METHODS: A user-friendly software was developed to accurately predict the individual size-specific dose estimation of paediatric patients undergoing computed tomography (CT) scans of the head, thorax, and abdomen. The software includes a calculation equation developed based on a novel SSDE prediction equation that used a population's pre-determined percentage difference between volume-weighted computed tomography dose index (CTDIvol) and SSDE with age. American Association of Physicists in Medicine (AAPM RPT 204) method (manual) and segmentation-based SSDE calculators (indoseCT and XXautocalc) were used to assess the proposed software predictions comparatively.
RESULTS: The results of this study show that the automated equation-based calculation of SSDE and the manual and segmentation-based calculation of SSDE are in good agreement for patients. The differences between the automated equation-based calculation of SSDE and the manual and segmentation-based calculation are less than 3%.
CONCLUSION: This study validated an accurate SSDE calculator that allows users to enter key input values and calculate SSDE.
IMPLICATION FOR PRACTICE: The automated equation-based SSDE software (PESSD) seems a promising tool for estimating individualised CT doses during CT scans.
METHODS: This study included 344 patients from the Korean Obstructive Lung Disease (KOLD) cohort. External validation was performed on a cohort of 112 patients. In total, 525 chest CT-based radiomics features were semi-automatically extracted. The five most useful features for survival prediction were selected by least absolute shrinkage and selection operation (LASSO) Cox regression analysis and used to generate a RS. The ability of the RS for classifying COPD patients into high or low mortality risk groups was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis.
RESULTS: The five features remaining after the LASSO analysis were %LAA-950, AWT_Pi10_6th, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA18mm. The RS demonstrated a C-index of 0.774 in the discovery group and 0.805 in the validation group. Patients with a RS greater than 1.053 were classified into the high-risk group and demonstrated worse overall survival than those in the low-risk group in both the discovery (log-rank test, < 0.001; hazard ratio [HR], 5.265) and validation groups (log-rank test, < 0.001; HR, 5.223). For both groups, RS was significantly associated with overall survival after adjustments for patient age and body mass index.
CONCLUSIONS: A radiomics approach for survival prediction and risk stratification in COPD patients is feasible, and the constructed radiomics model demonstrated acceptable performance. The RS derived from chest CT data of COPD patients was able to effectively identify those at increased risk of mortality.
KEY POINTS: • A total of 525 chest CT-based radiomics features were extracted and the five radiomics features of %LAA-950, AWT_Pi10_6th, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA18mm were selected to generate a radiomics model. • A radiomics model for predicting survival of COPD patients demonstrated reliable performance with a C-index of 0.774 in the discovery group and 0.805 in the validation group. • Radiomics approach was able to effectively identify COPD patients with an increased risk of mortality, and patients assigned to the high-risk group demonstrated worse overall survival in both the discovery and validation groups.