Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels

Simple Summary Artificial intelligence (AI) is a field that combines computer science with robust datasets to solve problems. AI in medicine uses machine learning and deep learning to analyze medical data and gain insight into the pathogenesis of diseases. This study summarizes and integrates our previous research and advances the analyses of macrophages. We used artificial neural networks and several types of machine learning to analyze the gene expression and protein levels by immunohistochemistry of several hematological neoplasia and pan-cancer series. As a result, the patients’ survival and disease subtype classification were achieved with high accuracy. Additionally, a review of the literature on the latest progress made by AI in the hematopathology field and future perspectives are given. Abstract Artificial intelligence (AI) can identify actionable oncology biomarkers. This research integrates our previous analyses of non-Hodgkin lymphoma. We used gene expression and immunohistochemical data, focusing on the immune checkpoint, and added a new analysis of macrophages, including 3D rendering. The AI comprised machine learning (C5, Bayesian network, C&R, CHAID, discriminant analysis, KNN, logistic regression, LSVM, Quest, random forest, random trees, SVM, tree-AS, and XGBoost linear and tree) and artificial neural networks (multilayer perceptron and radial basis function). The series included chronic lymphocytic leukemia, mantle cell lymphoma, follicular lymphoma, Burkitt, diffuse large B-cell lymphoma, marginal zone lymphoma, and multiple myeloma, as well as acute myeloid leukemia and pan-cancer series. AI classified lymphoma subtypes and predicted overall survival accurately. Oncogenes and tumor suppressor genes were highlighted (MYC, BCL2, and TP53), along with immune microenvironment markers of tumor-associated macrophages (M2-like TAMs), T-cells and regulatory T lymphocytes (Tregs) (CD68, CD163, MARCO, CSF1R, CSF1, PD-L1/CD274, SIRPA, CD85A/LILRB3, CD47, IL10, TNFRSF14/HVEM, TNFAIP8, IKAROS, STAT3, NFKB, MAPK, PD-1/PDCD1, BTLA, and FOXP3), apoptosis (BCL2, CASP3, CASP8, PARP, and pathway-related MDM2, E2F1, CDK6, MYB, and LMO2), and metabolism (ENO3, GGA3). In conclusion, AI with immuno-oncology markers is a powerful predictive tool. Additionally, a review of recent literature was made.


Introduction
Lymphoid neoplasms are tumors of the hematopoietic system derived from immature and mature B lymphocytes, T lymphocytes, and natural killer (NK) cells that evoke the normal stages of cell differentiation. Nevertheless, some neoplasms (such as hairy cell leukemia) show lineage heterogeneity and plasticity, and their normal counterparts cannot be found [1][2][3][4][5][6][7]. The 2016 revision of the World Health Organization (WHO) classification of lymphoid neoplasms [3] and the International Consensus Classification (ICC) [6] describe around 45 different subtypes of mature lymphoid neoplasms [3,6,7]. In this research, we analyzed the gene expression of some of the most relevant and frequent ones.
Extranodal marginal zone lymphoma of mucosa-associated lymphoid tissue is an extranodal lymphoma (MALT lymphoma) composed of a heterogeneous population of small B-cells [3]. It originates in the marginal zones, but it extends into the interfollicular and follicular regions and infiltrates the epithelium, forming the lymphoepithelial lesions [3,11].
Mantle cell lymphoma (MCL) is characterized by monomorphic small to mediumsized lymphoid cells with irregular nuclei and the CCND1 translocation, originating from peripheral B lymphocytes of the inner mantle zone, CD5+, and SOX11+ in the classical form [3,12,13].
Diffuse large B-cell lymphoma (DLBCL) is a neoplasm of medium or large B lymphoid cells that originate from the germinal center in the germinal center B-cell-like type, or from the post-germinal center in the activated B-cell-like type [3,14,15]. According to the clinical, morphological, and biological features, DLBCL can be subdivided into different subtypes; the remaining ones are not otherwise specified (NOS).
Burkitt lymphoma is a highly aggressive but curable lymphoma that often appears at extranodal sites or as acute leukemia. It is characterized by a monomorphic proliferation of medium-size B-cells, mitotic figures, and the MYC translocation to the immunoglobulin (IG) locus. It originates from the germinal centers. There are three epidemiological variants, with variable association with the Epstein-Barr virus (EBV): endemic, sporadic, and immunodeficiency-associated [3,[16][17][18]. Figure 1 shows the stages of the B-lymphocyte differentiation, and the relationship with the different lymphoma subtypes [19].
Nowadays, there has been rapid advance in the field of artificial intelligence (AI), and its role in medicine is gaining relevance. AI integrates computer science and datasets to make predictions or classifications based on input data.
There are two types of artificial intelligence, weak and strong AI. Weak AI, also known as narrow AI (NAI), is trained to perform specific tasks. Conversely, strong AI includes artificial general intelligence (AGI) or artificial super intelligence (ASI), and it is expected to surpass human abilities in the future [20][21][22][23][24][25][26].
In this research, we used weak artificial intelligence to predict the prognosis of the patients and to classify several subtypes of mature B-cell neoplasms (output). Gene expression (transcriptomics) and protein immunohistochemical data were used as predictors (input data). The research focused on artificial neural networks (mainly multilayer perceptron), but also used other neural networks such as the radial basis function and other machine learning techniques. Regarding the neural networks, "basic" but robust and reliable architectures were chosen as an elemental part of the analysis. Then, the "basic" networks were combined in more complex, multivariate analysis algorithms. Figure 2 describes the basic structure of the neural network. In the current theory of the pathogenesis of hematopoietic and lymphoid tissues, B-cell neoplasms correspond to various stages of B-cell differentiation. For example, follicular lymphoma, Burkitt lymphoma, and diffuse large B-cell lymphoma develop (or have a stage of differentiation) from mature B lymphocytes from the germinal centers of follicles of peripheral lymphoid tissues. Of note, follicular lymphoma is characterized by the IGH/BCL2 translocation (t14;18)(q32;q21) that occurs in the bone marrow. Nevertheless, this genetic alteration is not sufficient to generate lymphoma, and additional cumulative changes are necessary.
Nowadays, there has been rapid advance in the field of artificial intelligence (AI), and its role in medicine is gaining relevance. AI integrates computer science and datasets to make predictions or classifications based on input data.
There are two types of artificial intelligence, weak and strong AI. Weak AI, also known as narrow AI (NAI), is trained to perform specific tasks. Conversely, strong AI includes artificial general intelligence (AGI) or artificial super intelligence (ASI), and it is expected to surpass human abilities in the future [20][21][22][23][24][25][26].
In this research, we used weak artificial intelligence to predict the prognosis of the patients and to classify several subtypes of mature B-cell neoplasms (output). Gene expression (transcriptomics) and protein immunohistochemical data were used as predictors (input data). The research focused on artificial neural networks (mainly multilayer perceptron), but also used other neural networks such as the radial basis function and other machine learning techniques. Regarding the neural networks, "basic" but robust and reliable architectures were chosen as an elemental part of the analysis. Then, the "basic" networks were combined in more complex, multivariate analysis algorithms.  In the current theory of the pathogenesis of hematopoietic and lymphoid tissues, B-cell neoplasms correspond to various stages of B-cell differentiation. For example, follicular lymphoma, Burkitt lymphoma, and diffuse large B-cell lymphoma develop (or have a stage of differentiation) from mature B lymphocytes from the germinal centers of follicles of peripheral lymphoid tissues. Of note, follicular lymphoma is characterized by the IGH/BCL2 translocation (t14;18)(q32;q21) that occurs in the bone marrow. Nevertheless, this genetic alteration is not sufficient to generate lymphoma, and additional cumulative changes are necessary.
The immune checkpoints are regulators of the immune system that belong to the self-tolerance pathways. Without them, the immune system would attach to cells indiscriminately. Cancer uses several mechanisms to proliferate, including evading the host immune response using immune checkpoint molecules. There are two types of immune checkpoint molecules: stimulatory and inhibitory. Inhibitory checkpoint molecules inhibit the immune response and include several markers such as B7-H3 (CD276), BTLA, CTLA-4, LAG3, PD-1, TIM-3, and VISTA. Nowadays, immune checkpoints are important because they are the basis of cancer immunotherapy. Currently approved checkpoint inhibitors are anti CTLA-4, PD-1, and PD-L1 [19,[27][28][29][30][31][32][33][34][35]. In this research, artificial intelligence was used to classify and to predict the overall survival of different lymphoma subtypes using gene expression data, all the genes of the arrays, and specific panels of the immune checkpoint.
This manuscript integrates our previous publications to provide a general view of the results and adds new analysis on tumor-associated macrophages (TAMs).

