Evidence map›Paper›PMID 33710372›Full record

ArticleEuropean radiology2021

Application of artificial intelligence-based computer-assisted diagnosis on synthetic mammograms from breast tomosynthesis: comparison with digital mammograms.

Si Eun Lee, Kyunghwa Han, Eun-Kyung Kim

Open access · greenAbstract read
PubMed Publisher
In one paragraph

Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.4field-weighted citation impact, top 16% 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

6 citing papers in PubMed, 14 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

3 authors at 1 institution in 1 country.

Si Eun LeeDepartment of Radiology, Yongin Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu, Yongin-si, Gyeonggi-do, Republic of Korea.
Kyunghwa HanDepartment of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
Eun-Kyung KimDepartment of Radiology, Yongin Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu, Yongin-si, Gyeonggi-do, Republic of Korea. ekkim@yuhs.ac.ORCID http://orcid.org/0000-0002-3368-5013
Yonsei University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo compare the diagnostic agreement and performances of synthetic and conventional mammograms when artificial intelligence-based computer-assisted diagnosis (AI-CAD) is applied. MATERIAL AND

methodFrom January 2017 to April 2017, 192 patients (mean age 53.7 ± 11.7 years) diagnosed with 203 breast cancers were enrolled in this retrospective study. All patients underwent digital breast tomosynthesis (DBT) with digital mammograms (DM) simultaneously. Commercial AI-CAD was applied to the reconstructed synthetic mammograms (SM) from DBT and DM respectively and abnormality scores were calculated. We compared the median abnormality scores between DM and SM with the Wilcoxon signed-rank test and used the Bland-Altman analysis to evaluate agreements between the two mammograms and to investigate clinicopathological factors which might affect agreement. Diagnostic performances were compared using an area under the receiver operating characteristic curve (AUC).

resultThe abnormality scores showed a mean difference (bias) of - 3.26 (95% limits of agreement: - 32.69, 26.18) between the two mammograms by the Bland-Altman analysis. The concordance correlation coefficient was 0.934 (95% CI: 0.92, 0.946), suggesting high reproducibility. SM showed higher abnormality scores in cancer with distortion and occult findings, T1 and N0 cancer, and luminal type cancer than DM (all p ≤ 0.001). Diagnostic performance did not differ between the mammograms (AUC 0.945 for conventional mammograms, 0.938 for synthetic mammograms, p = 0.499).

conclusionAI-CAD can also work well on synthetic mammograms, showing good agreement and comparable diagnostic performance compared to its application to DM. KEY POINTS: • AI-CAD which was developed based on imaging findings of digital mammograms can also be applied to synthetic mammograms. • AI-CAD showed good agreement and similar diagnostic performance when applied to both synthetic and digital mammograms. • With AI-CAD, synthetic mammograms showed relatively higher abnormality scores in cancer with distortion and occult findings, T1 and N0 cancer, and luminal type cancer than digital mammograms.

Indexed as

Artificial IntelligenceBreast NeoplasmsAdultAgedBreastDiagnosis, Computer-AssistedFemaleHumansMammographyMiddle AgedReproducibility of ResultsRetrospective StudiesArtificial intelligenceBreast neoplasmsDiagnosis, computer-assistedDigital mammography

Identifiers

PMID33710372
OpenAlexW3135221298

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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.