Abstract
The purpose of this work was to evaluate the performance of an existing commercially available artificial intelligence (AI) software system in differentiating malignant and benign lung nodules. The AI tool consisted of a vessel-suppression function and a deep-learning-based computer-aided-detection (VS-CAD) analyzer. Fifty patients (32 females, mean age 52 years) with 75 lung nodules (47 malignant and 28 benign) underwent low-dose computed tomography (LDCT) followed by surgical excision and the pathological analysis of their 75 nodules within a 3 month time frame. All 50 cases were then processed by the AI software to generate corresponding VS images and CAD outcomes. All 75 pathologically proven lung nodules were well delineated by vessel-suppressed images. Three (6.4%) of the 47 lung cancer cases, and 11 (39.3%) of the 28 benign nodules were ignored and not detected by the AI without showing a CAD analysis summary. The AI system/radiologists produced a sensitivity and specificity (shown in %) of 93.6/89.4 and 39.3/82.1 in distinguishing malignant from benign nodules, respectively. AI sensitivity was higher than that of radiologists, though not statistically significant (p = 0.712). Specificity obtained by the radiologists was significantly higher than that of the VS-CAD AI (p = 0.003). There was no significant difference between the malignant and benign lesions with respect to age, gender, pure ground-glass pattern, the diameter and location of the nodules, or nodules <6 vs. ≥6 mm. However, more part-solid nodules were proven to be malignant than benign (90.9% vs. 9.1%), and more solid nodules were proven to be benign than malignant (86.7% vs. 13.3%) with statistical significance (p = 0.001 and <0.001, respectively). A larger cohort and prospective study are required to validate the AI performance.
Highlights
Lung cancer is currently the leading global cause of cancer-related death [1,2]
Sixty-one nodules summarized by the artificial intelligence (AI) analyzer were reviewed and showed that there was no significant difference in the 61 nodule sizes measured manually or by the AI analyzer (7.83 ± 3.06 vs. 8.13 ± 3.49, p = 0.624) with a Pearson correlation coefficient of 0.926
The results of this study showed that the vessel-suppression function and a deep-learning-based computer-aided-detection (VS-computer-aided detection (CAD)) AI system may have potential for the analysis of malignant and benign lung nodules on low-dose computed tomography (LDCT), this capability deviates from its current intended use
Summary
Lung cancer is currently the leading global cause of cancer-related death [1,2]. Current five-year survival estimates for non-small cell lung cancer range from 73% for Stage IA disease to 13% for StageIV disease [3]. Current five-year survival estimates for non-small cell lung cancer range from 73% for Stage IA disease to 13% for Stage. The identification of patients with lung cancer in early stages or Stage IA is associated with better prognosis for disease-free survival [9,10]. International Early Lung Cancer Action Program Investigators reported that the early detection and surgical excision of lung cancer presenting as GGN or part-solid nodules can provide a lung cancer-specific survival rate of up to 100% [11,12]. The early detection of lung cancer in patients using low-dose computed tomography (LDCT) was shown to be highly effective according to the report of National Lung Screening Trial (NLST) in 2011, with a relative reduction in lung cancer mortality by
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