Figure 2.
The basic structure of a neural network. The network is a function of predictors (also called inputs or independent variables) that minimize the prediction error of target variables (outputs). In the case of a multilayer perceptron, it is a feed-forward architecture because the connections flow from the input to the output layer without loops. Here, four genes predict the overall survival of patients. The input layer contains these genes. The hidden layer contains the unobservable nodes (units). The output layer contains the responses; the overall survival is a categorical variable (dead vs alive).
The immune checkpoints are regulators of the immune system that belong to the selftolerance pathways. Without them, the immune system would attach to cells indiscriminately. Cancer uses several mechanisms to proliferate, including evading the host immune response using immune checkpoint molecules. There are two types of immune checkpoint molecules: stimulatory and inhibitory. Inhibitory checkpoint molecules inhibit the immune response and include several markers such as B7-H3 (CD276), BTLA, CTLA-4, LAG3, PD-1, TIM-3, and VISTA. Nowadays, immune checkpoints are important because they are the basis of cancer immunotherapy. Currently approved checkpoint inhibitors are anti CTLA-4, PD-1, and PD-L1 [19,[27][28][29][30][31][32][33][34][35]. In this research, artificial intelligence was used to classify and to predict the overall survival of different lymphoma subtypes using gene expression data, all the genes of the arrays, and specific panels of the immune checkpoint.
This manuscript integrates our previous publications to provide a general view of the results and adds new analysis on tumor-associated macrophages (TAMs).

Machine Learning and Neural Networks
This research integrates all the previous analyses that were obtained using conventional biostatistics, machine learning, and artificial neural networks. Machine learning included Bayesian network, C&R tree, C5 tree, CHAID tree, discriminant analysis, KNN algorithm, logistic regression, LSVM, Quest tree, random forest, random trees, SVM, tree-AS, XGBoost linear, and XGBoost tree. Two types of artificial neural networks were used: the multilayer perceptron and radial basis function. The digital image quantification of markers was performed using the Waikato Environment for Knowledge Analysis (Weka), and the training of the classifier included fast random forest. All the materials and methods were thoroughly described in the previous publications [19,[27][28][29][30][31][32][33][34][35].

Multilayer Perceptron Artificial Neural Network
The multilayer perceptron architecture was chosen in most cases. Several parameters were chosen to optimize the neural network. The predictors were included in the input Figure 2. The basic structure of a neural network. The network is a function of predictors (also called inputs or independent variables) that minimize the prediction error of target variables (outputs). In the case of a multilayer perceptron, it is a feed-forward architecture because the connections flow from the input to the output layer without loops. Here, four genes predict the overall survival of patients. The input layer contains these genes. The hidden layer contains the unobservable nodes (units). The output layer contains the responses; the overall survival is a categorical variable (dead vs alive).

Machine Learning and Neural Networks
This research integrates all the previous analyses that were obtained using conventional biostatistics, machine learning, and artificial neural networks. Machine learning included Bayesian network, C&R tree, C5 tree, CHAID tree, discriminant analysis, KNN algorithm, logistic regression, LSVM, Quest tree, random forest, random trees, SVM, tree-AS, XGBoost linear, and XGBoost tree. Two types of artificial neural networks were used: the multilayer perceptron and radial basis function. The digital image quantification of markers was performed using the Waikato Environment for Knowledge Analysis (Weka), and the training of the classifier included fast random forest. All the materials and methods were thoroughly described in the previous publications [19,[27][28][29][30][31][32][33][34][35].

