{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T07:56:34Z","timestamp":1790668594406,"version":"4.1.0"},"reference-count":27,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":271,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BioMed Research International"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Support vector machines (SVMs) have been widely applied to high\u2010dimensional molecular and related biomedical data for cancer detection and other clinical classification tasks. The evidence spans diverse cancer types, specimens, platforms, objectives, and validation designs, limiting direct comparison.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We systematically reviewed studies evaluating SVM\u2010based models using molecular omics or high\u2010dimensional omics\u2010adjacent data for cancer\u2010related clinical applications. Article\u2010level characteristics, SVM implementation, feature selection, validation, comparator models, and reported performance measures were extracted. Risk of bias was evaluated with the Prediction Model Risk Of Bias Assessment Tool (PROBAST). Because the studies did not estimate a common clinically interpretable quantity, no meta\u2010analysis, pooled estimate, median performance comparison, or statistical subgroup comparison was performed. Findings were synthesized descriptively by data modality, clinical task, validation approach, comparative modeling, and methodological quality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Seventy\u2010five studies met the revised eligibility criteria. Sixty used molecular omics data; the other 15 used omics\u2010adjacent inputs (10 spectroscopy\u2010derived, two sensor derived, two mixed\u2010data, and one imaging\u2010derived). Cancer detection was the leading application. Internal validation alone was reported in 60 studies; eight combined internal and external validation, four relied on external validation only, two reported no validation, and one remained unclear. Seventy\u2010two studies contributed 544 article\u2010level SVM performance entries. In a separate PROBAST assessment, 72 studies were rated at high overall risk of bias and three at unclear risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>SVM\u2010based models have been studied across a broad range of cancer\u2010related applications, but the evidence is heterogeneous, predominantly internally validated, and frequently at high risk of bias. A single summary performance estimate would be misleading. This review maps the evidence and identifies cancer\u2010specific, modality\u2010specific, and externally validated questions suitable for future focused studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/bmri\/5935532","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T07:18:34Z","timestamp":1790666314000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Support Vector Machine Models for Cancer Detection and Related Clinical Applications Using Omics and Omics\u2010Adjacent Data: A Systematic 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