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      Diagnostic accuracy of dual-energy CT-based nomograms to predict lymph node metastasis in gastric cancer

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          Cancer statistics in China, 2015.

          With increasing incidence and mortality, cancer is the leading cause of death in China and is a major public health problem. Because of China's massive population (1.37 billion), previous national incidence and mortality estimates have been limited to small samples of the population using data from the 1990s or based on a specific year. With high-quality data from an additional number of population-based registries now available through the National Central Cancer Registry of China, the authors analyzed data from 72 local, population-based cancer registries (2009-2011), representing 6.5% of the population, to estimate the number of new cases and cancer deaths for 2015. Data from 22 registries were used for trend analyses (2000-2011). The results indicated that an estimated 4292,000 new cancer cases and 2814,000 cancer deaths would occur in China in 2015, with lung cancer being the most common incident cancer and the leading cause of cancer death. Stomach, esophageal, and liver cancers were also commonly diagnosed and were identified as leading causes of cancer death. Residents of rural areas had significantly higher age-standardized (Segi population) incidence and mortality rates for all cancers combined than urban residents (213.6 per 100,000 vs 191.5 per 100,000 for incidence; 149.0 per 100,000 vs 109.5 per 100,000 for mortality, respectively). For all cancers combined, the incidence rates were stable during 2000 through 2011 for males (+0.2% per year; P = .1), whereas they increased significantly (+2.2% per year; P < .05) among females. In contrast, the mortality rates since 2006 have decreased significantly for both males (-1.4% per year; P < .05) and females (-1.1% per year; P < .05). Many of the estimated cancer cases and deaths can be prevented through reducing the prevalence of risk factors, while increasing the effectiveness of clinical care delivery, particularly for those living in rural areas and in disadvantaged populations.
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            Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012.

            Estimates of the worldwide incidence and mortality from 27 major cancers and for all cancers combined for 2012 are now available in the GLOBOCAN series of the International Agency for Research on Cancer. We review the sources and methods used in compiling the national cancer incidence and mortality estimates, and briefly describe the key results by cancer site and in 20 large "areas" of the world. Overall, there were 14.1 million new cases and 8.2 million deaths in 2012. The most commonly diagnosed cancers were lung (1.82 million), breast (1.67 million), and colorectal (1.36 million); the most common causes of cancer death were lung cancer (1.6 million deaths), liver cancer (745,000 deaths), and stomach cancer (723,000 deaths). © 2014 UICC.
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              Updated nomogram predicting lymph node invasion in patients with prostate cancer undergoing extended pelvic lymph node dissection: the essential importance of percentage of positive cores.

              Few predictive models aimed at predicting the presence of lymph node invasion (LNI) in patients with prostate cancer (PCa) treated with extended pelvic lymph node dissection (ePLND) are available to date. Update a nomogram predicting the presence of LNI in patients treated with ePLND at the time of radical prostatectomy (RP). The study included 588 patients with clinically localised PCa treated between September 2006 and October 2010 at a single tertiary referral centre. All patients underwent RP and ePLND invariably including removal of obturator, external iliac, and hypogastric nodes. Prostate-specific antigen, clinical stage, and primary and secondary biopsy Gleason grade as well as percentage of positive cores were included in univariable (UVA) and multivariable (MVA) logistic regression models predicting LNI and formed the basis for the regression coefficient-based nomogram. The area under the curve (AUC) method was used to quantify the predictive accuracy (PA) of the model. The mean number of lymph nodes removed and examined was 20.8 (median: 19; range: 10-52). LNI was found in 49 of 588 patients (8.3%). All preoperative PCa characteristics differed significantly between LNI-positive and LNI-negative patients (all p<0.001). In UVA predictive accuracy analyses, percentage of positive cores was the most accurate predictor of LNI (AUC: 79.5%). At MVA, clinical stage, primary biopsy Gleason grade, and percentage of positive cores were independent predictors of LNI (all p≤0.006). The updated nomogram demonstrated a bootstrap-corrected PA of 87.6%. Using a 5% nomogram cut-off, 385 of 588 patients (65.5%) would be spared ePLND. and LNI would be missed in only 6 patients (1.5%). The sensitivity, specificity, and negative predictive value associated with the 5% cut-off were 87.8%, 70.3%, and 98.4%, respectively. The relatively low number of patients included as well as the lack of an external validation represent the main limitations of our study. We report the first update of a nomogram predicting the presence of LNI in patients treated with ePLND. The nomogram maintained high accuracy, even in more contemporary patients (87.6%). Because percentage of positive cores represents the foremost predictor of LNI, its inclusion should be mandatory in any LNI prediction model. Based on our model, those patients with a LNI risk<5% might be safely spared ePLND. Copyright © 2011 European Association of Urology. Published by Elsevier B.V. All rights reserved.
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                Author and article information

                Journal
                European Radiology
                Eur Radiol
                Springer Science and Business Media LLC
                0938-7994
                1432-1084
                December 2018
                June 4 2018
                December 2018
                : 28
                : 12
                : 5241-5249
                Article
                10.1007/s00330-018-5483-2
                29869176
                ab1729e8-bf58-4c0e-9fec-e73d6cee504d
                © 2018

                http://www.springer.com/tdm

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