Evidence map›Paper›PMID 37923984›Full record

ArticleBMC cancer2023

Clinical retrospective analysis with a predictive model for diffused-tenosynovial giant cell tumors of the temporomandibular joint.

Ying Liu, Yingying Huang, Dongwang Zhu, Jiang Li, Tongchao Zhao, Yining He, Ronghui Xia, Laiping Zhong

Open access · goldAbstract read
In one paragraph

Article in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 30% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

8 authors at 2 institutions in 2 countries.

Ying LiuDepartment of Oral & Maxillofacial Head & Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yingying HuangDepartment of Oral & Maxillofacial Head & Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Dongwang ZhuDepartment of Oral & Maxillofacial Head & Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jiang LiDepartment of Oral Pathology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Tongchao ZhaoDepartment of Oral & Maxillofacial Head & Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yining HeBiostatistics Office of Clinical Research Unit, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China.
Ronghui XiaDepartment of Oral Pathology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China. xrh335@163.com.
Laiping ZhongDepartment of Oral & Maxillofacial Head & Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China. zhonglp@hotmail.com.
Shanghai Ninth People's Hospital · CNShanghai Jiao Tong University · CN

Funding

Shanghai Ninth People's Hospital MDT Program 201916Shanghai Sailing Program 22YF1421700
6 · The paper itself

Abstract

backgroundThis study aimed to find out the characteristics in relation to tumor recurrence in diffused-tenosynovial giant cell tumor of temporomandibular joint and to develop and validate the prognostic model for personalized prediction.

methodsFrom April 2009 to January 2021, patients with diffused-tenosynovial giant cell tumor of temporomandibular joint at a single center were included in this study. The clinical features and local recurrence-free survival were assessed through the expression of the Ki-67 index and colony-stimulating factor 1 receptor expression. Both univariate and multivariate analyses were performed on the prognostic factors for local recurrence-free survival. An independent predictor nomogram and pertinent tumor characteristics were included.

resultsThe retrospective study enrolling seventy eligible patients at the Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine. During the follow-up time, eleven patients suffered tumor recurrence. Age was an independent risk factor for local recurrence-free survival (P = 0.032). The Ki-67 index varied significantly in different sites (P = 0.034) and tumor volume (P = 0.017). Multivariate logistic regression was used to develop the prediction model using both statistical significance and prognostic indicators. The C-index of the nomogram based on age, site, Ki-67, and colony-stimulating factor 1 receptor was 0.833. These variates provided good predicted accuracy for a nomogram on local recurrence-free survival. Diffused-tenosynovial giant cell tumor from the temporomandibular joint is extremely uncommon, and certain clinical traits are linked to the tumor proliferation index.

conclusionsWe identified the risk indicators and developed a nomogram in this study to forecast the likelihood of local recurrence-free survival in patients with diffused-tenosynovial giant cell tumor from temporomandibular joint.

Indexed as

Giant Cell Tumor of Tendon SheathNeoplasm Recurrence, LocalChinaHumansKi-67 AntigenMacrophage Colony-Stimulating FactorRetrospective StudiesTemporomandibular JointKi-67 AntigenMacrophage Colony-Stimulating FactorColony-stimulating factor 1 receptorDiffused-tenosynovial giant cell TumorKi-67 indexLocal recurrence-free survivalTemporomandibular joint

Identifiers

PMID37923984
PMCPMC10625237
OpenAlexW4388291081

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.