Deep learning algorithm to improve hypertrophic cardiomyopathy mutation prediction using cardiac cine images
- PDF / 2,401,162 Bytes
- 10 Pages / 595.276 x 790.866 pts Page_size
- 100 Downloads / 213 Views
CARDIAC
Deep learning algorithm to improve hypertrophic cardiomyopathy mutation prediction using cardiac cine images Hongyu Zhou 1,2,3 & Lu Li 4 & Zhenyu Liu 2,3 & Kankan Zhao 1 & Xiuyu Chen 4 & Minjie Lu 4 & Gang Yin 4 & Lei Song 5 & Shihua Zhao 4 & Hairong Zheng 1 & Jie Tian 2,3,6,7 Received: 4 August 2020 / Revised: 28 September 2020 / Accepted: 2 November 2020 # European Society of Radiology 2020
Abstract Objectives The high variability of hypertrophic cardiomyopathy (HCM) genetic phenotypes has prompted the establishment of risk-stratification systems that predict the risk of a positive genetic mutation based on clinical and echocardiographic profiles. This study aims to improve mutation-risk prediction by extracting cardiovascular magnetic resonance (CMR) morphological features using a deep learning algorithm. Methods We recruited 198 HCM patients (48% men, aged 47 ± 13 years) and divided them into training (147 cases) and test (51 cases) sets based on different genetic testing institutions and CMR scan dates (2012, 2013, respectively). All patients underwent CMR examinations, HCM genetic testing, and an assessment of established genotype scores (Mayo Clinic score I, Mayo Clinic score II, and Toronto score). A deep learning (DL) model was developed to classify the HCM genotypes, based on a nonenhanced four-chamber view of cine images. Results The areas under the curve (AUCs) for the test set were Mayo Clinic score I (AUC: 0.64, sensitivity: 64.29%, specificity: 47.83%), Mayo Clinic score II (AUC: 0.70, sensitivity: 64.29%, specificity: 65.22%), Toronto score (AUC: 0.74, sensitivity: 75.00%, specificity: 56.52%), and DL model (AUC: 0.80, sensitivity: 85.71%, specificity: 69.57%). The combination of the DL and the Toronto score resulted in a significantly higher predictive performance (AUC = 0.84, sensitivity: 83.33%, specificity: 78.26%), compared with Mayo I (p = 006), Mayo II (p = 022), and Toronto score (p = 0.029). Conclusions The combination of the DL model, based on nonenhanced cine CMR images and the Toronto score yielded significantly higher diagnostic performance in detecting HCM mutations. Key Points • Deep learning method could enable the extraction of image features from cine images. • Deep learning method based on cine images performed better than established scores in identifying HCM patients with positive genotypes. • The combination of the deep learning method based on cine images and the Toronto score could further improve the performance of the identification of HCM patients with positive genotypes. Hongyu Zhou and Lu Li contributed equally to this work. * Shihua Zhao [email protected]
4
Department of Magnetic Resonance Imaging, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases of China, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
5
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SZ University
Data Loading...