Multilayer Perceptron Artificial Neural Network
The multilayer perceptron architecture was chosen in most cases. Several parameters were chosen to optimize the neural network. The predictors were included in the input layer, the unobservable nodes or units in the hidden layer, and the responses in the output layer. Scale-dependent variables and covariates were rescaled to improve network training. The method for rescaling of covariates was standardized: subtract the mean and divide by the standard deviation, (x−mean)/s. The series of cases were randomly partitioned into training (70%) and testing (30%) datasets. The best performance was found using one hidden layer. The activation function linked the weighted sums of units in a layer to the values of units in the succeeding layer. The hyperbolic tangent was usually used. This function has the form γ(c) = tanh(c) = (e c -e -c )/(e c +e −c ). It takes real-valued arguments and transforms them into the range (-1, 1). When automatic architecture selection is used, this is the activation function for all units in the hidden layers. The number of units in each hidden layer was determined automatically by an estimation algorithm.
The output layer contained the target (dependent) variables and the activation function was softmax. This function has the form: γ(c k ) = exp(c k )/Σ j exp(c j ). It takes a vector of real-valued arguments and transforms it into a vector whose elements fall in the range (0,1) and sum to 1. Softmax is available only if all dependent variables are categorical. The training type determines how the network processes the records; the training type was batch. The training options were initial lambda (0.0000005), initial sigma (0.00005), interval center (0), and interval offset (+/−0.5). The network performance was assessed by the classification results, receiver operating characteristic (ROC) curve, cumulative gains chart, lift chart, predicted by observed chart, and residual by predicted chart. Using a sensitivity analysis, the independent variables were ranked according to their importance for predicting the dependent variable and in determining the neural network ( Figure 3).
Cancers 2022, 14, x FOR PEER REVIEW 6 of 55 Figure 3. Sensitivity analysis. Using a sensitivity analysis, the independent variables were ranked according to their importance for predicting the dependent variable and in determining the neural network.

Differential Gene Expression Using the GEOR2 Software
The GEO2R 1.0 software was used to compare the differential gene expression between subtypes simply. The Benjamini-Hochberg false discovery rate was applied to adjust the p values. Log transformation was applied if necessary. Limma precision weights and force normalization were not applied. The data were visualized using volcano and mean difference (MA) plots, contrasted with a level of cut-off significance set a priori at 0.05. This software runs in R 3.2.3, Biobase 2.30.0, GEOquery 2.40.0, limma 3.26.8. Webpage: https://www.ncbi.nlm.nih.gov/geo/info/geo2r.html (accessed on 23 July 2022).

Gene Set Enrichment Analysis
The Gene Set Enrichment Analysis (GSEA) was used to determine if a pathway of interest was associated with a particular biological state (for example, dead vs alive) [36,37]. The pathways were obtained from the Molecular Signatures Database (MSigDB 7.0 and greater) or designed in-house. The software GSEA v4.2.3 was downloaded from the webpage of UC San Diego, Broad Institute: http://www.gsea-msigdb.org/gsea/index.jsp (accessed on 23 July 2022).

Conventional Statistical Analyses
Comparisons between groups were performed using crosstabulation with Pearson Chi-Square and Fisher's exact tests, and with nonparametric Mann-Whitney U (2 groups) and Kruskal-Wallis H (≥3 groups) tests. Survival analyses used the Kaplan-Meier and Log-rank tests, and the univariate and multivariate Cox Regression. The criteria of survival and response were the standard [38]. Overall survival was calculated from the time of diagnosis to the last contact with the patient (event recorded as alive vs dead).

Risk Groups
Risk groups were created using the risk score (prognostic index), which was calculated by multiplying the beta coefficients of the Cox model by the gene expression values Figure 3. Sensitivity analysis. Using a sensitivity analysis, the independent variables were ranked according to their importance for predicting the dependent variable and in determining the neural network.
Hidden layer: J i units, a i:1 , . . . , a i:Ji ; with a i:k = γ i (c i:k ) and c i:k = ∑ Ji−1 j=0 w i:j,k a i_1:j where .a i−1:0 = 1 Output layer: J I = R units, a I:1 , . . . , a I:Ji ; with a I:k = γ I (c I:k ) and c I:k = ∑ J1 j=0 w I:j,k a i_ 1:j where .a i−1:0 = 1 Notation [34]: I Number of layers, discounting the input layer. J i Number of units in layer i. J 0 = P,J i = R, discounting the bias unit. w i:j,k Weight leading from layer i-1, unit j to layer i, unit k. No weights connect a m i−1:j and the bias a m i−j:0 ; that is, there is no w i:j,0 for any j. γ i (c) Activation function for layer i. w Weight vector containing all weights (w 1:0,1, w 1:0,2, . . . , w I:JI−1,JI ).

Differential Gene Expression Using the GEOR2 Software
The GEO2R 1.0 software was used to compare the differential gene expression between subtypes simply. The Benjamini-Hochberg false discovery rate was applied to adjust the p values. Log transformation was applied if necessary. Limma precision weights and force normalization were not applied. The data were visualized using volcano and mean difference (MA) plots, contrasted with a level of cut-off significance set a priori at 0.05. This software runs in R 3.

Gene Set Enrichment Analysis
The Gene Set Enrichment Analysis (GSEA) was used to determine if a pathway of interest was associated with a particular biological state (for example, dead vs alive) [36,37]. The pathways were obtained from the Molecular Signatures Database (MSigDB 7.0 and greater) or designed in-house. The software GSEA v4.2.3 was downloaded from the webpage of UC San Diego, Broad Institute: http://www.gsea-msigdb.org/gsea/index.jsp (accessed on 23 July 2022).

Conventional Statistical Analyses
Comparisons between groups were performed using crosstabulation with Pearson Chi-Square and Fisher's exact tests, and with nonparametric Mann-Whitney U (2 groups) and Kruskal-Wallis H (≥3 groups) tests. Survival analyses used the Kaplan-Meier and Log-rank tests, and the univariate and multivariate Cox Regression. The criteria of survival and response were the standard [38]. Overall survival was calculated from the time of diagnosis to the last contact with the patient (event recorded as alive vs dead).

Risk Groups
Risk groups were created using the risk score (prognostic index), which was calculated by multiplying the beta coefficients of the Cox model by the gene expression values (Risk score = B 1 X 1 + B 2 X 2 + . . . + B p X p , where x i is the expression value and B I is the beta value of the Cox table). In the Cox, all the genes are included in a unique model [39].
Several of the markers that were highlighted in the AI analyses (both machine learning and artificial neural network) were validated by immunohistochemistry at the protein level. The cases were selected from the lymphoma series of Tokai University Hospital. The series of cases ranged from 100 to 293 cases, depending on the project. Immunohistochemistry was performed using a Leica Bond Max autostainer following the manufacturer's instructions (Leica K.K., Tokyo, Japan). Table 1 details the primary antibodies that were used. The review section was made on the basis of PRISMA guidelines: https://prisma-statement.org/ (accessed on 29 September 2022), Carreras, J. (20 October 2022). Systematic review. https://doi.org/10.17605/OSF.IO/436JQ. The manuscripts were selected in PubMed using the keywords "lymphoma" and "artificial intelligence", and were organized according to the type of input data as PET/CT scan, histological images, immunophenotype, clinicopathological variables, and gene expression, mutational, and integrative analysis-based artificial intelligence.

