Abstract
As an emerging technology, artificial intelligence has been applied to identify various physical disorders. Here, we developed a three-layer diagnosis system for lung cancer, in which three machine learning approaches including decision tree C5.0, artificial neural network (ANN) and support vector machine (SVM) were involved. The area under the curve (AUC) was employed to evaluate their decision powers. In the first layer, the AUCs of C5.0, ANN and SVM were 0.676, 0.736 and 0.640, ANN was better than C5.0 and SVM. In the second layer, ANN was similar with SVM but superior to C5.0 supported by the AUCs of 0.804, 0.889 and 0.825. Much higher AUCs of 0.908, 0.910 and 0.849 were identified in the third layer, where the highest sensitivity of 94.12% was found in C5.0. These data proposed a three-layer diagnosis system for lung cancer: ANN was used as a broad-spectrum screening subsystem basing on 14 epidemiological data and clinical symptoms, which was firstly adopted to screen high-risk groups; then, combining with additional 5 tumor biomarkers, ANN was used as an auxiliary diagnosis subsystem to determine the suspected lung cancer patients; C5.0 was finally employed to confirm lung cancer patients basing on 22 CT nodule-based radiomic features.
Highlights
Lung cancer is the most common cause of cancer-related death worldwide due to insidious incidence, high metastasis, and poor prognosis [1]
These data proposed a three-layer diagnosis system for lung cancer: artificial neural network (ANN) was used as a broadspectrum screening subsystem basing on 14 epidemiological data and clinical symptoms, which was firstly adopted to screen high-risk groups; combining with additional 5 tumor biomarkers, ANN was used as an auxiliary diagnosis subsystem to determine the suspected lung cancer patients; C5.0 was employed to confirm lung cancer patients basing on 22 computed tomography (CT) nodule-based radiomic features
The results of the National Lung Screening Trial confirmed that low-dose CT (LDCT) adopted in the high-risk group could reduce the mortality rate of lung cancer by 20% compared with chest X-ray [6]
Summary
Lung cancer is the most common cause of cancer-related death worldwide due to insidious incidence, high metastasis, and poor prognosis [1]. As reported by the Annual Report of America in 2018, the five-year survival rate of lung and bronchus cancer ranged from 55.1% (stage I) to 4.2% (stage IV) for cases that were diagnosed from 2007 through 2013 [2]. Among them, computed tomography (CT)-based imaging diagnosis is the www.aging-us.com primary tool to detect lung cancer at early stages [4,5,6]. Several other studies demonstrated that CT scans should be implemented for the high-risk groups, but not for the general population, to detect early lung cancer, which could decrease the radiation hazard and financial costs [7,8,9]. The definition of the high-risk group for lung cancer is controversial, which is mainly assessed by age and smoking status [7]. Evidence showed that lung cancer could be indicated by other epidemiological characteristics and clinical symptoms such as the family history of cancer and hemoptysis [7, 9, 10]
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