Dépistage organisé du cancer du sein : point de vue du radiologue

Ceugnart, L.; Rocourt, N.; Haj-Amor, Mariem Ben; Bachelle, F.; Boulanger, Thomas; Chaveron, C.; Pouliquen, G.; Renaud, Armelle; Taïeb, S.

doi:10.1016/j.bulcan.2019.03.003

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…One of the objectives was "to combat inequality in uptake of and access to screening, and to increase the efficiency of programmes, in order to reduce avoidable deaths and the more severe treatments associated with delayed care" [7]. The French Cancer Registry confirms a positive impact of the screening campaigns on reducing the mortality rate among participants, although the participation rates are still lower than expected [8][9][10]. The participation rate in France for organized breast cancer screening was 48.6% in 2019 [11], colorectal cancer screening was 30.5% in 2018-2019 [12], and cervical cancer was 59.5% in 2019, a rate which has been stable since 2012 [13].…”

Section: Introductionmentioning

confidence: 99%

“…Given that cancer screening rates are suboptimal for lowincome, low-literate, and other disadvantaged populations in France [8][9][10], evidence is needed to develop responsive interventions that increase cancer screening uptake and reduce cancer inequities. Very little is known on the cancer-related knowledge, awareness, self-efficacy, and perceptions of screening barriers among low-income, illiterate immigrant women in France.…”

Section: Introductionmentioning

confidence: 99%

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Addressing Cancer Screening Inequities by Promoting Cancer Prevention Knowledge, Awareness, Self-Efficacy, and Screening Uptake Among Low-Income and Illiterate Immigrant Women in France

Jesús

Rodrigue²,

Rahmani³

et al. 2021

Int J Public Health

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Objective: Cancer screening rates are suboptimal for disadvantaged populations in France, yet little evidence exists on their cancer-related knowledge and screening barriers. The main objective of this study was to examine cancer-related knowledge, awareness, self-efficacy, and perceptions of screening barriers among low-income, illiterate immigrant women in France following an 8-weeks cancer educational intervention.Methods: Semi-structured qualitative interviews were conducted with 164 female participants in the Ain department of France between January 2019 and March 2020. Adopting the Health Belief Model as an intervention and analytic framework, salient themes were identified using qualitative thematic analysis.Results: Increased levels of perceived susceptibility to and perceived severity of cancer contributed to higher motivation to get screened. Barriers to screening included low French proficiency, shame surrounding illiteracy, and constant worries due to precarious living conditions. Perceived benefits (e.g., valuing one’s health and health-promoting behaviors), cues to action from a trusted source, and greater self-efficacy (e.g., more autonomous in healthcare-seeking) outweighed perceived barriers, including cultural barriers.Conclusions: Implications include developing audience-responsive targeted cancer screening communication strategies and educational materials to increase screening rates and reduce cancer and cancer screening inequities.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Addressing Cancer Screening Inequities by Promoting Cancer Prevention Knowledge, Awareness, Self-Efficacy, and Screening Uptake Among Low-Income and Illiterate Immigrant Women in France

Jesús

Rodrigue²,

Rahmani³

et al. 2021

Int J Public Health

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Impact of artificial intelligence in breast cancer screening with mammography

et al. 2022

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Objectives To demonstrate that radiologists, with the help of artificial intelligence (AI), are able to better classify screening mammograms into the correct breast imaging reporting and data system (BI-RADS) category, and as a secondary objective, to explore the impact of AI on cancer detection and mammogram interpretation time. Methods A multi-reader, multi-case study with cross-over design, was performed, including 314 mammograms. Twelve radiologists interpreted the examinations in two sessions delayed by a 4 weeks wash-out period with and without AI support. For each breast of each mammogram, they had to mark the most suspicious lesion (if any) and assign it with a forced BI-RADS category and a level of suspicion or “continuous BI-RADS 100”. Cohen’s kappa correlation coefficient evaluating the inter-observer agreement for BI-RADS category per breast, and the area under the receiver operating characteristic curve (AUC), were used as metrics and analyzed. Results On average, the quadratic kappa coefficient increased significantly when using AI for all readers [κ = 0.549, 95% CI (0.528–0.571) without AI and κ = 0.626, 95% CI (0.607–0.6455) with AI]. AUC was significantly improved when using AI (0.74 vs 0.77, p = 0.004). Reading time was not significantly affected for all readers (106 s without AI and vs 102 s with AI; p = 0.754). Conclusions When using AI, radiologists were able to better assign mammograms with the correct BI-RADS category without slowing down the interpretation time.

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