Abstract
Cardiac magnetic resonance (CMR) imaging has become an important technique for non-invasive diagnosis of takotsubo syndrome (TTS). The long-term prognostic value of CMR imaging in TTS has not been fully elucidated yet. This study sought to evaluate the prognostic value of texture analysis (TA) based on CMR images in patients with TTS using machine learning. In this multicenter study (InterTAK Registry), we investigated CMR imaging data of 58 patients (56 women, mean age 68 ± 12 years) with TTS. CMR imaging was performed in the acute to subacute phase (median time after symptom onset 4 days) of TTS. TA of the left ventricle was performed using free-hand regions-of-interest in short axis late gadolinium-enhanced and on T2-weighted (T2w) images. A total of 608 TA features adding the parameters age, gender, and body mass index were included. Dimension reduction was performed removing TA features with poor intra-class correlation coefficients (ICC ≤ 0.6) and those being redundant (correlation matrix with Pearson correlation coefficient r > 0.8). Five common machine-learning classifiers (artificial neural network Multilayer Perceptron, decision tree J48, NaïveBayes, RandomForest, and Sequential Minimal Optimization) with tenfold cross-validation were applied to assess 5-year outcome including major adverse cardiac and cerebrovascular events (MACCE). Dimension reduction yielded 10 TA features carrying prognostic information, which were all based on T2w images. The NaïveBayes machine learning classifier showed overall best performance with a sensitivity of 82.9% (confidence interval (CI) 80–86.2), specificity of 83.7% (CI 75.7–92), and an area-under-the receiver operating characteristics curve of 0.88 (CI 0.83–0.92). This proof-of-principle study is the first to identify unique T2w-derived TA features that predict long-term outcome in patients with TTS. These features might serve as imaging prognostic biomarkers in TTS patients.
Highlights
Cardiac magnetic resonance (CMR) imaging has become an important technique for non-invasive diagnosis of takotsubo syndrome (TTS)
T2-weighted (T2w) studies are able to detect myocardial oedema, which is a feature in the acute phase of TTS representing reversible myocardial injury and which matches the distribution of left ventricular (LV) dysfunction[6,8]
Fifty-eight patients with the diagnosis of TTS and with CMR examination with diagnostic image quality were included in the present study
Summary
Cardiac magnetic resonance (CMR) imaging has become an important technique for non-invasive diagnosis of takotsubo syndrome (TTS). This study sought to evaluate the prognostic value of texture analysis (TA) based on CMR images in patients with TTS using machine learning. The NaïveBayes machine learning classifier showed overall best performance with a sensitivity of 82.9% (confidence interval (CI) 80–86.2), specificity of 83.7% (CI 75.7–92), and an area-under-the receiver operating characteristics curve of 0.88 (CI 0.83–0.92) This proof-of-principle study is the first to identify unique T2w-derived TA features that predict long-term outcome in patients with TTS. CI Confidence interval ECG Electrocardiogram ICC Intraclass correlation coefficients InterTAK International Takotsubo IQR Interquartile ranges GLCM Grey-level co-occurrence matrix LGE Late gadolinium enhancement LV Left ventricle MACCE Major adverse cardiac and cerebrovascular events MI Myocardial infarction PRC Precision recall curve RLM Run-length matrix ROC Receiver operating characteristics ROI Regions-of-interest SMO Sequential minimal optimization T2w T2-weighted images TA Texture analysis TTS Takotsubo syndrome. It was discussed that LGE in the sub-acute phase of TTS may be associated with severity and prolonged recovery from the disease[11]
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