Radiomics approach for survival prediction in chronic obstructive pulmonary disease
Autor: | Choo-Khoom Ong, Young-Hoon Cho, Jeong Eun Hwang, Joon Beom Seo, Yeon-Mok Oh, Jae Seung Lee, Namkug Kim, Li-Cher Loh, Sang Min Lee, Jihye Yun, Sang Do Lee |
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Rok vydání: | 2021 |
Předmět: |
Oncology
Thorax medicine.medical_specialty COPD business.industry Proportional hazards model Hazard ratio General Medicine medicine.disease Obstructive lung disease 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine 030220 oncology & carcinogenesis Internal medicine Cohort medicine Radiology Nuclear Medicine and imaging Radiology business Body mass index Survival analysis |
Zdroj: | European Radiology. 31:7316-7324 |
ISSN: | 1432-1084 0938-7994 |
Popis: | To apply radiomics analysis for overall survival prediction in chronic obstructive pulmonary disease (COPD), and evaluate the performance of the radiomics signature (RS). 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. 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. 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. • A total of 525 chest CT-based radiomics features were extracted and the five radiomics features of %LAA −950 , AWT_Pi10_6 th , AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA 18mm 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. |
Databáze: | OpenAIRE |
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