Evidence map›Paper›PMID 33539664›Full record

ArticleCancer medicine2021

A novel prognostic nomogram for colorectal cancer liver metastasis patients with recurrence after hepatectomy.

Jie-Ying Liang, Hao-Cheng Lin, Jingwen Liu, De-Shen Wang, Yun-Fei Yuan, Bin-Kui Li, Yun Zheng, Xiao-Jun Wu, Gong Chen, Feng-Hua Wang and 5 more

Abstract readValidation Study
In one paragraph

Article in Cancer medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
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

7 citing papers in PubMed.

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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

15 authors.

Jie-Ying LiangState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.ORCID 0000-0002-9829-294X
Hao-Cheng LinState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.
Jingwen LiuZhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
De-Shen WangState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.ORCID 0000-0001-9657-4380
Yun-Fei YuanState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.
Bin-Kui LiState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.ORCID 0000-0002-9694-9522
Yun ZhengState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.ORCID 0000-0001-6717-3959
Xiao-Jun WuState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.
Gong ChenState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.
Feng-Hua WangState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.
Zhi-Qiang WangState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.
Zhi-Zhong PanState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.
De-Sen WanState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.
Rui-Hua XuState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.ORCID 0000-0001-9771-8534
Yu-Hong LiState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.ORCID 0000-0002-5710-9096

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWe aimed to construct a nomogram to predict personalized post-recurrence survival (PRS) among colorectal cancer liver metastasis (CRLM) patients with post-hepatectomy recurrence.

methodsColorectal cancer liver metastasis patients who received initial hepatectomy and had subsequent recurrence between 2001 and 2019 in Sun Yat-sen University Cancer Center from China were included in the study. Patients were randomly assigned to a training cohort and a validation cohort on a ratio of 2:1. Univariable analysis was first employed to select potential predictive factors for PRS. Then, the multivariable Cox regression model was applied to recognize independent prognostic factors. According to the model, a nomogram to predict PRS was established. The nomogram's predictive capacity was further assessed utilizing concordance index (C-index) values, calibration plots, and Kaplan-Meier curves.

resultsAbout 376 patients were finally enrolled, with a 3-year PRS rate of 37.3% and a 5-year PRS rate of 24.6%. The following five independent predictors for PRS were determined to construct the nomogram: the largest size of liver metastases at initial hepatectomy, relapse-free survival, CEA level at recurrence, recurrent sites, and treatment for recurrence. The nomogram displayed fairly good discrimination and calibration. The C-index value was 0.742 for the training cohort and 0.773 for the validation cohort. Patients were grouped into three risk groups very well by the nomogram, with 5-year PRS rates of 45.2%, 23.3%, and 9.0%, respectively (p < 0.001) in the training cohort and 36.0%, 9.2%, and 4.6%, respectively (p < 0.001) in the validation cohort.

conclusionA novel nomogram was built and validated to enable the prediction of personal PRS in CRLM patients with post-hepatectomy recurrence. The nomogram may help physicians in decision making.

Indexed as

NomogramsAnalysis of VarianceCarcinoembryonic AntigenCarcinoma, HepatocellularColorectal NeoplasmsDisease-Free SurvivalFemaleHepatectomyHumansLiver NeoplasmsMaleMiddle AgedNeoplasm Recurrence, LocalPostoperative ComplicationsPrognosisProportional Hazards ModelsCarcinoembryonic Antigencolorectal cancer liver metastasishepatectomynomogrampost-recurrence survivalprognostic factors

Identifiers

PMID33539664
PMCPMC7940234

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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.