Evidence map›Paper›PMID 40065354›Full record

ArticleJournal of translational medicine2025

Automated deep learning-based assessment of tumour-infiltrating lymphocyte density determines prognosis in colorectal cancer.

Joshua Millward, Zhen He, Aiden Nibali, Dmitri Mouradov, Lisa A Mielke, Kelly Tran, Angela Chou, Nicholas J Hawkins, Robyn L Ward, Anthony J Gill and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

12 authors.

Joshua Millward *School of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne, Australia. j.millward@latrobe.edu.au.ORCID 0000-0002-6374-1426
Zhen He *School of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne, Australia.
Aiden Nibali *School of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne, Australia.
Dmitri MouradovPersonalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia.
Lisa A MielkeOlivia Newton-John Cancer Research Institute, Melbourne, Australia.
Kelly TranOlivia Newton-John Cancer Research Institute, Melbourne, Australia.
Angela ChouDepartment of Anatomical Pathology, NSW Health Pathology, Royal North Shore Hospital, Sydney, Australia.
Nicholas J HawkinsSchool of Biomedical Sciences, UNSW Sydney, Sydney, Australia.
Robyn L WardFaculty of Medicine and Health, University of Sydney, Sydney, Australia.
Anthony J GillDepartment of Anatomical Pathology, NSW Health Pathology, Royal North Shore Hospital, Sydney, Australia.
Oliver M SieberPersonalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia.
David S WilliamsOlivia Newton-John Cancer Research Institute, Melbourne, Australia.

Funding

Austin Medical Research Foundation Austin Medical Research FoundationAustralian National Health and Medical Research Council 1185513
6 · The paper itself

Abstract

backgroundThe presence of tumour-infiltrating lymphocytes (TILs) is a well-established prognostic biomarker across multiple cancer types, with higher TIL counts being associated with lower recurrence rates and improved patient survival. We aimed to examine whether an automated intraepithelial TIL (iTIL) assessment could stratify patients by risk, with the ability to generalise across independent patient cohorts, using routine H&E slides of colorectal cancer (CRC). To our knowledge, no other existing fully automated iTIL system has demonstrated this capability.

methodsAn automated method employing deep neural networks was developed to enumerate iTILs in H&E slides of CRC. The method was applied to a Stage III discovery cohort (n = 353) to identify an optimal threshold of 17 iTILs per-mm

resultsSignificant stratification was observed in terms of overall survival for a combined validation cohort univariate (HR 1.67, 95%CI 1.39-2.00; p < 0.001) and multivariate (HR 1.37, 95%CI 1.13-1.66; p = 0.001) analysis. Our iTIL classifier was an independent prognostic factor within proficient DNA mismatch repair (pMMR) Stage II CRC cases with clinical high-risk features. Of these, those classified as TIL-High had outcomes similar to pMMR clinical low risk cases, and those classified TIL-Low had significantly poorer outcomes (univariate HR 2.38, 95%CI 1.57-3.61; p < 0.001, multivariate HR 2.17, 95%CI 1.42-3.33; p < 0.001).

conclusionsOur deep learning method is the first fully automated system to stratify patient outcome by analysing TILs in H&E slides of CRC, that has shown generalisation capabilities across multiple independent cohorts.

Indexed as

Colorectal NeoplasmsDeep LearningLymphocytes, Tumor-InfiltratingAgedAutomationDisease-Free SurvivalFemaleHumansMaleMiddle AgedMultivariate AnalysisPrognosisCell detectionComputational pathologyImage analysisTissue segmentation

Identifiers

PMID40065354
PMCPMC11892243

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LicenceCC BY
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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.