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. 2023 Mar 31:25:e44248.
doi: 10.2196/44248.

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review

Zainab Jan  1 , Farah El Assadi  1 , Alaa Abd-Alrazaq  2 , Puthen Veettil Jithesh  1
Affiliations

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review

Zainab Jan et al. J Med Internet Res. .

Abstract

Background: Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer.

Objective: This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature.

Methods: A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively.

Results: Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms.

Conclusions: This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.

Keywords: algorithm; artificial Intelligence; cancer; deep learning; diagnosis; diagnostic; machine learning; oncology; pancreatic; pancreatic cancer; predict; prediction; review method; scoping.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) flowchart of the study selection. AI: artificial intelligence.
Figure 2
Figure 2
Recommendations for designing an intelligent pancreatic cancer diagnosis and treatment platform. First, upload anonymized patient data from all over the world to a large federated database, and then perform extensive large-scale artificial intelligence (AI) training based on different demographic traits and geographical regions. To improve the current pancreatic cancer treatment methods, the updated and iterative AI system will be used for the precise prediction, screening, diagnosis, and treatment of pancreatic cancer. CT: computed tomography; MRI: magnetic resonance imaging.
See this image and copyright information in PMC

References

    1. Yamamoto T, Yagi S, Kinoshita H, Sakamoto Y, Okada K, Uryuhara K, Morimoto T, Kaihara S, Hosotani R. Long-term survival after resection of pancreatic cancer: a single-center retrospective analysis. World J Gastroenterol. 2015 Jan 07;21(1):262–8. doi: 10.3748/wjg.v21.i1.262. https://www.wjgnet.com/1007-9327/full/v21/i1/262.htm - DOI - PMC - PubMed
    1. Huang J, Lok V, Ngai CH, Zhang L, Yuan J, Lao XQ, Ng K, Chong C, Zheng Z, Wong MCS. Worldwide burden of, risk factors for, and trends in pancreatic cancer. Gastroenterology. 2021 Feb;160(3):744–754. doi: 10.1053/j.gastro.2020.10.007. https://linkinghub.elsevier.com/retrieve/pii/S0016-5085(20)35244-6 S0016-5085(20)35244-6 - DOI - PubMed
    1. Gheorghe G, Bungau S, Ilie M, Behl T, Vesa CM, Brisc C, Bacalbasa N, Turi V, Costache RS, Diaconu CC. Early diagnosis of pancreatic cancer: the key for survival. Diagnostics (Basel) 2020 Oct 24;10(11):1. doi: 10.3390/diagnostics10110869. https://www.mdpi.com/resolver?pii=diagnostics10110869 diagnostics10110869 - DOI - PMC - PubMed
    1. Hanada K, Okazaki A, Hirano N, Izumi Y, Minami T, Ikemoto J, Kanemitsu K, Hino F. Effective screening for early diagnosis of pancreatic cancer. Best Pract Res Clin Gastroenterol. 2015 Dec;29(6):929–39. doi: 10.1016/j.bpg.2015.09.017.S1521-6918(15)00130-4 - DOI - PubMed
    1. Qian L, Li Q, Baryeh K, Qiu W, Li K, Zhang J, Yu Q, Xu D, Liu W, Brand RE, Zhang X, Chen W, Liu G. Biosensors for early diagnosis of pancreatic cancer: a review. Transl Res. 2019 Nov;213:67–89. doi: 10.1016/j.trsl.2019.08.002.S1931-5244(19)30160-4 - DOI - PubMed

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