Evidence map›Paper›PMID 33748028›Full record

ArticleAnnals of surgical treatment and research2021

Diagnostic model for pancreatic cancer using a multi-biomarker panel.

Yoo Jin Choi, Woongchang Yoon, Areum Lee, Youngmin Han, Yoonhyeong Byun, Jae Seung Kang, Hongbeom Kim, Wooil Kwon, Young-Ah Suh, Yongkang Kim and 12 more

Erratum issuedOpen access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of surgical treatment and research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.8field-weighted citation impact, top 25% of its field
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

8 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
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  7. Diagnostic biomarkers for pancreatic cancer: An update.World journal of gastroenterology · 2021
    Article
  8. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

22 authors at 7 institutions in 1 country.

Yoo Jin ChoiDepartment of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-1142-1842
Woongchang YoonBio-MAX/N-Bio Institute, Seoul National University, Seoul, Korea.ORCID https://orcid.org/0000-0003-0544-1553
Areum LeeDepartment of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0001-9890-4435
Youngmin HanDepartment of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0003-0456-7824
Yoonhyeong ByunDepartment of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-4258-891X
Jae Seung KangDepartment of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0001-6587-9579
Hongbeom KimDepartment of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-1595-0135
Wooil KwonDepartment of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-4827-7805
Young-Ah SuhDepartment of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-4694-714X
Yongkang KimDepartment of Statistics, Seoul National University, Seoul, Korea.ORCID https://orcid.org/0000-0002-8837-2620
Seungyeoun LeeDepartment of Applied Mathematics, Sejong University, Seoul, Korea.ORCID https://orcid.org/0000-0001-7941-8933
Junghyun NamkungData Labs, AI Center, SK Telecom, Seoul, Korea.ORCID https://orcid.org/0000-0002-0246-0065
Sangjo HanData Labs, AI Center, SK Telecom, Seoul, Korea.ORCID https://orcid.org/0000-0002-7644-3671
Yonghwan ChoiData Labs, AI Center, SK Telecom, Seoul, Korea.ORCID https://orcid.org/0000-0001-5231-2434
Jin Seok HeoDepartment of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0001-6767-2790
Joon Oh ParkDepartment of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0001-6502-2612
Joo Kyung ParkDepartment of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Suwon, Korea.ORCID https://orcid.org/0000-0002-9652-5287
Song Cheol KimDepartment of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Korea.ORCID https://orcid.org/0000-0003-4552-4169
Chang Moo KangDepartment of Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-5382-4658
Woo Jin LeeCenter for Liver Cancer, National Cancer Center, Seoul, Korea.ORCID https://orcid.org/0000-0003-2115-6701
Taesung ParkDepartment of Statistics, Seoul National University, Seoul, Korea.ORCID https://orcid.org/0000-0002-8294-590X
Jin-Young JangDepartment of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0003-3312-0503
Seoul National University · KRSamsung Medical Center · KRKorea Telecom (South Korea) · KRNational Cancer Center · KRSejong University · KRUlsan College · KRYonsei University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeDiagnostic biomarkers of pancreatic ductal adenocarcinoma (PDAC) have been used for early detection to reduce its dismal survival rate. However, clinically feasible biomarkers are still rare. Therefore, in this study, we developed an automated multi-marker enzyme-linked immunosorbent assay (ELISA) kit using 3 biomarkers (leucine-rich alpha-2-glycoprotein [LRG1], transthyretin [TTR], and CA 19-9) that were previously discovered and proposed a diagnostic model for PDAC based on this kit for clinical usage.

methodsIndividual LRG1, TTR, and CA 19-9 panels were combined into a single automated ELISA panel and tested on 728 plasma samples, including PDAC (n = 381) and normal samples (n = 347). The consistency between individual panels of 3 biomarkers and the automated multi-panel ELISA kit were accessed by correlation. The diagnostic model was developed using logistic regression according to the automated ELISA kit to predict the risk of pancreatic cancer (high-, intermediate-, and low-risk groups).

resultsThe Pearson correlation coefficient of predicted values between the triple-marker automated ELISA panel and the former individual ELISA was 0.865. The proposed model provided reliable prediction results with a positive predictive value of 92.05%, negative predictive value of 90.69%, specificity of 90.69%, and sensitivity of 92.05%, which all simultaneously exceed 90% cutoff value.

conclusionThis diagnostic model based on the triple ELISA kit showed better diagnostic performance than previous markers for PDAC. In the future, it needs external validation to be used in the clinic.

Indexed as

BiomarkersEnzyme-linked immunosorbent assayPancreatic intraductal neoplasms

Identifiers

PMID33748028
PMCPMC7943279
OpenAlexW3134229160

What OpenQuestion holds

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LicenceCC BY-NC
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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.