ArticlePloS one2015
A Highly Efficient Gene Expression Programming (GEP) Model for Auxiliary Diagnosis of Small Cell Lung Cancer.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 26 citations in OpenAlex.
- Integrative Stacking Machine Learning Model for Small Cell Lung Cancer Prediction Using Metabolomics Profiling.Cancers · 2024Article
- Construction of a Diagnostic Model for Small Cell Lung Cancer Combining Metabolomics and Integrated Machine Learning.The oncologist · 2024Article
- Artificial Neural Networks in Lung Cancer Research: A Narrative Review.Journal of clinical medicine · 2023Review
- Cancer adjuvant chemotherapy prediction model for non-small cell lung cancer.IET systems biology · 2019Article
- A novel gene selection algorithm for cancer classification using microarray datasets.BMC medical genomics · 2019Article
- An efficient model for auxiliary diagnosis of hepatocellular carcinoma based on gene expression programming.Medical & biological engineering & computing · 2018Article
- Prediction of NSCLC recurrence from microarray data with GEP.IET systems biology · 2017Article
- An overview of the use of artificial neural networks in lung cancer research.Journal of thoracic disease · 2017Article
- Lung cancer prediction from microarray data by gene expression programming.IET systems biology · 2016Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLung cancer is an important and common cancer that constitutes a major public health problem, but early detection of small cell lung cancer can significantly improve the survival rate of cancer patients. A number of serum biomarkers have been used in the diagnosis of lung cancers; however, they exhibit low sensitivity and specificity.
methodsWe used biochemical methods to measure blood levels of lactate dehydrogenase (LDH), C-reactive protein (CRP), Na+, Cl-, carcino-embryonic antigen (CEA), and neuron specific enolase (NSE) in 145 small cell lung cancer (SCLC) patients and 155 non-small cell lung cancer and 155 normal controls. A gene expression programming (GEP) model and Receiver Operating Characteristic (ROC) curves incorporating these biomarkers was developed for the auxiliary diagnosis of SCLC.
resultsAfter appropriate modification of the parameters, the GEP model was initially set up based on a training set of 115 SCLC patients and 125 normal controls for GEP model generation. Then the GEP was applied to the remaining 60 subjects (the test set) for model validation. GEP successfully discriminated 281 out of 300 cases, showing a correct classification rate for lung cancer patients of 93.75% (225/240) and 93.33% (56/60) for the training and test sets, respectively. Another GEP model incorporating four biomarkers, including CEA, NSE, LDH, and CRP, exhibited slightly lower detection sensitivity than the GEP model, including six biomarkers. We repeat the models on artificial neural network (ANN), and our results showed that the accuracy of GEP models were higher than that in ANN. GEP model incorporating six serum biomarkers performed by NSCLC patients and normal controls showed low accuracy than SCLC patients and was enough to prove that the GEP model is suitable for the SCLC patients.
conclusionWe have developed a GEP model with high sensitivity and specificity for the auxiliary diagnosis of SCLC. This GEP model has the potential for the wide use for detection of SCLC in less developed regions.
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