A nomogram predicting pulmonary metastasis of hepatocellular carcinoma following partial hepatectomy

Li, J; Liu, Y; Yan, Zhenlin; Wan, Xia; Xia, Yong; Wang, K; Liu, J; Lau, W. Y.; Wu, Mengchao; Shen, Feng

doi:10.1038/bjc.2014.19

Cited by 58 publications

(63 citation statements)

References 31 publications

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“…Additionally, there is still no well-known predictive system based only on clinicopathological data for early recurrence. Although some nomogram-based systems using clinicopathological data have been proposed for predicting HCC recurrence (27)(28)(29)(30), their accuracy has been limited by the precision of physical markings. Therefore, these prediction systems have not been externally validated and have not been widely used.…”

Section: Discussionmentioning

confidence: 99%

A new prognostic model for hepatocellular carcinoma recurrence after curative hepatectomy

et al. 2018

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Abstract. We previously reported the effectiveness of the product of tumor number and size (NxS factor) for the prognosis of hepatocellular carcinoma (HCC) in patients following hepatectomy. The present study aimed to propose a new score based on the NxS factor to predict HCC recurrence following hepatectomy. A total of 406 patients who underwent hepatectomy for HCC at Osaka University Graduate School of Medicine were retrospectively analyzed to develop the new score. Among clinicopathological factors, including the NxS factor, the marker subset that achieved the best performance for prediction of early recurrence was assessed, and a prognostic model for HCC recurrence after curative hepatectomy (REACH) was developed. As the validation set, 425 patients who underwent hepatectomy for HCC at Yamaguchi University Graduate School of Medicine and Shimonoseki Medical Center were analyzed, and the prognostic ability of the REACH score was compared with that of well-known staging systems. Following analysis, the REACH score was constructed using six covariates (NxS factor, microscopic hepatic vein invasion, differentiation, serum albumin, platelet count and indocyanine green retention rate at 15 min). In the validation set, the REACH score predicted early recurrence in 73 of 81 samples, with a sensitivity of 89% and a specificity of 58%. The area under the curve (AUC) of the receiver operating characteristic curve of the REACH score was 0.78 and 0.74, respectively, for 1-and 2-year recurrence after hepatectomy; each AUC was higher than that of any of the other staging systems. Survival analysis indicated the REACH score had the best predictive value in disease-free and overall survival. The present findings demonstrated that the REACH score may be used to classify patients with HCC into high-and low-risk of recurrence, and to predict subsequent survival following hepatic resection.

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Section: Discussionmentioning

confidence: 99%

A new prognostic model for hepatocellular carcinoma recurrence after curative hepatectomy

et al. 2018

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“…First proposed in 2006, decision curves have been used in cancer research [2][3][4][5][6] and many other fields. 7-11 For example, decision curves have evaluated models that predict lung cancer in high-risk populations which can then be used as tools for recommending computed tomography (CT) screening.…”

Section: Decision Curvesmentioning

confidence: 99%

“…There is then a suggestion that high-risk individuals are those whose risks are in this region of the plot. 4 This erroneous approach tends to lead to high risk thresholds, at least when a single risk model is compared with treat all and treat none.…”

Section: Decision Curves Cannot Be Used To Choose a Risk Thresholdmentioning

confidence: 99%

Assessing the Clinical Impact of Risk Prediction Models With Decision Curves: Guidance for Correct Interpretation and Appropriate Use

Kerr

Brown

Zhu

et al. 2016

JCO

455

357

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The decision curve is a graphical summary recently proposed for assessing the potential clinical impact of risk prediction biomarkers or risk models for recommending treatment or intervention. It was applied recently in an article in Journal of Clinical Oncology to measure the impact of using a genomic risk model for deciding on adjuvant radiation therapy for prostate cancer treated with radical prostatectomy. We illustrate the use of decision curves for evaluating clinical- and biomarker-based models for predicting a man’s risk of prostate cancer, which could be used to guide the decision to biopsy. Decision curves are grounded in a decision-theoretical framework that accounts for both the benefits of intervention and the costs of intervention to a patient who cannot benefit. Decision curves are thus an improvement over purely mathematical measures of performance such as the area under the receiver operating characteristic curve. However, there are challenges in using and interpreting decision curves appropriately. We caution that decision curves cannot be used to identify the optimal risk threshold for recommending intervention. We discuss the use of decision curves for miscalibrated risk models. Finally, we emphasize that a decision curve shows the performance of a risk model in a population in which every patient has the same expected benefit and cost of intervention. If every patient has a personal benefit and cost, then the curves are not useful. If subpopulations have different benefits and costs, subpopulation-specific decision curves should be used. As a companion to this article, we released an R software package called DecisionCurve for making decision curves and related graphics.

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“…[14,25] Preoperative AFP has been incorporated in several prognostic nomograms for HCC as either a categorical variable or after a logarithmic transformation. [19,34,35] In this study, the AFP level was incorporated as a continuous variable because of its wide range in this cohort and its positive correlation with poor survival.…”

Section: Discussionmentioning

confidence: 99%

“…[14,16] Consistent with the present study, several studies reported MVI as an independent risk factor for prognosis and incorporated it into prognostic nomograms. [19,34,35,43,44] Nonencapsulated HCC was prone to direct liver invasion, tumor microsatellite formation, and vascular invasion. [45] Unfavorable outcomes were observed among patients with nonencapsulated HCC in most studies, which were in accordance with our results.…”

Section: Discussionmentioning

confidence: 99%

Prognostic nomogram for patients with hepatocellular carcinoma underwent adjuvant transarterial chemoembolization following curative resection

Jing

Fu²,

Zheng³

et al. 2017

Medicine

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A nomogram predicting pulmonary metastasis of hepatocellular carcinoma following partial hepatectomy

Cited by 58 publications

References 31 publications

A new prognostic model for hepatocellular carcinoma recurrence after curative hepatectomy

A new prognostic model for hepatocellular carcinoma recurrence after curative hepatectomy

Assessing the Clinical Impact of Risk Prediction Models With Decision Curves: Guidance for Correct Interpretation and Appropriate Use

Prognostic nomogram for patients with hepatocellular carcinoma underwent adjuvant transarterial chemoembolization following curative resection

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