Evidence map›Paper›PMID 30470933›Full record

ReviewVirchows Archiv : an international journal of pathology2019

Precision immunoprofiling by image analysis and artificial intelligence.

Viktor H Koelzer, Korsuk Sirinukunwattana, Jens Rittscher, Kirsten D Mertz

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Virchows Archiv : an international journal of pathology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
72citing papers in PubMed, 2 pooled it
–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

72 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  16. Why Is Wnt/β-Catenin Not Yet Targeted in Routine Cancer Care?Pharmaceuticals (Basel, Switzerland) · 2024
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12 more citing papers are in PubMed but not listed here.

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

4 authors.

Viktor H KoelzerInstitute of Cancer and Genomic Science, University of Birmingham, 6 Mindelsohn Way, Birmingham, B15 2SY, UK. vkoelzer@well.ox.ac.uk.ORCID http://orcid.org/0000-0001-9206-4885
Korsuk SirinukunwattanaInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Headington, Oxford, OX3 7DQ, UK.
Jens RittscherInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Headington, Oxford, OX3 7DQ, UK.
Kirsten D MertzInstitute of Pathology, Cantonal Hospital Baselland, Mühlemattstrasse 11, CH-4410, Liestal, Switzerland.

Funding

Krebsliga Beider Basel KLbB-4182-03-2017Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung P2SKP3_168322/2
6 · The paper itself

Abstract

Clinical success of immunotherapy is driving the need for new prognostic and predictive assays to inform patient selection and stratification. This requirement can be met by a combination of computational pathology and artificial intelligence. Here, we critically assess computational approaches supporting the development of a standardized methodology in the assessment of immune-oncology biomarkers, such as PD-L1 and immune cell infiltrates. We examine immunoprofiling through spatial analysis of tumor-immune cell interactions and multiplexing technologies as a predictor of patient response to cancer treatment. Further, we discuss how integrated bioinformatics can enable the amalgamation of complex morphological phenotypes with the multiomics datasets that drive precision medicine. We provide an outline to machine learning (ML) and artificial intelligence tools and illustrate fields of application in immune-oncology, such as pattern-recognition in large and complex datasets and deep learning approaches for survival analysis. Synergies of surgical pathology and computational analyses are expected to improve patient stratification in immuno-oncology. We propose that future clinical demands will be best met by (1) dedicated research at the interface of pathology and bioinformatics, supported by professional societies, and (2) the integration of data sciences and digital image analysis in the professional education of pathologists.

Indexed as

Artificial IntelligenceBiomarkers, TumorHumansImage Processing, Computer-AssistedImmunophenotypingPrecision MedicineBiomarkers, TumorArtificial intelligenceDigital pathologyImage analysisImmuno-oncologyImmunotherapyMachine learningPersonalized medicine

Identifiers

PMID30470933
PMCPMC6447694

What OpenQuestion holds

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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.