Evidence map›Paper›PMID 33902889›Full record

ReviewClinical radiology2021

Challenges and opportunities for artificial intelligence in oncological imaging.

H M C Cheung, D Rubin

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Clinical radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

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

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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

2 authors.

H M C CheungDepartment of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Canada.
D RubinDepartment of Radiology, Stanford University, CA, USA. Electronic address: dlrubin@stanford.edu.

Funding

Qualification and Deployment of Imaging Biomarkers of Cancer Treatment ResponseU01CA190214 · NCI · STANFORD UNIVERSITY · PI RUBIN, DANIEL L · 2015 to 2020
$3.1M
Distributed Learning of Deep Learning Models for Cancer ResearchU01CA242879 · NCI · STANFORD UNIVERSITY · PI KALPATHY-CRAMER, JAYASHREE, RUBIN, DANIEL L · 2019 to 2021
$1.2M
NCI NIH HHS U01 CA190214NCI NIH HHS U01 CA242879
6 · The paper itself

Abstract

Imaging plays a key role in oncology, including the diagnosis and detection of cancer, determining clinical management, assessing treatment response, and complications of treatment or disease. The current use of clinical oncology is predominantly qualitative in nature with some relatively crude size-based measurements of tumours for assessment of disease progression or treatment response; however, it is increasingly understood that there may be significantly more information about oncological disease that can be obtained from imaging that is not currently utilized. Artificial intelligence (AI) has the potential to harness quantitative techniques to improve oncological imaging. These may include improving the efficiency or accuracy of traditional roles of imaging such as diagnosis or detection. These may also include new roles for imaging such as risk-stratifying patients for different types of therapy or determining biological tumour subtypes. This review article outlines several major areas in oncological imaging where there may be opportunities for AI technology. These include (1) screening and detection of cancer, (2) diagnosis and risk stratification, (3) tumour segmentation, (4) precision oncology, and (5) predicting prognosis and assessing treatment response. This review will also address some of the potential barriers to AI research in oncological imaging.

Indexed as

Artificial IntelligenceDiagnostic ImagingHumansImage Interpretation, Computer-AssistedMedical OncologyNeoplasmsPrecision Medicine

Identifiers

PMID33902889
PMCPMC8434981

What OpenQuestion holds

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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.