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Article: Predicting prognosis in hepatocellular carcinoma after curative surgery with common clinicopathologic parameters
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TitlePredicting prognosis in hepatocellular carcinoma after curative surgery with common clinicopathologic parameters
 
AuthorsHao, K2
Luk, JM1 3
Lee, NPY1
Mao, M2
Zhang, C2
Ferguson, MD2
Lamb, J2
Dai, H2
Ng, IO1
Sham, PC1
Poon, RTP1
 
Issue Date2009
 
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmccancer/
 
CitationBmc Cancer, 2009, v. 9 [How to Cite?]
DOI: http://dx.doi.org/10.1186/1471-2407-9-389
 
AbstractBackground: Surgical resection is one important curative treatment for hepatocellular carcinoma (HCC), but the prognosis following surgery differs substantially and such large variation is mainly unexplained. A review of the literature yields a number of clinicopathologic parameters associated with HCC prognosis. However, the results are not consistent due to lack of systemic approach to establish a prediction model incorporating all these parameters. Methods: We conducted a retrospective analysis on the common clinicopathologic parameters from a cohort of 572 ethnic Chinese HCC patients who received curative surgery. The cases were randomly divided into training (n = 272) and validation (n = 300) sets. Each parameter was individually tested and the significant parameters were entered into a linear classifier for model building, and the prediction accuracy was assessed in the validation set. Results: Our findings based on the training set data reveal 6 common clinicopathologic parameters (tumor size, number of tumor nodules, tumor stage, venous infiltration status, and serum α-fetoprotein and total albumin levels) that were significantly associated with the overall HCC survival and disease-free survival (time to recurrence). We next built a linear classifier model by multivariate Cox regression to predict prognostic outcomes of HCC patients after curative surgery This analysis detected a considerable fraction of variance in HCC prognosis and the area under the ROC curve was about 70%. We further evaluated the model using two other protocols; leave-one-out procedure (n = 264) and independent validation (n = 300). Both were found to have excellent prediction power. The predicted score could separate patients into distinct groups with respect to survival (p-value = 1.8e-12) and disease free survival (p-value = 3.2e-7). Conclusion: This described model will provide valuable guidance on prognosis after curative surgery for HCC in clinical practice. The adaptive nature allows easy accommodation for future new biomarker inputs, and it may serve as the foundation for future modeling and prediction for HCC prognosis after surgical treatment. © 2009 Hao et al; licensee BioMed Central Ltd.
 
ISSN1471-2407
2013 Impact Factor: 3.319
2013 SCImago Journal Rankings: 1.686
 
DOIhttp://dx.doi.org/10.1186/1471-2407-9-389
 
PubMed Central IDPMC2785835
 
ISI Accession Number IDWOS:000272337500001
Funding AgencyGrant Number
Research Grants Council of Hong Kong and Innovation and Technology Fund of the Hong Kong Government
Funding Information:

The work was supported by Research Grants Council of Hong Kong and Innovation and Technology Fund of the Hong Kong Government to J. M. L. We would like to thank for the technical supports from Ashley Wong and Kit-Yuk Mak of the Queen Mary Hospital. IOL Ng is a Loke Yew Professor in Pathology.

 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorHao, K
 
dc.contributor.authorLuk, JM
 
dc.contributor.authorLee, NPY
 
dc.contributor.authorMao, M
 
dc.contributor.authorZhang, C
 
dc.contributor.authorFerguson, MD
 
dc.contributor.authorLamb, J
 
dc.contributor.authorDai, H
 
dc.contributor.authorNg, IO
 
dc.contributor.authorSham, PC
 
dc.contributor.authorPoon, RTP
 
dc.date.accessioned2010-09-06T09:45:52Z
 
dc.date.available2010-09-06T09:45:52Z
 
dc.date.issued2009
 
dc.description.abstractBackground: Surgical resection is one important curative treatment for hepatocellular carcinoma (HCC), but the prognosis following surgery differs substantially and such large variation is mainly unexplained. A review of the literature yields a number of clinicopathologic parameters associated with HCC prognosis. However, the results are not consistent due to lack of systemic approach to establish a prediction model incorporating all these parameters. Methods: We conducted a retrospective analysis on the common clinicopathologic parameters from a cohort of 572 ethnic Chinese HCC patients who received curative surgery. The cases were randomly divided into training (n = 272) and validation (n = 300) sets. Each parameter was individually tested and the significant parameters were entered into a linear classifier for model building, and the prediction accuracy was assessed in the validation set. Results: Our findings based on the training set data reveal 6 common clinicopathologic parameters (tumor size, number of tumor nodules, tumor stage, venous infiltration status, and serum α-fetoprotein and total albumin levels) that were significantly associated with the overall HCC survival and disease-free survival (time to recurrence). We next built a linear classifier model by multivariate Cox regression to predict prognostic outcomes of HCC patients after curative surgery This analysis detected a considerable fraction of variance in HCC prognosis and the area under the ROC curve was about 70%. We further evaluated the model using two other protocols; leave-one-out procedure (n = 264) and independent validation (n = 300). Both were found to have excellent prediction power. The predicted score could separate patients into distinct groups with respect to survival (p-value = 1.8e-12) and disease free survival (p-value = 3.2e-7). Conclusion: This described model will provide valuable guidance on prognosis after curative surgery for HCC in clinical practice. The adaptive nature allows easy accommodation for future new biomarker inputs, and it may serve as the foundation for future modeling and prediction for HCC prognosis after surgical treatment. © 2009 Hao et al; licensee BioMed Central Ltd.
 