Results
The different subtypes of hematological neoplasia (mainly non-Hodgkin lymphomas) were predicted using artificial neural networks, machine learning, and conventional biostatistics. The analysis used transcriptomic data and protein levels assessed by immunohistochemistry. The results are summarized as a bulleted list.

•
Using the whole array of 20,863 and a cancer transcriptome panel, the lymphoma subtypes were predicted by a neural network with high accuracy [19]. • A set of 30 genes derived from the neural network also predicted the overall survival of an independent series of diffuse large B-cell lymphoma, and a pan-cancer series of 7441 cases of The Cancer Genome Atlas (TCGA) [19] (Figure 4).  (C-F) Interestingly, the top 30 genes of the neural network not only predicted the lymphoma subtype but also managed to predict the overall survival of a large pan-cancer series from the TCGA of 7441 cases. Using a risk score formula, the cases of each series were stratified into high-and low-risk groups. The risk scores were calculated by multiplying the beta values of the Cox regression per gene expression values for each gene. The overall survival was calculated using the Kaplan-Meier and log-rank test and Cox regression analyses. These top 30 genes belonged to a pan-cancer transcriptome panel. Therefore, this may explain why they have predictive value in a pan-cancer series, and points out that there may be common cancer mechanisms in all human neoplasia. • An algorithm combined two types of neural networks (multilayer perceptron and radial basis function) to predict the overall survival, in combination with other clinically relevant variables [29]. • These variables were more than 60 years, the number of extranodal sites > 1, LDH-level ratio > 1, stage > 2, IPI score 2−3, with translocation (14;18) positive, immune response ratio 2:1 high (≥0.97), and overall survival up to 5 years vs alive from 10 years [29]. • As a result, new poor and favorable prognostic genes were identified, and were correlated with the immune microenvironment (M2-like tumor-associated macrophages) [29] ( Figures 5 and 6).

Follicular Lymphoma, Immune Response, and Microenvironment
 An algorithm combined two types of neural networks (multilayer perceptron and radial basis function) to predict the overall survival, in combination with other clinically relevant variables [29].
 As a result, new poor and favorable prognostic genes were identified, and were correlated with the immune microenvironment (M2-like tumor-associated macrophages) [29] ( Figures 5 and 6).
 Tridimensional (3D) analysis of tumor-associated macrophages (TAMs) of follicular lymphoma and transformation to diffuse large B-cell lymphoma was associated with increased numbers of TAMs, which created a network-like structure ( Figure 7). , and COX regression to highlight 43 genes with prognostic relevance; finally, a correlation with immuno-oncology genes was also performed. This figure shows the algorithm (method) that was used to analyze the gene expression data of follicular lymphoma using artificial neural networks.
From an initial set of 22,215 genes, a strategy of dimensionality reduction highlighted 43 genes, of which 18 were associated with poor and 25 with good overall survival of the patients. The first step consisted of several independent artificial neural networks. The network architecture included the 22,215 genes as predictors (inputs), a hidden layer, and an output layer with the predicted variable. The predicted variables were the overall survival of the patients (outcome dead vs alive), and other relevant clinicopathological variables of follicular lymphoma. The result of the neural network ranked all the genes according to their normalized importance for predicting the target variable. The results of the independent multiple neural networks were pooled resulting in 1005 genes, and the most relevant ones were highlighted using univariate and multivariate Cox regression analyses. The relevance of these genes was confirmed using gene set enrichment analysis (GSEA). Finally, these genes were also correlated with several immuno-oncology genes. The 43 genes were the following:  The set of 43 was used in addition to genes of the immune response as well as oncogenes and tumor suppressor genes related to the pathogenesis of follicular lymphoma. Of note, genes related to macrophages were highlighted, such as CD163. NOM p-val, nominal p value (the nominal p value estimates the statistical significance of the enrichment score for a single gene set); FDR q-val, false discovery rate.
• Tridimensional (3D) analysis of tumor-associated macrophages (TAMs) of follicular lymphoma and transformation to diffuse large B-cell lymphoma was associated with increased numbers of TAMs, which created a network-like structure ( Figure 7).


The random number generation created 120 independent multilayer perceptron solutions and 22,215 gene probes were ranked according to their averaged normalized importance for predicting the overall survival [35].
 The analysis identified new predictor genes, which were related to cell adhesion and migration, cell signaling, and metabolism. These genes were also correlated to the immuno-oncology markers of CD163, CSF1R, FOXP3, PDCD1 (PD-1), TNFRSF14 (HVEM), and IL10 [35].  A comparison with other machine learning techniques was also performed. Machine

Follicular Lymphoma, Random Number Generator-Based Strategy
• The random number generation created 120 independent multilayer perceptron solutions and 22,215 gene probes were ranked according to their averaged normalized importance for predicting the overall survival [35].
• The analysis identified new predictor genes, which were related to cell adhesion and migration, cell signaling, and metabolism. These genes were also correlated to the immuno-oncology markers of CD163, CSF1R, FOXP3, PDCD1 (PD-1), TNFRSF14 (HVEM), and IL10 [35]. • A comparison with other machine learning techniques was also performed. Machine learning included the following techniques: Bayesian network, C&R tree, C5 tree, CHAID tree, discriminant analysis, KNN algorithms, logistic regression, LSVM, Quest tree, random forest, random trees, SVM, tree-AS, XGBoost linear, and XGBoost tree. A neural network analysis was also made [35] (Figure 8).

