A systematic review of decision aids for mammography screening: Focus on outcomes and characteristics

Esmaeili, Marzieh; Ayyoubzadeh, Seyed Mohammad; Javanmard, Zohreh; Kalhori, Sharareh R. Niakan

doi:10.1016/j.ijmedinf.2021.104406

Cited by 11 publications

(12 citation statements)

References 71 publications

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“…Foreign literature has fed into the reflection of this study. International evidence on whether DA use modifies screening participation is scarce and results are contradictory ( 24 , 44 , 45 , 49 ). A Cochrane literature review ( 44 ) reported that better informing women about their cancer risk and screening may increase participation, reporting a low level of evidence based on 3 studies ( 44 , 50 ).…”

Section: Discussionmentioning

confidence: 99%

“…DAs should simply set out the best available evidence and the implications in terms of risks and benefits ( 21 – 23 ). Several DAs dealing with BC screening have been developed internationally ( 21 , 23 , 24 ). In 2018, the National Cancer Institute (INCa) funded the development of a DA specific to the French context as part of a call for research projects.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Breast cancer: a randomized controlled trial assessing the effect of a decision aid on mammography screening uptake: study protocol

Hild¹,

Teigné²,

Ferrat

et al. 2023

Front. Oncol.

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IntroductionBreast cancer (BC) is the primary cancer among women. The World Health Organization recommends a bilateral screening mammogram every 2 years for women aged 50 to 74 years. However, it has been shown that there is an absence of information about the benefits and risks of screening. Shared medical decision-making is important to ensure patients are involved in the decision process. Decision aids can facilitative this decision-making process. This article presents a protocol to evaluate the effect of a decision aid on participation rates in the French organized BC screening program.Methods and analysisDesign and setting. The design is a 2 arm randomized controlled study, performed in the Pays de la Loire region (French West Coast). Randomization will be based on general medicine practices (Primary Care).ParticipantsWomen aged between 50 and 74 years, eligible for BC screening. In this region, there are 75000 women, and 2800 general practitioners eligible for recruitment.InterventionIn the « Decision aid for organized cancer screening » arm, the intervention will distribute invitation letters to eligible women combined with the provision of decision aid to these women and their general practitioners and an incentive to implement shared medical decision-making. In the « Standard organized cancer screening » arm, only the screening invitation will be sent to eligible women.Primary endpointBC screening participation rates will be assessed after an 18-month follow-up period.Statistical analysisIn this non-inferiority trial, the percentage of women who are up-to-date with their screening at 18 months after the intervention will be compared across arms using a generalized mixed linear model.DiscussionThe research team expect to demonstrate that providing a better explanation of the benefits and risks of BC screening is not at odds with screening participation. The study results should help policy makers thinking about implementing shared medical decision-making within the framework of organized BC screening programs in the future.Ethics and disseminationOn 6 December 2021, the protocol received a favorable opinion from the French Committee for the Protection of Persons (2021-A01583-38). This study is registered with ClinicalTrials.gov, number NCT05607849. (Version 1, November 7, 2022; https://www.clinicaltrials.gov/ct2/show/NCT05607849). The study findings will be used for publication in peer-reviewed scientific journals and presentations in scientific meetings.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Breast cancer: a randomized controlled trial assessing the effect of a decision aid on mammography screening uptake: study protocol

Hild¹,

Teigné²,

Ferrat

et al. 2023

Front. Oncol.

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“…Unlike the brain abnormalities that are mostly detectable, breast cancer anomalous tissue detection using mammography images is prone to errors [69]. The variability in lesion morphology makes breast cancer detection and its characterization challenging [70,71].…”

Section: B Mias-mammo and Mias-patches-mammo Datasetsmentioning

confidence: 99%

Generative Adversarial Networks for Anomaly Detection in Biomedical Imaging: A Study on Seven Medical Image Datasets

Esmaeili

Toosi²,

Roshanpoor

et al. 2023

IEEE Access

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“…Mammography wears the crown of being the gold standard among several imaging modalities because it offers the potential of early detection of pathology [2]. However, mammography is a 2D method that reduces the ability to visualize lesions in case of prevalent glandular component in dense breast.…”

Section: Introductionmentioning

confidence: 99%

Shape-Based Breast Lesion Classification Using Digital Tomosynthesis Images: The Role of Explainable Artificial Intelligence

et al. 2022

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Computer-aided diagnosis (CAD) systems can help radiologists in numerous medical tasks including classification and staging of the various diseases. The 3D tomosynthesis imaging technique adds value to the CAD systems in diagnosis and classification of the breast lesions. Several convolutional neural network (CNN) architectures have been proposed to classify the lesion shapes to the respective classes using a similar imaging method. However, not only is the black box nature of these CNN models questionable in the healthcare domain, but so is the morphological-based cancer classification, concerning the clinicians. As a result, this study proposes both a mathematically and visually explainable deep-learning-driven multiclass shape-based classification framework for the tomosynthesis breast lesion images. In this study, authors exploit eight pretrained CNN architectures for the classification task on the previously extracted regions of interests images containing the lesions. Additionally, the study also unleashes the black box nature of the deep learning models using two well-known perceptive explainable artificial intelligence (XAI) algorithms including Grad-CAM and LIME. Moreover, two mathematical-structure-based interpretability techniques, i.e., t-SNE and UMAP, are employed to investigate the pretrained models’ behavior towards multiclass feature clustering. The experimental results of the classification task validate the applicability of the proposed framework by yielding the mean area under the curve of 98.2%. The explanability study validates the applicability of all employed methods, mainly emphasizing the pros and cons of both Grad-CAM and LIME methods that can provide useful insights towards explainable CAD systems.

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A systematic review of decision aids for mammography screening: Focus on outcomes and characteristics

Cited by 11 publications

References 71 publications

Breast cancer: a randomized controlled trial assessing the effect of a decision aid on mammography screening uptake: study protocol

Breast cancer: a randomized controlled trial assessing the effect of a decision aid on mammography screening uptake: study protocol

Generative Adversarial Networks for Anomaly Detection in Biomedical Imaging: A Study on Seven Medical Image Datasets

Shape-Based Breast Lesion Classification Using Digital Tomosynthesis Images: The Role of Explainable Artificial Intelligence

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