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Background In the first (prevalent) supplemental MRI screening round of the Dense Tissue and Early Breast Neoplasm Screening (DENSE) trial, a considerable number of breast cancers were found at the cost of an increased false-positive rate (FPR). In incident screening rounds, a lower cancer detection rate (CDR) is expected due to a smaller pool of prevalent cancers, and a reduced FPR, due to the availability of prior MRI examinations. Purpose To investigate screening performance indicators of the second round (incidence round) of the DENSE trial. Materials and Methods The DENSE trial (ClinicalTrials.gov: NCT01315015) is embedded within the Dutch population-based biennial mammography screening program for women aged 50-75 years. MRI examinations were performed between December 2011 and January 2016. Women were eligible for the second round when they again had a negative screening mammogram 2 years after their first MRI. The recall rate, biopsy rate, CDR, FPR, positive predictive values, and distributions of tumor characteristics were calculated and compared with results of the first round using 95% CIs and χ tests. Results A total of 3436 women (median age, 56 years; interquartile range, 48-64 years) underwent a second MRI screening. The CDR was 5.8 per 1000 screening examinations (95% CI: 3.8, 9.0) compared with 16.5 per 1000 screening examinations (95% CI: 13.3, 20.5) in the first round. The FPR was 26.3 per 1000 screening examinations (95% CI: 21.5, 32.3) in the second round versus 79.8 per 1000 screening examinations (95% CI: 72.4, 87.9) in the first round. The positive predictive value for recall was 18% (20 of 110 participants recalled; 95% CI: 12.1, 26.4), and the positive predictive value for biopsy was 24% (20 of 84 participants who underwent biopsy; 95% CI: 16.0, 33.9), both comparable to that of the first round. All tumors in the second round were stage 0-I and node negative. Conclusion The incremental cancer detection rate in the second round was 5.8 per 1000 screening examinations-compared with 16.5 per 1000 screening examinations in the first round. This was accompanied by a strong reduction in the number of false-positive results. © RSNA, 2021 See also the editorial by Moy and Gao in this issue.
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http://dx.doi.org/10.1148/radiol.2021203633 | DOI Listing |
Curr Comput Aided Drug Des
September 2025
Noida Institute of Engineering and Technology (Pharmacy Institute), 19 Knowledge Park-II, Institutional Area, Greater Noida, U.P, 201306, India.
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Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA.
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November 2025
The Key Lab of Health Chemistry & Molecular Diagnosis of Suzhou, College of Chemistry, Chemical Engineering & Materials Science, Soochow University, Suzhou, 215123, PR China; Center of Self-Propelled Nanotechnologies, Suzhou Industrial Park Institute of Services Outsourcing, Suzhou, 215123, PR China
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Department of Public Health, Institute of Tropical Medicine, Nationalestraat 155, 2000 Antwerp, Belgium.
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Drug Discovery and Development Laboratory (DDD Lab), Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India. Electronic address:
Organic pesticide molecules pose toxicity risks to aquatic species such as Chironomus riparius and Lemna gibba. However, limited toxicity data and resource-intensive laboratory tests impede comprehensive assessment. To overcome these obstacles, computational techniques like Quantitative Structure-Toxicity Relationship (QSTR) offer an efficient and effective approach.
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