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Machine learning for postoperative complication prediction and early recurrence risk assessment across cancer types:
Wen Chen1, Xinliang Liu2, Zhenheng Wu3
1Department of Hepatobiliary Surgery, Fuzhou First General Hospital Affiliated of Fujian Medical University, Fuzhou, 350009, China.
Background:
Although machine learning is often used in medical diagnosis, its effectiveness in cancer diagnosis remains uncertain.
Objective:
To explore the ability of machine learning to predict cancer postoperative complications and early recurrence.
Methods:
From 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.
Results:
Ultimately, 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.
Conclusion:
Machine 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.
