2021, 41(11): 1478-1484. |
[10] | JIANG Xun-wei, SUN Xing-hua, ZHANG Han, XIAO Ting-ting, ZHANG Yong-wei, XIE Li-jian. Change of left ventricular torsion function and systolic synchronization in children with hypertrophic cardiomyopathy [J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2020, 40(7): 929-935. |
[11] | FENG Ze-hao1*, ZHANG Qing1*, CHAI Ye-zi1, SU Xuan1, SUN Bao-hang-xing1, LIU Qi-ming1, YAN Fu-hua2, JIANG Meng1#, PU Jun1#. Evaluation of effect of smoking on myocardial injury and prognosis in patients with acute ST-segment elevation myocardial infarction [J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2020, 40(5): 573-582. |
[12] | WANG Wei, ZHAO Hang, GE Heng, DING Song, SHEN Xue-dong, PU Jun. Value of two-dimensional speckle-tracking echocardiography in accessing myocardial viability and predicting left ventricular remodeling after acute myocardial infarction [J]. , 2018, 38(12): 1447-. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||