The authors define ordered subset processing for standard algorithms (such as expectation maximization, EM) for image restoration from projections. Ordered subsets methods group projection data into an ordered sequence of subsets (or blocks). An iteration of ordered subsets EM is defined as a single pass through all the subsets, in each subset using the current estimate to initialize application of EM with that data subset. This approach is similar in concept to block-Kaczmarz methods introduced by Eggermont et al. (1981) for iterative reconstruction. Simultaneous iterative reconstruction (SIRT) and multiplicative algebraic reconstruction (MART) techniques are well known special cases. Ordered subsets EM (OS-EM) provides a restoration imposing a natural positivity condition and with close links to the EM algorithm. OS-EM is applicable in both single photon (SPECT) and positron emission tomography (PET). In simulation studies in SPECT, the OS-EM algorithm provides an order-of-magnitude acceleration over EM, with restoration quality maintained.

Background:The host inflammatory response has a vital role in carcinogenesis and tumour progression. We examined the prognostic value of inflammatory markers (albumin, white-cell count and its components, and platelets) in pre-treated patients with advanced renal cell carcinoma (RCC).Methods:Using data from a randomised trial, multivariable proportional hazards models were generated to examine the impact of inflammatory markers and established prognostic factors (performance status, calcium, and haemoglobin) on overall survival (OS). We evaluated a new prognostic classification incorporating additional information from inflammatory markers.Results:Of the 416 patients, 362 were included in the analysis. Elevated neutrophil counts, elevated platelet counts, and a high neutrophil–lymphocyte ratio were significant independent predictors for shorter OS in a model with established prognostic factors. The addition of inflammatory markers improves the discriminatory value of the prognostic classification as compared with established factors alone (C-statistic 0.673 vs 0.654, P=0.002 for the difference), with 25.8% (P=0.004) of patients more appropriately classified using the new classification.Conclusion:Markers of systemic inflammation contribute significantly to prognostic classification in addition to established factors for pre-treated patients with advanced RCC. Upon validation of these data in independent studies, stratification of patients using these markers in future clinical trials is recommended.

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Background Risk prediction models may be valuable to identify women at risk of pre-eclampsia to guide aspirin prophylaxis in early pregnancy.Objective To assess the performance of 'simple' risk models for pre-eclampsia that use routinely collected maternal characteristics; compare with 'specialised' models that include specialised tests; and to guideline recommended decision rules.Search strategy MEDLINE, Embase and PubMed were searched to June 2014.Selection criteria We included studies that developed or validated pre-eclampsia risk models using maternal characteristics with or without specialised tests and reported model performance.Data collection and analysis We extracted data on study characteristics; model predictors, validation and performance including area under the curve (AUC), sensitivity and specificity.Main results We identified 29 studies that developed 70 models including 22 simple models. Studies included 151-9149 women with a pre-eclampsia prevalence of 1.2-9.5%. No single predictor was included in all models. Four simple models were externally validated, with a model using parity, pre-eclampsia history, race, chronic hypertension and conception method to predict early-onset pre-eclampsia achieving the highest AUC (0.76, 95% CI 0.74-0.77). Nine studies comparing simple versus specialized models in the same population reported AUC favouring specialised models. A simple model achieved fewer false positives than a guideline recommended risk factor list, but sensitivity to classify risk for aspirin prophylaxis was not assessed.Conclusion Validated simple pre-eclampsia risk models demonstrate good risk discrimination that can be improved with specialised tests. Further research is needed to determine their clinical value to guide aspirin prophylaxis compared with decision rules.Keywords Aspirin, pre-eclampsia, risk factors, risk prediction models, systematic review, validation.Tweetable abstract Pre-eclampsia risk models using maternal factors show good risk discrimination to guide aspirin prophylaxis.Please cite this paper as: Al-Rubaie ZTA, Askie LM, Ray JG, Hudson HM, Lord SJ. The performance of risk prediction models for pre-eclampsia using routinely collected maternal characteristics and comparison with models that include specialised tests and with clinical guideline decision rules: a systematic review.