| 1. |
- Chatzidionysiou, K, et al.
(författare)
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Risk of lung cancer in rheumatoid arthritis and in relation to autoantibody positivity and smoking
- 2022
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Ingår i: RMD open. - : BMJ. - 2056-5933. ; 8:2
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Tidskriftsartikel (refereegranskat)abstract
- Lung cancer is a common malignancy in rheumatoid arthritis (RA). Since smoking is a risk factor for both (seropositive) RA and lung cancer, it remains unclear whether RA, in itself, increases lung cancer risk.MethodsWe performed a population-based cohort study of patients with RA and individually matched general population reference individuals identified in Swedish registers and from the Epidemiological Investigation of RA early RA study, prospectively followed for lung cancer occurrence 1995–2018. We calculated incidence rates and performed Cox regression to estimate HRs including 95% CIs of lung cancer, taking smoking and RA serostatus into account.ResultsOverall, we included 44 101 patients with RA (590 incident lung cancers, 56 per 100 000), and 216 495 matched general population individuals (1691 incident lung cancers, 33 per 100 000), corresponding to a crude HR (95% CI) of 1.76 (1.60 to 1.93). In subset analyses, this increased risk remained after adjustment for smoking (HR 1.77, 95% CI 1.06 to 2.97). Compared with general population subjects who were never smokers, patients with RA who were ever smokers had almost seven times higher risk of lung cancer. In RA, seropositivity was a significant lung cancer risk factor, even when adjusted for smoking, increasing the incidence 2–6 times. At 20 years, the risk in patients with RA was almost 3%, overall and over 4% for patients who were ever smokers and had at least one RA autoantibody.ConclusionsSeropositive RA is a risk factor for lung cancer over and above what can be explained by smoking, although residual confounding by smoking or other airway exposures cannot be formally excluded. There is a need for increased awareness and potentially for regular lung cancer screening, at least in a subset of patients with RA.
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| 2. |
- Georgescu, R., et al.
(författare)
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Particle PHD forward filter-backward simulator for targets in close proximity
- 2013
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Ingår i: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - 1520-6149. - 9781479903566 ; , s. 6387-6391
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Konferensbidrag (refereegranskat)abstract
- In this work, we introduce the particle PHD forward filter - backward simulator (PHD-FFBSi) capable of dealing with uncertainties in the labeling of tracks that appear when tracking two targets in close proximity with measurements that do not discriminate between them. The Forward Filter Backward Simulator is a smoothing technique based on rejection sampling for the calculation of the probabilities of association between targets and tracks. The forward filter is a particle implementation of the Probability Hypothesis Density (PHD) filter that presents advantages over an SIR filter. Difficulties that arise due to the presence of target birth and death processes are addressed through modifications to the fast FFBSi. Simulations show the new particle filter of asymptotically linear complexity in the number of particles calculates correct target label probabilities at varying levels of measurement noise.
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| 3. |
- Georgescu, R., et al.
(författare)
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Two linear complexity particle filters capable of maintaining target label probabilities for targets in close proximity
- 2012
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Ingår i: 15th International Conference on Information Fusion, FUSION 2012. Singapore, 7 - 12 September 2012. - 9780982443859 ; , s. 2370-2377
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Konferensbidrag (refereegranskat)abstract
- In this work, we introduce two particle filters of linear complexity in the number of particles that take distinct approaches to solving the problem of tracking two targets in close proximity. We operate in the regime in which measurements do not discriminate between targets and hence uncertainties in the labeling of the tracks arise. For simplicity, we limit our study to the two target case for which there are only two possible associations between targets and tracks. The proposed Approximate Set Particle Filter (ASPF) introduces some approximations but has similar complexity and still provides much more accurate descriptions of the posterior uncertainties compared to standard particle filters. The fast Forward Filter Unlabeled Backward Simulator (fast FFUBSi) employs a smoothing technique based on rejection sampling for the calculation of target label probabilities. Simulations show that neither particle filter suffers from track coalescence (when outputting MMOSPA estimates) and both calculate correct target label probabilities.
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