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15K19061"],"award-info":[{"award-number":["KAKEN 15K19061"]}]},{"name":"Rifat Hamoudi was funded by Al-Jalila Foundation","award":["18K15100"],"award-info":[{"award-number":["18K15100"]}]},{"name":"Rifat Hamoudi was funded by Al-Jalila Foundation","award":["24590430"],"award-info":[{"award-number":["24590430"]}]},{"name":"Rifat Hamoudi was funded by Al-Jalila Foundation","award":["2021-B04"],"award-info":[{"award-number":["2021-B04"]}]},{"name":"Rifat Hamoudi was funded by Al-Jalila Foundation","award":["AJF2018090"],"award-info":[{"award-number":["AJF2018090"]}]},{"name":"Rifat Hamoudi was funded by Al-Jalila Foundation","award":["22010902103"],"award-info":[{"award-number":["22010902103"]}]},{"name":"University of Sharjah","award":["KAKEN 15K19061"],"award-info":[{"award-number":["KAKEN 15K19061"]}]},{"name":"University of Sharjah","award":["18K15100"],"award-info":[{"award-number":["18K15100"]}]},{"name":"University of Sharjah","award":["24590430"],"award-info":[{"award-number":["24590430"]}]},{"name":"University of Sharjah","award":["2021-B04"],"award-info":[{"award-number":["2021-B04"]}]},{"name":"University of Sharjah","award":["AJF2018090"],"award-info":[{"award-number":["AJF2018090"]}]},{"name":"University of Sharjah","award":["22010902103"],"award-info":[{"award-number":["22010902103"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>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&amp;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.<\/jats:p>","DOI":"10.3390\/cancers14215318","type":"journal-article","created":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T23:45:00Z","timestamp":1667087100000},"page":"5318","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6129-8299","authenticated-orcid":false,"given":"Joaquim","family":"Carreras","sequence":"first","affiliation":[{"name":"Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9807-2875","authenticated-orcid":false,"given":"Giovanna","family":"Roncador","sequence":"additional","affiliation":[{"name":"Monoclonal Antibodies Unit, Spanish National Cancer Research Center (Centro Nacional de Investigaciones Oncologicas, CNIO), Melchor Fernandez Almagro 3, 28029 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1402-0868","authenticated-orcid":false,"given":"Rifat","family":"Hamoudi","sequence":"additional","affiliation":[{"name":"Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates"},{"name":"Division of Surgery and Interventional Science, University College London, Gower Street, London WC1E 6BT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1093\/annonc\/11.suppl_1.S3","article-title":"Lymphoma classification\u2014From controversy to consensus: The R.E.A.L. and WHO Classification of lymphoid neoplasms","volume":"11","author":"Harris","year":"2000","journal-title":"Ann. 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