2020
DOI: 10.1002/jcla.23682
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Development of risk prediction models for lung cancer based on tumor markers and radiological signs

Abstract: Lung cancer is one of the leading causes of cancer-related deaths worldwide, accounting for about 787,000 deaths each year in China. 1,2 Many patients are identified in the advanced stages of lung cancer during initial diagnosis. The age-standardized 5-year relative survival of lung cancer was only 19.7% in China, but if diagnosed at an early stage, then surgical resection offers a favorable prognosis. 3-5 There are several methods for diagnosing lung cancer. The most commonly recommended method for screening … Show more

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Cited by 6 publications
(6 citation statements)
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“…The level of CEA in MPE is much higher than in serum because the molecular weight of CEA is considerably larger, and it is difficult to transfer CEA to the liver to decompose and metabolize, 38 which is consistent with our experimental results. Tu et al indicated that the total specificity and sensitivity in the diagnosis of lung cancer‐related MPE for CEA were 93.5% and 78.2%, respectively, and the summary AUC was 0.90 39 . In our study, the cutoff value of PE CEA for the diagnosis of MPE was 3.38 ng/ml, with a sensitivity and specificity of 77.78% and 92.59%, respectively.…”
Section: Discussionsupporting
confidence: 50%
“…The level of CEA in MPE is much higher than in serum because the molecular weight of CEA is considerably larger, and it is difficult to transfer CEA to the liver to decompose and metabolize, 38 which is consistent with our experimental results. Tu et al indicated that the total specificity and sensitivity in the diagnosis of lung cancer‐related MPE for CEA were 93.5% and 78.2%, respectively, and the summary AUC was 0.90 39 . In our study, the cutoff value of PE CEA for the diagnosis of MPE was 3.38 ng/ml, with a sensitivity and specificity of 77.78% and 92.59%, respectively.…”
Section: Discussionsupporting
confidence: 50%
“…With the development of artificial intelligence technology, the machine learning models provided a better alternative for creating applicable predictive clinical diagnosis tools. In this study, we developed and validated a diagnostic nomogram model to improve the diagnostic accuracy of lung cancer based on AI tools and clinical data ( 3 , 10 , 13 ).…”
Section: Discussionmentioning
confidence: 99%
“…Guo et al [ 33 ] proposed a deep neural network framework to detect lung nodules from low-dose CT images and determine them benign or malignant to identify lung cancer with an accuracy of 99.02%. Accurately predicting the risk of malignant pulmonary lesions in pleural effusion allows for the early diagnosis of lung cancer [ 34 ]. Ahmad et al [ 35 ] used a random forest, decision tree algorithm for the early diagnosis prediction of lung cancer by analyzing the data of multiple risk factors and pulmonary symptoms.…”
Section: Related Workmentioning
confidence: 99%