Mantle Cell Lymphoma, Use of Immuno-Oncology Panels to Predict Survival
• An analysis algorithm included several analysis techniques such as neural networks (both the multilayer perceptron artificial and radial basis function), GSEA, and conventional statistics. In this analysis, 20,862 genes were correlated with 28 prognostic genes of mantle cell lymphoma. After dimensionality reduction, the patients' overall survival was predicted, and new markers were highlighted ( Figure 9) [34].  Figure 9. Prediction of the overall survival of mantle cell lymphoma using an algorithm based on neural networks. Two methods (A and B algorithms) were designed. Method 1 used as input 20,862 genes to predict the overall survival outcome (dead vs. alive) and other prognostic markers; because of dimensionality reduction, a final set of 19 genes were highlighted. The analysis also included testing the final 19 genes with other machine learning analysis, and conventional overall survival with log-rank test. Method 2 used as input several gene panels to predict the overall survival. As a result, 125 pan-cancer and immuno-oncology genes were highlighted. The association with the patients overall survival was confirmed by GSEA and conventional overall survival with log-rank test. OS, overall survival; MLP, multilayer perceptron; RBF, radial basis function; GSEA, gene set enrichment analysis; D/A, dead/Alive; AUC, area under the curve; NI, normalized importance. Figure 9. Prediction of the overall survival of mantle cell lymphoma using an algorithm based on neural networks. Two methods (A and B algorithms) were designed. Method 1 used as input 20,862 genes to predict the overall survival outcome (dead vs. alive) and other prognostic markers; because of dimensionality reduction, a final set of 19 genes were highlighted. The analysis also included testing the final 19 genes with other machine learning analysis, and conventional overall survival with log-rank test. Method 2 used as input several gene panels to predict the overall survival. As a result, 125 pan-cancer and immuno-oncology genes were highlighted. The association with the patients overall survival was confirmed by GSEA and conventional overall survival with log-rank test. OS, overall survival; MLP, multilayer perceptron; RBF, radial basis function; GSEA, gene set enrichment analysis; D/A, dead/Alive; AUC, area under the curve; NI, normalized importance.

•
The highlighted genes were related to the cell cycle, apoptosis, and metabolism. The genes not only predicted the survival of mantle cell lymphoma, but also of diffuse large B-cell lymphoma and a large pan-cancer series of the TCGA [34]. • A neural network algorithm that combined 10 oncology and immuno-oncology panels predicted overall survival ( Figure 9) [34].

•
Other machine learning techniques were used. Additionally, a correlation with the MCL35 proliferation assay, which was created by the Lymphoma/Leukemia Molecular Profiling Project, was made [34] (Figure 9).

Diffuse Large B-Cell Lymphoma, Identification of the 25 Genes Set
• A multilayer perceptron analysis predicted the overall survival of 100 cases using as input 54,614 gene probes, and highlighted 25 genes with prognostic value [27]. • Correlation with known diffuse large B-cell lymphoma markers showed that high expression of MYC, BCL2, and ENO3 was associated with worse outcome [27] (Figures 10 and 11).


The TNFAIP8 marker was highlighted in this analysis. Because of TNFAIP8's importance in the apoptotic pathway, it was validated by immunohistochemistry (i.e., at protein level) in an independent series of 97 cases from Tokai University. Digital image quantification of TNFAIP8 was performed using an AI-based method. Correlations with the prognosis of the patients showed that high TNFAIP8 is associated with poor survival [28].
 TNFAIP8 correlated positively with high M2-like CD163-positive tumor-associated macrophages (TAMs) and non-GCB cell of origin phenotype [28] (Figure 12). The TNFAIP8 marker was highlighted in this analysis. Because of TNFAIP8's importance in the apoptotic pathway, it was validated by immunohistochemistry (i.e., at protein level) in an independent series of 97 cases from Tokai University. Digital image quantification of TNFAIP8 was performed using an AI-based method. Correlations with the prognosis of the patients showed that high TNFAIP8 is associated with poor survival [28]. • TNFAIP8 correlated positively with high M2-like CD163-positive tumor-associated macrophages (TAMs) and non-GCB cell of origin phenotype [28] (Figure 12).     The caspase-8 protein expression was also modeled using several machine learning and artificial neural networks [31] (Figures 13 and 14).   • An algorithm included multilayer perceptron, radial basis function, GSEA, COX regression, and several machine learning techniques to predict the overall survival of 414 cases of diffuse large B-cell lymphoma [30].

•
The machine learning techniques were Bayesian network, C5.0 algorithm, chi-squared automatic interaction detection CHAID tree, classification and regression (C&R) tree, discriminant analysis, logistic regression, Quest tree, random trees, and tree-AS. The neural network was the multilayer perceptron [30].

•
The association of PD-L1 (CD274) and IKAROS with the overall survival was validated in an independent series of 113 cases by immunohistochemistry. The quantification included an AI-based method [30] (Figure 15).

Diffuse Large B-Cell Lymphoma, CSF1R
• The protein expression of CSF1R was analyzed by immunohistochemistry in 198 cases of diffuse large B-cell lymphoma, and it was found that high CSF1R-positive TAMs were associated with poor progression-free survival ( Figure 16) [32].

Diffuse Large B-Cell Lymphoma, CSF1R
 The protein expression of CSF1R was analyzed by immunohistochemistry in 198 cases of diffuse large B-cell lymphoma, and it was found that high CSF1R-positive TAMs were associated with poor progression-free survival ( Figure 16) [32].


The gene expression of CSF1R was predicted by all the genes, and by an immunooncology pattern, and correlated with SIRPA and CD47 [32] (Figures 17 and 18). Figure 16. Role of CSF1R in the prognosis of diffuse large B-cell lymphoma. CSF1R was analyzed by immunohistochemistry in a series of 198 cases, and two histological patterns were found. A CSF1Rpositive B-cell pattern was characterized by favorable progression-free survival; this pattern was less frequent (around 30% of the cases). Conversely, the most frequent pattern was of CSF1Rpositive tumor-associated macrophages (TAMs) and was associated with an unfavorable outcome. Additionally, the prediction of the immunohistochemical expression of CSF1R by other CSF1R-related markers was performed using neural networks. The CSF1R-related markers were CSF1, STAT3, NFKB, MYC, and Ki67. All markers were quantified using digital image analysis. Of note, the multilayer perceptron network analyses were performed to predict both the TAM and the B-cell patterns. Our data suggested that the use of a CSF1R inhibitor such as Pexidartinib could be used in the CSF1R + TAM pattern. CSF1R, macrophage colony-stimulating factor 1 receptor; DLBCL, diffuse large B-cell lymphoma; TAM, tumor-associated macrophage, PFS, progression-free survival.

•
The gene expression of CSF1R was predicted by all the genes, and by an immunooncology pattern, and correlated with SIRPA and CD47 [32] (Figures 17 and 18).

