| 1. |
- Alves, Guido, et al.
(författare)
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CSF Aβ42 predicts early-onset dementia in Parkinson disease.
- 2014
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Ingår i: Neurology. - 1526-632X. ; 82:20, s. 1784-90
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Tidskriftsartikel (refereegranskat)abstract
- To test in vivo the proposal from clinicopathologic studies that β-amyloid (Aβ) pathology shortens the time to dementia in Parkinson disease (PD), and to explore the utility of CSF Aβ and related measures as early prognostic biomarkers of dementia in an incident PD cohort.
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| 2. |
- Førland, Marthe Gurine, et al.
(författare)
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Validation of a new assay for α-synuclein detection in cerebrospinal fluid.
- 2017
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Ingår i: Clinical chemistry and laboratory medicine. - : Walter de Gruyter GmbH. - 1437-4331 .- 1434-6621. ; 55:2, s. 254-260
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Tidskriftsartikel (refereegranskat)abstract
- Abnormal α-synuclein aggregation and deposition is the pathological hallmark of Parkinson's disease (PD) and dementia with Lewy bodies (DLB), but is also found in Alzheimer disease (AD). Therefore, there is a gaining interest in α-synuclein in cerebrospinal fluid (CSF) as potential biomarker for these neurodegenerative diseases. To broaden the available choices of α-synuclein measurement in CSF, we developed and validated a new assay for detecting total α-synuclein.
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| 3. |
- Larsen, Marthe, et al.
(författare)
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AI Risk Score on Screening Mammograms Preceding Breast Cancer Diagnosis
- 2023
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Ingår i: Radiology. - 1527-1315. ; 309:1, s. 1-8
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Tidskriftsartikel (refereegranskat)abstract
- Background Few studies have evaluated the role of artificial intelligence (AI) in prior screening mammography. Purpose To examine AI risk scores assigned to screening mammography in women who were later diagnosed with breast cancer. Materials and Methods Image data and screening information of examinations performed from January 2004 to December 2019 as part of BreastScreen Norway were used in this retrospective study. Prior screening examinations from women who were later diagnosed with cancer were assigned an AI risk score by a commercially available AI system (scores of 1-7, low risk of malignancy; 8-9, intermediate risk; and 10, high risk of malignancy). Mammographic features of the cancers based on the AI score were also assessed. The association between AI score and mammographic features was tested with a bivariate test. Results A total of 2787 prior screening examinations from 1602 women (mean age, 59 years ± 5.1 [SD]) with screen-detected (n = 1016) or interval (n = 586) cancers showed an AI risk score of 10 for 389 (38.3%) and 231 (39.4%) cancers, respectively, on the mammograms in the screening round prior to diagnosis. Among the screen-detected cancers with AI scores available two screening rounds (4 years) before diagnosis, 23.0% (122 of 531) had a score of 10. Mammographic features were associated with AI score for invasive screen-detected cancers (P < .001). Density with calcifications was registered for 13.6% (43 of 317) of screen-detected cases with a score of 10 and 4.6% (15 of 322) for those with a score of 1-7. Conclusion More than one in three cases of screen-detected and interval cancers had the highest AI risk score at prior screening, suggesting that the use of AI in mammography screening may lead to earlier detection of breast cancers.
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| 4. |
- 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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| 5. |
- Larsen, Marthe, et al.
(författare)
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Mammographic density and interval cancers in mammographic screening : Moving towards more personalized screening
- 2023
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Ingår i: Breast. - : Elsevier BV. - 0960-9776. ; 69, s. 306-311
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: The European Society on Breast Imaging has recommended supplemental magnetic resonance imaging (MRI) every two to four years for women with mammographically dense breasts. This may not be feasible in many screening programs. Also, the European Commission Initiative on Breast Cancer suggests not implementing screening with MRI. By analyzing interval cancers and time from screening to diagnosis by density, we present alternative screening strategies for women with dense breasts. Methods: Our BreastScreen Norway cohort included 508 536 screening examinations, including 3125 screen-detected and 945 interval breast cancers. Time from screening to interval cancer was stratified by density measured by an automated software and classified into Volpara Density Grades (VDGs) 1–4. Examinations with volumetric density ≤3.4% were categorized as VDG1, 3.5%–7.4% as VDG2, 7.5%–15.4% as VDG3, and ≥15.5% as VDG4. Interval cancer rates were also determined by continuous density measures. Results: Median time from screening to interval cancer was 496 (IQR: 391–587) days for VDG1, 500 (IQR: 350–616) for VDG2, 482 (IQR: 309–595) for VDG3 and 427 (IQR: 266–577) for VDG4. A total of 35.9% of the interval cancers among VDG4 were detected within the first year of the biennial screening interval. For VDG2, 26.3% were detected within the first year. The highest annual interval cancer rate (2.7 per 1000 examinations) was observed for VDG4 in the second year of the biennial interval. Conclusions: Annual screening of women with extremely dense breasts may reduce the interval cancer rate and increase program-wide sensitivity, especially in settings where supplemental MRI screening is not feasible.
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