Liver stiffness value-based risk estimation of late recurrence after curative resection of hepatocellular carcinoma: Development and validation of a predictive model

Kyu Sik Jung, Ji Hong Kim, Seung Up Kim, Kijun Song, Beom Kyung Kim, Jun Yong Park, Do Young Kim, Sang Hoon Ahn, Do Chang Moon, In Ji Song, Gi Hong Choi, Young Nyun Park, Kwang Hyub Han

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Background: Preoperative liver stiffness (LS) measurement using transient elastography (TE) is useful for predicting late recurrence after curative resection of hepatocellular carcinoma (HCC). We developed and validated a novel LS value-based predictive model for late recurrence of HCC. Methods: Patients who were due to undergo curative resection of HCC between August 2006 and January 2010 were prospectively enrolled and TE was performed prior to operations by study protocol. The predictive model of late recurrence was constructed based on a multiple logistic regression model. Discrimination and calibration were used to validate the model. Results: Among a total of 139 patients who were finally analyzed, late recurrence occurred in 44 patients, with a median follow-up of 24.5 months (range, 12.4-68.1). We developed a predictive model for late recurrence of HCC using LS value, activity grade II-III, presence of multiple tumors, and indocyanine green retention rate at 15 min (ICG R15), which showed fairly good discrimination capability with an area under the receiver operating characteristic curve (AUROC) of 0.724 (95% confidence intervals [CIs], 0.632-0.816). In the validation, using a bootstrap method to assess discrimination, the AUROC remained largely unchanged between iterations, with an average AUROC of 0.722 (95% CIs, 0.718-0.724). When we plotted a calibration chart for predicted and observed risk of late recurrence, the predicted risk of late recurrence correlated well with observed risk, with a correlation coefficient of 0.873 (P<0.001). Conclusion: A simple LS value-based predictive model could estimate the risk of late recurrence in patients who underwent curative resection of HCC.

Original languageEnglish
Article numbere99167
JournalPloS one
Volume9
Issue number6
DOIs
Publication statusPublished - 2014 Jun 9

Fingerprint

risk estimate
resection
hepatoma
Liver
Hepatocellular Carcinoma
Stiffness
Recurrence
liver
ROC Curve
Elasticity Imaging Techniques
confidence interval
calibration
Calibration
Indocyanine Green
Logistic Models
Confidence Intervals
Logistics
Tumors
neoplasms
methodology

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

Cite this

@article{d7d361ad4aae4cfdbe001ee6924b4ff9,
title = "Liver stiffness value-based risk estimation of late recurrence after curative resection of hepatocellular carcinoma: Development and validation of a predictive model",
abstract = "Background: Preoperative liver stiffness (LS) measurement using transient elastography (TE) is useful for predicting late recurrence after curative resection of hepatocellular carcinoma (HCC). We developed and validated a novel LS value-based predictive model for late recurrence of HCC. Methods: Patients who were due to undergo curative resection of HCC between August 2006 and January 2010 were prospectively enrolled and TE was performed prior to operations by study protocol. The predictive model of late recurrence was constructed based on a multiple logistic regression model. Discrimination and calibration were used to validate the model. Results: Among a total of 139 patients who were finally analyzed, late recurrence occurred in 44 patients, with a median follow-up of 24.5 months (range, 12.4-68.1). We developed a predictive model for late recurrence of HCC using LS value, activity grade II-III, presence of multiple tumors, and indocyanine green retention rate at 15 min (ICG R15), which showed fairly good discrimination capability with an area under the receiver operating characteristic curve (AUROC) of 0.724 (95{\%} confidence intervals [CIs], 0.632-0.816). In the validation, using a bootstrap method to assess discrimination, the AUROC remained largely unchanged between iterations, with an average AUROC of 0.722 (95{\%} CIs, 0.718-0.724). When we plotted a calibration chart for predicted and observed risk of late recurrence, the predicted risk of late recurrence correlated well with observed risk, with a correlation coefficient of 0.873 (P<0.001). Conclusion: A simple LS value-based predictive model could estimate the risk of late recurrence in patients who underwent curative resection of HCC.",
author = "Jung, {Kyu Sik} and Kim, {Ji Hong} and Kim, {Seung Up} and Kijun Song and Kim, {Beom Kyung} and Park, {Jun Yong} and Kim, {Do Young} and Ahn, {Sang Hoon} and Moon, {Do Chang} and Song, {In Ji} and Choi, {Gi Hong} and Park, {Young Nyun} and Han, {Kwang Hyub}",
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language = "English",
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journal = "PLoS One",
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Liver stiffness value-based risk estimation of late recurrence after curative resection of hepatocellular carcinoma : Development and validation of a predictive model. / Jung, Kyu Sik; Kim, Ji Hong; Kim, Seung Up; Song, Kijun; Kim, Beom Kyung; Park, Jun Yong; Kim, Do Young; Ahn, Sang Hoon; Moon, Do Chang; Song, In Ji; Choi, Gi Hong; Park, Young Nyun; Han, Kwang Hyub.

