Abstract
The improvement of pulmonary nodule detection, which is a challenging task when using chest radiographs, may help to elevate the role of chest radiographs for the diagnosis of lung cancer. To assess the performance of a deep learning-based nodule detection algorithm for the detection of lung cancer on chest radiographs from participants in the National Lung Screening Trial (NLST). This diagnostic study used data from participants in the NLST ro assess the performance of a deep learning-based artificial intelligence (AI) algorithm for the detection of pulmonary nodules and lung cancer on chest radiographs using separate training (in-house) and validation (NLST) data sets. Baseline (T0) posteroanterior chest radiographs from 5485 participants (full T0 data set) were used to assess lung cancer detection performance, and a subset of 577 of these images (nodule data set) were used to assess nodule detection performance. Participants aged 55 to 74 years who currently or formerly (ie, quit within the past 15 years) smoked cigarettes for 30 pack-years or more were enrolled in the NLST at 23 US centers between August 2002 and April 2004. Information on lung cancer diagnoses was collected through December 31, 2009. Analyses were performed between August 20, 2019, and February 14, 2020. Abnormality scores produced by the AI algorithm. The performance of an AI algorithm for the detection of lung nodules and lung cancer on radiographs, with lung cancer incidence and mortality as primary end points. A total of 5485 participants (mean [SD] age, 61.7 [5.0] years; 3030 men [55.2%]) were included, with a median follow-up duration of 6.5 years (interquartile range, 6.1-6.9 years). For the nodule data set, the sensitivity and specificity of the AI algorithm for the detection of pulmonary nodules were 86.2% (95% CI, 77.8%-94.6%) and 85.0% (95% CI, 81.9%-88.1%), respectively. For the detection of all cancers, the sensitivity was 75.0% (95% CI, 62.8%-87.2%), the specificity was 83.3% (95% CI, 82.3%-84.3%), the positive predictive value was 3.8% (95% CI, 2.6%-5.0%), and the negative predictive value was 99.8% (95% CI, 99.6%-99.9%). For the detection of malignant pulmonary nodules in all images of the full T0 data set, the sensitivity was 94.1% (95% CI, 86.2%-100.0%), the specificity was 83.3% (95% CI, 82.3%-84.3%), the positive predictive value was 3.4% (95% CI, 2.2%-4.5%), and the negative predictive value was 100.0% (95% CI, 99.9%-100.0%). In digital radiographs of the nodule data set, the AI algorithm had higher sensitivity (96.0% [95% CI, 88.3%-100.0%] vs 88.0% [95% CI, 75.3%-100.0%]; P = .32) and higher specificity (93.2% [95% CI, 89.9%-96.5%] vs 82.8% [95% CI, 77.8%-87.8%]; P = .001) for nodule detection compared with the NLST radiologists. For malignant pulmonary nodule detection on digital radiographs of the full T0 data set, the sensitivity of the AI algorithm was higher (100.0% [95% CI, 100.0%-100.0%] vs 94.1% [95% CI, 82.9%-100.0%]; P = .32) compared with the NLST radiologists, and the specificity (90.9% [95% CI, 89.6%-92.1%] vs 91.0% [95% CI, 89.7%-92.2%]; P = .91), positive predictive value (8.2% [95% CI, 4.4%-11.9%] vs 7.8% [95% CI, 4.1%-11.5%]; P = .65), and negative predictive value (100.0% [95% CI, 100.0%-100.0%] vs 99.9% [95% CI, 99.8%-100.0%]; P = .32) were similar to those of NLST radiologists. In this study, the AI algorithm performed better than NLST radiologists for the detection of pulmonary nodules on digital radiographs. When used as a second reader, the AI algorithm may help to detect lung cancer.
Highlights
For the detection of malignant pulmonary nodules in all images of the full T0 data set, the sensitivity was 94.1%, the specificity was 83.3%, the positive predictive value was 3.4%, and the negative predictive value was 100.0%
In digital radiographs of the nodule data set, the artificial intelligence (AI) algorithm had higher sensitivity (96.0% [95% CI, 88.3%-100.0%] vs 88.0% [95% CI, 75.3%-100.0%]; P = .32) and higher specificity (93.2% [95% CI, 89.9%-96.5%] vs 82.8% [95% CI, 77.8%-87.8%]; P = .001) for nodule detection compared with the National Lung Screening Trial (NLST) radiologists
Meaning The study findings suggest that an artificial intelligence algorithm trained to detect pulmonary nodules can help to improve lung cancer detection on chest radiographs
Summary
Large randomized clinical trials investigating chest radiography and low-dose computed tomography (CT) as screening tools for lung cancer have reported that low-dose CT screening reduces lung cancer mortality in high-risk populations.[1,2] only low-dose CT is recommended for lung cancer screening among high-risk populations in most countries.[3,4,5] a substantial cost is associated with low-dose CT; compared with chest radiography, CT is less accessible and more expensive, exposes patients to a higher dose of radiation, and produces a higher proportion of false-positive and incidental findings, which may lead to additional laboratory testing and increase patient anxiety.[6,7]Chest radiography avoids many of the problems associated with low-dose CT, but the survival benefits of chest radiography as a screening tool have not been fully examined.[8]. The detection of lung cancer on chest radiographs is challenging for radiologists because of the limited contrast resolution and 2-dimensional projectional nature of radiography, which can obscure findings owing to the superimposition of lesions by anatomical structures and produce high false-negative rates with low intraobserver and interobserver agreement.[9,10] Previous studies have reported that, in retrospect, radiographic evidence of cancer was present in up to 90% of patients with peripheral cancer and in 65% to 70% of patients with centrally located cancer before the actual cancer diagnosis was made.[10,11] Tumor characteristics, such as lesion size, conspicuity, and location, are all independent factors in detection error and can lead to missed lesions during the interpretation of chest radiographs.[12]
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