Evidence map›Paper›PMID 37230111›Full record

ArticleThe Journal of pathology2023

Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large-scale studies.

Gregory Verghese, Mengyuan Li, Fangfang Liu, Amit Lohan, Nikhil Cherian Kurian, Swati Meena, Patrycja Gazinska, Aekta Shah, Aasiyah Oozeer, Terry Chan and 13 more

Open access · hybridAbstract read
In one paragraph

Article in The Journal of pathology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
4.0field-weighted citation impact, top 5% 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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

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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

23 authors at 7 institutions in 7 countries.

Gregory Verghese *Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Mengyuan Li *Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.ORCID 0000-0001-5526-3228
Fangfang Liu *Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, PR China.
Amit LohanDepartment of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India.
Nikhil Cherian KurianDepartment of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India.
Swati MeenaDepartment of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India.
Patrycja GazinskaBreast Cancer Now Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Aekta ShahCancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Aasiyah OozeerKing's Health Partners Cancer Biobank, King's College London, London, UK.
Terry ChanDivision of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.ORCID 0000-0003-2150-1296
Mark OpdamDivision of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.ORCID 0000-0001-5832-6386
Sabine LinnDivision of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
Cheryl GillettKing's Health Partners Cancer Biobank, King's College London, London, UK.
Elena AlbertsCancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Thomas HardimanCancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Samantha JonesCentre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, London, UK.
Selvam ThavarajFaculty of Dentistry, Oral & Craniofacial Science, King's College London, London, UK.
J Louise JonesCentre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, London, UK.ORCID 0000-0003-3880-3274
Roberto SalgadoDepartment of Pathology, GZA-ZNA Hospitals, Antwerp, Belgium.
Sarah E PinderSchool of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
Swapnil RaneDepartment of Pathology, Tata Memorial Centre-ACTREC, HBNI, Mumbai, India.ORCID 0000-0002-5374-3903
Amit SethiDepartment of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India.ORCID 0000-0002-8634-1804
Anita GrigoriadisCancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.ORCID 0000-0003-3434-201X
King's College London · GBIndian Institute of Technology Bombay · INThe Netherlands Cancer Institute · NLQueen Mary University of London · GBPeter MacCallum Cancer Centre · AUTata Memorial Hospital · INTianjin Medical University Cancer Institute and Hospital · CN

Funding

Cancer Research UK CRUK/07/012Cancer Research UK CTRQQR-2021/100004Cancer Research UK KCL-BCN-Q3Medical Research Council MR/X012476/1
6 · The paper itself

Abstract

The suggestion that the systemic immune response in lymph nodes (LNs) conveys prognostic value for triple-negative breast cancer (TNBC) patients has not previously been investigated in large cohorts. We used a deep learning (DL) framework to quantify morphological features in haematoxylin and eosin-stained LNs on digitised whole slide images. From 345 breast cancer patients, 5,228 axillary LNs, cancer-free and involved, were assessed. Generalisable multiscale DL frameworks were developed to capture and quantify germinal centres (GCs) and sinuses. Cox regression proportional hazard models tested the association between smuLymphNet-captured GC and sinus quantifications and distant metastasis-free survival (DMFS). smuLymphNet achieved a Dice coefficient of 0.86 and 0.74 for capturing GCs and sinuses, respectively, and was comparable to an interpathologist Dice coefficient of 0.66 (GC) and 0.60 (sinus). smuLymphNet-captured sinuses were increased in LNs harbouring GCs (p < 0.001). smuLymphNet-captured GCs retained clinical relevance in LN-positive TNBC patients whose cancer-free LNs had on average ≥2 GCs, had longer DMFS (hazard ratio [HR] = 0.28, p = 0.02) and extended GCs' prognostic value to LN-negative TNBC patients (HR = 0.14, p = 0.002). Enlarged smuLymphNet-captured sinuses in involved LNs were associated with superior DMFS in LN-positive TNBC patients in a cohort from Guy's Hospital (multivariate HR = 0.39, p = 0.039) and with distant recurrence-free survival in 95 LN-positive TNBC patients of the Dutch-N4plus trial (HR = 0.44, p = 0.024). Heuristic scoring of subcapsular sinuses in LNs of LN-positive Tianjin TNBC patients (n = 85) cross-validated the association of enlarged sinuses with shorter DMFS (involved LNs: HR = 0.33, p = 0.029 and cancer-free LNs: HR = 0.21 p = 0.01). Morphological LN features reflective of cancer-associated responses are robustly quantifiable by smuLymphNet. Our findings further strengthen the value of assessment of LN properties beyond the detection of metastatic deposits for prognostication of TNBC patients. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Indexed as

Deep LearningTriple Negative Breast NeoplasmsClinical Trials as TopicFemaleHumansLymphatic MetastasisLymph NodesNeoplasm StagingPrognosisRetrospective Studiescomputational pathologydeep learningdigital pathologygerminal centrelymph nodesinustriple negative breast cancer

Identifiers

PMID37230111
PMCPMC10720675
OpenAlexW4378174280

What OpenQuestion holds

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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.