A Combination of Multilayer Perceptron, Radial Basis Function Artificial Neural Networks and Machine Learning Image Segmentation for the Dimension Reduction and the Prognosis Assessment of Diffuse Large B-Cell Lymphoma
The prognosis of diffuse large B-cell lymphoma (DLBCL) is heterogeneous. Therefore, we aimed to highlight predictive biomarkers. First, artificial intelligence was applied into a discovery series of gene expression of 414 patients (GSE10846). A dimension reduction algorithm aimed to correlate with t...
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MDPI AG
2021-03-01
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| author | Joaquim Carreras Yara Yukie Kikuti Masashi Miyaoka Shinichiro Hiraiwa Sakura Tomita Haruka Ikoma Yusuke Kondo Atsushi Ito Naoya Nakamura Rifat Hamoudi |
| author_facet | Joaquim Carreras Yara Yukie Kikuti Masashi Miyaoka Shinichiro Hiraiwa Sakura Tomita Haruka Ikoma Yusuke Kondo Atsushi Ito Naoya Nakamura Rifat Hamoudi |
| author_sort | Joaquim Carreras |
| collection | DOAJ |
| description | The prognosis of diffuse large B-cell lymphoma (DLBCL) is heterogeneous. Therefore, we aimed to highlight predictive biomarkers. First, artificial intelligence was applied into a discovery series of gene expression of 414 patients (GSE10846). A dimension reduction algorithm aimed to correlate with the overall survival and other clinicopathological variables; and included a combination of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) artificial neural networks, gene-set enrichment analysis (GSEA), Cox regression and other machine learning and predictive analytics modeling [C5.0 algorithm, logistic regression, Bayesian Network, discriminant analysis, random trees, tree-AS, Chi-squared Automatic Interaction Detection CHAID tree, Quest, classification and regression (C&R) tree and neural net)]. From an initial 54,613 gene-probes, a set of 488 genes and a final set of 16 genes were defined. Secondly, two identified markers of the immune checkpoint, PD-L1 (<i>CD274</i>) and IKAROS (<i>IKZF4</i>), were validated in an independent series from Tokai University, and the immunohistochemical expression was quantified, using a machine-learning-based Weka segmentation. High PD-L1 associated with poor overall and progression-free survival, non-GCB phenotype, Epstein–Barr virus infection (EBER+), high RGS1 expression and several clinicopathological variables, such as high IPI and absence of clinical response. Conversely, high expression of IKAROS was associated with a good overall and progression-free survival, GCB phenotype and a positive clinical response to treatment. Finally, the set of 16 genes (<i>PAF1, USP28, SORT1, MAP7D3, FITM2, CENPO, PRCC, ALDH6A1, CSNK2A1, TOR1AIP1, NUP98, UBE2H, UBXN7, SLC44A2, NR2C2AP</i> and <i>LETM1</i>), in combination with <i>PD-L1</i>, <i>IKAROS</i>, <i>BCL2</i>, <i>MYC</i>, <i>CD163</i> and <i>TNFAIP8</i>, predicted the survival outcome of DLBCL with an overall accuracy of 82.1%. In conclusion, building predictive models of DLBCL is a feasible analytical strategy. |
| first_indexed | 2024-03-09T05:00:21Z |
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| id | doaj.art-a6461498faed4acf8626366e89aa9e72 |
| institution | Directory Open Access Journal |
| issn | 2673-2688 |
| language | English |
| last_indexed | 2024-03-09T05:00:21Z |
| publishDate | 2021-03-01 |
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| spelling | doaj.art-a6461498faed4acf8626366e89aa9e722023-12-03T13:00:59ZengMDPI AGAI2673-26882021-03-012110613410.3390/ai2010008A Combination of Multilayer Perceptron, Radial Basis Function Artificial Neural Networks and Machine Learning Image Segmentation for the Dimension Reduction and the Prognosis Assessment of Diffuse Large B-Cell LymphomaJoaquim Carreras0Yara Yukie Kikuti1Masashi Miyaoka2Shinichiro Hiraiwa3Sakura Tomita4Haruka Ikoma5Yusuke Kondo6Atsushi Ito7Naoya Nakamura8Rifat Hamoudi9Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, JapanDepartment of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, JapanDepartment of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, JapanDepartment of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, JapanDepartment of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, JapanDepartment of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, JapanDepartment of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, JapanDepartment of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, JapanDepartment