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

Carreras, Joaquim; Kikuti, Yara Yukie; Miyaoka, Masashi; Hiraiwa, Shinichiro; Tomita, Sakura; Ikoma, Haruka; Kondo, Yusuke; Ito, Atsushi; Nakamura, Naoya; Hamoudi, Rifat

doi:10.3390/ai2010008

Cited by 30 publications

(56 citation statements)

References 25 publications

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“…Finally, high expression of PD-L1 was associated with poor prognosis in DLBCL [44]. This result was also recently confirmed by our group [45], but, in this research, the CSF1R did not correlate with the PD-L1 expression. Another marker that we have recently described is the apoptosis inhibitor TNFAIP8, which is associated with a poor prognosis of the patients [28].…”

Section: Discussionsupporting

confidence: 84%

Integrative Statistics, Machine Learning and Artificial Intelligence Neural Network Analysis Correlated CSF1R with the Prognosis of Diffuse Large B-Cell Lymphoma

Carreras

Kikuti

Miyaoka

et al. 2021

Hemato

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Tumor-associated macrophages (TAMs) of the immune microenvironment play an important role in the Diffuse Large B-cell Lymphoma (DLBCL) pathogenesis. This research aimed to characterize the expression of macrophage colony-stimulating factor 1 receptor (CSF1R) at the gene and protein level in correlation with survival. First, the immunohistochemical expression of CSF1R was analyzed in a series of 198 cases from Tokai University Hospital and two patterns of histological expression were found, a TAMs, and a diffuse B-lymphocytes pattern. The clinicopathological correlations showed that the CSF1R + TAMs pattern associated with a poor progression-free survival of the patients, disease progression, higher MYC proto-oncogene expression, lower MDM2 expression, BCL2 translocation, and a MYD88 L265P mutation. Conversely, a diffuse CSF1R + B-cells pattern was associated with a favorable progression-free survival. Second, the histological expression of CSF1R was also correlated with 10 CSF1R-related markers including CSF1, STAT3, NFKB1, Ki67, MYC, PD-L1, TNFAIP8, IKAROS, CD163, and CD68. CSF1R moderately correlated with STAT3, TNFAIP8, CD68, and CD163 in the cases with the CSF1R + TAMs pattern. In addition, machine learning modeling predicted the CSF1R immunohistochemical expression with high accuracy using regression, generalized linear, an artificial intelligence neural network (multilayer perceptron), and support vector machine (SVM) analyses. Finally, a multilayer perceptron analysis predicted the genes associated with the CSF1R gene expression using the GEO GSE10846 DLBCL series of the Lymphoma/Leukemia Molecular Profiling Project (LLMPP), with correlation to the whole set of 20,683 genes as well as with an immuno-oncology cancer panel of 1790 genes. In addition, CSF1R positively correlated with SIRPA and inversely with CD47. In conclusion, the CSF1R histological pattern correlated with the progression-free survival of the patients of the Tokai series, and predictive analytics is a feasible strategy in DLBCL.

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Section: Discussionsupporting

confidence: 84%

Integrative Statistics, Machine Learning and Artificial Intelligence Neural Network Analysis Correlated CSF1R with the Prognosis of Diffuse Large B-Cell Lymphoma

Carreras

Kikuti

Miyaoka

et al. 2021

Hemato

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“…The analysis was performed to predict all subtypes simultaneously and then each subtype against the others. The multilayer perceptron was performed as we have recently described [17,[19][20][21][22][23].…”

Section: Multilayer Perceptron Analysismentioning

confidence: 99%

Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy

Carreras

Hamoudi

2021

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Predictive analytics using artificial intelligence is a useful tool in cancer research. A multilayer perceptron neural network used gene expression data to predict the lymphoma subtypes of 290 cases of non-Hodgkin lymphoma (GSE132929). The input layer included both the whole array of 20,863 genes and a cancer transcriptome panel of 1769 genes. The output layer was lymphoma subtypes, including follicular lymphoma, mantle cell lymphoma, diffuse large B-cell lymphoma, Burkitt lymphoma, and marginal zone lymphoma. The neural networks successfully classified the cases consistent with the lymphoma subtypes, with an area under the curve (AUC) that ranged from 0.87 to 0.99. The most relevant predictive genes were LCE2B, KNG1, IGHV7_81, TG, C6, FGB, ZNF750, CTSV, INGX, and COL4A6 for the whole set; and ARG1, MAGEA3, AKT2, IL1B, S100A7A, CLEC5A, WIF1, TREM1, DEFB1, and GAGE1 for the cancer panel. The characteristic predictive genes for each lymphoma subtypes were also identified with high accuracy (AUC = 0.95, incorrect predictions = 6.2%). Finally, the topmost relevant 30 genes of the whole set, which belonged to apoptosis, cell proliferation, metabolism, and antigen presentation pathways, not only predicted the lymphoma subtypes but also the overall survival of diffuse large B-cell lymphoma (series GSE10846, n = 414 cases), and most relevant cancer subtypes of The Cancer Genome Atlas (TCGA) consortium including carcinomas of breast, colorectal, lung, prostate, and gastric, melanoma, etc. (7441 cases). In conclusion, neural networks predicted the non-Hodgkin lymphoma subtypes with high accuracy, and the highlighted genes also predicted the survival of a pan-cancer series.

