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- Abate, E., et al.
(author)
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Combined performance tests before installation of the ATLAS Semiconductor and Transition Radiation Tracking Detectors
- 2008
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In: Journal of Instrumentation. - 1748-0221. ; 3
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Journal article (peer-reviewed)abstract
- The ATLAS (A Toroidal LHC ApparatuS) Inner Detector provides charged particle tracking in the centre of the ATLAS experiment at the Large Hadron Collider (LHC). The Inner Detector consists of three subdetectors: the Pixel Detector, the Semiconductor Tracker (SCT), and the Transition Radiation Tracker (TRT). This paper summarizes the tests that were carried out at the final stage of SCT+TRT integration prior to their installation in ATLAS. The combined operation and performance of the SCT and TRT barrel and endcap detectors was investigated through a series of noise tests, and by recording the tracks of cosmic rays. This was a crucial test of hardware and software of the combined tracker detector systems. The results of noise and cross-talk tests on the SCT and TRT in their final assembled configuration, using final readout and supply hardware and software, are reported. The reconstruction and analysis of the recorded cosmic tracks allowed testing of the offline analysis chain and verification of basic tracker performance parameters, such as efficiency and spatial resolution, in combined operation before installation.
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- Dadaev, T, et al.
(author)
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Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants
- 2018
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In: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 9:1, s. 2256-
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Journal article (peer-reviewed)abstract
- Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling.
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- Egevad, L, et al.
(author)
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Identification of areas of grading difficulties in prostate cancer and comparison with artificial intelligence assisted grading
- 2020
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In: Virchows Archiv : an international journal of pathology. - : Springer Science and Business Media LLC. - 1432-2307. ; 477:6, s. 777-786
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Journal article (peer-reviewed)abstract
- The International Society of Urological Pathology (ISUP) hosts a reference image database supervised by experts with the purpose of establishing an international standard in prostate cancer grading. Here, we aimed to identify areas of grading difficulties and compare the results with those obtained from an artificial intelligence system trained in grading. In a series of 87 needle biopsies of cancers selected to include problematic cases, experts failed to reach a 2/3 consensus in 41.4% (36/87). Among consensus and non-consensus cases, the weighted kappa was 0.77 (range 0.68–0.84) and 0.50 (range 0.40–0.57), respectively. Among the non-consensus cases, four main causes of disagreement were identified: the distinction between Gleason score 3 + 3 with tangential cutting artifacts vs. Gleason score 3 + 4 with poorly formed or fused glands (13 cases), Gleason score 3 + 4 vs. 4 + 3 (7 cases), Gleason score 4 + 3 vs. 4 + 4 (8 cases) and the identification of a small component of Gleason pattern 5 (6 cases). The AI system obtained a weighted kappa value of 0.53 among the non-consensus cases, placing it as the observer with the sixth best reproducibility out of a total of 24. AI may serve as a decision support and decrease inter-observer variability by its ability to make consistent decisions. The grading of these cancer patterns that best predicts outcome and guides treatment warrants further clinical and genetic studies. Results of such investigations should be used to improve calibration of AI systems.
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