Wei Wu1,2, Larry A Pierce1, Yuzheng Zhang3, Sudhakar N J Pipavath1, Timothy W Randolph4, Kristin J Lastwika5,6, Paul D Lampe5,6, A McGarry Houghton4,6,7, Haining Liu2, Liming Xia8, Paul E Kinahan9. 1. Department of Radiology, Tongji Hospital, Tongji Medical College affiliated to Huazhong University of Science and Technology, 1095 Jiefang Ave, Wuhan, Hubei, 430000, People's Republic of China. 2. Department of Radiology, University of Washington, 1959 NE Pacific St, Seattle, WA, 98105, USA. 3. Program in Biostatistics and Biomathematics, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. 4. Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. 5. Translational Research Program, Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. 6. Human Biology Divisions, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. 7. Division of Pulmonary and Critical Care, University of Washington Medical Center, Seattle, WA, USA. 8. Department of Radiology, Tongji Hospital, Tongji Medical College affiliated to Huazhong University of Science and Technology, 1095 Jiefang Ave, Wuhan, Hubei, 430000, People's Republic of China. xialiming2017@outlook.com. 9. Department of Radiology, University of Washington, 1959 NE Pacific St, Seattle, WA, 98105, USA. kinahan@uw.edu.
Abstract
PURPOSE: To compare the ability of radiological semantic and quantitative texture features in lung cancer diagnosis of pulmonary nodules. MATERIALS AND METHODS: A total of N = 121 subjects with confirmed non-small-cell lung cancer were matched with 117 controls based on age and gender. Radiological semantic and quantitative texture features were extracted from CT images with or without contrast enhancement. Three different models were compared using LASSO logistic regression: "CS" using clinical and semantic variables, "T" using texture features, and "CST" using clinical, semantic, and texture variables. For each model, we performed 100 trials of fivefold cross-validation and the average receiver operating curve was accessed. The AUC of the cross-validation study (AUCCV) was calculated together with its 95% confidence interval. RESULTS: The AUCCV (and 95% confidence interval) for models T, CS, and CST was 0.85 (0.71-0.96), 0.88 (0.77-0.96), and 0.88 (0.77-0.97), respectively. After separating the data into two groups with or without contrast enhancement, the AUC (without cross-validation) of the model T was 0.86 both for images with and without contrast enhancement, suggesting that contrast enhancement did not impact the utility of texture analysis. CONCLUSIONS: The models with semantic and texture features provided cross-validated AUCs of 0.85-0.88 for classification of benign versus cancerous nodules, showing potential in aiding the management of patients. KEY POINTS: • Pretest probability of cancer can aid and direct the physician in the diagnosis and management of pulmonary nodules in a cost-effective way. • Semantic features (qualitative features reported by radiologists to characterize lung lesions) and radiomic (e.g., texture) features can be extracted from CT images. • Input of these variables into a model can generate a pretest likelihood of cancer to aid clinical decision and management of pulmonary nodules.
PURPOSE: To compare the ability of radiological semantic and quantitative texture features in lung cancer diagnosis of pulmonary nodules. MATERIALS AND METHODS: A total of N = 121 subjects with confirmed non-small-cell lung cancer were matched with 117 controls based on age and gender. Radiological semantic and quantitative texture features were extracted from CT images with or without contrast enhancement. Three different models were compared using LASSO logistic regression: "CS" using clinical and semantic variables, "T" using texture features, and "CST" using clinical, semantic, and texture variables. For each model, we performed 100 trials of fivefold cross-validation and the average receiver operating curve was accessed. The AUC of the cross-validation study (AUCCV) was calculated together with its 95% confidence interval. RESULTS: The AUCCV (and 95% confidence interval) for models T, CS, and CST was 0.85 (0.71-0.96), 0.88 (0.77-0.96), and 0.88 (0.77-0.97), respectively. After separating the data into two groups with or without contrast enhancement, the AUC (without cross-validation) of the model T was 0.86 both for images with and without contrast enhancement, suggesting that contrast enhancement did not impact the utility of texture analysis. CONCLUSIONS: The models with semantic and texture features provided cross-validated AUCs of 0.85-0.88 for classification of benign versus cancerous nodules, showing potential in aiding the management of patients. KEY POINTS: • Pretest probability of cancer can aid and direct the physician in the diagnosis and management of pulmonary nodules in a cost-effective way. • Semantic features (qualitative features reported by radiologists to characterize lung lesions) and radiomic (e.g., texture) features can be extracted from CT images. • Input of these variables into a model can generate a pretest likelihood of cancer to aid clinical decision and management of pulmonary nodules.
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