Evidence map›Paper›PMID 36845714›Full record

ArticleFrontiers in oncology2023

A clinical prediction model for lung metastasis risk in osteosarcoma: A multicenter retrospective study.

Shengping Zheng, Longhao Chen, Jiaming Wang, Haosheng Wang, Zhaohui Hu, Wanying Li, Chan Xu, Minmin Ma, Bing Wang, Yangjun Huang and 6 more

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In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 2 pooled it
–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

4 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
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

16 authors.

Shengping ZhengDepartment of Orthopedics, Xianyang Central Hospital, Xianyang, China.
Longhao ChenFaculty of Orthopaedics and Traumatology, Guangxi University of Traditional Chinese Medicine, Nanning, China.
Jiaming WangDepartment of Orthopedics, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
Haosheng WangDepartment of Orthopaedics, The Second Hospital of Jilin University, Changchun, China.
Zhaohui HuDepartment of Spinal Surgery, Liuzhou People's Hospital, Liuzhou, China.
Wanying LiClinical Medical Research Center, Xianyang Central Hospital, Xianyang, China.
Chan XuClinical Medical Research Center, Xianyang Central Hospital, Xianyang, China.
Minmin MaClinical Medical Research Center, Xianyang Central Hospital, Xianyang, China.
Bing WangClinical Medical Research Center, Xianyang Central Hospital, Xianyang, China.
Yangjun HuangFaculty of Orthopaedics and Traumatology, Guangxi University of Traditional Chinese Medicine, Nanning, China.
Qiang LiuDepartment of Orthopedics, Xianyang Central Hospital, Xianyang, China.
Zhi-Ri TangSchool of Physics and Technology, Wuhan University, Wuhan, China.
Guanyu LiuFaculty of Medicine, Macau University of Science and Technology, Macau, Macau SAR, China.
Tingting WangFaculty of Medicine, Macau University of Science and Technology, Macau, Macau SAR, China.
Wenle LiState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, China.
Chengliang YinFaculty of Medicine, Macau University of Science and Technology, Macau, Macau SAR, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Lung metastases (LM) have a poor prognosis of osteosarcoma. This study aimed to predict the risk of LM using the nomogram in patients with osteosarcoma. Methods: A total of 1100 patients who were diagnosed as osteosarcoma between 2010 and 2019 in the Surveillance, Epidemiology and End Results (SEER) database were selected as the training cohort. Univariate and multivariate logistic regression analyses were used to identify independent prognostic factors of osteosarcoma lung metastases. 108 osteosarcoma patients from a multicentre dataset was as valiation data. The predictive power of the nomogram model was assessed by receiver operating characteristic curves (ROC) and calibration plots, and decision curve analysis (DCA) was utilized to interpret the accurate validity in clinical practice. Results: A total of 1208 patients with osteosarcoma from both the SEER database(n=1100) and the multicentre database (n=108) were analyzed. Univariate and multivariate logistic regression analyses showed that Survival time, Sex, T-stage, N-stage, Surgery, Radiation, and Bone metastases were independent risk factors for lung metastasis. We combined these factors to construct a nomogram for estimating the risk of lung metastasis. Internal and external validation showed significant predictive differences (AUC 0.779, 0.792 respectively). Calibration plots showed good performance of the nomogram model. Conclusions: In this study, a nomogram model for predicting the risk of lung metastases in osteosarcoma patients was constructed and turned out to be accurate and reliable through internal and external validation. Moreover we built a webpage calculator (https://drliwenle.shinyapps.io/OSLM/) taken into account nomogram model to help clinicians make more accurate and personalized predictions.

Indexed as

lung metastasisnomogramosteosarcomaSEERwebpage calculator

Identifiers

PMID36845714
PMCPMC9950508

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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.