Value of a virtual liver surgery planning system in predicting hepatic dysfunction after hepatectomy for liver cancer
Kecan Lin, Jingfeng Liu, Jinhua Zeng, Min‐Hui Chi, Yongyi Zeng
Abstract
Kecan Lin, Jingfeng Liu, Jinhua Zeng, Min‐Hui Chi, Yongyi Zeng
Abstract
Objective To calculate the residoal liver volume using a virtaal liver surgery planning system,and to investigate the value of standardized estimated liver remnant volume ratio (STELR) in prcdicting hepatic dysfunction after hepatectomy.Methods The clinical data of 76 patients with primary liver cancer who were admitted to the First Affiliated Hospital of Fujian Medical University from April 2007 to October 2011 were retrospectivcly analyzed.The virtual resection and residual liver volume measurements were carried out using Liv 1.0 software.The value of STELR in predicting hepatic dysfunction was assessed using receiver operator characteristic (ROC) curves.Effects of different risk factors on postoperative hepatic dysfunction were analyzed using univariate analysis of variance and multivariate Logistic regression models. Results The mean residual liver volumes predicted by the software and resected during operation were (489 ± 206)ml and (459 ± 199 )ml,respectively,with a positive correlation between predicted and actual resection volumes (r =0.916,P < 0.05).Of the 76 patients,48 had mild hepatic dysfunction,19 had moderate hepatic dysfunction and 9 had severe hepatic dysfunction.A critical STELR of 53% was associated with severe hepatic dysfunction.Severe hepatic dysfunction was detected in 2 out of 59 (3%) patients with STELR ≥ 53% and 7 out of 17 (41%) patients with STELR < 53%,which represented a significant difference ( x2 =5.085,P < 0.05 ).The result of univariate analysis revealed that STEL,R,operating time,intraoperative blood loss were significant prognostic indicators for severe hepatic dysfunction ( F =7.227,8.630,13.809,P <0.05).Multivariate Logistic regession revealed that STELR was a significant independent predictor of severe hepatic dysfunction ( Wald =6.675,P < 0.05 ).Conclusion The likelihood of severe hepatic dysfunction after hepatectomy can be predicted by STELR. Key words: Liver neoplasms; Surgical planning; Liver volume
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Objective To calculate the residoal liver volume using a virtaal liver surgery planning system,and to investigate the value of standardized estimated liver remnant volume ratio (STELR) in prcdicting hepatic dysfunction after hepatectomy.Methods The clinical data of 76 patients with primary liver cancer who were admitted to the First Affiliated Hospital of Fujian Medical University from April 2007 to October 2011 were retrospectivcly analyzed.The virtual resection and residual liver volume measurements were carried out using Liv 1.0 software.The value of STELR in predicting hepatic dysfunction was assessed using receiver operator characteristic (ROC) curves.Effects of different risk factors on postoperative hepatic dysfunction were analyzed using univariate analysis of variance and multivariate Logistic regression models. Results The mean residual liver volumes predicted by the software and resected during operation were (489 ± 206)ml and (459 ± 199 )ml,respectively,with a positive correlation between predicted and actual resection volumes (r =0.916,P < 0.05).Of the 76 patients,48 had mild hepatic dysfunction,19 had moderate hepatic dysfunction and 9 had severe hepatic dysfunction.A critical STELR of 53% was associated with severe hepatic dysfunction.Severe hepatic dysfunction was detected in 2 out of 59 (3%) patients with STELR ≥ 53% and 7 out of 17 (41%) patients with STELR < 53%,which represented a significant difference ( x2 =5.085,P < 0.05 ).The result of univariate analysis revealed that STEL,R,operating time,intraoperative blood loss were significant prognostic indicators for severe hepatic dysfunction ( F =7.227,8.630,13.809,P <0.05).Multivariate Logistic regession revealed that STELR was a significant independent predictor of severe hepatic dysfunction ( Wald =6.675,P < 0.05 ).Conclusion The likelihood of severe hepatic dysfunction after hepatectomy can be predicted by STELR. Key words: Liver neoplasms; Surgical planning; Liver volume
Key concepts: Medicine, Liver dysfunction, Hepatic dysfunction, Hepatectomy, Univariate analysis, Logistic regression, Internal medicine, Receiver operating characteristic