Diffuse Large B-Cell Lymphoma, Pan-Cancer Immuno-Oncology Panel
• An immuno-oncology panel of 730 genes predicted the overall survival and cell-oforigin phenotype (Lymph2Cx assay) of a series of 106 diffuse large B-cell lymphoma cases, using artificial neural networks and machine learning [33].

•
The association of MAPK3 with the GCB phenotype was confirmed by immunohistochemistry [33] (Figure 19).  An immuno-oncology panel of 730 genes predicted the overall survival and cell-oforigin phenotype (Lymph2Cx assay) of a series of 106 diffuse large B-cell lymphoma cases, using artificial neural networks and machine learning [33].


The association of MAPK3 with the GCB phenotype was confirmed by immunohistochemistry [33] (Figure 19).

Diffuse Large B-Cell Lymphoma, Integrative Analysis of Macrophage Markers
Gene expression profiling of 233 DLBCL patients treated with chemotherapy plus Rituximab was obtained from the series GSE10846, present in the NCBI Gene Expression Omnibus database. The prognostic value for overall survival of the gene expression of CD163 was first tested and 100 representative cases were selected, which contained high-risk (i.e., high CD163) and low-risk cases (i.e., low CD163) (Figure 20).  A functional protein association network was created using the five macrophage and one regulatory T lymphocyte (Treg) markers: CD68, CD16, CD163, PTX3, MITF, and FOXP3 as the initial nodes (identifies). Then, the resulting network (i.e., pathway) that contained 57 markers was tested for GSEA analysis in the GSE10846 series of gene expression of diffuse large B-cell lymphoma. We identified the most relevant pathological markers (i.e., genes) that are associated with the prognosis of the patients as follows: high-risk (bad prognosis, and with high CD163 expression) vs low-risk (good prognosis, low CD163). We found that this pathway was enriched in the high-risk phenotype with a NOM p-val < 0.001 and FDR q-val < 0.001. In the enrichment score, we could identify the markers: CD163 (2nd in the list with a rank metric score of 0.515), CD16 (FCGR3B, 4th), CD68 (10th), PTX3 (15th), and MITF (23rd). Of note, FOXP3 was outside the enrichment set of genes so it was not associated with the high-risk group. Importantly, at fifth position, IL10, was identified. GSEA with markers belonging to the immune regulatory M2c-like TAM pathway was also tested with similar results (Figure 20).
The macrophage markers were analyzed at protein level by immunohistochemistry in the series of Tokai University (n = 132) ( Figure 21). The distribution of the markers in the normal reactive tonsil was also evaluated.  The histological analysis in reactive tonsil, a secondary lymphoid organ, showed a different distribution of the different markers. CD68-positive and MITF-positive macrophages were widely distributed in all areas. CD16-positive cells were scarce and only identified in the lympho-epithelium, the epithelial barrier. CD163-positive macrophages were mainly present in the interfollicular regions and infrequently in the germinal centers of the follicles. PTX3-positive cells were of macrophage morphology in all areas and in the germinal centers PTX3-positive cells also had a morphology of B lymphocytes (mainly centroblasts). IL10positive macrophages were scarce but present in all areas. Double IHC showed mutually exclusive distribution between CD163 and CD16 and partially exclusive with MITF.
Additional analysis consisted of validation the macrophage markers in an independent series of cases of diffuse large B-cell lymphoma, from the Lymphoma/Leukemia Molecular Profiling Project (LLMPP), the GSE10846 (webpage: https://www.ncbi.nlm.nih.gov/geo/ query/acc.cgi?acc=GSE10846, accessed on 21 September 2022). Only the cases treated with R-CHOP-like therapy were selected (n = 233). Several machine learning and artificial neural networks (multilayer perceptron) were used. The dependent (target) variable was the overall survival (outcome dead vs alive). As predictors, the macrophage genes of CD163, CSF1R, PTX3, CD274 (PD-L1), and IL10 were used. Additional immuno-oncology predictors were markers previously highlighted in the analyses, including MYC, BCL2, TP53, FOXP3, CSF1, IL34, PDCD1 (PD-1), TNFRSF14, TNFAIP8, IKZF1, STAT3, NFKB1, MYD88, RELA, CASP8, CASP3, PARP1, BCL2, MKI67, ENO3, and GGA3. In total, 25 genes were analyzed and the overall survival was successfully predicted. Table 2 shows the machine learning and neural network models, the number of predictors used in the models, and the overall accuracy. Figure 16 shows the most relevant models and the most relevant genes. The models confirmed the importance of the immuno-oncology markers ( Figure 23). Table 2. Machine learning and artificial neural network analysis using gene expression data.

No. of Predictors Overall Accuracy (%)
XGBoost  Figure 22. Prediction of the overall survival of diffuse large B-cell lymphoma by M2c-like macrophages using an artificial neural network. The overall survival of the patients was predicted using an artificial neural network using the histochemical data of the tissue samples. The network confirmed that the most relevant markers were PTX3 and IL10, which characterized the immune regulatory M2c-like macrophages. A conventional survival analysis using the Kaplan-Meier with logrank test confirmed the association of high M2c-like macrophages with poor overall and progression-free survival of the patients. Original magnification: 400×.   Table 2.
Using the random forest, the markers were ranked according to their significance for predicting the patients' overall survival. The random forest uses a tree model and a bagging method.
The Bayesian network is a graphical model that shows variables (nodes) in a dataset and the probabilistic, or conditional, independences between them. It constructs a probability model by combining observed and recorded evidence. The network's links (arcs) do not always depict cause and effect.
The LSVM method permits the classification of data using a linear support vector machine. With large datasets, or ones with numerous predictor fields, LSVM is an especially adequate method. In this LSVM analysis, the predictors were ranked in order of relevance.
Nearest Neighbor Analysis classifies the cases based on the resemblance to others and patterns; this chart is a lower-dimensional projection of the predictor space, which contains 25 predictors (genes).  Table 2.

Discussion
Using the random forest, the markers were ranked according to their significance for predicting the patients' overall survival. The random forest uses a tree model and a bagging method.
The Bayesian network is a graphical model that shows variables (nodes) in a dataset and the probabilistic, or conditional, independences between them. It constructs a probability model by combining observed and recorded evidence. The network's links (arcs) do not always depict cause and effect.
The LSVM method permits the classification of data using a linear support vector machine. With large datasets, or ones with numerous predictor fields, LSVM is an especially adequate method. In this LSVM analysis, the predictors were ranked in order of relevance.
Nearest Neighbor Analysis classifies the cases based on the resemblance to others and patterns; this chart is a lower-dimensional projection of the predictor space, which contains 25 predictors (genes).

