Evidence map›Paper›PMID 38995172›Full record

ArticleRadiology. Imaging cancer2024

AI-enhanced Mammography With Digital Breast Tomosynthesis for Breast Cancer Detection: Clinical Value and Comparison With Human Performance.

Daphne Resch, Roberto Lo Gullo, Jonas Teuwen, Friedrich Semturs, Johann Hummel, Alexandra Resch, Katja Pinker

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Imaging cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 3 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

10 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Guideline
  3. Pooled it
  4. Article
  5. Article
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  8. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Daphne Resch *From the Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Austria (D.R.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (R.L.G., J.T.); Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria (F.S., J.H.); St Francis Hospital Vienna, Vienna, Austria (A.R.); Sigmund Freud University Medical School, Vienna, Austria (A.R.); and Department of Radiology, Division of Breast Imaging, Columbia University Irving Medical Center, 161 Fort Washington Ave, New York, NY 10032 (K.P.).
Roberto Lo Gullo *From the Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Austria (D.R.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (R.L.G., J.T.); Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria (F.S., J.H.); St Francis Hospital Vienna, Vienna, Austria (A.R.); Sigmund Freud University Medical School, Vienna, Austria (A.R.); and Department of Radiology, Division of Breast Imaging, Columbia University Irving Medical Center, 161 Fort Washington Ave, New York, NY 10032 (K.P.).
Jonas TeuwenFrom the Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Austria (D.R.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (R.L.G., J.T.); Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria (F.S., J.H.); St Francis Hospital Vienna, Vienna, Austria (A.R.); Sigmund Freud University Medical School, Vienna, Austria (A.R.); and Department of Radiology, Division of Breast Imaging, Columbia University Irving Medical Center, 161 Fort Washington Ave, New York, NY 10032 (K.P.).
Friedrich SemtursFrom the Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Austria (D.R.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (R.L.G., J.T.); Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria (F.S., J.H.); St Francis Hospital Vienna, Vienna, Austria (A.R.); Sigmund Freud University Medical School, Vienna, Austria (A.R.); and Department of Radiology, Division of Breast Imaging, Columbia University Irving Medical Center, 161 Fort Washington Ave, New York, NY 10032 (K.P.).
Johann HummelFrom the Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Austria (D.R.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (R.L.G., J.T.); Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria (F.S., J.H.); St Francis Hospital Vienna, Vienna, Austria (A.R.); Sigmund Freud University Medical School, Vienna, Austria (A.R.); and Department of Radiology, Division of Breast Imaging, Columbia University Irving Medical Center, 161 Fort Washington Ave, New York, NY 10032 (K.P.).
Alexandra Resch *From the Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Austria (D.R.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (R.L.G., J.T.); Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria (F.S., J.H.); St Francis Hospital Vienna, Vienna, Austria (A.R.); Sigmund Freud University Medical School, Vienna, Austria (A.R.); and Department of Radiology, Division of Breast Imaging, Columbia University Irving Medical Center, 161 Fort Washington Ave, New York, NY 10032 (K.P.).
Katja Pinker *From the Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Austria (D.R.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (R.L.G., J.T.); Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria (F.S., J.H.); St Francis Hospital Vienna, Vienna, Austria (A.R.); Sigmund Freud University Medical School, Vienna, Austria (A.R.); and Department of Radiology, Division of Breast Imaging, Columbia University Irving Medical Center, 161 Fort Washington Ave, New York, NY 10032 (K.P.).ORCID 0000-0002-2722-7331

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

Purpose To compare two deep learning-based commercially available artificial intelligence (AI) systems for mammography with digital breast tomosynthesis (DBT) and benchmark them against the performance of radiologists. Materials and Methods This retrospective study included consecutive asymptomatic patients who underwent mammography with DBT (2019-2020). Two AI systems (Transpara 1.7.0 and ProFound AI 3.0) were used to evaluate the DBT examinations. The systems were compared using receiver operating characteristic (ROC) analysis to calculate the area under the ROC curve (AUC) for detecting malignancy overall and within subgroups based on mammographic breast density. Breast Imaging Reporting and Data System results obtained from standard-of-care human double-reading were compared against AI results with use of the DeLong test. Results Of 419 female patients (median age, 60 years [IQR, 52-70 years]) included, 58 had histologically proven breast cancer. The AUC was 0.86 (95% CI: 0.85, 0.91), 0.93 (95% CI: 0.90, 0.95), and 0.98 (95% CI: 0.96, 0.99) for Transpara, ProFound AI, and human double-reading, respectively. For Transpara, a rule-out criterion of score 7 or lower yielded 100% (95% CI: 94.2, 100.0) sensitivity and 60.9% (95% CI: 55.7, 66.0) specificity. The rule-in criterion of higher than score 9 yielded 96.6% sensitivity (95% CI: 88.1, 99.6) and 78.1% specificity (95% CI: 73.8, 82.5). For ProFound AI, a rule-out criterion of lower than score 51 yielded 100% sensitivity (95% CI: 93.8, 100) and 67.0% specificity (95% CI: 62.2, 72.1). The rule-in criterion of higher than score 69 yielded 93.1% (95% CI: 83.3, 98.1) sensitivity and 82.0% (95% CI: 77.9, 86.1) specificity. Conclusion Both AI systems showed high performance in breast cancer detection but lower performance compared with human double-reading.

Indexed as

Artificial IntelligenceBreast NeoplasmsMammographyAgedBreastDeep LearningFemaleHumansMiddle AgedRadiographic Image Interpretation, Computer-AssistedRetrospective StudiesSensitivity and SpecificityArtificial IntelligenceBreastDeep LearningDigital Breast TomosynthesisMammographyOncology

Identifiers

PMID38995172
PMCPMC11287230

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.