Po-Hao Feng1,2, Yin-Tzu Lin1,3, Chung-Ming Lo3,4. 1. School of Medicine, College of Medicine, Taipei Medical University, Taipei, 11031, Taiwan. 2. Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, 23561, Taiwan. 3. Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, 10675, Taiwan. 4. Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, 11031, Taiwan.
Abstract
PURPOSE: Bronchoscopy is useful in lung cancer detection, but cannot be used to differentiate cancer types. A computer-aided diagnosis (CAD) system was proposed to distinguish malignant cancer types to achieve objective diagnoses. METHODS: Bronchoscopic images of 12 adenocarcinoma and 10 squamous cell carcinoma patients were collected. The images were transformed from a red-blue-green (RGB) to a hue-saturation-value (HSV) color space to obtain more meaningful color textures. By combining significant textural features (P < 0.05) in a machine learning classifier, a prediction model of malignant types was established. RESULTS: The performance of the CAD system achieved an accuracy of 86% (19/22), a sensitivity of 90% (9/10), a specificity of 83% (10/12), a positive predictive value of 82% (9/11), and a negative predictive value of 91% (10/11) in distinguishing lung cancer types. The area under the receiver operating characteristic curve was 0.82. CONCLUSIONS: On the basis of extracted HSV textures of bronchoscopic images, the CAD system can provide recommendations for clinical diagnoses of lung cancer types.
PURPOSE: Bronchoscopy is useful in lung cancer detection, but cannot be used to differentiate cancer types. A computer-aided diagnosis (CAD) system was proposed to distinguish malignant cancer types to achieve objective diagnoses. METHODS: Bronchoscopic images of 12 adenocarcinoma and 10 squamous cell carcinomapatients were collected. The images were transformed from a red-blue-green (RGB) to a hue-saturation-value (HSV) color space to obtain more meaningful color textures. By combining significant textural features (P < 0.05) in a machine learning classifier, a prediction model of malignant types was established. RESULTS: The performance of the CAD system achieved an accuracy of 86% (19/22), a sensitivity of 90% (9/10), a specificity of 83% (10/12), a positive predictive value of 82% (9/11), and a negative predictive value of 91% (10/11) in distinguishing lung cancer types. The area under the receiver operating characteristic curve was 0.82. CONCLUSIONS: On the basis of extracted HSV textures of bronchoscopic images, the CAD system can provide recommendations for clinical diagnoses of lung cancer types.