Discussion
Artificial intelligence (AI) is a recently developed field that integrates computer science with datasets to perform out calculations. In medicine, both machine learning and deep learning analyze medical data and gain insights on diseases. Artificial intelligence has many applications, including diagnosis, disease classification, image analysis, etc. [20][21][22][23][24].
Machine learning is a specialty in artificial intelligence. By using statistics, algorithms are trained to make classifications or predictions [20][21][22][23]. An algorithm of machine learning is composed of three parts: (1) Decision process. Based on the labeled or unlabeled input data, an estimated pattern is produced by the algorithm. (2) Error function, which evaluates the prediction of the model.
(3) Model optimization process. During the fitting, the weights are adjusted to reduce discrepancy between the known and the estimates, and weights are updated autonomously until a threshold of accuracy is met.
There are three categories of machine learning models: (1) Supervised, which use labeled datasets, such as linear regression, logistic regression, random forest, and support vector machine (SVM). (2) Unsupervised, which use unlabeled datasets and discover hidden patterns or data groupings without the need of human intervention, such as principal component analysis (PCA), singular value decomposition (SVD), and k-means clustering.
A linear regression algorithm is used to predict numerical values based on a linear relationship between predictors. Logistic regression is a type of supervised learning that predicts a categorical variable (binary). The clustering analysis uses unsupervised learning and identifies patterns to group the cases. Decision trees can be used to predict numerical values or to classify the data into categories; they use a branching sequence of link decisions that are represented in a tree diagram. Random forests predict a value or category by combining the results of decision trees [20].Artificial neural networks (ANNs) are algorithms that, in essence, mimic the human brain. Many data mining applications use neural networks because they are flexible and powerful for complex processes [25].
A neural network is composed of an input layer, multiple hidden layers (deep neural network), and an output layer. Most neural networks are feed-forward, which means that the flow moves in one direction from the input to the output [20-24]. The "deep" term refers to the number of layers (inclusive of input, hidden, and output layer); more than three layers can be considered in a deep learning algorithm [21]. The multilayer perceptron (MLP) and radial basis function (RBF) are used in predictive applications, and are supervised because the results can be compared with the known values of the target variables [20][21][22][23][24][25][26]. The input layer contains the predictors (for example, the genes). The hidden layer contains unobservable nodes (units). The value of each hidden unit is some function of the predictors. The output layer contains the responses (Figure 2). This research predicted the prognosis (mainly the overall survival) and classified the different subtypes of mature B-cell neoplasms (non-Hodgkin lymphomas) with high accuracy. Therefore, machine learning and artificial neural networks are useful biostatistical tools in biomedical research, and it is expected that the importance of artificial intelligence in medicine will increase in the future.
This research used basic types of neural networks to obtain reliable results. The single neural networks created the basis for more complex algorithms, making the analysis similar to a classical multivariate analysis. The neural networks were also complemented with other conventional biostatistical analyses, such as gene set enrichment analysis (GSEA) and Cox regression. Additionally, other machine learning techniques were used to complement the results. Each type of machine learning has special uses, and in the results, the information that is provided was complementary.
In the different algorithms, the input data comprised all the genes of the array or specific panels. The panels that were used were carefully selected, and included cancer tran-scriptome, pan-cancer, cancer progression, and metabolic pathways that incorporate many oncogenes and tumor suppressor genes, but also immune-related panels such as immune exhaustion, human inflammation, host response, autoimmune, and immuno-oncology. Nowadays, immuno-oncology panels are particularly relevant. This research highlighted many important immuno-oncology markers such as CD163, CSF1R, CSF1, PD-L1, IL10, TN-FRSF14, TNFAIP8, PD-1, and FOXP3 which are markers of tumor-associated macrophages (TAMs), T lymphocytes, and regulatory T lymphocytes (Tregs). A complete discussion can be found in the previous publications [19,[27][28][29][30][31][32][33][34][35]. Most of these markers can be targeted using inhibitors. In diffuse large B-cell lymphoma, the use of immunomodulatory drugs and immune checkpoint inhibitors is a new and promising field for treating the patients beyond the classical R-CHOP [58] (Table 3). Interestingly, some of the identified markers were also relevant for the prognosis of nonhematological neoplasia, which suggests that there are common pathogenic mechanisms in all types of neoplasia.
AI analysis combined neural networks such as multilayer perceptron and radial basis function, and several machine learning techniques such as Bayesian network, C&R tree, C5 tree, CHAID tree, discriminant analysis, KNN algorithm, logistic regression, LSVM, Quest tree, random forest, random trees, SVM, tree-AS, XGBoost linear, XGBoost tree. It is impossible to decide which the best technique is because each method has some strengths and weaknesses, and its applicability depends on the type of data, number of cases, and number of variables (inputs).
The term neural network refers to a family of loosely related models that are characterized by large parameter spaces and flexible structures, derived from the study of brain function. Neural networks are the tools of choice in many data mining applications because of their power and flexibility, especially if the underlying process is complex [28].
Artificial neural networks used in prediction applications, such as multilayer perceptron (MLP) and radial basis function (RBF) networks, are supervised in the sense that the results predicted by the model are compared to known values of target variables. The choice between the MLP and RBF methods depends on the type of data and the level of complexity of the problem. The MLP method can find more complex relationships, while RBF is generally faster [30]. Deep neural networks have been criticized for being opaque because their predictions are incomprehensible to humans; their multi-layered nonlinear structure is a "black box model" [31].
We recently modeled celiac disease and ulcerative colitis using AI [59,60]. In the case of ulcerative colitis, we analyzed a series of 43 cases, including 13 healthy controls, 8 inactive ulcerative colitis, 7 non-involved active ulcerative colitis, and 15 involved active ulcerative colitis. As input, 734 genes were included. A total of 16 models were used to predict ulcerative colitis. The overall accuracy was as follows: C5 decision tree (100%, 2 fields used); logistic regression, discriminant analysis, LSVM, SVM, XGBoost linear, XGBoost tree, and neural network (100%, 734 fields); CHAID (97.7%, 2 fields); random forest (97.7%, 734); KNN algorithm (95.4%, 734); C&R tree (95.4%, 12); Quest (83.7%, 6); Bayesian network (65.1%, 734); random trees (0%, 734). In this research, most of the machine learning methods and neural networks had accuracy above 85%. Nevertheless, the number of fields that were used was variable. As also observed in the data of mature B-cell neoplasms, decision trees have difficulties in handling a large set of variables. Bayesian networks provide acceptable results, but are not superior to neural networks. Logistic regression accuracy is usually high and uses many variables. In the end, the most practical strategy is to test all methods and select the ones that predict better. In Table 2, the same 16 models are applied to our data of diffuse large B-cell lymphoma. Generally, the machine learning methods successfully predicted the overall survival of patients with diffuse large B-cell lymphoma using immuno-oncology and immune checkpoint markers. In this particular experiment, neural networks did not have high accuracy.
In conclusion, artificial intelligence analysis is a useful tool for analyzing the prognosis and classification of non-Hodgkin lymphomas.

