Evidence map›Paper›PMID 37689775›Full record

ArticleEuropean journal of medical research2023

Development of nomograms to predict recurrence after conversion hepatectomy for hepatocellular carcinoma previously treated with transarterial interventional therapy.

Min Deng, Rongce Zhao, Renguo Guan, Shaohua Li, Zhijun Zuo, Wenping Lin, Wei Wei, Rongping Guo

Open access · goldAbstract read
In one paragraph

Article in European journal of medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 27% 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

4 citing papers in PubMed, 4 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

8 authors at 3 institutions in 1 country.

Min Deng *Department of Liver Surgery, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, China.
Rongce Zhao *Department of Liver Surgery, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, China.
Renguo GuanDepartment of Liver Surgery, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, China.
Shaohua LiDepartment of Liver Surgery, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, China.
Zhijun ZuoDepartment of Liver Surgery, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, China.
Wenping LinDepartment of Liver Surgery, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, China.
Wei WeiDepartment of Liver Surgery, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, China.
Rongping GuoDepartment of Liver Surgery, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, China. guorongpingsysucc@outlook.com.
Sun Yat-sen University · CNState Key Laboratory of Oncology in South ChinaSun Yat-sen University Cancer Center · CN

Funding

Clinical Trials Project (308 Project) of Sun Yat-sen University Cancer Center 308-2015-014Clinical Trials Project (5010 Project) of Sun Yat-sen University 5010-2017009Medical Scientific Research Foundation of Guangdong Province A2017477National Natural Science Foundation of China 81871985National Natural Science Foundation of China 82172579Natural Science Foundation of Guangdong Province 2017A030310203Natural Science Foundation of Guangdong Province 2018A0303130098Science and Technology Planning Project of Guangdong Province 2017A020215112Science and Technology Planning Project of Guangzhou 201903010017Science and Technology Planning Project of Guangzhou 201904010479
6 · The paper itself

Abstract

backgroundLack of opportunity for radical surgery and postoperative tumor recurrence are challenges for surgeons and hepatocellular carcinoma (HCC) patients. This study aimed to develop nomograms to predict recurrence risk and recurrence-free survival (RFS) probability after conversion hepatectomy for patients previously receiving transarterial interventional therapy.

methodsIn total, 261 HCC patients who underwent conversion liver resection and previously received transarterial interventional therapy were retrospectively enrolled. Nomograms to predict recurrence risk and RFS were developed, with discriminative ability and calibration evaluated by C-statistics, calibration plots, and the Area under the Receiver Operator Characteristic (AUROC) curves.

resultsUnivariate/multivariable logistic regression and Cox regression analyses were used to identify predictive factors for recurrence risk and RFS, respectively. The following factors were selected as predictive of recurrence: age, tumor number, microvascular invasion (MVI) grade, preoperative alpha-fetoprotein (AFP), preoperative carbohydrate antigen 19-9 (CA19-9), and Eastern Cooperative Oncology Group performance score (ECOG PS). Similarly, age, tumor number, postoperative AFP, postoperative protein induced by vitamin K absence or antagonist-II (PIVKA-II), and ECOG PS were incorporated for the prediction of RFS. The discriminative ability and calibration of the nomograms revealed good predictive ability. Calibration plots showed good agreement between the nomogram predictions of recurrence and RFS and the actual observations.

conclusionsA pair of reliable nomograms was developed to predict recurrence and RFS in HCC patients after conversion resection who previously received transarterial interventional therapy. These predictive models can be used as guidance for clinicians to help with treatment strategies.

Indexed as

Carcinoma, HepatocellularLiver Neoplasmsalpha-FetoproteinsHepatectomyHumansNomogramsRetrospective Studiesalpha-FetoproteinsConversionHepatectomyHepatocellular carcinomaNomogramRecurrence

Identifiers

PMID37689775
PMCPMC10492285
OpenAlexW4386572959

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.