Accuracy of machine learning in diagnosing microsatellite instability in gastric cancer: A systematic review and meta-analysis

Background: Significant challenges persist in the early identification of microsatellite instability (MSI) within current clinical practice. In recent years, with the growing utilization of machine learning (ML) in the diagnosis and management of gastric cancer (GC), numerous researchers have explored the effectiveness of ML methodologies in detecting MSI. Nevertheless, the predictive value of these approaches still lacks comprehensive evidence. Accordingly, this study was carried out to consolidate the accuracy of ML in the prompt detection of MSI in GC.

Methods: PubMed, the Cochrane Library, the Web of Science, and Embase were retrieved up to March 20, 2024. The risk of bias in the encompassed studies was evaluated utilizing a risk assessment tool for predictive models. Models were then subjected to subgroup analysis based on the modeling variables.

Results: A total of 12 studies, encompassing 11,912 patients with GC, satisfied the predefined inclusion criteria. ML models established in these studies were primarily based on pathological images, clinical features, and radiomics. The results suggested that in the validation sets, the pathological image-based models had a synthesized c-index of 0.86 [95 % CI (0.83-0.89)], with sensitivity and specificity being 0.86 [95 % CI (0.76-0.92)] and 0.83 [95 % CI (0.78-0.87)], respectively; radiomics feature-based models achieved respective values of 0.87 [95 % CI (0.81-0.92)], 0.77 [95 % CI (0.70-0.83)] and 0.81 [95 % CI (0.74-0.87)]; radiomics feature-based models + clinical feature-based models achieved respective values of 0.87 [95 % CI (0.81-0.93)], 0.78 [95 % CI (0.70-0.84)] and 0.79 [95 % CI (0.69-0.86)].

Conclusions: ML has demonstrated optimal performance in detecting MSI in GC and could serve as a prospective early adjunctive detection tool for MSI in GC. Future research should contemplate minimally invasive or non-invasive, readily collectible, and efficient predictors to augment the predictive accuracy of ML.