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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

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. [ABSTRACT FROM AUTHOR]

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

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 enddiastolic 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. [ABSTRACT FROM AUTHOR]

Purpose: A multiphasic cine sequence performed during magnetic resonance enterography (MRE) has been shown to increase diagnostic accuracy of MRE demonstrating limited movement in inflamed intestine in patients with Crohn's disease (CD). Our aim was to confirm in our study population that intestinal inflammation was associated with decreased motility and determine if factors suggestive of complicated disease such as the presence of a stricture or fistula were associated with decreased motility on the MRE cine sequence.Methods: This was a retrospective study of 59 patients (mean age 40.8 ± 16.1) with Crohn's disease who had a small bowel lesion on MRE. Two gastrointestinal radiologists independently scored MRE findings using a qualitative, subjective scoring system. Univariate and multivariable ordered logistic regression models were used to evaluate the associations between cine sequence score, radiologic image findings, and clinical data.Results: On univariate analysis, radiologic findings reflecting active inflammation, the presence of a stricture, and penetrating disease were associated with decreased motility. On multivariable analysis, hyper-enhancement, the presence of a comb sign, and global evidence of active inflammation remained associated with decreased motility. Of the factors suggesting complicated disease, the presence of stricture (Odds Ratio 0.40, 95% Confidence Interval 0.17-0.95, p-value 0.038) was associated with decreased motility.Conclusions: As previously shown, well-established radiologic findings of bowel inflammation were associated with decreased small bowel motility. In this study, we have added that the radiologic finding of a fixed stricture is also associated with decreased motility. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: We investigated whether an additional cine sequence in MR enteroclysis examination is practicable and would provide additional findings in the diagnostics of patients with abdominal diseases. Materials and methods: Seventeen consecutively presenting patients with known or presumed abdominal disease (known or presumed intestinal illness; age range, 21–57 years; mean age, 34.1 years; male:female ratio, 4:13) were retrospectively studied. All patients underwent MRI enteroclysis at 1.5 T subsequent to conventional ileocolonoscopy, which was used as reference standard. A gastroenterologist and a radiologist evaluated the MRI examinations together to see whether the additional cine sequences would provide any additional findings. Results: The cine sequences provided relevant diagnostic information in at least 11 of 17 patients (60.6%). In five patients, we were able to determine a stenosis as scarred; in two patients, normal peristalsis of the bowel was demonstrated, showing that the stenosis had not been scarred. Conclusion: This study provides strong evidence that the implementation of cine sequences in MRI enteroclysis examination provides additional findings with clinical relevance, especially for distinguishing scarred and functional stenoses. [Copyright &y& Elsevier]