Objective ·To assess the feasibility of using 3D imaging features extracted from cardiac magnetic resonance (CMR) short-axis cine images to predict left ventricular ejection fraction (LVEF). Methods ·A total of 100 left ventricular hypertrophy (LVH) patients who visited the Department of Cardiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine from January 2018 to December 2021, as well as 100 healthy control (HC) subjects during the same period, were included. All subjects completed CMR examinations under the supervision of experienced cardiologists and radiologists. The endocardial and epicardial contours were then manually delineated by cardiologists. Measurements of cardiac function and morphology were completed and data was recorded, including LVEF, left ventricular end-diastolic volume (LVEDV), and left ventricular end-diastolic mass (LVEDM). Myocardial 3D radiomic features of CMR-cine sequences were extracted by the Pyradiomics package, and selected and sorted by using correlation coefficient and K-best method. The LVEF prediction was performed with linear regression (LR), random forest (RF) and gradient boost (GB) methods. Results were also compared with LVEF prediction based on clinical information and CMR parameters. Results ·In terms of clinical indicators, there were significant differences between the LVH and HC groups, such as LVEDV and LVEDM (all P<0.05); after extracting 3D radiomic features, the top 10 features were selected for further analysis. LR regression model, GB regression model and RF regression model were constructed for predicting the LVEF, and RF regression models showed the best results with seven features, in which the mean absolute error (MAE) was 0.066±0.002. Further comparison results showed that the model using radiomic information with CMR parameters (MAE=0.056±0.001) had the best performance and it was significantly better than the model using radiomic features (MAE=0.066±0.002) or CMR parameters (MAE=0.060±0.001) alone (both P<0.05). Conclusion ·The use of radiomic features for LVEF prediction has certain feasibility, and combining radiomic features with CMR parameters can further improve the prediction accuracy of the model.
Keywords：cardiac magnetic resonance (CMR)
;
radiomics
;
left ventricular hypertrophy
;
cine sequence
LIU Qiming, LU Qifan, CHAI Yezi, JIANG Meng, PU Jun. Radiomics-based left ventricular ejection fraction prediction: a feasibility study. Journal of Shanghai Jiao Tong University (Medical Science)[J], 2023, 43(9): 1162-1168 doi:10.3969/j.issn.1674-8115.2023.09.010
左室射血分数（left ventricular ejection fraction，LVEF）作为评估心功能的一项重要指标，在疾病诊断、患者用药及预后评估等多方面均有重要价值［1-2］。目前获取LVEF的主要方式包括超声心动图（echocardiography，ECG）［3］与心脏磁共振（cardiac magnetic resonance，CMR）［4］，其中ECG因高性价比及高普及率目前在临床上最常应用［5］，但是基于ECG的LVEF多采用2D图像直接进行计算，结果因操作者熟练度及图像采集习惯等因素，常导致观察者内与观察者间出现一定的差异［6］。CMR作为目前无创心脏形态与功能检查的金标准，虽然普及率较低，但是在评估结果准确性及可重复性方面表现更佳［7］。其他心脏影像学检查，例如胸部CT等虽然仪器配备有心电门控，但采集过程中仍缺少时间轴信息，难以直接计算射血分数；即使部分研究使用了CT电影序列，仍存在放射剂量较高的问题［8］。
Tab 3 LVEF prediction performance based on clinical and radiomic information
Feature group①
MAE
P value②
Basic information③
0.071±0.002
0.000
Radiomics
0.066±0.002
0.000
CMR④
0.060±0.001
0.001
CMR+radiomics
0.056±0.001
NA
Note:①indicates best radiomics-based model with RF algorithm. ②P value is calculated compared with the CMR+radiomics group. ③The clinical data include age, gender, weight, height, BMI and BSA. ④The CMR parameters include LVEDV, LVEDM, LVEDV/BSA and LVEDM/BSA.
JIANG Meng, PU Jun and LIU Qiming participated in research design. LIU Qiming, CHAI Yezi and LU Qifan participated in data collection. LIU Qiming participated in data analysis. LIU Qiming, CHAI Yezi and LU Qifan drafted and revised the manuscript. All the authors have read the last version of paper and consented for submission.
Lang RM, Badano LP, Mor-Avi V, et al. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging[J]. J Am Soc Echocardiogr, 2015, 28(1): 1-39.e14.
OMMEN S R, MITAL S, BURKE M A, et al. 2020 AHA/ACC guideline for the diagnosis and treatment of patients with hypertrophic cardiomyopathy: executive summary: a report of the American college of cardiology/American heart association joint committee on clinical practice guidelines[J]. Circulation, 2020, 142(25): e533-e557.
IBANEZ B, ALETRAS A H, ARAI A E, et al. Cardiac MRI endpoints in myocardial infarction experimental and clinical trials[J]. J Am Coll Cardiol, 2019, 74(2): 238-256.
YU X, YAO X X, WU B F, et al. Using deep learning method to identify left ventricular hypertrophy on echocardiography[J].Int J Cardiovasc Imaging, 2022, 38(4): 759-769.
FUNADA A, KANZAKI H, NOGUCHI T, et al. Prognostic significance of late gadolinium enhancement quantification in cardiac magnetic resonance imaging of hypertrophic cardiomyopathy with systolic dysfunction[J]. Heart Vessels, 2016, 31(5): 758-770.
LYU Q, SHAN H M, XIE Y B, et al. Cine cardiac MRI motion artifact reduction using a recurrent neural network[J]. IEEE Trans Med Imaging, 2021, 40(8): 2170-2181.
LARKMAN D J, HERLIHY A H, COUTTS G A, et al. Elimination of magnetic field foldover artifacts in MR images[J]. J Magn Reson Imaging, 2000, 12(5): 795-797.
RAJIAH P, KAY F, BOLEN M, et al. Cardiac magnetic resonance in patients with cardiac implantable electronic devices: challenges and solutions[J]. J Thorac Imaging, 2020, 35(1): W1-W17.
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.
KELLER H, WANGER K C, GOEPFRICH M, et al. Morphological quantification and differentiation of left ventricular hypertrophy in hypertrophic cardiomyopathy and hypertensive heart disease. A two dimensional echocardiographic study[J]. Eur Heart J, 1990, 11(1): 65-74.
ZWANENBURG A, VALLIÈRES M, ABDALAH M A, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping[J]. Radiology, 2020, 295(2): 328-338.
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.
BUSTIN A, FUIN N, BOTNAR R M, et al. From compressed-sensing to artificial intelligence-based cardiac MRI reconstruction[J]. Front Cardiovasc Med, 2020, 7: 17.
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.
RAISI-ESTABRAGH Z, JAGGI A, GKONTRA P, et al. Cardiac magnetic resonance radiomics reveal differential impact of sex, age, and vascular risk factors on cardiac structure and myocardial tissue[J]. Front Cardiovasc Med, 2021, 8: 763361.
ZHENG X Y, YAO Z, HUANG Y N, et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer[J]. Nat Commun, 2020, 11(1): 1236.
BERNARD O, LALANDE A, ZOTTI C, et al. Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?[J]. IEEE Trans Med Imaging, 2018, 37(11): 2514-2525.
CAMPELLO V M, MARTÍN-ISLA C, IZQUIERDO C, et al. Minimising multi-centre radiomics variability through image normalisation: a pilot study[J]. Sci Rep, 2022, 12(1): 12532.
PIANTADOSI G, SANSONE M, FUSCO R, et al. Multi-planar 3D breast segmentation in MRI via deep convolutional neural networks[J]. Artif Intell Med, 2020, 103: 101781.
