Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis.

Background: Early diagnosis can significantly improve survival rate of Pancreatic ductal adenocarcinoma (PDAC), but due to the insidious and non-specific early symptoms, most patients are not suitable for surgery when diagnosed. Traditional imaging techniques and an increasing number of non-imaging diagnostic methods have been used for the early diagnosis of pancreatic cancer (PC) through deep learning (DL). Objective: This review summarizes diagnosis methods for pancreatic cancer with the technique of deep learning and looks forward to the future development directions of deep learning for early diagnosis of pancreatic cancer. Methods: This study follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, retrieving studies on deep learning for early pancreatic cancer diagnosis from PubMed, Embase, Web of Science, IEEE, and Cochrane Library over the past 5 years. Inclusion criteria were studies involving PDAC patients, using deep learning algorithms for diagnosis evaluation, using histopathological results as the reference standard, and having sufficient data. Two reviewers independently screened and extracted data. Quality was assessed using QUADAS-2, with StataMP 17 for meta-analysis. Results: In this study, 422 articles were retrieved, and 7 were finally included for meta-analysis. The analysis showed that the accuracy of deep learning in the early diagnosis of pancreatic cancer was 80%-98.9%, and the combined sensitivity, specificity and AUC were 0.92 (95% CI: 0.85-0.96), 0.92 (95% CI: 0.85-0.96), and 0.97 (95% CI: 0.95-0.98). The positive and negative likelihood ratio were 11.52 (95% CI, 6.15-21.55) and 0.09 (95% CI, 0.04-0.17). Endoscopic ultrasound (EUS) and Contrast-Enhanced Computed Tomography (CE-CT) were the main diagnostic methods. Non-imaging diagnostic methods such as deep learning urine markers, disease trajectory also performed good diagnostic potential. Conclusions: Artificial intelligence (AI) technology holds promise for clinical guidance in pancreatic cancer risk prediction and diagnosis. Future research may focus on leveraging diverse data sources like genomics and biomarkers through deep learning; utilizing multi - center or international samples; tackling the challenge of early diagnosis for small pancreatic cancers; enhancing the explainability of AI models and multi-modal approaches.