Evidence map›Paper›PMID 31601270›Full record

ArticleCancer communications (London, England)2019

A nomogram predicting the recurrence of hepatocellular carcinoma in patients after laparoscopic hepatectomy.

Yang-Xun Pan, Jian-Cong Chen, Ai-Ping Fang, Xiao-Hui Wang, Jin-Bin Chen, Jun-Cheng Wang, Wei He, Yi-Zhen Fu, Li Xu, Min-Shan Chen and 3 more

Open access · goldAbstract read
In one paragraph

Article in Cancer communications (London, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
3.9field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed, 49 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 2 institutions in 2 countries.

Yang-Xun PanState Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, Guangdong, P. R. China.
Jian-Cong ChenState Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, Guangdong, P. R. China.
Ai-Ping FangDepartment of Public Health, Sun Yat-sen University, Guangzhou, 510080, Guangdong, P. R. China.
Xiao-Hui WangState Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, Guangdong, P. R. China.
Jin-Bin ChenState Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, Guangdong, P. R. China.
Jun-Cheng WangState Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, Guangdong, P. R. China.
Wei HeState Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, Guangdong, P. R. China.
Yi-Zhen FuState Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, Guangdong, P. R. China.
Li XuState Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, Guangdong, P. R. China.
Min-Shan ChenState Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, Guangdong, P. R. China.
Yao-Jun ZhangState Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, Guangdong, P. R. China.
Qi-Jiong LiState Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, Guangdong, P. R. China. liqj@sysucc.org.cn.
Zhong-Guo ZhouState Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, Guangdong, P. R. China. zhouzhg@sysucc.org.cn.ORCID 0000-0002-1929-4278
Sun Yat-sen University · CNScripps Research Institute · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatients with hepatocellular carcinoma (HCC) undergoing surgical resection still have a high 5-year recurrence rate (~ 60%). With the development of laparoscopic hepatectomy (LH), few studies have compared the efficacy between LH and traditional surgical approach on HCC. The objective of this study was to establish a nomogram to evaluate the risk of recurrence in HCC patients who underwent LH.

methodsThe clinical data of 432 patients, pathologically diagnosed with HCC, underwent LH as initial treatment and had surgical margin > 1 cm were collected. The significance of their clinicopathological features to recurrence-free survival (RFS) was assessed, based on which a nomogram was constructed using a training cohort (n = 324) and was internally validated using a temporal validation cohort (n = 108).

resultsHepatitis B surface antigen (hazard ratio [HR], 1.838; P = 0.044), tumor number (HR, 1.774; P = 0.003), tumor thrombus (HR, 2.356; P = 0.003), cancer cell differentiation (HR, 0.745; P = 0.080), and microvascular tumor invasion (HR, 1.673; P  =0.007) were found to be independent risk factors for RFS in the training cohort, and were used for constructing the nomogram. The C-index for RFS prediction in the training cohort using the nomogram was 0.786, which was higher than that of the 8th edition of the American Joint Committee on Cancer TNM classification (C-index, 0.698) and the Barcelona Clinic Liver Cancer staging system (C-index, 0.632). A high consistency between the nomogram prediction and actual observation was also demonstrated by a calibration curve. An improved predictive benefit in RFS and higher threshold probability of the nomogram were determined by receiver operating characteristic curve analysis, which was also confirmed in the validation cohort compared to other systems.

conclusionsWe constructed and validated a nomogram able to quantify the risk of recurrence after initial LH for HCC patients, which can be clinically implemented in assisting the planification of individual postoperative surveillance protocols.

Indexed as

Neoplasm Recurrence, LocalAdultCarcinoma, HepatocellularFemaleHepatectomyHumansKaplan-Meier EstimateLaparoscopyLiver NeoplasmsMaleMiddle AgedNomogramsAmerican Joint Committee on Cancer TNM classificationBarcelona Clinic Liver Cancer staging systemHepatitis B surface antigenHepatocellular carcinomaLaparoscopic hepatectomyNomogramRecurrenceTumor invasionTumor thrombus

Identifiers

PMID31601270
PMCPMC6788088
OpenAlexW2980134631

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Registered trials

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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.