In: PloS one, Vol. 9, No. 6, e99167, 09.06.2014.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Liver stiffness value-based risk estimation of late recurrence after curative resection of hepatocellular carcinoma

T2 - Development and validation of a predictive model

AU - Jung, Kyu Sik

AU - Kim, Ji Hong

AU - Kim, Seung Up

AU - Song, Kijun

AU - Kim, Beom Kyung

AU - Park, Jun Yong

AU - Kim, Do Young

AU - Ahn, Sang Hoon

AU - Moon, Do Chang

AU - Song, In Ji

AU - Choi, Gi Hong

AU - Park, Young Nyun

AU - Han, Kwang Hyub

PY - 2014/6/9

Y1 - 2014/6/9

N2 - Background: Preoperative liver stiffness (LS) measurement using transient elastography (TE) is useful for predicting late recurrence after curative resection of hepatocellular carcinoma (HCC). We developed and validated a novel LS value-based predictive model for late recurrence of HCC. Methods: Patients who were due to undergo curative resection of HCC between August 2006 and January 2010 were prospectively enrolled and TE was performed prior to operations by study protocol. The predictive model of late recurrence was constructed based on a multiple logistic regression model. Discrimination and calibration were used to validate the model. Results: Among a total of 139 patients who were finally analyzed, late recurrence occurred in 44 patients, with a median follow-up of 24.5 months (range, 12.4-68.1). We developed a predictive model for late recurrence of HCC using LS value, activity grade II-III, presence of multiple tumors, and indocyanine green retention rate at 15 min (ICG R15), which showed fairly good discrimination capability with an area under the receiver operating characteristic curve (AUROC) of 0.724 (95% confidence intervals [CIs], 0.632-0.816). In the validation, using a bootstrap method to assess discrimination, the AUROC remained largely unchanged between iterations, with an average AUROC of 0.722 (95% CIs, 0.718-0.724). When we plotted a calibration chart for predicted and observed risk of late recurrence, the predicted risk of late recurrence correlated well with observed risk, with a correlation coefficient of 0.873 (P<0.001). Conclusion: A simple LS value-based predictive model could estimate the risk of late recurrence in patients who underwent curative resection of HCC.

AB - Background: Preoperative liver stiffness (LS) measurement using transient elastography (TE) is useful for predicting late recurrence after curative resection of hepatocellular carcinoma (HCC). We developed and validated a novel LS value-based predictive model for late recurrence of HCC. Methods: Patients who were due to undergo curative resection of HCC between August 2006 and January 2010 were prospectively enrolled and TE was performed prior to operations by study protocol. The predictive model of late recurrence was constructed based on a multiple logistic regression model. Discrimination and calibration were used to validate the model. Results: Among a total of 139 patients who were finally analyzed, late recurrence occurred in 44 patients, with a median follow-up of 24.5 months (range, 12.4-68.1). We developed a predictive model for late recurrence of HCC using LS value, activity grade II-III, presence of multiple tumors, and indocyanine green retention rate at 15 min (ICG R15), which showed fairly good discrimination capability with an area under the receiver operating characteristic curve (AUROC) of 0.724 (95% confidence intervals [CIs], 0.632-0.816). In the validation, using a bootstrap method to assess discrimination, the AUROC remained largely unchanged between iterations, with an average AUROC of 0.722 (95% CIs, 0.718-0.724). When we plotted a calibration chart for predicted and observed risk of late recurrence, the predicted risk of late recurrence correlated well with observed risk, with a correlation coefficient of 0.873 (P<0.001). Conclusion: A simple LS value-based predictive model could estimate the risk of late recurrence in patients who underwent curative resection of HCC.

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