Background70 years ago, it was put forward that the diseased liver was not a favorable soil for metastatic tumor cells. In addition, a few studies have demonstrated that rare occurrence of colorectal liver metastases among patients with fatty liver, cirrhosis or chronic hepatitis B and C virus infection. We performed a meta-analysis to verify the association between the incidences of colorectal liver metastases with chronically diseased livers.MethodsRelevant studies were identified by a search of electronic database PubMed, Cochrane Library, OVID, Web of Science and CNKI (up to February 24, 2014). Pooled odds ratio (OR) with 95% confidence interval (CI) was calculated using random- or fixed-effect models when appropriate. Meta-analysis and publication bias (Bgger's test) was evaluated with STATA 12.0.ResultsA total of 10,349 colorectal cancer patients from 10 studies were included. The meta-analysis result showed there was a significant difference in the incidences of colorectal liver metastases between patients with normal and chronically diseased livers (OR = 0.32; 95% CI 95%: 0.26–0.38, P = 0.000 fixed-effects model). The result of Begg's test (Pr>|z| = 0.089; P>0.05) revealed no publication bias.ConclusionsThe results of this meta-analysis demonstrated that patients with chronically diseased livers had significantly lower incidences of colorectal liver metastases than those with normal livers.
ObjectivePeritoneal metastasis is difficult to diagnose using traditional imaging techniques. The main aim of the current study was to develop and validate a nomogram for effectively predicting the risk of peritoneal metastasis in colorectal cancer (PMCC).MethodsA retrospective case-control study was conducted using clinical data from 1284 patients with colorectal cancer who underwent surgery at the First Affiliated Hospital of Guangxi Medical University from January 2010 to December 2015. Least absolute shrinkage and selection operator (LASSO) regression was applied to optimize feature selection of the PMCC risk prediction model and multivariate logistic regression analysis conducted to determine independent risk factors. Using the combined features selected in the LASSO regression model, we constructed a nomogram model and evaluated its predictive value via receiver operating characteristic (ROC) curve analysis. The bootstrap method was employed for repeated sampling for internal verification and the discrimination ability of the prediction models evaluated based on the C-index. The consistency between the predicted and actual results was assessed with the aid of calibration curves.ResultsOverall, 96 cases of PMCC were confirmed via postoperative pathological diagnosis. Logistic regression analysis showed that age, tumor location, perimeter ratio, tumor size, pathological type, tumor invasion depth, CEA level, and gross tumor type were independent risk factors for PMCC. A nomogram composed of these eight factors was subsequently constructed. The calibration curve revealed good consistency between the predicted and actual probability, with a C-index of 0.882. The area under the curve (AUC) of the nomogram prediction model was 0.882 and its 95% confidence interval (CI) was 0.845–0.919. Internal validation yielded a C-index of 0.868.ConclusionWe have successfully constructed a highly sensitive nomogram that should facilitate early diagnosis of PMCC, providing a robust platform for further optimization of clinical management strategies.
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