Evidence map›Paper›PMID 32214844›Full record

ArticleCancer management and research2020

Novel Prognostic Nomograms for Predicting Early and Late Recurrence of Hepatocellular Carcinoma After Curative Hepatectomy.

Wei Xu, Ruineng Li, Fei Liu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
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Article in Cancer management and research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
2.5field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 27 citations in OpenAlex.

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  12. Advances in post-operative prognostic models for hepatocellular carcinoma.Journal of Zhejiang University. Science. B · 2023
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Wei XuDepartment of Hepatobiliary Surgery, Hunan Provincial People's Hospital, The First Hospital Affiliated with Hunan Normal University, Changsha 410005, People's Republic of China.
Ruineng LiDepartment of Hepatobiliary Surgery, Hunan Provincial People's Hospital, The First Hospital Affiliated with Hunan Normal University, Changsha 410005, People's Republic of China.
Fei LiuDepartment of Hepatobiliary Surgery, Hunan Provincial People's Hospital, The First Hospital Affiliated with Hunan Normal University, Changsha 410005, People's Republic of China.
Hunan Normal University · CNHunan Provincial People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimHepatectomy is the main curative method for patients with hepatocellular carcinoma (HCC) in China. Unfortunately, high recurrence rate after hepatectomy poses negative impact on the prognosis of patients. This study aimed to develop prognostic nomograms to predict early recurrence (ER) and late recurrence (LR) of HCC after curative hepatectomy. PATIENTS AND

methodsTotal of 318 HCC patients undergoing curative hepatectomy from January 2012 to January 2018 were retrospectively recruited. Potential risk factors that were significant for predicting ER and LR in univariate analysis were selected for multivariate survival model analysis using the backward stepwise method. Risk factors identified in multivariate analysis were used to develop nomograms to predict ER and LR. The nomogram was internally validated using 2,000 bootstrap samples from 75% of the original data.

resultsAmong 318 patients, 164 showed postoperative recurrence, of which 140 and 24 had ER (≤2 years) and LR (>2 years), respectively. Multivariate analysis showed that age, Hong Kong Liver Cancer Stage, albumin-bilirubin, METAVIR fibrosis grade, and microvascular invasion were risk factors of ER for HCC after curative hepatectomy. The AUC of the ROC curve for ER in the development set (D-set) was 0.888 while that in the validation set (V-set) was 0.812. Neutrophil/lymphocyte ratio and glypican-3 (+) were risk factors for LR in HCC patients after curative hepatectomy. The AUC of the ROC curve for LR predictive nomogram that integrated all independent predictors was 0.831. The AUC of the ROC curve for LR in the D-set was 0.833, while that for LR in the V-set was 0.733. The C-index and AUC of ROC for the proposed nomograms were more satisfactory than three conventional HCC staging systems used in this study.

conclusionWe developed novel nomograms to predict ER and LR of HCC patients after curative hepatectomy for clinical use to individualize follow-up and therapeutic strategies.

Indexed as

hepatectomyhepatocellular carcinomanomogramprognosisrisk factor

Identifiers

PMID32214844
PMCPMC7082541
OpenAlexW3010006517

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.