Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia

Abstract

Purpose: This study aims to explore the value of clinical features, CT imaging signs, and radiomics features in differentiating between adults and children with Mycoplasma pneumonia and seeking quantitative radiomic representations of CT imaging signs.

Materials and methods: In a retrospective analysis of 981 cases of mycoplasmal pneumonia patients from November 2021 to December 2023, 590 internal data (adults:450, children: 140) randomly divided into a training set and a validation set with an 8:2 ratio and 391 external test data (adults:121; children:270) were included. Using univariate analysis, CT imaging signs and clinical features with significant differences (p < 0.05) were selected. After segmenting the lesion area on the CT image as the region of interest, 1,904 radiomic features were extracted. Then, Pearson correlation analysis (PCC) and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features. Based on the selected features, multivariable logistic regression analysis was used to establish the clinical model, CT image model, radiomic model, and combined model. The predictive performance of each model was evaluated using ROC curves, AUC, sensitivity, specificity, accuracy, and precision. The AUC between each model was compared using the Delong test. Importantly, the radiomics features and quantitative and qualitative CT image features were analyzed using Pearson correlation analysis and analysis of variance, respectively.

Results: For the individual model, the radiomics model, which was built using 45 selected features, achieved the highest AUCs in the training set, validation set, and external test set, which were 0.995 (0.992, 0.998), 0.952 (0.921, 0.978), and 0.969 (0.953, 0.982), respectively. In all models, the combined model achieved the highest AUCs, which were 0.996 (0.993, 0.998), 0.972 (0.942, 0.995), and 0.986 (0.976, 0.993) in the training set, validation set, and test set, respectively. In addition, we selected 11 radiomics features and CT image features with a correlation coefficient r greater than 0.35.

Conclusion: The combined model has good diagnostic performance for differentiating between adults and children with mycoplasmal pneumonia, and different CT imaging signs are quantitatively represented by radiomics.

Keywords: adults; children; computed tomography (CT); mycoplasma pneumonia; radiomics.

Figure 1

Research flowchart.

Figure 2

(A) Mycoplasmal pneumonia in a child (male, 9 years old), mainly manifested as large patchy consolidation, with air bronchogram sign visible; (C) Mycoplasmal pneumonia in an adult (female, 57 years old), characterized by focal and small patchy consolidation; (B–D) Lesion annotation.

Figure 3

The correlation ranking of the top 20 radiomic features in the training set.

Figure 4

The ROC curves of the four models in the training set, validation set, and test set.

Figure 5

Visualization through scatter plot and box plot analysis of the correlation between quantitative and qualitative imaging features and radiomics features.

Figure 6

Correlation analysis between radiomics and CT imaging signs: case presentation. (A) Child (male, 9 years old) with mycoplasmal pneumonia: Consolidation pattern:3; original_glrlm_ShortRunLowGrayLevelEmphasis:0.031; normalize_glrlm_GrayLevelNonUniformityNormalized:0.513. (B) Adult (female, 57 years old) with mycoplasmal pneumonia: Consolidation pattern:1; original_glrlm_ShortRunLowGrayLevelEmphasis:0.046; normalize_glrlm_GrayLevelNonUniformityNormalized:0.534.