Evidence map›Paper›PMID 39098998›Full record

ArticleThoracic cancer2024

Construction of a nomogram model based on biomarkers for liver metastasis in non-small cell lung cancer.

Tian Zhang, Yajuan Zhang, Yunfeng Ni, Xiaohui Jia, Yanlin Li, Ziyang Mao, Panpan Jiang, Xiaolan Fu, Min Jiao, Lili Jiang and 4 more

Abstract read
In one paragraph

Article in Thoracic cancer, 2024. 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
–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

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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.

  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

14 authors.

Tian ZhangDepartment of Medical Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Yajuan ZhangDepartment of Medical Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Yunfeng NiDepartment of Thoracic Surgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, People's Republic of China.
Xiaohui JiaDepartment of Medical Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Yanlin LiDepartment of Medical Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Ziyang MaoDepartment of Medical Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Panpan JiangDepartment of Medical Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Xiaolan FuDepartment of Medical Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Min JiaoDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Lili JiangDepartment of Medical Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Wenjuan WangDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Hui GuoDepartment of Medical Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Ying ZanDepartment of Medical Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Mengjie LiuDepartment of Medical Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.ORCID 0000-0001-7823-1851

Funding

Key Industry Innovation Chain (Group) of Shaanxi Province 2022ZDLSF04-11Natural Science Basic Research Program of Shaanxi Province 2022JQ-796Science and Technology Program of Shaanxi Province 2022JQ-784
6 · The paper itself

Abstract

backgroundPatients with non-small cell lung cancer (NSCLC) with liver metastasis have a poor prognosis, and there are no reliable biomarkers for predicting disease progression. Currently, no recognized and reliable prediction model exists to anticipate liver metastasis in NSCLC, nor have the risk factors influencing its onset time been thoroughly explored.

methodsThis study conducted a retrospective analysis of 434 NSCLC patients from two hospitals to assess the association between the risk and timing of liver metastasis, as well as several variables.

resultsThe patients were divided into two groups: those without liver metastasis and those with liver metastasis. We constructed a nomogram model for predicting liver metastasis in NSCLC, incorporating elements such as T stage, N stage, M stage, lack of past radical lung cancer surgery, and programmed death ligand 1 (PD-L1) levels. Furthermore, NSCLC patients with wild-type EGFR, no prior therapy with tyrosine kinase inhibitors (TKIs), and no prior radical lung cancer surgery showed an elevated risk of early liver metastasis.

conclusionIn conclusion, the nomogram model developed in this study has the potential to become a simple, intuitive, and customizable clinical tool for assessing the risk of liver metastasis in NSCLC patients following validation. Furthermore, it provides a framework for investigating the timing of metachronous liver metastasis.

Indexed as

Biomarkers, TumorCarcinoma, Non-Small-Cell LungLiver NeoplasmsLung NeoplasmsNomogramsAdultAgedFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesBiomarkers, Tumordriver genesimmunotherapy biomarkersliver metastasis in NSCLCnomogram modeltemporal heterogeneity

Identifiers

PMID39098998
PMCPMC11462952

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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.