Evidence map›Paper›PMID 37000507›Full record

ArticleJournal of medical Internet research2023

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

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

Abstract readScoping Review
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  4. Unravelling the pathogenesis and therapeutic approaches of pancreatic ductal adenocarcinoma.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Zainab JanCollege of Health & Life Sciences, Hamad Bin Khalifa University, Doha, Qatar.ORCID 0000-0002-4050-8496
Farah El AssadiCollege of Health & Life Sciences, Hamad Bin Khalifa University, Doha, Qatar.ORCID 0000-0001-7145-5473
Alaa Abd-AlrazaqAI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.ORCID 0000-0001-7695-4626
Puthen Veettil JitheshCollege of Health & Life Sciences, Hamad Bin Khalifa University, Doha, Qatar.ORCID 0000-0001-7747-0930

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPancreatic 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.

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

methodsA 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.

resultsOf 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.

conclusionsThis 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.

Indexed as

Artificial IntelligencePancreatic NeoplasmsAlgorithmsEarly Detection of CancerHumansalgorithmartificial Intelligencecancerdeep learningdiagnosisdiagnosticmachine learningoncologypancreaticpancreatic cancerpredictpredictionreview methodscoping

Identifiers

PMID37000507
PMCPMC10131763

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.