The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning
Abstract Colorectal cancer (CRC) is a form of cancer that impacts both the rectum and colon. Typically, it begins with a small abnormal growth known as a polyp, which can either be non-cancerous or cancerous. Therefore, early detection of colorectal cancer as the second deadliest cancer after lung c...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-024-84023-w |
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| author | Fatemeh Bahrambanan Meysam Alizamir Kayhan Moradveisi Salim Heddam Sungwon Kim Seunghyun Kim Meysam Soleimani Saeid Afshar Amir Taherkhani |
| author_facet | Fatemeh Bahrambanan Meysam Alizamir Kayhan Moradveisi Salim Heddam Sungwon Kim Seunghyun Kim Meysam Soleimani Saeid Afshar Amir Taherkhani |
| author_sort | Fatemeh Bahrambanan |
| collection | DOAJ |
| description | Abstract Colorectal cancer (CRC) is a form of cancer that impacts both the rectum and colon. Typically, it begins with a small abnormal growth known as a polyp, which can either be non-cancerous or cancerous. Therefore, early detection of colorectal cancer as the second deadliest cancer after lung cancer, can be highly beneficial. Moreover, the standard treatment for locally advanced colorectal cancer, which is widely accepted around the world, is chemoradiotherapy. Then, in this study, seven artificial intelligence models including decision tree, K-nearest neighbors, Adaboost, random forest, Gradient Boosting, multi-layer perceptron, and convolutional neural network were implemented to detect patients responder and non-responder to radiochemotherapy. For finding the potential predictors (genes), three feature selection strategies were employed including mutual information, F-classif, and Chi-Square. Based on feature selection models, four different scenarios were developed and five, ten, twenty and thirty features selected for designing a more accurate classification paradigm. The results of this study confirm that random forest, Gradient Boosting, decision tree, and K-nearest neighbors provided more accurate results in terms of accuracy, by 93.8%. Moreover, Among the feature selection methods, mutual information and F-classif showed the best results, while Chi-Square produced the worst results. Therefore, the suggested artificial intelligence models can be successfully applied as a robust approach for classification of colorectal cancer response to radiochemotherapy for medical studies. |
| format | Article |
| id | doaj-art-4f3e3600b7e34a1cb7940efa1127891e |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4f3e3600b7e34a1cb7940efa1127891e2025-01-05T12:15:36ZengNature PortfolioScientific Reports2045-23222025-01-0115113610.1038/s41598-024-84023-wThe development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learningFatemeh Bahrambanan0Meysam Alizamir1Kayhan Moradveisi2Salim Heddam3Sungwon Kim4Seunghyun Kim5Meysam Soleimani6Saeid Afshar7Amir Taherkhani8Research Center for Molecular Medicine, Hamadan University of Medical SciencesInstitute of Research and Development, Duy Tan UniversityCivil Engineering Department, University of KurdistanFaculty of Science, Agronomy Department, Hydraulics Division, University 20 Août 1955Department of Railroad Construction and Safety Engineering, Dongyang UniversityDepartment of Biology, University of California San DiegoDepartment of Pharmaceutical Biotechnology, School of Pharmacy, Hamadan University of Medical SciencesDepartment of Molecular Medicine and Genetics, Medical School, Hamadan University of Medical SciencesResearch Center for Molecular Medicine, Hamadan University of Medical SciencesAbstract Colorectal cancer (CRC) is a form of cancer that impacts both the rectum and colon. Typically, it begins with a small abnormal growth known as a polyp, which can either be non-cancerous or cancerous. Therefore, early detection of colorectal cancer as the second deadliest cancer after lung cancer, can be highly beneficial. Moreover, the standard treatment for locally advanced colorectal cancer, which is widely accepted around the world, is chemoradiotherapy. Then, in this study, seven artificial intelligence models including decision tree, K-nearest neighbors, Adaboost, random forest, Gradient Boosting, multi-layer perceptron, and convolutional neural network were implemented to detect patients responder and non-responder to radiochemotherapy. For finding the potential predictors (genes), three feature selection strategies were employed including mutual information, F-classif, and Chi-Square. Based on feature selection models, four different scenarios were developed and five, ten, twenty and thirty features selected for designing a more accurate classification paradigm. The results of this study confirm that random forest, Gradient Boosting, decision tree, and K-nearest neighbors provided more accurate results in terms of accuracy, by 93.8%. Moreover, Among the feature selection methods, mutual information and F-classif showed the best results, while Chi-Square produced the worst results. Therefore, the suggested artificial intelligence models can be successfully applied as a robust approach for classification of colorectal cancer response to radiochemotherapy for medical studies.https://doi.org/10.1038/s41598-024-84023-wColorectal cancerDeep learningConvolutional neural networkClassificationRandom forestGradient Boosting |
| spellingShingle | Fatemeh Bahrambanan Meysam Alizamir Kayhan Moradveisi Salim Heddam Sungwon Kim Seunghyun Kim Meysam Soleimani Saeid Afshar Amir Taherkhani The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning Scientific Reports Colorectal cancer Deep learning Convolutional neural network Classification Random forest Gradient Boosting |
| title | The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning |
| title_full | The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning |
| title_fullStr | The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning |
| title_full_unstemmed | The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning |
| title_short | The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning |
| title_sort | development of an efficient artificial intelligence based classification approach for colorectal cancer response to radiochemotherapy deep learning vs machine learning |
| topic | Colorectal cancer Deep learning Convolutional neural network Classification Random forest Gradient Boosting |
| url | https://doi.org/10.1038/s41598-024-84023-w |
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