Evidence map›Paper›PMID 37973858›Full record

ArticleScientific reports2023

Quantifying the potential benefits of early detection for pancreatic cancer through a counterfactual simulation modeling analysis.

Jiheum Park, Francesca Lim, Matthew Prest, Jennifer S Ferris, Zainab Aziz, Alice Agyekum, Sophie Wagner, Roman Gulati, Chin Hur

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.9field-weighted citation impact, top 20% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Review
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 at 2 institutions in 1 country.

Jiheum ParkDepartment of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA. jp4147@cumc.columbia.edu.
Francesca LimDepartment of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA.
Matthew PrestDepartment of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA.
Jennifer S FerrisDepartment of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA.
Zainab AzizDepartment of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA.
Alice AgyekumDepartment of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA.
Sophie WagnerDepartment of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA.
Roman GulatiDivision of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA.
Chin HurDepartment of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA. ch447@cumc.columbia.edu.
Columbia University Irving Medical Center · USFred Hutch Cancer Center · US

Funding

The Organoid and Cell Culture CoreP30DK132710 · NIDDK · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Hiroshi Nakagawa · 2022 to 2026
$7.2M
Modeling and analytics for cancer diagnostics: traversing the data-evidence divideR35CA274442 · NCI · FRED HUTCHINSON CANCER CENTER · PI RUTH D ETZIONI · 2022 to 2026
$4.9M
Statistical modeling to support population and translational cancer researchR50CA221836 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Roman Gulati · 2017 to 2026
$2.0M
Early Detection of Pancreatic Cancer with Human-in-the-Loop Deep LearningK25CA267052 · NCI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Jiheum Park · 2023 to 2026
$680k
Domain-Knowledge Informed Deep Learning for Early Detection of Pancreatic CancerR21CA265400 · NCI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HUR, CHIN, TATONETTI, NICHOLAS P · 2021 to 2022
$400k
NCI NIH HHS K25 CA267052NCI NIH HHS R21 CA265400NCI NIH HHS R35 CA274442NCI NIH HHS R50 CA221836NIDDK NIH HHS P30 DK132710
6 · The paper itself

Abstract

The benefits of cancer early detection depend on various factors, including cancer type, screening method performance, stage at diagnosis, and subsequent treatment. Although numerous studies have evaluated the effectiveness of screening interventions for identifying cancer at earlier stages, there is no quantitative analysis that studies the optimal early detection time interval that results in the greatest mortality benefit; such data could serve as a target and benchmark for cancer early detection strategies. In this study, we focus on pancreatic ductal adenocarcinoma (PDAC), a cancer known for its lack of early symptoms. Consequently, it is most often detected at late stages when the 5-year survival rate is only 3%. We developed a PDAC population model that simulates an individual patient's age and stage at diagnosis, while replicating overall US cancer incidence and mortality rates. The model includes "cancer sojourn time," serving as a proxy for the speed of cancer progression, with shorter times indicating rapid progression and longer times indicating slower progression. In our PDAC model, our hypothesis was that earlier cancer detection, potentially through a hypothetical screening intervention in the counterfactual analysis, would yield reduced mortality as compared to a no-screening group. We found that the benefits of early detection, such as increased life-years gained, are greater when the sojourn time is shorter, reaching their maximum when identification is made 4-6 years prior to clinical diagnosis (e.g., when a symptomatic diagnosis is made). However, when early detection occurs even earlier, for example 6-10 years prior to clinical diagnosis, the benefits significantly diminish for shorter sojourn time cancers, and level off for longer sojourn time cancers. Our study clarifies the potential benefits of PDAC early detection that explicitly incorporates individual patient heterogeneity in cancer progression and identifies quantitative benchmarks for future interventions.

Indexed as

Carcinoma, Pancreatic DuctalPancreatic NeoplasmsEarly Detection of CancerHumansMass Screening

Identifiers

PMID37973858
PMCPMC10654404
OpenAlexW4388728222

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.