ReviewCancer cell international2026
Machine learning for postoperative complication prediction and early recurrence risk assessment across cancer types: a systematic review and meta-analysis.
Review in Cancer cell international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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8 authors.
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Abstract
backgroundAlthough machine learning is often used in medical diagnosis, its effectiveness in cancer diagnosis remains uncertain.
objectiveTo explore the ability of machine learning to predict cancer postoperative complications and early recurrence.
methodsFrom the creation of the database until October 4, 2024, we conducted a comprehensive search of PubMed, Web of Science (WoS), Embase, Scopus, Cochrane Library, Wanfang, and the China National Knowledge Infrastructure (CNKI). The pooled sensitivity, specificity, Fagan plot analysis, and area under the curve (AUC) were used to assess the overall test performance of machine learning. In addition, meta-regression analysis was used to explore the sources of heterogeneity further. Furthermore, Deeks' funnel plot asymmetry test was used to assess publication bias.
resultsUltimately, 31 publications were identified and incorporated into this meta-analysis. In the subgroup of postoperative complications, the combined sensitivity, specificity, and AUC values of all studies were 0.75 (95% CI, 0.65-0.83), 0.78 (95% CI, 0.65-0.87), and 0.83 (95% CI, 0.79-0.86), respectively. Moreover, the combined sensitivity, specificity, and AUC values of proposed studies (studies that proposed the best predictive model) were 0.85 (95% CI, 0.71-0.93), 0.76 (95% CI, 0.39-0.94), and 0.88 (95% CI, 0.85-0.91), respectively. In the subgroup of early recurrence, the combined sensitivity, specificity, and AUC values of all studies were 0.74 (95% CI, 0.68-0.80), 0.73 (95% CI, 0.67-0.77), and 0.80 (95% CI, 0.76-0.83), respectively. Furthermore, the combined sensitivity, specificity, and AUC values of proposed studies were 0.78 (95% CI, 0.70-0.85), 0.76 (95% CI, 0.70-0.82), and 0.84 (95% CI, 0.80-0.87), respectively. In addition, Deeks' Funnel Plot, p-value > 0.05, indicating no publication bias. Furthermore, meta-regression analysis showed that sample size and machine learning may be the main influencing factors.
conclusionMachine learning can accurately predict cancer postoperative complications and early recurrence. However, its accuracy is influenced by multiple factors, including the type of machine learning model, tumor type, sample size, year of publication, and country of publication. Therefore, more studies with larger sample sizes and more standardized methodology are needed to improve the reliability of its prediction.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.