BACKGROUND Major issues in the implementation of screening for lung cancer by means of low-dose computed tomography (CT) are the definition of a positive result and the management of lung nodules detected on the scans. We conducted a population-based prospective study to determine factors predicting the probability that lung nodules detected on the first screening low-dose CT scans are malignant or will be found to be malignant on follow-up. METHODS We analyzed data from two cohorts of participants undergoing low-dose CT screening. The development data set included participants in the Pan-Canadian Early Detection of Lung Cancer Study (PanCan). The validation data set included participants involved in chemoprevention trials at the British Columbia Cancer Agency (BCCA), sponsored by the U.S. National Cancer Institute. The final outcomes of all nodules of any size that were detected on baseline low-dose CT scans were tracked. Parsimonious and fuller multivariable logistic-regression models were prepared to estimate the probability of lung cancer. RESULTS In the PanCan data set, 1871 persons had 7008 nodules, of which 102 were malignant, and in the BCCA data set, 1090 persons had 5021 nodules, of which 42 were malignant. Among persons with nodules, the rates of cancer in the two data sets were 5.5% and 3.7%, respectively. Predictors of cancer in the model included older age, female sex, family history of lung cancer, emphysema, larger nodule size, location of the nodule in the upper lobe, part-solid nodule type, lower nodule count, and spiculation. Our final parsimonious and full models showed excellent discrimination and calibration, with areas under the receiver-operating-characteristic curve of more than 0.90, even for nodules that were 10 mm or smaller in the validation set. CONCLUSIONS Predictive tools based on patient and nodule characteristics can be used to accurately estimate the probability that lung nodules detected on baseline screening low-dose CT scans are malignant. (Funded by the Terry Fox Research Institute and others; ClinicalTrials.gov number, NCT00751660.)

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ObjeCtivesTo determine if coronary computed tomographic angiography enhances prediction of perioperative risk in patients before non-cardiac surgery and to assess the preoperative coronary anatomy in patients who experience a myocardial infarction after non-cardiac surgery.Design Prospective cohort study. setting 12 centers in eight countries.PartiCiPants 955 patients with, or at risk of, atherosclerotic disease who underwent non-cardiac surgery.interventiOns Coronary computed tomographic angiography was performed preoperatively; clinicians were blinded to the results unless left main disease was suspected. Results were classified as normal, non-obstructive (<50% stenosis), obstructive (one or two vessels with ≥50% stenosis), or extensive obstructive (≥50% stenosis in two vessels including the proximal left anterior descending artery, three vessels, or left main).

Commercial anion exchangers are unable to selectively remove dissolved chromate or CrO 4 2-at neutral to alkaline pH in the presence of other competing anions, namely, sulfate, chloride, bicarbonate, and nitrate. This study reports the results of a new anion exchanger, referred to as polymeric ligand exchanger or PLE, which shows very high chromate affinity under otherwise identical conditions. Laboratory results indicate that the PLE is also amenable to efficient regeneration and chemically stable in the presence of sorbed chromate species. IntroductionBackground and Previous Studies. Unlike other toxic heavy metals, oxyanions of Cr(VI) or chromates are quite soluble in the aqueous phase almost over the entire pH range and, thus, quite mobile in the natural environment. Widespread industrial applications and high mobility are the two primary reasons why chromate is frequently found in contaminated sites and groundwater. 1-3 Also, natural geochemical contamination has been held responsible for leaching of Cr(VI) in groundwaters in some arid areas. 4-6 Contrary to Cr-(VI) species or chromates, Cr(III) is less toxic and very insoluble at neutral to alkaline pH. As a result, chemical reduction of Cr(VI) to Cr(III), followed by precipitation or coprecipitation as chromic hydroxide, Cr(OH) 3 (s), has been a traditional approach for treating Cr(VI)-laden wastewater. However, at very low concentrations of Cr(VI) species (from mg/L to µg/L), redox reaction is kinetically slow and unable to attain nearzero level of residual chromate in treated water as often required by regulatory agencies. Also, the process often produces an inordinate amount of sludge due to the presence of the co-precipitant. Ideally, a fixed-bed sorption process is quite effective under such circumstances provided the sorbent is selective toward target chromate species in preference to other competing solutes.Chromate ions exist in the aqueous phase in different ionic forms, with total chromate concentration and pH dictating which particular chromate species will predominate. The following are the important equilibrium reactions. 7,8

Background Current lung cancer screening guidelines use either mean diameter, volume, or density of the largest lung nodule on the previous CT scan or appearance of a new nodule to ascertain the timing of the next CT scan. We aimed to develop an accurate screening protocol by estimating the 3-year lung cancer risk after two screening CT scans using deep learning of radiologists' CT readings and other universally available clinical information. Methods A deep learning algorithm (referred to as DeepLR) was developed using data from participants who had received at least two CT screening scans up to 2 years apart in the National Lung Screening Trial (NLST; training cohort). Double-blinded validation was done using data from participants in the Pan-Canadian Early Detection of Lung Cancer (PanCan) study (validation cohort). The primary analysis was to compare accuracy of DeepLR scores to predict lung cancer incidence at 1 year, 2 years, and 3 years with the Lung CT Screening Reporting & Data System (Lung-RADS) and volume doubling time, using time-dependent area under the receiver operating characteristic curve (AUC) analysis. Findings The training cohort consisted of 25 097 participants from NLST and the validation cohort comprised 2294 individuals from PanCan. In the validation cohort, DeepLR showed good discrimination, with 1-year, 2-year, and 3-year time-dependent AUC values for cancer diagnosis of 0•968 (SD 0•013), 0•946 (0•013), and 0•899 (0•017), respectively. Among individuals deemed high risk by DeepLR, 94%, 85%, and 71% of incident and interval lung cancers diagnosed within 1 year, 2 years, and 3 years, respectively, after the second screening CT scan were identified. Furthermore, individuals with high DeepLR scores had a significantly higher risk of mortality (hazard ratio 16•07, 95% CI 10•15-25•44; p<0•0001) among people with high scores on Lung-RADS. Interpretation DeepLR recognises patterns in both temporal and spatial changes and synergy among changes in nodule and non-nodule features. DeepLR scores could be used to accurately guide clinical management after the next scheduled repeat screening CT scan.