Evidence map›Paper›PMID 40841408›Full record

ArticleScientific reports2025

Discovery of tumour indicating morphological changes in benign prostate biopsies through AI.

Eduard Chelebian, Christophe Avenel, Helena Järemo, Pernilla Andersson, Anders Bergh, Carolina Wählby

Abstract read
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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Spatial architecture of development and disease.Nature reviews. Genetics · 2026
    Review
  3. 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

6 authors.

Eduard ChelebianDepartment of Information Technology and SciLifeLab, Uppsala University, 75237, Uppsala, Sweden.
Christophe AvenelDepartment of Information Technology and SciLifeLab, Uppsala University, 75237, Uppsala, Sweden.
Helena JäremoDepartment of Medical Biosciences, Pathology, Umeå University, 90187, Umeå, Sweden.
Pernilla AnderssonDepartment of Medical Biosciences, Pathology, Umeå University, 90187, Umeå, Sweden.
Anders BerghDepartment of Medical Biosciences, Pathology, Umeå University, 90187, Umeå, Sweden.
Carolina WählbyDepartment of Information Technology and SciLifeLab, Uppsala University, 75237, Uppsala, Sweden. carolina.wahlby@it.uu.se.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diagnostic needle biopsies that miss clinically significant prostate cancer (PCa) often sample benign tissue near hidden cancers. Such benign samples might still display subtle morphological signs of cancer elsewhere in the prostate. This study examined if artificial intelligence (AI) could detect these morphological clues in benign biopsies from men with elevated prostate-specific antigen (PSA) levels to predict subsequent diagnosis of clinically significant PCa within 30 months. We analysed biopsies from 232 men initially diagnosed as benign, matched for age, diagnosis year, and PSA levels-half were later diagnosed with PCa, while the rest remained cancer-free for at least eight years. The AI model accurately predicted future PCa diagnosis from initial benign biopsies (AUC = 0.82), highlighting patterns such as changes in stromal collagen and altered glandular epithelial cells. This demonstrates that AI analysis of routine haematoxylin-eosin biopsy sections can detect subtle signs indicating clinically significant PCa before it becomes histologically apparent. Such morphological patterns shed light on the broader tissue alterations induced by prostate cancer, even in benign tissue, potentially enhancing early detection and clinical decision-making.

Indexed as

Artificial IntelligenceProstateProstatic NeoplasmsAgedBiopsyHumansMaleMiddle AgedProstate-Specific AntigenProstate-Specific Antigen

Identifiers

PMID40841408
PMCPMC12370971

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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.