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The future of breast cancer screening: what do participants in a breast cancer screening program think about automation using artificial intelligence?

ABSTRACT: Background:If screening participants do not trust computerized decision-making, screening participation may be affected by the introduction of such methods. Purpose:To survey breast cancer screening participants' attitudes towards potential future uses of computerization. Material and Methods:A survey was constructed. Women in a breast cancer screening program were invited via the final report letter to participate. Data were collected from February 2018 to March 2019 and 2196 surveys were completed. Questions asked participants to rate propositions using Likert scales. Data analysis was done using ?2 and logistic regression tests. Results:The mean age of participants was 61 years. Response rate was 1.3%. Of the submitted surveys, 97.5% were complete; 38% of respondents reported a preference for a computer-only examination. The highest level of confidence was given a computer-only reading followed by a physician reading. Participants with?>?12 years of education were more likely to prefer a computer-only reading (odds ratio [OR] 1.655, 95% confidence interval [CI] 1.168-2.344), had a greater trust in letting a computer determine screening intervals and the need for a supplemental MRI (OR 1.606, 95% CI 1.171-2.202 and OR 1.577, 95% CI 1.107-2.247, respectively). Age was not found to be a significant predictor. Conclusion:A high level of trust in computerized decision-making was expressed. Higher age was associated with a lower understanding of technology but did not affect attitudes to computerized decision-making. A lower level of education was associated with a lower trust in computerization. This may be valuable knowledge for future studies.

SUBMITTER: Jonmarker O

PROVIDER: S-EPMC6901736 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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The future of breast cancer screening: what do participants in a breast cancer screening program think about automation using artificial intelligence?

Jonmarker Olof O Strand Fredrik F Brandberg Yvonne Y Lindholm Peter P

Acta radiologica open 20191204 12

<h4>Background</h4>If screening participants do not trust computerized decision-making, screening participation may be affected by the introduction of such methods.<h4>Purpose</h4>To survey breast cancer screening participants' attitudes towards potential future uses of computerization.<h4>Material and methods</h4>A survey was constructed. Women in a breast cancer screening program were invited via the final report letter to participate. Data were collected from February 2018 to March 2019 and 2 ...[more]

PMID: 31839989

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Project description:ObjectivesDigital breast tomosynthesis (DBT) can detect more cancers than the current standard breast screening method, digital mammography (DM); however, it can substantially increase the reading workload and thus hinder implementation in screening. Artificial intelligence (AI) might be a solution. The aim of this study was to retrospectively test different ways of using AI in a screening workflow.MethodsAn AI system was used to analyse 14,772 double-read single-view DBT examinations from a screening trial with paired DM double reading. Three scenarios were studied: if AI can identify normal cases that can be excluded from human reading; if AI can replace the second reader; if AI can replace both readers. The number of detected cancers and false positives was compared with DM or DBT double reading.ResultsBy excluding normal cases and only reading 50.5% (7460/14,772) of all examinations, 95% (121/127) of the DBT double reading detected cancers could be detected. Compared to DM screening, 27% (26/95) more cancers could be detected (p < 0.001) while keeping recall rates at the same level. With AI replacing the second reader, 95% (120/127) of the DBT double reading detected cancers could be detected-26% (25/95) more than DM screening (p < 0.001)-while increasing recall rates by 53%. AI alone with DBT has a sensitivity similar to DM double reading (p = 0.689).ConclusionAI can open up possibilities for implementing DBT screening and detecting more cancers with the total reading workload unchanged. Considering the potential legal and psychological implications, replacing the second reader with AI would probably be most the feasible approach.Key points• Breast cancer screening with digital breast tomosynthesis and artificial intelligence can detect more cancers than mammography screening without increasing screen-reading workload. • Artificial intelligence can either exclude low-risk cases from double reading or replace the second reader. • Retrospective study based on paired mammography and digital breast tomosynthesis screening data.

