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Multicenter Study
. 2024 May 28;26(1):85.
doi: 10.1186/s13058-024-01846-1.

Optimising the diagnostic accuracy of First post-contrAst SubtracTed breast MRI (FAST MRI) through interpretation-training: a multicentre e-learning study, mapping the learning curve of NHS Breast Screening Programme (NHSBSP) mammogram readers using an enriched dataset

Lyn I Jones  1 , Andrea Marshall  2 , Rebecca Geach  3 , Premkumar Elangovan  4 , Elizabeth O'Flynn  5 , Tony Timlin  3 , Sadie McKeown-Keegan  3 , Janice Rose  6 , Sarah Vinnicombe  7 , Sian Taylor-Phillips  2 , Mark Halling-Brown  4 , Janet A Dunn  2 ; FAST MRI Study Group
Collaborators, Affiliations
Multicenter Study

Optimising the diagnostic accuracy of First post-contrAst SubtracTed breast MRI (FAST MRI) through interpretation-training: a multicentre e-learning study, mapping the learning curve of NHS Breast Screening Programme (NHSBSP) mammogram readers using an enriched dataset

Lyn I Jones et al. Breast Cancer Res. .

Abstract

Background: Abbreviated breast MRI (FAST MRI) is being introduced into clinical practice to screen women with mammographically dense breasts or with a personal history of breast cancer. This study aimed to optimise diagnostic accuracy through the adaptation of interpretation-training.

Methods: A FAST MRI interpretation-training programme (short presentations and guided hands-on workstation teaching) was adapted to provide additional training during the assessment task (interpretation of an enriched dataset of 125 FAST MRI scans) by giving readers feedback about the true outcome of each scan immediately after each scan was interpreted (formative assessment). Reader interaction with the FAST MRI scans used developed software (RiViewer) that recorded reader opinions and reading times for each scan. The training programme was additionally adapted for remote e-learning delivery.

Study design: Prospective, blinded interpretation of an enriched dataset by multiple readers.

Results: 43 mammogram readers completed the training, 22 who interpreted breast MRI in their clinical role (Group 1) and 21 who did not (Group 2). Overall sensitivity was 83% (95%CI 81-84%; 1994/2408), specificity 94% (95%CI 93-94%; 7806/8338), readers' agreement with the true outcome kappa = 0.75 (95%CI 0.74-0.77) and diagnostic odds ratio = 70.67 (95%CI 61.59-81.09). Group 1 readers showed similar sensitivity (84%) to Group 2 (82% p = 0.14), but slightly higher specificity (94% v. 93%, p = 0.001). Concordance with the ground truth increased significantly with the number of FAST MRI scans read through the formative assessment task (p = 0.002) but by differing amounts depending on whether or not a reader had previously attended FAST MRI training (interaction p = 0.02). Concordance with the ground truth was significantly associated with reading batch size (p = 0.02), tending to worsen when more than 50 scans were read per batch. Group 1 took a median of 56 seconds (range 8-47,466) to interpret each FAST MRI scan compared with 78 (14-22,830, p < 0.0001) for Group 2.

Conclusions: Provision of immediate feedback to mammogram readers during the assessment test set reading task increased specificity for FAST MRI interpretation and achieved high diagnostic accuracy. Optimal reading-batch size for FAST MRI was 50 reads per batch. Trial registration (25/09/2019): ISRCTN16624917.

Keywords: Abbreviated breast MRI; Breast cancer; Diagnostic accuracy; FAST MRI; Formative assessment; Medical education; Screening; e-learning.

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Conflict of interest statement

Other than the funding sources declared above, the authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
RiViewer software display of an example cancer case from the test set a Maximum intensity projections (MIP). The whole RiViewer screen is shown, including the control panel and timer. b Axial and sagittal slices as they would appear to a reader who has correctly identified a cancer and committed to their opinion for the case. The ground truth is displayed as a red, volume region of interest (ROI) and the reader’s point ROI is displayed as white and black superimposed crosses. Pop-up white text describes the ground truth/cancer histology. c Axial and sagittal slices with the reader’s point ROI hidden to enable the reader to review the cancer appearance prior to moving on to view the next scan. This figure demonstrates how the software enabled trainee readers to compare their own opinion with the ground truth of each scan during formative assessment in the current study
Fig. 2
Fig. 2
Flow diagram detailing participation in FAST MRI interpretation e-learning study
Fig. 3
Fig. 3
Diagnostic accuracy in the receiver operating characteristic (ROC) space a Point estimates of accuracy for individual readers in ROC space b Plot of accuracy in ROC space for each group and attendance or non-attendance with error bars for 95% Confidence Intervals (95%CIs)
Fig. 4
Fig. 4
Changes in reader specificity with number of test-set FAST MRI scans read over time Multi-level generalised mixed model using restricted cubic splines with 4 knots to the number of scans read over time by attendance or non-attendance at previous FAST-MRI training and by group
Fig. 5
Fig. 5
Changes in concordance with the true outcome by number of test-set FAST-MRIs read over time Multi-level generalised mixed model using restricted cubic splines with 4 knots to the number of scans read over time by attendance or non-attendance at previous FAST-MRI training and by group
Fig. 6
Fig. 6
Changes in concordance with the true outcome by number of test-set FAST-MRIs read per batch Multi-level generalised mixed model using restricted cubic splines with 4 knots fitted to the rank order of FAST-MRI scans read per batch by reader group
See this image and copyright information in PMC

References

    1. Bakker MF, De Lange SV, Pijnappel RM, Mann RM, Peeters PHM, Monninkhof EM, et al. Supplemental MRI screening for women with extremely dense breast tissue. New England J Med [Internet]. 2019;381(22):2091–2102. doi: 10.1056/NEJMoa1903986. - DOI - PubMed
    1. Veenhuizen SG, De Lange SV, Bakker MF, Pijnappel RM, Mann RM, Monninkhof EM, et al. Supplemental breast MRI for women with extremely dense breasts : results of the second screening round of the DENSE trial. Radiology [Internet]. 2021;00:1–9. doi: 10.1148/radiol.2021203633. - DOI - PubMed
    1. Comstock CE, Gatsonis C, Newstead GM, Snyder BS, Gareen IF, Bergin JT, et al. Comparison of abbreviated breast MRI vs digital breast tomosynthesis for breast cancer detection among women with dense breasts undergoing screening. JAMA - J Am Med Assoc [Internet]. 2020;323(8):746–756. doi: 10.1001/jama.2020.0572. - DOI - PMC - PubMed
    1. Kuhl CK, Schrading S, Strobel K, Schild HH, Hilgers RD, Bieling HB. Abbreviated breast magnetic resonance imaging (MRI): first postcontrast subtracted images and maximum-intensity projection - A novel approach to breast cancer screening with MRI. J f Clin Oncol [Internet]. 2014;32:2304–2310. doi: 10.1200/JCO.2013.52.5386. - DOI - PubMed
    1. Vinnicombe S, Harvey H, Healy N. Introduction of an abbreviated breast MRI service in the UK as part of the BRAID trial: practicalities, challenges and future directions. Clin Radiol [Internet]. 2021;76(6):427–433. doi: 10.1016/j.crad.2021.01.020. - DOI - PubMed

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