Objective: To evaluate the performance of 2D and 3D radiomics features with different machine learning approaches to classify SPLs based on magnetic resonance(MR) T2 weighted imaging (T2WI).Material and Methods: A total of 132 patients with pathologically confirmed SPLs were examined and randomly divided into training (n = 92) and test datasets (n = 40). A total of 1692 3D and 1231 2D radiomics features per patient were extracted. Both radiomics features and clinical data were evaluated. A total of 1260 classification models, comprising 3 normalization methods, 2 dimension reduction algorithms, 3 feature selection methods, and 10 classifiers with 7 different feature numbers (confined to 3–9), were compared. The ten-fold cross-validation on the training dataset was applied to choose the candidate final model. The area under the receiver operating characteristic curve (AUC), precision-recall plot, and Matthews Correlation Coefficient were used to evaluate the performance of machine learning approaches.Results: The 3D features were significantly superior to 2D features, showing much more machine learning combinations with AUC greater than 0.7 in both validation and test groups (129 vs. 11). The feature selection method Analysis of Variance(ANOVA), Recursive Feature Elimination(RFE) and the classifier Logistic Regression(LR), Linear Discriminant Analysis(LDA), Support Vector Machine(SVM), Gaussian Process(GP) had relatively better performance. The best performance of 3D radiomics features in the test dataset (AUC = 0.824, AUC-PR = 0.927, MCC = 0.514) was higher than that of 2D features (AUC = 0.740, AUC-PR = 0.846, MCC = 0.404). The joint 3D and 2D features (AUC=0.813, AUC-PR = 0.926, MCC = 0.563) showed similar results as 3D features. Incorporating clinical features with 3D and 2D radiomics features slightly improved the AUC to 0.836 (AUC-PR = 0.918, MCC = 0.620) and 0.780 (AUC-PR = 0.900, MCC = 0.574), respectively.Conclusions: After algorithm optimization, 2D feature-based radiomics models yield favorable results in differentiating malignant and benign SPLs, but 3D features are still preferred because of the availability of more machine learning algorithmic combinations with better performance. Feature selection methods ANOVA and RFE, and classifier LR, LDA, SVM and GP are more likely to demonstrate better diagnostic performance for 3D features in the current study.

IRS4, a member of the insulin receptor substrate protein family, can induce constitutive PI3K/AKT hyperactivation and cell proliferation even in the absence of insulin or growth factors and promote tumorigenesis, but its regulation has only been explored at the transcriptional level.

This study aimed to investigate the potential of intravoxel incoherent motion (IVIM) diffusion-weighted MR imaging in assessing solitary pulmonary lesions (SPLs). Sixty-two patients with pathologically confirmed SPLs, including 51 and 11 cases of malignant and benign lesions, respectively, were assessed. Diffusion weighted imaging (DWI) with 13 b values was used to derive apparent diffusion coefficient (ADC) and IVIM parameters, including true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f). Our results showed that, there was an excellent inter-observer agreement on the measurements of D and ADC between observers (inter-class correlation coefficient, ICC = 0.902 and 0.884, respectively). Meanwhile, f and D* showed good and substantial reproducibility (ICC = 0.787 and 0.623, respectively). D and ADC of malignant lesions were significantly lower than those of benign lesions (both P ≤ 0.001), while similar values were obtained in both groups for D* and f (both P > 0.05). In receiver operating characteristic (ROC) analysis, D showed the highest area under curve (AUC) for distinguishing malignant from benign lesions, followed by ADC. Accompanying signs of SPLs have specific features on IVIM maps. In conclusion, IVIM provides functional information in characterizing SPLs which is helpful to differential diagnosis. D and ADC have a significantly higher diagnostic value than f and D*.