{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:35:22Z","timestamp":1767706522872,"version":"3.41.2"},"reference-count":36,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T00:00:00Z","timestamp":1735516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,1,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>The primary objective of this study is to investigate various applications of artificial intelligence (AI) and statistical methodologies for analyzing and managing peritoneal metastases (PM) caused by gastrointestinal cancers.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>Relevant keywords and search criteria were comprehensively researched on PubMed and Google Scholar to identify articles and reviews related to the topic. The AI approaches considered were conventional machine learning (ML) and deep learning (DL) models, and the relevant statistical approaches included biostatistics and logistic models.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The systematic literature review yielded nearly 30 articles meeting the predefined criteria. Analyses of these studies showed that AI methodologies consistently outperformed traditional statistical approaches. In the AI approaches, DL consistently produced the most precise results, while classical ML demonstrated varied performance but maintained high predictive accuracy. The sample size was the recurring factor that increased the accuracy of the predictions for models of the same type.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>AI and statistical approaches can detect PM developing among patients with gastrointestinal cancers. Therefore, if clinicians integrated these approaches into diagnostics and prognostics, they could better analyze and manage PM, enhancing clinical decision-making and patients\u2019 outcomes. Collaboration across multiple institutions would also help in standardizing methods for data collection and allowing consistent results.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bfgp\/elae049","type":"journal-article","created":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T11:49:51Z","timestamp":1735559391000},"source":"Crossref","is-referenced-by-count":1,"title":["Using artificial intelligence and statistics for managing peritoneal metastases from gastrointestinal cancers"],"prefix":"10.1093","volume":"24","author":[{"given":"Adam","family":"Wojtulewski","sequence":"first","affiliation":[{"name":"Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute , 12902 Magnolia Drive, Tampa FL 33612 ,","place":["United States"]},{"name":"Department of Computer and Information Science and Engineering, University of Florida , 432 Newell Dr, Gainesville, FL 32611 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aleksandra","family":"Sikora","sequence":"additional","affiliation":[{"name":"Department of Medicine, Medical University of Warsaw , \u017bwirki i Wigury 61, 02-091 Warszawa ,","place":["Poland"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sean","family":"Dineen","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Oncology, H. 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