dc.description.naturepublished_or_final_version
 
dc.identifier.citationBmc Cancer, 2009, v. 9 [How to Cite?]
DOI: http://dx.doi.org/10.1186/1471-2407-9-389
 
dc.identifier.citeulike6075470
 
dc.identifier.doihttp://dx.doi.org/10.1186/1471-2407-9-389
 
dc.identifier.hkuros168602
 
dc.identifier.isiWOS:000272337500001
Funding AgencyGrant Number
Research Grants Council of Hong Kong and Innovation and Technology Fund of the Hong Kong Government
Funding Information:

The work was supported by Research Grants Council of Hong Kong and Innovation and Technology Fund of the Hong Kong Government to J. M. L. We would like to thank for the technical supports from Ashley Wong and Kit-Yuk Mak of the Queen Mary Hospital. IOL Ng is a Loke Yew Professor in Pathology.

 
dc.identifier.issn1471-2407
2013 Impact Factor: 3.319
2013 SCImago Journal Rankings: 1.686
 
dc.identifier.openurl
 
dc.identifier.pmcidPMC2785835
 
dc.identifier.pmid19886989
 
dc.identifier.scopuseid_2-s2.0-71549154370
 
dc.identifier.urihttp://hdl.handle.net/10722/88627
 
dc.identifier.volume9
 
dc.languageeng
 
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmccancer/
 
dc.publisher.placeUnited Kingdom
 
dc.relation.ispartofBMC Cancer
 
dc.relation.referencesReferences in Scopus
 
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
 
dc.subject.meshArea Under Curve
 
dc.subject.meshCarcinoma, Hepatocellular - mortality - pathology - surgery
 
dc.subject.meshDisease-Free Survival
 
dc.subject.meshHumans
 
dc.subject.meshLiver Neoplasms - mortality - pathology - surgery
 
dc.titlePredicting prognosis in hepatocellular carcinoma after curative surgery with common clinicopathologic parameters
 
dc.typeArticle
 
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<contributor.author>Mao, M</contributor.author>
<contributor.author>Zhang, C</contributor.author>
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<description.abstract>Background: Surgical resection is one important curative treatment for hepatocellular carcinoma (HCC), but the prognosis following surgery differs substantially and such large variation is mainly unexplained. A review of the literature yields a number of clinicopathologic parameters associated with HCC prognosis. However, the results are not consistent due to lack of systemic approach to establish a prediction model incorporating all these parameters. Methods: We conducted a retrospective analysis on the common clinicopathologic parameters from a cohort of 572 ethnic Chinese HCC patients who received curative surgery. The cases were randomly divided into training (n = 272) and validation (n = 300) sets. Each parameter was individually tested and the significant parameters were entered into a linear classifier for model building, and the prediction accuracy was assessed in the validation set. Results: Our findings based on the training set data reveal 6 common clinicopathologic parameters (tumor size, number of tumor nodules, tumor stage, venous infiltration status, and serum &#945;-fetoprotein and total albumin levels) that were significantly associated with the overall HCC survival and disease-free survival (time to recurrence). We next built a linear classifier model by multivariate Cox regression to predict prognostic outcomes of HCC patients after curative surgery This analysis detected a considerable fraction of variance in HCC prognosis and the area under the ROC curve was about 70%. We further evaluated the model using two other protocols; leave-one-out procedure (n = 264) and independent validation (n = 300). Both were found to have excellent prediction power. The predicted score could separate patients into distinct groups with respect to survival (p-value = 1.8e-12) and disease free survival (p-value = 3.2e-7). Conclusion: This described model will provide valuable guidance on prognosis after curative surgery for HCC in clinical practice. The adaptive nature allows easy accommodation for future new biomarker inputs, and it may serve as the foundation for future modeling and prediction for HCC prognosis after surgical treatment. &#169; 2009 Hao et al; licensee BioMed Central Ltd.</description.abstract>
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Author Affiliations
  1. The University of Hong Kong
  2. Rosetta Inpharmatics LLC
  3. National University of Singapore