Evidence map›Paper›PMID 30143405›Full record

ArticlePancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]2018

Progression to pancreatic ductal adenocarcinoma from pancreatic intraepithelial neoplasia: Results of a simulation model.

Mary Linton B Peters, Andrew Eckel, Peter P Mueller, Angela C Tramontano, Davis T Weaver, Anna Lietz, Chin Hur, Chung Yin Kong, Pari V Pandharipande

Abstract read
In one paragraph

Article in Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.], 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 1 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

36 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Pancreatic cancer.Nature reviews. Disease primers · 2026
    Review
  5. Opportunities to Avoid Invasive Cancer by Diagnosis and Interception of Preneoplastic Lesions and Cancer Risk Conditions.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026
    Article
  6. A Scoping Review on Calibration Methods for Cancer Simulation Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2025
    Article
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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

9 authors.

Mary Linton B PetersDivision of Hematology/Oncology, Beth Israel Deaconess Medical Center, USA; Institute for Technology Assessment, Massachusetts General Hospital, USA. Electronic address: mbpeters@bidmc.harvard.edu.
Andrew EckelInstitute for Technology Assessment, Massachusetts General Hospital, USA.
Peter P MuellerInstitute for Technology Assessment, Massachusetts General Hospital, USA.
Angela C TramontanoInstitute for Technology Assessment, Massachusetts General Hospital, USA.
Davis T WeaverInstitute for Technology Assessment, Massachusetts General Hospital, USA.
Anna LietzInstitute for Technology Assessment, Massachusetts General Hospital, USA.
Chin HurInstitute for Technology Assessment, Massachusetts General Hospital, USA.
Chung Yin KongInstitute for Technology Assessment, Massachusetts General Hospital, USA.
Pari V PandharipandeInstitute for Technology Assessment, Massachusetts General Hospital, USA.

Funding

Optimizing the Treatment of Pancreatic AdenocarcinomaR01CA212086 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI HUR, CHIN, KONG, CHUNG YIN · 2017 to 2021
$2.1M
NCI NIH HHS R01 CA212086
6 · The paper itself

Abstract

objectivesTo gain insight into the natural history and carcinogenesis pathway of Pancreatic Intraepithelial Neoplasia (PanIN) lesions by building a calibrated simulation model of PanIN progression to pancreatic ductal adenocarcinoma (PDAC)

methodsWe revised a previously validated simulation model of solid PDAC, calibrating the model to fit data from the National Cancer Institute's Surveillance, Epidemiology, and End Results program and published literature on PanIN prevalence by age. We estimated the likelihood of progression from PanIN states (1, 2, and 3) to PDAC and the time between PanIN onset and PDAC (dwell time). We evaluated a hypothetical intervention to test for and treat PanIN 3 lesions to estimate the potential benefits from PanIN detection.

resultsWe estimated the lifetime probability of progressing from PanIN 1 to PDAC to be 1.5% (men), 1.3% (women). Progression from PanIN 1 to PDAC took 33.6 years and 35.3 years, respectively, and from PanIN 3 to PDAC took 11.3 years and 12.3 years. A hypothetical test for PanIN 3 detection and treatment could provide a maximum, average life expectancy gain of 40 days.

conclusionsOur modeling analysis estimates PanINs have a relatively indolent course to PDAC, supporting the feasibility of potential future early detection strategies.

Indexed as

AdenocarcinomaAdultAgedAged, 80 and overAge FactorsBiomarkers, TumorCarcinoma in SituCarcinoma, Pancreatic DuctalComputer SimulationDisease ProgressionFemaleHumansIncidenceLife ExpectancyMaleMiddle AgedBiomarkers, Tumorpancreatic cancerpancreatic intraepithelial neoplasiasimulation modeling

Identifiers

PMID30143405
PMCPMC9201992

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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.