Wenjing Zhao1, Ziqi Xiong1, Yining Jiang1, Kunpeng Wang2, Min Zhao3, Xiwei Lu4, Ailian Liu1, Dongxue Qin5, Zhiyong Li6. 1. Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Zhongshan road No.222, Xigang District, Dalian, 116011, Liaoning Province, China. 2. Department of Radiology, Dalian Public Health Clinical Center, Dalian, Liaoning Province, China. 3. GE Healthcare, Beijing, China. 4. Department of Tuberculosis, Dalian Public Health Clinical Center, Dalian, Liaoning Province, China. 5. Department of Radiology, The Second Hospital of Dalian Medical University, Zhongshan road No.467, Shahekou District, Dalian, 116011, Liaoning Province, China. 17709875038@163.com. 6. Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Zhongshan road No.222, Xigang District, Dalian, 116011, Liaoning Province, China. zjy_lzy@126.com.
Abstract
PURPOSE: To investigate the incremental value of enhanced CT-based radiomics in discriminating between pulmonary tuberculosis (PTB) and pulmonary adenocarcinoma (PAC) presenting as solid nodules or masses and to develop an optimal radiomics model. METHODS: A total of 128 lesions (from 123 patients) from three hospitals were retrospectively analyzed and were randomly divided into training and test datasets at a ratio of 7:3. Independent predictors in subjective image features were used to develop the subjective image model (SIM). The plain CT-based and enhanced CT-based radiomics features were screened by the correlation coefficient method, univariate analysis, and the least absolute shrinkage and selection operator, then used to build the plain CT radiomics model (PRM) and enhanced CT radiomics model (ERM), respectively. Finally, the combined model (CM) combining PRM and ERM was established. In addition, the performance of three radiologists and one respiratory physician was evaluated. The areas under the receiver operating characteristic curve (AUCs) were used to assess the performance of each model. RESULTS: The differential diagnostic capability of the ERM (training: AUC = 0.933; test: AUC = 0.881) was better than that of the PRM (training: AUC = 0.861; test: AUC = 0.756) and the SIM (training: AUC = 0.760; test: AUC = 0.611). The CM was optimal (training: AUC = 0.948; test: AUC = 0.917) and outperformed the respiratory physician and most radiologists. CONCLUSIONS: The ERM was more helpful than the PRM for identifying PTB and PAC that present as solid nodules or masses, and the CM was the best.
PURPOSE: To investigate the incremental value of enhanced CT-based radiomics in discriminating between pulmonary tuberculosis (PTB) and pulmonary adenocarcinoma (PAC) presenting as solid nodules or masses and to develop an optimal radiomics model. METHODS: A total of 128 lesions (from 123 patients) from three hospitals were retrospectively analyzed and were randomly divided into training and test datasets at a ratio of 7:3. Independent predictors in subjective image features were used to develop the subjective image model (SIM). The plain CT-based and enhanced CT-based radiomics features were screened by the correlation coefficient method, univariate analysis, and the least absolute shrinkage and selection operator, then used to build the plain CT radiomics model (PRM) and enhanced CT radiomics model (ERM), respectively. Finally, the combined model (CM) combining PRM and ERM was established. In addition, the performance of three radiologists and one respiratory physician was evaluated. The areas under the receiver operating characteristic curve (AUCs) were used to assess the performance of each model. RESULTS: The differential diagnostic capability of the ERM (training: AUC = 0.933; test: AUC = 0.881) was better than that of the PRM (training: AUC = 0.861; test: AUC = 0.756) and the SIM (training: AUC = 0.760; test: AUC = 0.611). The CM was optimal (training: AUC = 0.948; test: AUC = 0.917) and outperformed the respiratory physician and most radiologists. CONCLUSIONS: The ERM was more helpful than the PRM for identifying PTB and PAC that present as solid nodules or masses, and the CM was the best.
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