Evidence map›Paper›PMID 33068222›Full record

ArticleInternational journal of clinical oncology2021

A novel nomogram for predicting local recurrence-free survival after surgical resection for retroperitoneal liposarcoma from a Chinese tertiary cancer center.

Guoqiang Xue, Zhen Wang, Chengpeng Li, Ang Lv, Xiuyun Tian, Jianhui Wu, Hui Qiu, Chunyi Hao

Abstract read
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In one paragraph

Article in International journal of clinical oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
1.9field-weighted citation impact, top 11% 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

16 citing papers in PubMed, 24 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 1 institution in 2 countries.

Guoqiang XueKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery, Peking University Cancer Hospital & Institute, Beijing, People's Republic of China.
Zhen WangKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery, Peking University Cancer Hospital & Institute, Beijing, People's Republic of China.
Chengpeng LiKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery, Peking University Cancer Hospital & Institute, Beijing, People's Republic of China.
Ang LvKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery, Peking University Cancer Hospital & Institute, Beijing, People's Republic of China.
Xiuyun TianKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery, Peking University Cancer Hospital & Institute, Beijing, People's Republic of China.
Jianhui WuKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery, Peking University Cancer Hospital & Institute, Beijing, People's Republic of China.
Hui QiuKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery, Peking University Cancer Hospital & Institute, Beijing, People's Republic of China.
Chunyi HaoKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery, Peking University Cancer Hospital & Institute, No. 52 Fucheng Road, Haidian District, Beijing, People's Republic of China. haochunyi@bjmu.edu.cn.ORCID http://orcid.org/0000-0002-1233-1689
Peking University · CN

Funding

Beijing Municipal Administration of Hospitals' Ascent Plan DFL20181104Beijing Municipal Administration of Hospitals' Youth Program QML20181104Capital Health Research and Development of Special Funds 2020-1-1021
6 · The paper itself

Abstract

backgroundLocal recurrence is the most difficult postoperative challenge and the leading cause of death in patients with retroperitoneal liposarcoma (RLPS). We aimed to establish a postoperative nomogram exclusively focused on RLPS for predicting local recurrence-free survival (LRFS).

methodsA cohort of 124 patients after surgical resection with curative intent in the Peking University Cancer Hospital Sarcoma Center were included in the study. Demographic, clinicopathologic, and treatment variables were analyzed using the Cox regression model. Significant clinically relevant variables in multivariable analysis were incorporated into the RLPS-specific nomogram. The discriminative ability and predictive accuracy of the nomogram were assessed by calculating the concordance index and drawing a calibration plot.

resultsAt a median follow-up of 26.5 (interquartile range 10.9-39.4) months, 71 patients had recurrent disease. The 3-year and 5-year LRFS rates were 35.6% (95% confidence interval, 27.0-46.9%) and 28.2% (95% CI 15.8-38.6%), respectively. Multivariate analysis identified the French Federation of Cancer Centers Sarcoma Group (FNCLCC) grade and completeness of resection as independent predictors of LRFS. Variables included in our nomogram were: presentation status, multifocality, completeness of resection, histologic subtypes, and FNCLCC grade. The concordance index of our nomogram was 0.732 (95% CI 0.667-0.797) and the calibration plot was excellent.

conclusionsOur novel nomogram for patients with resected RLPS could improve recurrence risk stratification to explore molecular analysis associated with recurrence.

Indexed as

LiposarcomaNomogramsChinaHumansNeoplasm Recurrence, LocalPrognosisRetroperitoneal NeoplasmsRetrospective StudiesLocal recurrence-free survivalNomogramPrognosisRetroperitoneal liposarcoma

Identifiers

PMID33068222
OpenAlexW3092827846

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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.