Development and Validation of a Radiomics Nomogram for Differentiating Mycoplasma Pneumonia and Bacterial Pneumonia

Li, Honglin; Li, Ting; Cai, Qinxin; Wang, Xiaozhuan; Liao, Yuting; Cheng, Yuanxiong; Zhou, Quan

doi:10.3390/diagnostics11081330

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…The internal training set had an AUC value of 0.996 (0.993, 0.998), the validation set had an AUC value of 0.972 (0.942, 0.995), and the external test set had an AUC value of 0.986 (0.976, 0.993), which is higher than that of the single model. Consistent with the study by Honglin Li et al ( 10 ), a combined nomogram combining radiological and clinical features was established and validated for distinguishing Mycoplasma pneumonia and bacterial pneumonia with similar CT manifestations. In the radiomics model, the AUC of the training set was 0.877 and the AUC of the test set was 0.810.…”

Section: Discussionsupporting

confidence: 55%

“…Wang et al ( 24 ) combined deep learning-radiomics models to distinguish COVID-19 from non-COVID-19 viral pneumonia. Honglin Li ( 10 ) confirmed that radiomics-clinical nomograms have good discriminative power for mycoplasmal pneumonia and bacterial pneumonia. These studies demonstrate the feasibility of using radiomics to identify lung inflammation.…”

Section: Discussionmentioning

confidence: 91%

“…Xie et al ( 9 ) applied CT radiomics to conduct a comparative analysis of ground-glass density shadows in COVID-19 and non-COVID-19 and proposed that the CT radiomics model can help to differentiate between COVID-19 and non-COVID-19 ground-glass density shadows. At the same time, Honglin Li et al ( 10 ) confirmed that radiomics-clinical nomograms have good discriminative effects on mycoplasmal pneumonia and bacterial pneumonia, which is helpful for clinical decision-making. In addition, radiomics also plays an important role in grading severity ( 11 ) and prognostic evaluation ( 12 ) of pneumonia.…”

Section: Introductionmentioning

confidence: 95%

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

Meng,

Wang,

Zhuo

et al. 2024

Front. Med.

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PurposeThis 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 methodsIn 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.ResultsFor 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.ConclusionThe 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.

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Section: Discussionsupporting

confidence: 55%

Section: Discussionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 95%

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

Meng,

Wang,

Zhuo

et al. 2024

Front. Med.

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“…Chi-square test or Fisher's exact test was used for the nominal variable. T-test was used for continuous variables 14 . The "rmda" package was used to construct the decision curve.…”

Section: Methodsmentioning

confidence: 99%

Model based on the automated AI-Driven CT quantification is effective for the diagnosis of refractory Mycoplasma pneumoniae pneumonia

Qian,

Tao,

et al. 2023

Preprint

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Refractory Mycoplasma pneumoniae pneumonia（RMPP）prediction is a challenging but clinically significant challenge. A model based on AI-derived quantitative determination of lung lesions extent on initial computed tomography (CT) scan and clinical indicators has the potential to facilitate early RMPP prediction in hospitalized children. In this study, we conducted a retrospective cohort as a training set including 126 children with M. pneumoniae pneumonia (MP) admitted to Children’s Hospital of Nanjing Medical University, China from January 2019 to December 2020. We defined a constant Φ which can combine the volume and CT value of pulmonary lesions and be further used to calculate the logarithm of Φ to the base of 2 (Log2Φ). Finally, a clinical-imaging prediction model was developed using Log2Φ and clinical characteristics. The area under the receiver operating characteristic curve (ROC-AUC) was applied to performance evaluation. Then we conducted a prospective study including 54 children with MP as a test set to validate the predictive model for RMPP from January to December 2021. The clinical model yielded the AUC of 0.810 and 0.782, and the imaging model yielded the AUC of 0.764 and 0.769 in the training set and the test set, respectively. The clinical-imaging model combining Log2Φ, temperature(T), aspartate aminotransferase (AST), preadmission fever duration (PFD), and preadmission macrolides therapy duration (PMTD) produced the highest AUC values of 0.897 and 0.895 in the training set and the test set, respectively. Our work demonstrated that using automated quantification of lung disease at CT combined with clinical data in MPP is useful to predict RMPP.

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Lung CT‐based multi‐lesion radiomic model to differentiate between nontuberculous mycobacteria and Mycobacterium tuberculosis

Hu,

Zhong,

Liu

et al. 2024

Medical Physics

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BackgroundNontuberculous mycobacterial lung disease (NTM‐LD) and Mycobacterium tuberculosis lung disease (MTB‐LD) are difficult to distinguish based on conventional imaging examinations. In recent years, radiomics has been used to discriminate them. However, existing radiomic methods mainly focus on specific lesion types, and have limitations in handling the presence of multiple lesion types that vary among different patients.PurposeWe aimed to establish a radiomic model based on multiple lesion types in the patient's CT scans, and analyzed the importance of different lesion types in distinguishing the two diseases.Methods120 NTM‐LD and 120 MTB‐LD patients were retrospectively enrolled in this study and randomly split into the training (168) and testing (72) sets. A total of 1037 radiomic features were extracted separately for each lesion type. The univariate analysis, least absolute shrinkage, and selection operator were used to select the significant radiomic features. The radiomic signature score (Radscore) from each lesion type was estimated and aggregated to construct the multi‐lesion feature vector for each patient. A multi‐lesion radiomic (MLR) model was then established using the random forest classifier, which can estimate importance coefficients for different lesion types. The performances of the MLR model and single radomic models were investigated by the receiver operating characteristic curve (ROC). The impact of the predicted lesion importance was also evaluated in subjective imaging diagnosis.ResultsThe MLR model achieved an area under the curve (AUC) of 90.2% (95% CI: 86.2% 94.1%) in differentiating NTM‐LD and MTB‐LD, outperforming the models using specific lesion types following existing radiomic models by 1% to 13%. Among different lesion types, tree‐in‐bud pattern demonstrated the highest distinguishing value, followed by consolidation, nodules, and lymph node enlargement. Given the estimated lesion importance, two senior radiologists exhibited improved accuracy in diagnosis, with an increased accuracy of 8.33% and 8.34%, respectively.ConclusionsThis is the first radiomic study to use multiple lesion types to distinguish NTM‐LD and MTB‐LD. The developed MLR model performed well in differentiating the two diseases, and the lesion types with high importance exhibited the potential to assist experienced radiologists in clinical decision‐making.

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Development and Validation of a Radiomics Nomogram for Differentiating Mycoplasma Pneumonia and Bacterial Pneumonia

Cited by 5 publications

References 22 publications

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

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

Model based on the automated AI-Driven CT quantification is effective for the diagnosis of refractory Mycoplasma pneumoniae pneumonia

Lung CT‐based multi‐lesion radiomic model to differentiate between nontuberculous mycobacteria and Mycobacterium tuberculosis

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