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- Botteri, Edoardo, et al.
(författare)
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Characteristics of non-participants in a randomized colorectal cancer screening trial comparing sigmoidoscopy and faecal immunochemical testing.
- 2022
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Ingår i: International journal of cancer. - : Wiley. - 1097-0215 .- 0020-7136. ; 151:3, s. 361-371
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Tidskriftsartikel (refereegranskat)abstract
- Public health systems should guarantee universal access to health care services, including cancer screening. We assessed whether certain population subgroups were underrepresented among participants in colorectal cancer screening with sigmoidoscopy and faecal immunochemical testing (FIT). Between 2012 and 2019, about 140,000 individuals aged 50-74years were randomly invited to once-only sigmoidoscopy or first round of FIT screening. This study included 46,919 individuals invited to sigmoidoscopy and 70,019 to FIT between 2012 and 2017. We used logistic regression models to evaluate if demographic and socioeconomic factors and use of certain drugs were associated with participation. 24,159 (51.5%) individuals attended sigmoidoscopy and 40,931 (58.5%) FIT screening. Male gender, young age, low education and income, being retired or unemployed, living alone, being an immigrant, long driving time to screening centre, and use of antidiabetic and psychotropic drugs were associated with low participation in both screening groups. Many of these factors also predicted low acceptance of colonoscopy after positive FIT. While male gender, young age and living alone were more strongly associated with non-participation in FIT than sigmoidoscopy, low education and income, being retired or immigrant and long driving time were more strongly associated with non-participation in sigmoidoscopy than FIT. In conclusion, participation was lower in sigmoidoscopy than FIT. Predictors of non-participation were similar between arms. However, low socioeconomic status, being an immigrant and long driving time affected participation more in sigmoidoscopy screening, suggesting that FIT may guarantee more equal access to screening services than sigmoidoscopy.
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| 2. |
- Larsen, Marthe, et al.
(författare)
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Artificial Intelligence Evaluation of 122 969 Mammography Examinations from a Population-based Screening Program
- 2022
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Ingår i: Radiology. - : Radiological Society of North America (RSNA). - 1527-1315 .- 0033-8419. ; 303:3, s. 502-511
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Tidskriftsartikel (refereegranskat)abstract
- Background Artificial intelligence (AI) has shown promising results for cancer detection with mammographic screening. However, evidence related to the use of AI in real screening settings remain sparse. Purpose To compare the performance of a commercially available AI system with routine, independent double reading with consensus as performed in a population-based screening program. Furthermore, the histopathologic characteristics of tumors with different AI scores were explored. Materials and Methods In this retrospective study, 122 969 screening examinations from 47 877 women performed at four screening units in BreastScreen Norway from October 2009 to December 2018 were included. The data set included 752 screen-detected cancers (6.1 per 1000 examinations) and 205 interval cancers (1.7 per 1000 examinations). Each examination had an AI score between 1 and 10, where 1 indicated low risk of breast cancer and 10 indicated high risk. Threshold 1, threshold 2, and threshold 3 were used to assess the performance of the AI system as a binary decision tool (selected vs not selected). Threshold 1 was set at an AI score of 10, threshold 2 was set to yield a selection rate similar to the consensus rate (8.8%), and threshold 3 was set to yield a selection rate similar to an average individual radiologist (5.8%). Descriptive statistics were used to summarize screening outcomes. Results A total of 653 of 752 screen-detected cancers (86.8%) and 92 of 205 interval cancers (44.9%) were given a score of 10 by the AI system (threshold 1). Using threshold 3, 80.1% of the screen-detected cancers (602 of 752) and 30.7% of the interval cancers (63 of 205) were selected. Screen-detected cancer with AI scores not selected using the thresholds had favorable histopathologic characteristics compared to those selected; opposite results were observed for interval cancer. Conclusion The proportion of screen-detected cancers not selected by the artificial intelligence (AI) system at the three evaluated thresholds was less than 20%. The overall performance of the AI system was promising according to cancer detection.
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