Optimizing the pairs of radiologists that double read screening mammograms

Author(s):  
Jessie Gommers ◽  
Craig Abbey ◽  
Fredrik Strand ◽  
Mireille Broeders ◽  
Ioannis Sechopoulos
Keyword(s):  
2021 ◽  
pp. 000313482096628
Author(s):  
Erica Choe ◽  
Hayoung Park ◽  
Ma’at Hembrick ◽  
Christine Dauphine ◽  
Junko Ozao-Choy

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.


Author(s):  
Kristina Lång ◽  
Solveig Hofvind ◽  
Alejandro Rodríguez-Ruiz ◽  
Ingvar Andersson

Abstract Objectives To investigate whether artificial intelligence (AI) can reduce interval cancer in mammography screening. Materials and methods Preceding screening mammograms of 429 consecutive women diagnosed with interval cancer in Southern Sweden between 2013 and 2017 were analysed with a deep learning–based AI system. The system assigns a risk score from 1 to 10. Two experienced breast radiologists reviewed and classified the cases in consensus as true negative, minimal signs or false negative and assessed whether the AI system correctly localised the cancer. The potential reduction of interval cancer was calculated at different risk score thresholds corresponding to approximately 10%, 4% and 1% recall rates. Results A statistically significant correlation between interval cancer classification groups and AI risk score was observed (p < .0001). AI scored one in three (143/429) interval cancer with risk score 10, of which 67% (96/143) were either classified as minimal signs or false negative. Of these, 58% (83/143) were correctly located by AI, and could therefore potentially be detected at screening with the aid of AI, resulting in a 19.3% (95% CI 15.9–23.4) reduction of interval cancer. At 4% and 1% recall thresholds, the reduction of interval cancer was 11.2% (95% CI 8.5–14.5) and 4.7% (95% CI 3.0–7.1). The corresponding reduction of interval cancer with grave outcome (women who died or with stage IV disease) at risk score 10 was 23% (8/35; 95% CI 12–39). Conclusion The use of AI in screen reading has the potential to reduce the rate of interval cancer without supplementary screening modalities. Key Points • Retrospective study showed that AI detected 19% of interval cancer at the preceding screening exam that in addition showed at least minimal signs of malignancy. Importantly, these were correctly localised by AI, thus obviating supplementary screening modalities. • AI could potentially reduce a proportion of particularly aggressive interval cancers. • There was a correlation between AI risk score and interval cancer classified as true negative, minimal signs or false negative.


2019 ◽  
Vol 34 (12) ◽  
pp. 2720-2722 ◽  
Author(s):  
Nancy L. Schoenborn ◽  
Jin Huang ◽  
Cynthia M. Boyd ◽  
Sarah Nowak ◽  
Craig E. Pollack

2009 ◽  
Vol 18 (4) ◽  
pp. 765-773 ◽  
Author(s):  
JirÍ Grim ◽  
Petr Somol ◽  
Michal Haindl ◽  
Jan Danes

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