Prognostic prediction of left ventricular myocardial noncompaction using machine learning and cardiac magnetic resonance radiomics

Abstract

Background Although there are many studies on the prognostic factors of left ventricular myocardial noncompaction (LVNC), the determinants are varied and not entirely consistent. This study aimed to build predictive models using radiomics features and machine learning to predict major adverse cardiovascular events (MACEs) in patients with LVNC. Methods In total, 96 patients with LVNC were included and randomly divided into training and test cohorts. A total of 105 cine cardiac magnetic resonance (CMR)-derived radiomics features and 35 clinical characteristics were extracted. Five different oversampling algorithms were compared for selection of the optimal imbalanced processing. Feature importance was assessed with extreme gradient boosting (XGBoost). We compared the performance of 5 machine learning classification methods with different sample:feature ratios to determine the optimal hybrid classification strategy. Subsequently, radiomics, clinical, and combined radiomics-clinical models were developed and compared. Results The machine learning pipeline included an adaptive synthetic (ADASYN) algorithm for imbalanced processing, XGBoost feature selection with a sample:feature ratio of 10, and support vector machine (SVM) modeling. The areas under the receiver operating characteristic curves (AUCs) of the radiomics model, clinical model, and combined model in the validation cohort were 0.87 (sensitivity 83.33%, specificity 64.29%), 0.65 (sensitivity 16.67%, specificity 78.57%), and 0.92 (specificity 33.33%, sensitivity 100.00%), respectively. The radiomics model performed similarly to the clinical and combined models (P=0.124 and P=0.621, respectively). The performance of the combined model was significantly better than that of the clinical model (P=0.003). Conclusions The machine learning-based cine CMR radiomics model performed well at predicting MACEs in patients with LVNC. Adding radiomics features offered incremental prognostic value over clinical factors alone.

MRI radiomics-based decision support tool for a personalized classification of cervical disc degeneration: a two-center study

“…Through mRMR feature selection, the top 10 features were selected for further modeling. For imbalance processing, an adaptive synthetic (ADASYN) algorithm, as a valuable oversampled method in radiomics ( Han et al, 2023 ), was performed to balance the training data. Then, the logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and support vector machine (SVM) were implemented for model construction and comparison.…”

Watchful Waiting

“… 5 , 6 Machine learning has also been shown to aid in the prediction of major adverse cardiovascular events in patients with LVNC. 7 , 8 LVNC continues to be the subject of international discussions as complete alignment regarding morphologic development, diagnosis, and classification has not been realized. The addition of machine learning to the work presented in this issue of the journal may offer even greater opportunities to identify LVNC with greater accuracy and begin to better risk stratify newborns and children with this diagnosis.…”