Evidence map›Paper›PMID 33486604›Full record

ArticleEuropean radiology2021

Can artificial intelligence reduce the interval cancer rate in mammography screening?

Kristina Lång, Solveig Hofvind, Alejandro Rodríguez-Ruiz, Ingvar Andersson

Registry-linked trialOpen access · hybridAbstract read
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. It is linked to trial NCT05048095 (The Use of AI as a Third Reader and During Consensus in a Double Reading Breast Cancer Screening Program in Sweden), which is not on this map. Cited by 50 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed, 2 pooled it
9.4field-weighted citation impact, top 2% 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.

NCT05048095 completednot on this map

The Use of AI as a Third Reader and During Consensus in a Double Reading Breast Cancer Screening Program in Sweden

TypeobservationalSponsorOstergotland County Council, SwedenRan2021 to 2022Enrolled15,500ConditionsBreast CancerArmsAI cancer detection system
3 · Its place in the literature

Who cites it

50 citing papers in PubMed, 2 syntheses or guidelines pooled it, 95 citations in OpenAlex.

  1. Pooled it
  2. Artificial intelligence in mammography: a systematic review of the external validation.Revista brasileira de ginecologia e obstetricia : revista da Federacao Brasileira das Sociedades de Ginecologia e Obstetricia · 2024
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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

4 authors at 2 institutions in 4 countries.

Kristina LångDiagnostic Radiology, Department of Translational Medicine, Lund University, Inga Maria Nilssons gata 47, SE-20502, Malmö, Sweden. kristina.lang@med.lu.se.ORCID http://orcid.org/0000-0001-9250-1897
Solveig HofvindCancer Registry of Norway, Oslo, Norway.
Alejandro Rodríguez-RuizScreenPoint Medical BV, Toernooiveld 300, 6525 EC, Nijmegen, The Netherlands.
Ingvar AnderssonDiagnostic Radiology, Department of Translational Medicine, Lund University, Inga Maria Nilssons gata 47, SE-20502, Malmö, Sweden.
Lund University · SECancer Registry of Norway · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo investigate whether artificial intelligence (AI) can reduce interval cancer in mammography screening. MATERIALS AND

methodsPreceding screening mammograms of 429 consecutive women diagnosed with interval cancer in Southern Sweden between 2013 and 2017 were analysed with a deep learning-based AI system. The system assigns a risk score from 1 to 10. Two experienced breast radiologists reviewed and classified the cases in consensus as true negative, minimal signs or false negative and assessed whether the AI system correctly localised the cancer. The potential reduction of interval cancer was calculated at different risk score thresholds corresponding to approximately 10%, 4% and 1% recall rates.

resultsA statistically significant correlation between interval cancer classification groups and AI risk score was observed (p < .0001). AI scored one in three (143/429) interval cancer with risk score 10, of which 67% (96/143) were either classified as minimal signs or false negative. Of these, 58% (83/143) were correctly located by AI, and could therefore potentially be detected at screening with the aid of AI, resulting in a 19.3% (95% CI 15.9-23.4) reduction of interval cancer. At 4% and 1% recall thresholds, the reduction of interval cancer was 11.2% (95% CI 8.5-14.5) and 4.7% (95% CI 3.0-7.1). The corresponding reduction of interval cancer with grave outcome (women who died or with stage IV disease) at risk score 10 was 23% (8/35; 95% CI 12-39).

conclusionThe use of AI in screen reading has the potential to reduce the rate of interval cancer without supplementary screening modalities. KEY POINTS: • Retrospective study showed that AI detected 19% of interval cancer at the preceding screening exam that in addition showed at least minimal signs of malignancy. Importantly, these were correctly localised by AI, thus obviating supplementary screening modalities. • AI could potentially reduce a proportion of particularly aggressive interval cancers. • There was a correlation between AI risk score and interval cancer classified as true negative, minimal signs or false negative.

Indexed as

Breast NeoplasmsEarly Detection of CancerArtificial IntelligenceFemaleHumansMammographyMass ScreeningRetrospective StudiesSwedenArtificial intelligenceBreast cancerMammographyMass screening

Identifiers

PMID33486604
PMCPMC8270858
OpenAlexW3121131956

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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.