Review of the Literature and Future Perspective in Hematological Neoplasia Using AI
Other groups have also used artificial intelligence in the field of hematopathology research. Table 4 provides precise updates on the latest progress made in hematological malignancies using machine learning and neural networks. The manuscripts were selected in PubMed using the keywords "lymphoma" and "artificial intelligence". Among all articles that were found within the past 3-4 years, a selection of the most recent research was made. Because of limited space, not all relevant manuscripts are included in Table 4.  Dice score [64] (2) Histological images-based AI   Development and evaluation of a leukemia diagnosis system using deep learning in real clinical scenarios A total of 1732 bone marrow, raw images of 89 children with leukemia were analyzed with convolutional neural networks, with a performance accuracy of 89%. Apart from detecting leukocytes, the system also detected bone marrow metastasis of lymphoma and neuroblastomas.  A clinically applicable approach to the classification of B-cell non-Hodgkin lymphomas with flow cytometry and machine learning The immunophenotype data from flow cytometry of 1465 B-cell non-Hodgkin lymphoma (NHL) cases were analyzed. The cases included chronic lymphocytic leukemia (CLL), diffuse large B-cell lymphoma (DLBCL), Burkitt lymphoma (BL), follicular cell lymphoma (FCL), hairy cell leukemia (HCL), splenic lymphoma (SL), mantle cell lymphoma (MCL), marginal zone lymphoma (MZL), and lymphoplasmacytic lymphoma (LPL). The accuracy of the classification ranged from 92% to 100%.
Classification trees [78]   Artificial intelligence analysis of gene expression data predicted the prognosis of patients with diffuse large B-cell lymphoma The gene expression of a series of 414 cases of diffuse large B-cell lymphoma (DLBCL) was analyzed to predict the overall survival, and was correlated with other known pathogenic genes such as BCL2 and MYC.
A refined cell of origin classifier with targeted NGS and artificial intelligence showed robust predictive value in DLBCL The series of diffuse large B-cell lymphoma of 418 cases included immunohistochemical, gene expression, DNA in situ hybridization, array CGH, and NGS sequencing. Using an autoencoder, the cases were classified according to the cell of origin and the survival (overall survival and progression-free survival). Logic learning machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables The data of 130 patients diagnosed with Hodgkin's lymphoma, including a small set of clinical variables and more than 54,000 gene features, were used to predict the prognosis.
K-nearest neighbor (KNN), artificial neural network (ANN), support vector machine (SVM), decision tree, and the innovative logic learning machine method [83] Cancers 2022, 14  The data of 574 diffuse large B-cell lymphoma cases, which included exome and transcriptome sequencing, array-based DNA copy-number analysis, and targeted amplicon resequencing of 372 genes, were used to identify genetic subtypes.
Random forest [84] H&E, hematoxylin and eosin. The publications were selected from PubMed using the keywords "artificial intelligence" and "lymphoma". The manuscripts were organized according to the type of input data, i.e., PET/CT scan, histological images, immunophenotype, clinicopathological variables, and gene expression, mutational, and integrative analysis-based artificial intelligence .
Worth mentioning is the work of Schmitz R et al. published in the New England Journal of Medicine in 2018. The genetics and pathogenesis of diffuse large B-cell lymphoma were analyzed using random forest. The input data from 574 diffuse large B-cell lymphoma cases included exome and transcriptome sequencing, whole-genome copy-number arraybased DNA analysis, and targeted amplicon resequencing of 372 genes to identify genetic subtypes [84].
A similar work was published by Xu-Monette ZY et al. in 2020 in Blood Advances. Based on targeted next-generation sequencing (NGS), a correlation with the cell of origin subtypes was made using AI in diffuse large B-cell lymphoma. The series of 418 cases included immunohistochemical, gene expression, DNA in situ hybridization, array CGH, and NGS sequencing. Using autoencoders and CPH models, the cases were classified according to the cell of origin and the patients' survival (overall survival and progression-free survival) [81].
Li D et al. reported in 2020 in Nature Communications a deep learning diagnostic platform for diffuse large B-cell lymphoma. The method included data from multiple hospitals. This research used histological images of H&E to classify diffuse large B-cell lymphoma (DLBCL) vs non-DLBCL. Non-DLBCL included cases of metastatic carcinoma, melanoma, and other lymphomas. The lymphoma subtypes were chronic lymphocytic leukemia, mantle cell lymphoma, follicular lymphoma, and classical Hodgkin lymphoma. Seventeen types of convolutional neural networks were used, and the model had an accuracy of 99.7-100% [74].
In the past five years, there has been a significant increase in the use of artificial intelligence in cancer research, and many applications in hematological neoplasia have been published [85]. Many studies have used convolutional neural networks to classify digitalized histological images. Machine learning and artificial neural networks have also been used to analyze gene expression and mutational data. It is expected that in the future, artificial intelligence techniques will become a standard part of the biostatistical analysis, and complementary to "conventional" bioinformatics.
Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/cancers14215318/s1, Table S1: Multilayer perceptron analysis (MLP). Table S2: Radial basis function analysis (RBF). Table S3: Genes associated to poor prognosis in the multivariate Cox survival analysis. Table S4: Genes associated to good prognosis in the multivariate Cox survival analysis. Table S5

Institutional Review Board Statement:
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Tokai University, School of Medicine (protocol code IRB14R-080, and IRB20-156).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: All the data are available upon request to Joaquim Carreras.