BACKGROUND AND AIMS: Margin involvement following liver resection for colorectal cancer is associated with early disease recurrence and shorter long-term survival. This study aimed to develop a predictive index for quantifying the likelihood of a positive resection margin (R1) for patients undergoing hepatic resection for metastatic colorectal cancer. METHODS: Clinical, pathological and complete follow-up data were prospectively collected from 1005 consecutive liver resections performed in 929 patients for colorectal liver metastases with curative intent at a single centre between 1987 and 2005. Ninety-four resections in 81 patients with extra-hepatic disease were excluded, leaving 911 resections (844 primary and 67 repeat) in 848 patients for analysis. Multivariate logistic regression was used to identify independent predictors of margin involvement and from the beta-coefficients generated, develop a predictive model that was validated using measures of discrimination and calibration. RESULTS: There were 80 (8.8%) R1 resections, with a 5-year cancer-specific survival for R0 and R1 hepatic resections of 39.7% and 17.8%, respectively; p<0.001. On multivariate analysis, five risk factors were found to be independent predictors of an R1 resection: non-anatomical resection vs. anatomical resection (odds ratio (OR)=4.3, p=0.001), >3 hepatic metastases involving >50% of the liver vs. <3 metastases (OR=4.0, p<0.001); bilobar vs. unilobar disease (OR=2.9, p<0.001); repeat vs. primary hepatic resection (OR=3.1, p=0.006); abnormal vs. normal pre-operative liver function tests (OR=1.6, p=0.044). These five factors were used to develop a predictive model, which when tested, fitted the data well, with an area under the receiver operating characteristic curve of 78.1% (S.E.=2.7%). CONCLUSIONS: This study describes an accurate model for quantifying the risk of a positive margin following hepatic resection for liver metastases. It may be used pre-operatively by multi-disciplinary teams to identify patients who may benefit from neoadjuvant therapy prior to liver surgery, thus minimizing the risk of a positive resection margin.
BACKGROUND AND AIMS: Margin involvement following liver resection for colorectal cancer is associated with early disease recurrence and shorter long-term survival. This study aimed to develop a predictive index for quantifying the likelihood of a positive resection margin (R1) for patients undergoing hepatic resection for metastatic colorectal cancer. METHODS: Clinical, pathological and complete follow-up data were prospectively collected from 1005 consecutive liver resections performed in 929 patients for colorectal liver metastases with curative intent at a single centre between 1987 and 2005. Ninety-four resections in 81 patients with extra-hepatic disease were excluded, leaving 911 resections (844 primary and 67 repeat) in 848 patients for analysis. Multivariate logistic regression was used to identify independent predictors of margin involvement and from the beta-coefficients generated, develop a predictive model that was validated using measures of discrimination and calibration. RESULTS: There were 80 (8.8%) R1 resections, with a 5-year cancer-specific survival for R0 and R1 hepatic resections of 39.7% and 17.8%, respectively; p<0.001. On multivariate analysis, five risk factors were found to be independent predictors of an R1 resection: non-anatomical resection vs. anatomical resection (odds ratio (OR)=4.3, p=0.001), >3 hepatic metastases involving >50% of the liver vs. <3 metastases (OR=4.0, p<0.001); bilobar vs. unilobar disease (OR=2.9, p<0.001); repeat vs. primary hepatic resection (OR=3.1, p=0.006); abnormal vs. normal pre-operative liver function tests (OR=1.6, p=0.044). These five factors were used to develop a predictive model, which when tested, fitted the data well, with an area under the receiver operating characteristic curve of 78.1% (S.E.=2.7%). CONCLUSIONS: This study describes an accurate model for quantifying the risk of a positive margin following hepatic resection for liver metastases. It may be used pre-operatively by multi-disciplinary teams to identify patients who may benefit from neoadjuvant therapy prior to liver surgery, thus minimizing the risk of a positive resection margin.
Authors: Carlo Pulitanò; Federico Castillo; Luca Aldrighetti; Martin Bodingbauer; Rowan W Parks; Gianfranco Ferla; Stephen J Wigmore; O James Garden Journal: HPB (Oxford) Date: 2010-05 Impact factor: 3.647
Authors: Nicolas Golse; Petru O Bucur; René Adam; Denis Castaing; Antonio Sa Cunha; Eric Vibert Journal: J Gastrointest Surg Date: 2012-11-18 Impact factor: 3.452
Authors: Pieter M H Nierop; Diederik J Höppener; Eric P van der Stok; Boris Galjart; Florian E Buisman; Vinod P Balachandran; William R Jarnagin; T Peter Kingham; Peter J Allen; Jinru Shia; Peter B Vermeulen; Bas Groot Koerkamp; Dirk J Grünhagen; Cornelis Verhoef; Michael I D'Angelica Journal: HPB (Oxford) Date: 2019-11-15 Impact factor: 3.647
Authors: C S van Kessel; M S van Leeuwen; R van Hillegersberg; I H M Borel Rinkes; M A A J van den Bosch; I Q Molenaar Journal: J Gastrointest Surg Date: 2013-04-25 Impact factor: 3.452
Authors: Oliver Ziff; Ibrahim Rajput; Robert Adair; Giles J Toogood; K Rajendra Prasad; J Peter A Lodge Journal: HPB (Oxford) Date: 2013-07-22 Impact factor: 3.647