Dynamic 3D radiomics analysis using artificial intelligence to assess the stage of COVID-19 on CT images.

OBJECTIVE: To develop a dynamic 3D radiomics analysis method using artificial intelligence technique for automatically assessing four disease stages (i.e., early, progressive, peak, and absorption stages) of COVID-19 patients on CT images. METHODS: The dynamic 3D radiomics analysis method was composed of three AI algorithms (the lung segmentation, lesion segmentation, and stage-assessing AI algorithms) that were trained and tested on 313,767 CT images from 520 COVID-19 patients. This proposed method used 3D lung lesion that was segmented by the lung and lesion segmentation algorithms to extract radiomics features, and then combined with clinical metadata to assess the possible stage of COVID-19 patients using stage-assessing algorithm. Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to evaluate diagnostic performance. RESULTS: Of 520 patients, 66 patients (mean age, 57 years ± 15 [standard deviation]; 35 women), including 203 CT scans, were tested. The dynamic 3D radiomics analysis method used 30 features, including 27 radiomics features and 3 clinical features to assess the possible disease stage of COVID-19 with an accuracy of 90%. For the prediction of each stage, the AUC of stage 1 was 0.965 (95% CI: 0.934, 0.997), AUC of stage 2 was 0.958 (95% CI: 0.931, 0.984), AUC of stage 3 was 0.998 (95% CI: 0.994, 1.000), and AUC of stage 4 was 0.975 (95% CI: 0.956, 0.994). CONCLUSION: With high diagnostic performance, the dynamic 3D radiomics analysis using artificial intelligence could represent a potential tool for helping hospitals make appropriate resource allocations and follow-up of treatment response. KEY POINTS: • The AI segmentation algorithms were able to accurately segment the lung and lesion of COVID-19 patients of different stages. • The dynamic 3D radiomics analysis method successfully extracted the radiomics features from the 3D lung lesion. • The stage-assessing AI algorithm combining with clinical metadata was able to assess the four stages with an accuracy of 90%, a macro-average AUC of 0.975.

A new computed tomography score-based staging for melioidosis pneumonia to predict progression.

Background: Melioidosis pneumonia, caused by the bacterium Burkholderia pseudomallei, is a serious infectious disease prevalent in tropical regions. Chest computed tomography (CT) has emerged as a valuable tool for assessing the severity and progression of lung involvement in melioidosis pneumonia. However, there persists a need for the quantitative assessment of CT characteristics and staging methodologies to precisely anticipate disease progression. This study aimed to quantitatively extract CT features and evaluate a CT score-based staging system in predicting the progression of melioidosis pneumonia. Methods: This study included 97 patients with culture-confirmed melioidosis pneumonia who presented between January 2002 and December 2021. Lung segmentation and annotation of lesions (consolidation, nodules, and cavity) were used for feature extraction. The features, including the involved area, amount, and intensity, were extracted. The CT scores of the lesion features were defined by the feature importance weight and qualitative stage of melioidosis pneumonia. Gaussian process regression (GPR) was used to predict patients with severe or critical melioidosis pneumonia according to CT scores. Results: The melioidosis pneumonia stages included acute stage (0-7 days), subacute stage (8-28 days), and chronic stage (>28 days). In the acute stage, the CT scores of all patients ranged from 2.5 to 6.5. In the subacute stage, the CT scores for the severe and mild patients were 3.0-7.0 and 2.0-5.0, respectively. In the chronic stage, the CT score of the mild patients fluctuated approximately between 2.5 and 3.5 in a linear distribution. Consolidation was the most common type of lung lesion in those with melioidosis pneumonia. Between stages I and II, the percentage of severe scans with nodules dropped from 72.22% to 47.62% (P<0.05), and the percentage of severe scans with cavities significantly increased from 16.67% to 57.14% (P<0.05). The GPR optimization function yielded area under the receiver operating characteristic curves of 0.71 for stage I, 0.92 for stage II, and 0.87 for all stages. Conclusions: In patients with melioidosis pneumonia, it is reasonable to divide the period (the whole progression of melioidosis pneumonia) into three stages to determine the prognosis.