Evidence map›Paper›PMID 37405479›Full record

Trial reportJournal of cancer research and clinical oncology2023

Web-based nomograms for predicting overall survival and cancer-specific survival in retroperitoneal leiomyosarcoma: a population-based analysis.

Zhile Feng, Yongxiang Li

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

2 authors.

Zhile FengGeneral Surgery Department, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, People's Republic of China.
Yongxiang LiGeneral Surgery Department, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, People's Republic of China. liyongxiang@ahmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRetroperitoneal leiomyosarcoma is a type of carcinoma with low incidence and poor prognosis, and prognostic factors are currently unknown. Therefore, our study aimed to investigate the predictive factors of RPLMS and establish prognostic nomograms.

methodsPatients diagnosed with RPLMS between 2004 and 2017 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Prognostic factors were identified by univariate and multivariate COX regression analyses and used to generate nomograms to predict overall survival (OS) and cancer-specific survival (CSS).

results646 eligible patients were randomly divided into training set (n = 323) and validation set (n = 323). Multivariate COX regression analysis indicated that the independent risk factors for OS and CSS were age, tumor size, grade, SEER stage, and surgery. In the nomogram of OS, the concordance indices (C-index) of the training and validation sets were 0.72 and 0.691, and in the nomogram of CSS, the C-indices of the training and validation sets were 0.737 and 0.737. Furthermore, calibration plots showed that the predicted results of the nomograms in the training and validation sets agree well with the actual observations.

conclusionAge, tumor size, grade, SEER stage, and surgery were independent prognostic factors for RPLMS. The nomograms developed and validated in this study can accurately predict the OS and CSS of patients, which could help clinicians make individualized survival predictions. Finally, we make the two nomograms into two web calculators for the convenience of clinicians.

Indexed as

LeiomyosarcomaRetroperitoneal NeoplasmsHumansInternetNeoplasm StagingNomogramsPrognosisSEER ProgramCancer-specific survivalNomogramOverall survivalRPLMS

Identifiers

PMID37405479
PMCPMC11797845

What Socratic holds

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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 Socratic graph.