Evidence map›Paper›PMID 34579788›Full record

ReviewGenome medicine2021

Deep learning in cancer diagnosis, prognosis and treatment selection.

Khoa A Tran, Olga Kondrashova, Andrew Bradley, Elizabeth D Williams, John V Pearson, Nicola Waddell

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Genome medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 449 papers, 12 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
449citing papers in PubMed, 12 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

449 citing papers in PubMed, 12 syntheses or guidelines pooled it.

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  7. Causal inference in the diagnosis and prognosis of ovarian cancer: current state and future directions.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025
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389 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

6 authors.

Khoa A TranDepartment of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, 4006, Australia.
Olga KondrashovaDepartment of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, 4006, Australia.
Andrew BradleyFaculty of Engineering, Queensland University of Technology (QUT), Brisbane, 4000, Australia.
Elizabeth D WilliamsSchool of Biomedical Sciences, Faculty of Health, Queensland University of Technology (QUT), Brisbane, 4059, Australia.
John V PearsonDepartment of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, 4006, Australia.
Nicola WaddellDepartment of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, 4006, Australia. Nic.Waddell@qimrberghofer.edu.au.ORCID 0000-0002-3950-2476

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across healthcare domains together with the availability of highly characterised cancer datasets has accelerated research into the utility of deep learning in the analysis of the complex biology of cancer. While early results are promising, this is a rapidly evolving field with new knowledge emerging in both cancer biology and deep learning. In this review, we provide an overview of emerging deep learning techniques and how they are being applied to oncology. We focus on the deep learning applications for omics data types, including genomic, methylation and transcriptomic data, as well as histopathology-based genomic inference, and provide perspectives on how the different data types can be integrated to develop decision support tools. We provide specific examples of how deep learning may be applied in cancer diagnosis, prognosis and treatment management. We also assess the current limitations and challenges for the application of deep learning in precision oncology, including the lack of phenotypically rich data and the need for more explainable deep learning models. Finally, we conclude with a discussion of how current obstacles can be overcome to enable future clinical utilisation of deep learning.

Indexed as

Deep LearningArtificial IntelligenceGenomicsHumansMachine LearningMedical OncologyNeoplasmsNeural Networks, ComputerPharmacogeneticsPrecision MedicinePrognosisTumor MicroenvironmentArtificial intelligenceCancer genomicsCancer of unknown primaryDeep learningExplainabilityMolecular subtypesMulti-modal learningPharmacogenomicsPrecision oncologyPrognosisTumour microenvironment

Identifiers

PMID34579788
PMCPMC8477474

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.