Artificial Neural Network for Prediction of Distant Metastasis in Colorectal Cancer

Biglarian, Akbar; Bakhshi, Enayatollah; Gohari, Mahmood Reza; Khodabakhshi, Reza

doi:10.7314/apjcp.2012.13.3.927

Cited by 38 publications

(27 citation statements)

References 22 publications

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“…The Naives Bayes algorithm and the support vector machine (SVM) (Gopinath and Shanthi, 2013), Fuzzy Logic and Artificial Neural Networks (ANN) (Biglarian et al, 2012) have been used as auxiliary tools in diagnosis and prognosis of cancer. These methods train as a classifier according to the features of essential biomarkers and other index.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Lung Cancer Based on Serum Biomarkers by Gene Expression Programming Methods

Chen²,

Cui³

et al. 2014

Asian Pacific Journal of Cancer Prevention

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In diagnosis of lung cancer, rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important. Serum markers, including lactate dehydrogenase (LDH), C-reactive protein (CRP), carcino-embryonic antigen (CEA), neurone specific enolase (NSE) and Cyfra21-1, are reported to reflect lung cancer characteristics. In this study classification of lung tumors was made based on biomarkers (measured in 120 NSCLC and 60 SCLC patients) by setting up optimal biomarker joint models with a powerful computerized tool -gene expression programming (GEP). GEP is a learning algorithm that combines the advantages of genetic programming (GP) and genetic algorithms (GA). It specifically focuses on relationships between variables in sets of data and then builds models to explain these relationships, and has been successfully used in formula finding and function mining. As a basis for defining a GEP environment for SCLC and NSCLC prediction, three explicit predictive models were constructed. CEA and NSE are requentlyused lung cancer markers in clinical trials, CRP, LDH and Cyfra21-1 have significant meaning in lung cancer, basis on CEA and NSE we set up three GEP models-GEP 1(CEA, NSE, Cyfra21-1), GEP2 (CEA, NSE, LDH), GEP3 (CEA, NSE, CRP). The best classification result of GEP gained when CEA, NSE and Cyfra21-1 were combined: 128 of 135 subjects in the training set and 40 of 45 subjects in the test set were classified correctly, the accuracy rate is 94.8% in training set; on collection of samples for testing, the accuracy rate is 88.9%. With GEP2, the accuracy was significantly decreased by 1.5% and 6.6% in training set and test set, in GEP3 was 0.82% and 4.45% respectively. Serum Cyfra21-1 is a useful and sensitive serum biomarker in discriminating between NSCLC and SCLC. GEP modeling is a promising and excellent tool in diagnosis of lung cancer.

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Section: Introductionmentioning

confidence: 99%

Prediction of Lung Cancer Based on Serum Biomarkers by Gene Expression Programming Methods

Chen²,

Cui³

et al. 2014

Asian Pacific Journal of Cancer Prevention

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show abstract

“…In this study, the NN model was identi ed as the optimal model for predicting distant metastases, while Catboost and LR models were shown to be optimal models for predicting LNM in COAD and READ samples, respectively. Consistent with these observations Biglarian et al showed that the ROC for NN and LR models predicting distant metastasis of CRC were 0.82 and 0.77, respectively, suggesting that the NN model was more suitable for the prediction of distant metastasis in CRC [16]. The feature genes from the optimal models were signi cantly enriched in calcium ion homeostasis, transmembrane transport/ion transport, T cell chemotaxis, chemokine signaling pathway amongst others.…”

Section: Discussionmentioning

confidence: 71%

Machine learning models for predicting lymph node and distant metastases in colorectal cancer

Dai

Yang

et al. 2020

Preprint

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Background: Colorectal cancer (CRC) is the third most common malignancy in the world and metastasis is responsible for a major proportion of the cancer-related deaths in CRC patients.Aims: To construct machine learning models for predicting lymph node and distant metastases in colorectal cancer and analyze biological functions features of metastasis-related genes.Methods: RNA-seq and miRNA-seq data as well as corresponding clinical data from colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ) were obtained from The Cancer Genome Atlas (TCGA) database. The differentially expressed RNAs (DE-RNAs) in non-LNM (N0) and LNM (N1/N2) as well as non-distant metastases (M0) and distant metastases (M1) were analyzed. Six machine learning models including logistic regression (LR), random forest (RF), support vector machine (SVM), Catboost, gradient boosting decision tree (GBDT), and artificial neural network (NN) were constructed to predict cancer metastasis and the feature genes of the optimal model were further analyzed by functional enrichment, protein-protein interaction (PPI) network, and drug-target analyses.Results: Differential RNA expression profiles of LNM and non-LNM as well as M0 vs. M1 were observed in both COAD and READ samples. NN model was determined to be the optimal model for predicting distant metastases, while Catboost and LR models were the optimal models for predicting LNM in COAD and READ samples, respectively. PPI analysis indicated that KIR2DL4, chemokine-related genes CXCL9/10/11/13 and CCL25, and gamma-aminobutyric acid (GABA) receptor genes (GABRR1, GABRB2 and GABRA3) were key genes in metastasis. In addition, atorvastatin and eszopiclone were identified as potential therapeutic agents as they target these genes.Conclusions: We constructed six machine learning models for predicting colorectal cancer metastases and identify the optimal model. We analyzed biological functions features of metastasis-related RNAs in colorectal cancer.

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“…SOM is an effective method for predicting cancer outcomes (Golub et al, 1999;Gohari et al, 2011, Biglarian et al, 2012Valarmathi and Radhakrishna, 2013). In this study, we formed a system based on a KohonenSOM neural network to predict the localization of colon tumors.…”

Section: Methodsmentioning

confidence: 99%

Classification of colon cancer based on the expression of randomly selected genes

Cheng

et al. 2015

Genet. Mol. Res.

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ABSTRACT. In order to ascertain the relationship between gene expression and colon cancer localization, a classification method based on random gene selection and a self-organizing map network is proposed. Different numbers of genes were selected randomly from 54,675 genes of 53 colon cancer patients in stage union for international cancer control II. These patients were then divided into two sets: a training set of 36 and a validation set of 17 patients. In this study, we randomly selected 1000, 100, 50, 30, 10, 5, and 3 genes, 1000 times, respectively. The minimum misclassification ratio of each gene group was 3/17 to 4/17, and the percentage of gene groups that were less than 0.25 was approximately 1-7%. Moreover, the misclassification ratio of most gene groups (about 82-89%) was lower than 0.4. Through the analysis of these low misclassification ratio gene groups, we found that there were few common genes between them. This revealed that colon cancer localization is not associated with a single gene group but with many gene groups. Furthermore, K-fold cross validation was used to test the reliability of the possible informative genes, and the results indicated that using gene expression to classify colon tumor localization was not feasible.

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Artificial Neural Network for Prediction of Distant Metastasis in Colorectal Cancer

Cited by 38 publications

References 22 publications

Prediction of Lung Cancer Based on Serum Biomarkers by Gene Expression Programming Methods

Prediction of Lung Cancer Based on Serum Biomarkers by Gene Expression Programming Methods

Machine learning models for predicting lymph node and distant metastases in colorectal cancer

Classification of colon cancer based on the expression of randomly selected genes

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