TY - JOUR
T1 - Classification of lung cancer subtypes based on autofluorescence bronchoscopic pattern recognition
T2 - A preliminary study
AU - Feng, Po Hao
AU - Chen, Tzu Tao
AU - Lin, Yin Tzu
AU - Chiang, Shang Yu
AU - Lo, Chung Ming
N1 - Funding Information:
The authors thank the Ministry of Science and Technology , Taiwan ( MOST106-2221-E-038-018 ) and Taipei Medical University /Shuang Ho Hospital ( 105TMU-SHH-02-4 ) for financially supporting this study.
Funding Information:
The authors thank the Ministry of Science and Technology, Taiwan (MOST106-2221-E-038-018) and Taipei Medical University/Shuang Ho Hospital (105TMU-SHH-02-4) for financially supporting this study.
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/9/1
Y1 - 2018/9/1
N2 - 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.
AB - 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.
KW - Autofluorescent bronchoscopy
KW - Color texture
KW - Computer-aided diagnosis
KW - Lung cancer
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U2 - 10.1016/j.cmpb.2018.05.016
DO - 10.1016/j.cmpb.2018.05.016
M3 - Article
AN - SCOPUS:85048463910
SN - 0169-2607
VL - 163
SP - 33
EP - 38
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
ER -