کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
3986508 1601418 2012 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Prognostic models for outcome following liver resection for colorectal cancer metastases: A systematic review
موضوعات مرتبط
علوم پزشکی و سلامت پزشکی و دندانپزشکی تومور شناسی
پیش نمایش صفحه اول مقاله
Prognostic models for outcome following liver resection for colorectal cancer metastases: A systematic review
چکیده انگلیسی

BackgroundLiver resection provides the best chance for cure in colorectal cancer (CRC) liver metastases. A variety of factors that might influence survival and recurrence have been identified. Predictive models can help in risk stratification, to determine multidisciplinary treatment and follow-up for individual patients.AimsTo systematically review available prognostic models described for outcome following resection of CRC liver metastases and to assess their differences and applicability.MethodsThe Pubmed, Embase and Cochrane Library databases were searched for articles proposing a prognostic model or risk stratification system for resection of CRC liver metastases. Search terms included ‘colorectal’, ‘liver’, ‘metastasis’, ‘resection’, ‘prognosis’ and ‘prediction’. The articles were systematically reviewed.ResultsFifteen prognostic systems were identified, published between 1996 and 2009. The median study population was 305 patients and the median follow-up was 32 months. All studies used Cox proportional hazards for multi-variable analysis. No prognostic factor was common in all models, though there was a tendency towards the number of metastases, CRC spread to lymph nodes, maximum size of metastases, preoperative CEA level and extrahepatic spread as representing independent risk factors. Seven models assigned more weight to selected factors considered of higher predictive value.ConclusionThe existing predictive models are diverse and their prognostic factors are often not weighed according to their impact. For the development of future predictive models, the complex relations within datasets and differences in relevance of individual factors should be taken into account, for example by using artificial neural networks.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: European Journal of Surgical Oncology (EJSO) - Volume 38, Issue 1, January 2012, Pages 16–24
نویسندگان
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