Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study

Abstract Objectives In breast cancer screening, two readers separately examine each woman’s mammograms for signs of cancer. We examined whether preventing the two readers from seeing each other’s decisions (blinding) affects behaviour and outcomes. Methods This cohort study used data from the CO-OPS breast-screening trial (1,119,191 women from 43 screening centres in England) where all discrepant readings were arbitrated. Multilevel models were fitted using Markov chain Monte Carlo to measure whether reader 2 conformed to the decisions of reader 1 when they were not blinded, and the effect of blinding on overall rates of recall for further tests and cancer detection. Differences in positive predictive value (PPV) were assessed using Pearson’s chi-squared test. Results When reader 1 recalls, the probability of reader 2 also recalling was higher when not blinded than when blinded, suggesting readers may be influenced by the other’s decision. Overall, women were less likely to be recalled when reader 2 was blinded (OR 0.923; 95% credible interval 0.864, 0.986), with no clear pattern in cancer detection rate (OR 1.029; 95% credible interval 0.970, 1.089; Bayesian p value 0.832). PPV was 22.1% for blinded versus 20.6% for not blinded (p < 0.001). Conclusions Our results suggest that when not blinded, reader 2 is influenced by reader 1’s decisions to recall (alliterative bias) which would result in bypassing arbitration and negate some of the benefits of double-reading. We found a relationship between blinding the second reader and slightly higher PPV of breast cancer screening, although this analysis may be confounded by other centre characteristics. Key Points • In Europe, it is recommended that breast screening mammograms are analysed by two readers but there is little evidence on the effect of ‘blinding’ the readers so they cannot see each other’s decisions. • We found evidence that when the second reader is not blinded, they are more likely to agree with a recall decision from the first reader and less likely to make an independent judgement (alliterative error). This may reduce overall accuracy through bypassing arbitration. • This observational study suggests an association between blinding the second reader and higher positive predictive value of screening, but this may be confounded by centre characteristics.

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Artificial intelligence (AI) to enhance breast cancer screening: protocol for population-based cohort study of cancer detection

BMJ Open ◽

10.1136/bmjopen-2021-054005 ◽

2022 ◽

Vol 12 (1) ◽

pp. e054005

Author(s):

M Luke Marinovich ◽

Elizabeth Wylie ◽

William Lotter ◽

Alison Pearce ◽

Stacy M Carter ◽

...

Keyword(s):

Breast Cancer ◽

Artificial Intelligence ◽

Cancer Screening ◽

Breast Cancer Screening ◽

Cancer Detection ◽

Screening Programme ◽

Ethics Committee ◽

Population Screening ◽

Interval Cancers ◽

Double Reading

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.

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Artificial Intelligence in Mammography-Based Breast Cancer Screening

Case Medical Research ◽

10.31525/ct1-nct04156880 ◽

2019 ◽

Author(s):

Keyword(s):

Breast Cancer ◽

Artificial Intelligence ◽

Cancer Screening ◽

Breast Cancer Screening

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Evaluation of Patterns in Access to Breast Cancer Care and Breast Cancer Presentation in a Safety Net Patient Population

The American Surgeon ◽

10.1177/0003134820966288 ◽

2021 ◽

pp. 000313482096628

Author(s):

Erica Choe ◽

Hayoung Park ◽

Ma’at Hembrick ◽

Christine Dauphine ◽

Junko Ozao-Choy

Keyword(s):

Breast Cancer ◽

Cancer Screening ◽

Breast Cancer Screening ◽

Cancer Care ◽

Patient Population ◽

Safety Net ◽

Minority Women ◽

Breast Cancer Care ◽

Safety Net Hospital ◽

Screening Mammograms

Background While prior studies have shown the apparent health disparities in breast cancer diagnosis and treatment, there is a gap in knowledge with respect to access to breast cancer care among minority women. Methods We performed a retrospective analysis of patients with newly diagnosed breast cancer from 2014 to 2016 to evaluate how patients presented and accessed cancer care services in our urban safety net hospital. Patient demographics, cancer stage, history of breast cancer screening, and process of referral to cancer care were collected and analyzed. Results Of the 202 patients identified, 61 (30%) patients were younger than the age of 50 and 75 (63%) were of racial minority background. Only 39% of patients with a new breast cancer were diagnosed on screening mammogram. Women younger than the age of 50 ( P < .001) and minority women ( P < .001) were significantly less likely to have had any prior screening mammograms. Furthermore, in patients who met the screening guideline age, more than half did not have prior screening mammograms. Discussion Future research should explore how to improve breast cancer screening rates within our county patient population and the potential need for revision of screening guidelines for minority patients.

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