Evidence map›Paper›PMID 36119479›Full record

ReviewFrontiers in oncology2022

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction.

Meredith A Jones, Warid Islam, Rozwat Faiz, Xuxin Chen, Bin Zheng

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
5.9field-weighted citation impact, top 3% 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

27 citing papers in PubMed, 42 citations in OpenAlex.

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  11. Bridging the Gap: Scenario Planning for Science and Technology in Breast Cancer.Medical journal of the Islamic Republic of Iran · 2025
    Article
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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

5 authors at 1 institution in 1 country.

Meredith A JonesSchool of Biomedical Engineering, University of Oklahoma, Norman, OK, United States.
Warid IslamSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States.
Rozwat FaizSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States.
Xuxin ChenSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States.
Bin ZhengSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States.
University of Oklahoma · US

Funding

Use of 3D Quantitative Optical Methods to Optimize Mebendazole Treatment of Ovarian CancerP20GM135009 · NIGMS · UNIVERSITY OF OKLAHOMA · PI Yuye Ling · 2022 to 2026
$13.6M
NIGMS NIH HHS P20 GM135009
6 · The paper itself

Abstract

Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.

Indexed as

breast cancercomputer aided detectioncomputer aided diagnosisdeep learningmachine learningmammography

Identifiers

PMID36119479
PMCPMC9471147
OpenAlexW4293724450

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.