A machine learning texture model for classifying lung cancer subtypes using preliminary bronchoscopic findings. Issue 12 (8th November 2018)

• Record Type: Journal Article
• Authors: Feng, Po‐Hao; Lin, Yin‐Tzu; Lo, Chung‐Ming
• Is Part Of: Medical physics. Volume 45:Issue 12(2018)
• Page Start: 5509
• Page End: 5514
• Publication Date: 2018-11-08
• Subjects: bronchoscopy -- color texture -- computer‐aided diagnosis -- lung cancer; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13241
• Languages: English
• ISSNs: 0094-2405

Abstract

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.