Li-Xiang Zhang1, Pan-Quan Luo2, Lei Chen3,4, Dong-da Song5, A-Man Xu2, Peng Xu2, Jia Xu5. 1. Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China. 2. Department of General Surgery, The Fourth Affiliated Hospital of Anhui Medical University, Hefei, China. 3. Department of General Surgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. 4. Department of Hepatobiliary Surgery, Xiang'an Hospital of Xiamen University, Xiamen, China. 5. Oncology Department, Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing, China.
Abstract
BACKGROUND: The prognosis of patients with hepatocellular carcinoma (HCC) remains difficult to accurately predict. The purpose of this study was to establish a prognostic model for HCC based on a novel scoring system. METHODS: Five hundred and sixty patients who underwent a curative hepatectomy for treatment of HCC at our hospital between January 2007 and January 2014 were included in this study. Univariate and multivariate analyses were used to screen for prognostic risk factors. The nomogram construction was based on Cox proportional hazard regression models, and the development of the new scoring model was analyzed using receiver operating characteristic (ROC) curve analysis and then compared with other clinical indexes. The novel scoring system was then validated with an external dataset from a different medical institution. RESULTS: Multivariate analysis showed that tumor size, portal vein tumor thrombus (PVTT), invasion of adjacent tissues, microvascular invasion, and levels of fibrinogen and total bilirubin were independent prognostic factors. The new scoring model had higher area under the curve (AUC) values compared to other systems, and the C-index of the nomogram was highly consistent for evaluating the survival of HCC patients in the validation and training datasets, as well as the external validation dataset. CONCLUSIONS: Based on serum markers and other clinical indicators, a precise model to predict the prognosis of patients with HCC was developed. This novel scoring system can be an effective tool for both surgeons and patients.
BACKGROUND: The prognosis of patients with hepatocellular carcinoma (HCC) remains difficult to accurately predict. The purpose of this study was to establish a prognostic model for HCC based on a novel scoring system. METHODS: Five hundred and sixty patients who underwent a curative hepatectomy for treatment of HCC at our hospital between January 2007 and January 2014 were included in this study. Univariate and multivariate analyses were used to screen for prognostic risk factors. The nomogram construction was based on Cox proportional hazard regression models, and the development of the new scoring model was analyzed using receiver operating characteristic (ROC) curve analysis and then compared with other clinical indexes. The novel scoring system was then validated with an external dataset from a different medical institution. RESULTS: Multivariate analysis showed that tumor size, portal vein tumor thrombus (PVTT), invasion of adjacent tissues, microvascular invasion, and levels of fibrinogen and total bilirubin were independent prognostic factors. The new scoring model had higher area under the curve (AUC) values compared to other systems, and the C-index of the nomogram was highly consistent for evaluating the survival of HCC patients in the validation and training datasets, as well as the external validation dataset. CONCLUSIONS: Based on serum markers and other clinical indicators, a precise model to predict the prognosis of patients with HCC was developed. This novel scoring system can be an effective tool for both surgeons and patients.
Authors: Oliver Zivanovic; Lindsay M Jacks; Alexia Iasonos; Mario M Leitao; Robert A Soslow; Emanuela Veras; Dennis S Chi; Nadeem R Abu-Rustum; Richard R Barakat; Murray F Brennan; Martee L Hensley Journal: Cancer Date: 2011-07-12 Impact factor: 6.860
Authors: Lindsey A Torre; Freddie Bray; Rebecca L Siegel; Jacques Ferlay; Joannie Lortet-Tieulent; Ahmedin Jemal Journal: CA Cancer J Clin Date: 2015-02-04 Impact factor: 508.702