A machine learning texture model for classifying lung cancer subtypes using preliminary bronchoscopic findings

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.

Radiation Oncology in the Era of Big Data and Machine Learning for Precision Medicine

“…Numerous studies [19][20][21][22] have demonstrated the application of CADx tools for diagnosing lung [19][20][21] and breast [19,22] lesions. Cheng et al [19] investigated the deep learning capability for the diagnosis of breast lesions in ultrasound (US) images and pulmonary nodules in CT scans.…”

“…Figure 4 illustrates several cases of breast lesions and pulmonary nodules in US and CT images, respectively, differentiated with deep learning-based CADx [19]. Feng et al [20] and Beig et al [21] studied the classification of lung lesions on endo-bronchoscopic images [20] with logistic regressions, and non-small cell lung cancer (NSCLC) adenocarcinomas distinctions from granulomas on non-contrast CT [21] using support vector machine (SVM) and neural network (NN). The reported results indicated an accuracy of 86% in distinguishing lung cancer types, e.g., adenocarcinoma and squamous cell carcinoma [20].…”

“…Feng et al [20] and Beig et al [21] studied the classification of lung lesions on endo-bronchoscopic images [20] with logistic regressions, and non-small cell lung cancer (NSCLC) adenocarcinomas distinctions from granulomas on non-contrast CT [21] using support vector machine (SVM) and neural network (NN). The reported results indicated an accuracy of 86% in distinguishing lung cancer types, e.g., adenocarcinoma and squamous cell carcinoma [20]. Surprisingly, the reported results [21] in distinguishing non-small cell lung cancer adenocarcinomas from granulomas on non-contrast CT images showed that the developed CADx systems outperformed the radiologist readers.…”

Application of artificial intelligence in respiratory medicine: Has the time arrived?

“…Using ML in bronchoscopy, to analyse images and diagnose potential cancers, has been explored. One study achieved 86% diagnostic accuracy . However, treatment decisions are better informed by the definitive histology results, which are typically reported within days.…”

“…One study achieved 86% diagnostic accuracy. 5 However, treatment decisions are better informed by the definitive histology results, which are typically reported within days. Real-time highlighting of potentially missed lesions could be a more useful approach, and analogous efforts have shown promise in colonoscopy.…”