of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, JapanDepartment of Clinical Sciences, College of Medicine, University of Sharjah, P.O. Box 27272 Sharjah, United Arab EmiratesThe prognosis of diffuse large B-cell lymphoma (DLBCL) is heterogeneous. Therefore, we aimed to highlight predictive biomarkers. First, artificial intelligence was applied into a discovery series of gene expression of 414 patients (GSE10846). A dimension reduction algorithm aimed to correlate with the overall survival and other clinicopathological variables; and included a combination of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) artificial neural networks, gene-set enrichment analysis (GSEA), Cox regression and other machine learning and predictive analytics modeling [C5.0 algorithm, logistic regression, Bayesian Network, discriminant analysis, random trees, tree-AS, Chi-squared Automatic Interaction Detection CHAID tree, Quest, classification and regression (C&R) tree and neural net)]. From an initial 54,613 gene-probes, a set of 488 genes and a final set of 16 genes were defined. Secondly, two identified markers of the immune checkpoint, PD-L1 (<i>CD274</i>) and IKAROS (<i>IKZF4</i>), were validated in an independent series from Tokai University, and the immunohistochemical expression was quantified, using a machine-learning-based Weka segmentation. High PD-L1 associated with poor overall and progression-free survival, non-GCB phenotype, Epstein–Barr virus infection (EBER+), high RGS1 expression and several clinicopathological variables, such as high IPI and absence of clinical response. Conversely, high expression of IKAROS was associated with a good overall and progression-free survival, GCB phenotype and a positive clinical response to treatment. Finally, the set of 16 genes (<i>PAF1, USP28, SORT1, MAP7D3, FITM2, CENPO, PRCC, ALDH6A1, CSNK2A1, TOR1AIP1, NUP98, UBE2H, UBXN7, SLC44A2, NR2C2AP</i> and <i>LETM1</i>), in combination with <i>PD-L1</i>, <i>IKAROS</i>, <i>BCL2</i>, <i>MYC</i>, <i>CD163</i> and <i>TNFAIP8</i>, predicted the survival outcome of DLBCL with an overall accuracy of 82.1%. In conclusion, building predictive models of DLBCL is a feasible analytical strategy.https://www.mdpi.com/2673-2688/2/1/8overall survivaldiffuse large B-cell lymphomaartificial intelligenceMultilayer PerceptronRadial Basis FunctionPD-L1 (CD274) |
| spellingShingle | Joaquim Carreras Yara Yukie Kikuti Masashi Miyaoka Shinichiro Hiraiwa Sakura Tomita Haruka Ikoma Yusuke Kondo Atsushi Ito Naoya Nakamura Rifat Hamoudi A Combination of Multilayer Perceptron, Radial Basis Function Artificial Neural Networks and Machine Learning Image Segmentation for the Dimension Reduction and the Prognosis Assessment of Diffuse Large B-Cell Lymphoma AI overall survival diffuse large B-cell lymphoma artificial intelligence Multilayer Perceptron Radial Basis Function PD-L1 (CD274) |
| title | A Combination of Multilayer Perceptron, Radial Basis Function Artificial Neural Networks and Machine Learning Image Segmentation for the Dimension Reduction and the Prognosis Assessment of Diffuse Large B-Cell Lymphoma |
| title_full | A Combination of Multilayer Perceptron, Radial Basis Function Artificial Neural Networks and Machine Learning Image Segmentation for the Dimension Reduction and the Prognosis Assessment of Diffuse Large B-Cell Lymphoma |
| title_fullStr | A Combination of Multilayer Perceptron, Radial Basis Function Artificial Neural Networks and Machine Learning Image Segmentation for the Dimension Reduction and the Prognosis Assessment of Diffuse Large B-Cell Lymphoma |
| title_full_unstemmed | A Combination of Multilayer Perceptron, Radial Basis Function Artificial Neural Networks and Machine Learning Image Segmentation for the Dimension Reduction and the Prognosis Assessment of Diffuse Large B-Cell Lymphoma |
| title_short | A Combination of Multilayer Perceptron, Radial Basis Function Artificial Neural Networks and Machine Learning Image Segmentation for the Dimension Reduction and the Prognosis Assessment of Diffuse Large B-Cell Lymphoma |
| title_sort | combination of multilayer perceptron radial basis function artificial neural networks and machine learning image segmentation for the dimension reduction and the prognosis assessment of diffuse large b cell lymphoma |
| topic | overall survival diffuse large B-cell lymphoma artificial intelligence Multilayer Perceptron Radial Basis Function PD-L1 (CD274) |
| url | https://www.mdpi.com/2673-2688/2/1/8 |
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