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“…Another inhibitor, the Survivin, was also found overexpressed in DLBCL [47] and in ABC molecular type DLBCL the overexpression was also associated to a poor prognosis [47]. Besides, we recently described that high expression of another apoptosis inhibitor (TNFAIP8) was associated with a poor prognosis of DLBCL [40]. In this project the protein expression of Caspase-8 was analyzed in a series of Tokai University's, and we found that high expression was associated with a favorable survival of the patients.…”

Section: Discussionmentioning

confidence: 94%

“…Several software were used in this research according to the manufacturer's instructions: R software for statistical computing version 3.6. 2a-d) and Radial Basis Function analysis using the immunohistochemical data was performed following the manufacturer's instructions, and as we have thoroughly described in our previous publications [39,40]. In this neural network analysis, the prediction of Caspase-8 by the other related markers of the pathway was performed using the immunohistochemical data of Caspase-8 as a dichotomic variable (high vs. low, with the same cut-off of the overall survival).…”

Section: Software and Artificial Neural Network Analysismentioning

confidence: 99%

High Expression of Caspase-8 Associated with Improved Survival in Diffuse Large B-Cell Lymphoma: Machine Learning and Artificial Neural Networks Analyses

et al. 2021

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High expression of the anti-apoptotic TNFAIP8 is associated with poor survival of the patients with diffuse large B-cell lymphoma (DLBCL), and one of the functions of TNFAIP8 is to inhibit the pro-apoptosis Caspase-8. We aimed to analyze the immunohistochemical expression of Caspase-8 (active subunit p18; CASP8) in a series of 97 cases of DLBCL from Tokai University Hospital, and to correlate with other Caspase-8 pathway-related markers, including cleaved Caspase-3, cleaved PARP, BCL2, TP53, MDM2, MYC, Ki67, E2F1, CDK6, MYB and LMO2. After digital image quantification, the correlation with several clinicopathological characteristics of the patients showed that high protein expression of Caspase-8 was associated with a favorable overall and progression-free survival (Hazard Risks = 0.3; p = 0.005 and 0.03, respectively). Caspase-8 also positively correlated with cCASP3, MDM2, E2F1, TNFAIP8, BCL2 and Ki67. Next, the Caspase-8 protein expression was modeled using predictive analytics, and a high overall predictive accuracy (>80%) was obtained with CHAID decision tree, Bayesian network, discriminant analysis, C5 tree, logistic regression, and Artificial Intelligence Neural Network methods (both Multilayer perceptron and Radial basis function); the most relevant markers were cCASP3, E2F1, TP53, cPARP, MDM2, BCL2 and TNFAIP8. Finally, the CASP8 gene expression was also successfully modeled in an independent DLBCL series of 414 cases from the Lymphoma/Leukemia Molecular Profiling Project (LLMPP). In conclusion, high protein expression of Caspase-8 is associated with a favorable prognosis of DLBCL. Predictive modeling is a feasible analytic strategy that results in a solution that can be understood (i.e., explainable artificial intelligence, “white-box” algorithms).

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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

Cited by 30 publications

References 25 publications

Integrative Statistics, Machine Learning and Artificial Intelligence Neural Network Analysis Correlated CSF1R with the Prognosis of Diffuse Large B-Cell Lymphoma

Integrative Statistics, Machine Learning and Artificial Intelligence Neural Network Analysis Correlated CSF1R with the Prognosis of Diffuse Large B-Cell Lymphoma

Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy

High Expression of Caspase-8 Associated with Improved Survival in Diffuse Large B-Cell Lymphoma: Machine Learning and Artificial Neural Networks Analyses

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