Evidence map›Paper›PMID 40108402›Full record

ArticleScientific reports2025

Stroma and lymphocytes identified by deep learning are independent predictors for survival in pancreatic cancer.

Xiuxiang Tan, Mika Rosin, Simone Appinger, Julia Campello Deierl, Konrad Reichel, Mariëlle Coolsen, Liselot Valkenburg-van Iersel, Judith de Vos-Geelen, Evelien J M de Jong, Jan Bednarsch and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
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

21 authors.

Xiuxiang TanDepartment of Surgery and Transplantation, University Hospital RWTH Aachen, Pauwelsstrasse 30, 52074, Aachen, Germany. tanxiuxiang2017@outlook.com.
Mika RosinDepartment of Surgery and Transplantation, University Hospital RWTH Aachen, Pauwelsstrasse 30, 52074, Aachen, Germany.
Simone AppingerDepartment of Surgery and Transplantation, University Hospital RWTH Aachen, Pauwelsstrasse 30, 52074, Aachen, Germany.
Julia Campello DeierlDepartment of Surgery and Transplantation, University Hospital RWTH Aachen, Pauwelsstrasse 30, 52074, Aachen, Germany.
Konrad ReichelDepartment of General, Visceral and Transplantation Surgery, Universitats Klinikum Essen, Essen, Germany.
Mariëlle CoolsenDepartment of Surgery, Maastricht University Medical Center, Maastricht, The Netherlands.
Liselot Valkenburg-van IerselDepartment of Internal Medicine, Division of Medical Oncology, GROW, Maastricht University Medical Center, Maastricht, The Netherlands.
Judith de Vos-GeelenDepartment of Internal Medicine, Division of Medical Oncology, GROW, Maastricht University Medical Center, Maastricht, The Netherlands.
Evelien J M de JongDepartment of Internal Medicine, Division of Medical Oncology, GROW, Maastricht University Medical Center, Maastricht, The Netherlands.
Jan BednarschDepartment of General, Visceral and Transplantation Surgery, Universitats Klinikum Essen, Essen, Germany.
Bas GrootkoerkampDepartment of Surgery, Erasmus University Medical Centre, Rotterdam, The Netherlands.
Michail DoukasDepartment of Pathology, Erasmus University Medical Center, Rotterdam, The Netherlands.
Casper van EijckDepartment of Surgery, Erasmus University Medical Centre, Rotterdam, The Netherlands.
Tom LueddeDepartment of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Duesseldorf, Duesseldorf, Germany.
Edgar DahlInstitute of Pathology, University Hospital RWTH Aachen, Aachen, Germany.
Jakob Nikolas KatherElse Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
Shivan SivakumarDepartment of Immunology and Immunotherapy, School of Infection, Inflammation and Immunology, College of Medicine and Health, University of Birmingham, Birmingham, UK.
Wolfram Trudo KnoefelDepartment of General, Visceral and Pediatric Surgery, Heinrich Heine University, Düsseldorf, Germany.
Georg WiltbergerDepartment of Surgery and Transplantation, University Hospital RWTH Aachen, Pauwelsstrasse 30, 52074, Aachen, Germany.
Ulf Peter NeumannDepartment of General, Visceral and Transplantation Surgery, Universitats Klinikum Essen, Essen, Germany.
Lara R HeijDepartment of General, Visceral and Transplantation Surgery, Universitats Klinikum Essen, Essen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers known to humans. However, not all patients fare equally poor survival, and a minority of patients even survives advanced disease for months or years. Thus, there is a clinical need to search corresponding prognostic biomarkers which forecast survival on an individual basis. To dig more information and identify potential biomarkers from PDAC pathological slides, we trained a deep learning (DL) model based U-net-shaped backbone. This DL model can automatically detect tumor, stroma and lymphocytes on whole slide images (WSIs) of PDAC patients. We performed an analysis of 800 PDAC scans, categorizing stroma in percentage (SIP) and lymphocytes in percentage (LIP) into two and three categories, respectively. The presented model achieved remarkable accuracy results with a total accuracy of 94.72%, a mean intersection of union rate of 78.66%, and a mean dice coefficient of 87.74%. Survival analysis revealed that SIP-mediate and LIP-high groups correlated with enhanced median overall survival (OS) across all cohorts. These findings underscore the potential of SIP and LIP as prognostic biomarkers for PDAC and highlight the utility of DL as a tool for PDAC biomarkers detecting on WSIs.

Indexed as

Carcinoma, Pancreatic DuctalDeep LearningLymphocytesPancreatic NeoplasmsStromal CellsAgedBiomarkers, TumorFemaleHumansMaleMiddle AgedPrognosisSurvival AnalysisBiomarkers, TumorDeep learningImmune infiltrationPancreatic cancerPrognostic biomarkerTumor proportion

Identifiers

PMID40108402
PMCPMC11923104

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.