Classification of lung cancer subtypes based on autofluorescence bronchoscopic pattern recognition: A preliminary study. (September 2018)

• Record Type: Journal Article
• Authors: Feng, Po-Hao; Chen, Tzu-Tao; Lin, Yin-Tzu; Chiang, Shang-Yu; Lo, Chung-Ming
• Is Part Of: Computer methods and programs in biomedicine. Volume 163(2018)
• Page Start: 33
• Page End: 38
• Publication Date: 2018-09
• Subjects: Lung cancer -- Autofluorescent bronchoscopy -- Computer-aided diagnosis -- Color texture; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.05.016
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Numerous quantitative image features were developed from fluorescence bronchoscopy images to characterize different lung cancer types. Image texture features were extracted from HSV color channels for malignancy evaluation. The likelihoods of malignancy of tumors were predicted by a logistic regression model using image features in a computer-aided diagnosis system which can distinguish malignant types to achieve objective and consistent diagnoses. Abstract: Background and objectives: Lung cancer is the leading cause of cancer deaths worldwide. With current use of autofluorescent bronchoscopic imaging to detect early lung cancer and limitations of pathologic examinations, a computer-aided diagnosis (CAD) system based on autofluorescent bronchoscopy was proposed to distinguish different pathological cancer types to achieve objective and consistent diagnoses. Methods: The collected database consisted of 12 adenocarcinomas and 11 squamous cell carcinomas. The corresponding autofluorescent bronchoscopic images were first transformed to a hue (H), saturation (S), and value (V) color space to obtain better interpretation of the color information. Color textural features were respectively extracted from the H, S, and V channels and combined in a logistic regression classifier to classify malignant types by machine learning. Results: After feature selection, the proposed CAD system achieved an accuracy of 83% (19/23), a sensitivity of 73% (8/11), a specificity of 92% (11/12), a positive predictive value of 89% (8/9), a negative predictive value of 79% (11/14), and an area under the receiver operating characteristic curve of 0.81 for distinguishing lung cancer types. Conclusions: The proposed CAD system based on color textures of autofluorescent bronchoscopic images provides a diagnostic method of malignant types in clinical use.