Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis
BackgroundEarly 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 di...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1597969/full |
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| author | Yuanbo Bi Dongrui Li Ruochen Pang Chengxv Du Da Li Xiaoyv Zhao Haitao Lv |
| author_facet | Yuanbo Bi Dongrui Li Ruochen Pang Chengxv Du Da Li Xiaoyv Zhao Haitao Lv |
| author_sort | Yuanbo Bi |
| collection | DOAJ |
| description | BackgroundEarly 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).ObjectiveThis 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.MethodsThis 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.ResultsIn 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.ConclusionsArtificial 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. |
| format | Article |
| id | doaj-art-0f3fccd6b5ea4bedaa0ea9b28543a708 |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-0f3fccd6b5ea4bedaa0ea9b28543a7082025-08-20T04:14:16ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-08-011510.3389/fonc.2025.15979691597969Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysisYuanbo Bi0Dongrui Li1Ruochen Pang2Chengxv Du3Da Li4Xiaoyv Zhao5Haitao Lv6Department of Hepatobiliary Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, ChinaDepartment of Hepatobiliary Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, ChinaDepartment of Spine Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, ChinaDepartment of Hepatobiliary Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, ChinaDepartment of Hepatobiliary Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, ChinaDepartment of Oncology, Hebei Medical University, Shijiazhuang, Hebei, ChinaDepartment of Hepatobiliary Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, ChinaBackgroundEarly 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).ObjectiveThis 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.MethodsThis 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.ResultsIn 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.ConclusionsArtificial 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.https://www.frontiersin.org/articles/10.3389/fonc.2025.1597969/fullpancreatic cancer (PC)deep learningdiagnosis methodsresearch trendsmeta-analysis |
| spellingShingle | Yuanbo Bi Dongrui Li Ruochen Pang Chengxv Du Da Li Xiaoyv Zhao Haitao Lv Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis Frontiers in Oncology pancreatic cancer (PC) deep learning diagnosis methods research trends meta-analysis |
| title | Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis |
| title_full | Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis |
| title_fullStr | Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis |
| title_full_unstemmed | Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis |
| title_short | Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis |
| title_sort | diagnosis methods for pancreatic cancer with the technique of deep learning a review and a meta analysis |
| topic | pancreatic cancer (PC) deep learning diagnosis methods research trends meta-analysis |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1597969/full |
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