The value of machine learning approaches in the diagnosis of early gastric cancer: a systematic review and meta-analysis.
World J Surg Oncol
; 22(1): 40, 2024 Feb 01.
Article
en En
| MEDLINE
| ID: mdl-38297303
ABSTRACT
BACKGROUND:
The application of machine learning (ML) for identifying early gastric cancer (EGC) has drawn increasing attention. However, there lacks evidence-based support for its specific diagnostic performance. Hence, this systematic review and meta-analysis was implemented to assess the performance of image-based ML in EGC diagnosis.METHODS:
We performed a comprehensive electronic search in PubMed, Embase, Cochrane Library, and Web of Science up to September 25, 2022. QUADAS-2 was selected to judge the risk of bias of included articles. We did the meta-analysis using a bivariant mixed-effect model. Sensitivity analysis and heterogeneity test were performed.RESULTS:
Twenty-one articles were enrolled. The sensitivity (SEN), specificity (SPE), and SROC of ML-based models were 0.91 (95% CI 0.87-0.94), 0.85 (95% CI 0.81-0.89), and 0.94 (95% CI 0.39-1.00) in the training set and 0.90 (95% CI 0.86-0.93), 0.90 (95% CI 0.86-0.92), and 0.96 (95% CI 0.19-1.00) in the validation set. The SEN, SPE, and SROC of EGC diagnosis by non-specialist clinicians were 0.64 (95% CI 0.56-0.71), 0.84 (95% CI 0.77-0.89), and 0.80 (95% CI 0.29-0.97), and those by specialist clinicians were 0.80 (95% CI 0.74-0.85), 0.88 (95% CI 0.85-0.91), and 0.91 (95% CI 0.37-0.99). With the assistance of ML models, the SEN of non-specialist physicians in the diagnosis of EGC was significantly improved (0.76 vs 0.64).CONCLUSION:
ML-based diagnostic models have greater performance in the identification of EGC. The diagnostic accuracy of non-specialist clinicians can be improved to the level of the specialists with the assistance of ML models. The results suggest that ML models can better assist less experienced clinicians in diagnosing EGC under endoscopy and have broad clinical application value.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Neoplasias Gástricas
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
/
Systematic_reviews
Límite:
Humans
Idioma:
En
Revista:
World J Surg Oncol
Año:
2024
Tipo del documento:
Article
País de afiliación:
China