Project description:IntroductionArtiﬁcial intelligence (AI) algorithms for interpreting mammograms have the potential to improve the effectiveness of population breast cancer screening programmes if they can detect cancers, including interval cancers, without contributing substantially to overdiagnosis. Studies suggesting that AI has comparable or greater accuracy than radiologists commonly employ 'enriched' datasets in which cancer prevalence is higher than in population screening. Routine screening outcome metrics (cancer detection and recall rates) cannot be estimated from these datasets, and accuracy estimates may be subject to spectrum bias which limits generalisabilty to real-world screening. We aim to address these limitations by comparing the accuracy of AI and radiologists in a cohort of consecutive of women attending a real-world population breast cancer screening programme.Methods and analysisA retrospective, consecutive cohort of digital mammography screens from 109 000 distinct women was assembled from BreastScreen WA (BSWA), Western Australia's biennial population screening programme, from November 2016 to December 2017. The cohort includes 761 screen-detected and 235 interval cancers. Descriptive characteristics and results of radiologist double-reading will be extracted from BSWA outcomes data collection. Mammograms will be reinterpreted by a commercial AI algorithm (DeepHealth). AI accuracy will be compared with that of radiologist single-reading based on the diﬀerence in the area under the receiver operating characteristic curve. Cancer detection and recall rates for combined AI-radiologist reading will be estimated by pairing the first radiologist read per screen with the AI algorithm, and compared with estimates for radiologist double-reading.Ethics and disseminationThis study has ethical approval from the Women and Newborn Health Service Ethics Committee (EC00350) and the Curtin University Human Research Ethics Committee (HRE2020-0316). Findings will be published in peer-reviewed journals and presented at national and international conferences. Results will also be disseminated to stakeholders in Australian breast cancer screening programmes and policy makers in population screening.

Project description:ObjectiveTo examine the accuracy of artificial intelligence (AI) for the detection of breast cancer in mammography screening practice.DesignSystematic review of test accuracy studies.Data sourcesMedline, Embase, Web of Science, and Cochrane Database of Systematic Reviews from 1 January 2010 to 17 May 2021.Eligibility criteriaStudies reporting test accuracy of AI algorithms, alone or in combination with radiologists, to detect cancer in women's digital mammograms in screening practice, or in test sets. Reference standard was biopsy with histology or follow-up (for screen negative women). Outcomes included test accuracy and cancer type detected.Study selection and synthesisTwo reviewers independently assessed articles for inclusion and assessed the methodological quality of included studies using the QUality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A single reviewer extracted data, which were checked by a second reviewer. Narrative data synthesis was performed.ResultsTwelve studies totalling 131 822 screened women were included. No prospective studies measuring test accuracy of AI in screening practice were found. Studies were of poor methodological quality. Three retrospective studies compared AI systems with the clinical decisions of the original radiologist, including 79 910 women, of whom 1878 had screen detected cancer or interval cancer within 12 months of screening. Thirty four (94%) of 36 AI systems evaluated in these studies were less accurate than a single radiologist, and all were less accurate than consensus of two or more radiologists. Five smaller studies (1086 women, 520 cancers) at high risk of bias and low generalisability to the clinical context reported that all five evaluated AI systems (as standalone to replace radiologist or as a reader aid) were more accurate than a single radiologist reading a test set in the laboratory. In three studies, AI used for triage screened out 53%, 45%, and 50% of women at low risk but also 10%, 4%, and 0% of cancers detected by radiologists.ConclusionsCurrent evidence for AI does not yet allow judgement of its accuracy in breast cancer screening programmes, and it is unclear where on the clinical pathway AI might be of most benefit. AI systems are not sufficiently specific to replace radiologist double reading in screening programmes. Promising results in smaller studies are not replicated in larger studies. Prospective studies are required to measure the effect of AI in clinical practice. Such studies will require clear stopping rules to ensure that AI does not reduce programme specificity.Study registrationProtocol registered as PROSPERO CRD42020213590.

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The future of breast cancer screening: what do participants in a breast cancer screening program think about automation using artificial intelligence?

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The future of breast cancer screening: what do participants in a breast cancer screening program think about automation using artificial intelligence?

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