Radiomics with structural magnetic resonance imaging, surface morphometry features, neurology scales, and clinical metrics to evaluate the neurodevelopment of preschool children with corrected tetralogy of Fallot

Abstract

Background: Despite the improved survival rates of children with tetralogy of Fallot (TOF), various degrees of neurodevelopmental disorders persist. Currently, there is a lack of quantitative and objective imaging markers to assess the neurodevelopment of individuals with TOF. This study aimed to noninvasively examine potential quantitative imaging markers of TOF neurodevelopment by combining radiomics signatures and morphological features and to further clarify the relationship between imaging markers and clinical neurodevelopment metrics.

Methods: This study included 33 preschool children who had undergone surgical correction for TOF and 29 healthy controls (36 in the training cohort and 26 in the testing cohort), all of whom underwent three-dimensional T1-weighted high-resolution (T1-3D) head magnetic resonance imaging (MRI). Radiomics features were extracted by Pyradiomics to construct radiomics models, while surface morphometry (surface and volumetric) features were analyzed to build morphometry models. Merged models integrating radiomics and morphometry features were subsequently developed. The optimal discriminative radiomics signatures were identified via least absolute shrinkage and selection operator (LASSO). Machine learning classification models include support vector machine (SVM) with radial basis function (RBF) and multivariable logistic regression (MLR) models, both of which were used to evaluate the potential imaging biomarkers. Performances of models were evaluated based on their calibration and classification metrics. The area under the receiver operating characteristic curves (AUCs) of the models were evaluated using the Delong test. Neurodevelopmental assessments for children with corrected TOF were conducted with the Wechsler Preschool and Primary Scale of Intelligence-Fourth Edition (WPPSI-IV). Furthermore, the correlation of the significant discriminative indicators with clinical metrics and neurodevelopmental scales was evaluated.

Results: Twelve discriminative radiomics signatures, optimized for classification, were identified. The performance of the merged model (AUCs of 0.922 and 0.917 for the training set and test set with SVM, respectively) was superior to that of the single radiomics model (AUCs of 0.915 and 0.917 for the training set and test set with SVM, respectively) and that of the single morphometric models (AUCs of 0.803 and 0.756 for the training set and test set with SVM, respectively). The radiomics model demonstrated higher significance than did the morphometric models in training set with SVM (AUC: 0.915 vs. 0.803; P<0.001). Additionally, the significant indicators showed a correlation with clinical indicators and neurodevelopmental scales.

Conclusions: MRI-based radiomics features combined with morphometry features can provide complementary information to identify neurodevelopmental abnormalities in children with corrected TOF, which will provide potential evidence for clinical diagnosis and treatment.

Keywords: Radiomics; machine learning; magnetic resonance imaging (MRI); neurodevelopment; tetralogy of Fallot (TOF).

Figure 1

The flowchart of participant recruitment in this study. TOF, tetralogy of Fallot; HC, healthy control.

Figure 2

The radiomics workflow for integrative analysis of TOF neurodevelopment. TOF, tetralogy of Fallot; SVM, support vector machine; LASSO, least absolute shrinkage and selection operator; WPPSI-IV, Wechsler Preschool and Primary Intelligence Scale Fourth Edition.

Figure 3

Feature selection by LASSO regression. (A) LASSO coefficient indicated that important features tended to 0 with increasing lambda. (B) The deviance was determined by adjusting the optimal regularization parameters (λ) based on 10-fold cross-validation. The left dashed line delineates the minimum norm, and the right indicates 1-standard error norm (1-SE). This study used the minimum norm. LASSO, least absolute shrinkage and selection operator.

Figure 4

The AUC curve of the merger model, the radiomics model, and the morphologic model. (A) The training set (AUC_radiomics =88.5%, AUC_morphologic =80.8%, AUC_merger =88.8%) and (B) the validation set (AUC_radiomics =88.1%, AUC_morphologic =79.2%, AUC_merger =90.0%).

Figure 5

Correlation bubble map of neurodevelopmental scales (A)/clinical metrics (B) and radiomics signatures. The color bar on the right represents the size of the correlation coefficient; the size of the ball represents a significant difference. GAI, general ability index; NVI, non-verbal index; VAI, verbal reception index; FSIQ, full scales intelligence quotient; WMI, working memory index; VSI, visual spatial index; VCI, verbal comprehension index; ACC, aortic cross-clamp; CPB, cardiopulmonary bypass; BMI, body mass index; VSD, ventricular septal defect; SPO₂, pulse oxygen saturation; ORA, overriding aorta; SALGLE, small area low gray level emphasis; LRE, long-run emphasis; SZNU, size zone nonuniformity normalized.

Figure 6

The scatter plots of the radiomics features and neurodevelopmental scales/clinical metrics. VCI, verbal comprehension index; GAI, general ability index; FSIQ, full scales intelligence quotient; VAI, verbal reception index; VSI, visual spatial index; CPB, cardiopulmonary bypass; ACC, aortic cross-clamp; SpO₂, pulse oxygen saturation; ORA, overriding aorta; SALGLE, small area low gray level emphasis; LRE, long-run emphasis; VSD, ventricular septal defect; SZNU, size zone nonuniformity normalized.