Evidence map›Paper›PMID 33717561›Full record

ArticleJournal of thoracic disease2021

A nomogram for predicting brain metastases of EGFR-mutated lung adenocarcinoma patients and estimating the efficacy of therapeutic strategies.

Jing Wang, Baozhong Zhang, Qingsong Pang, Tian Zhang, Xi Chen, Puchun Er, Yuwen Wang, Jinqiang You, Ping Wang

Abstract read
In one paragraph

Article in Journal of thoracic disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
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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.

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

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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

9 authors.

Jing WangDepartment of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Centre for Cancer, Tianjin, China.
Baozhong ZhangDepartment of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Centre for Cancer, Tianjin, China.
Qingsong PangDepartment of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Centre for Cancer, Tianjin, China.
Tian ZhangDepartment of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Centre for Cancer, Tianjin, China.
Xi ChenDepartment of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Centre for Cancer, Tianjin, China.
Puchun ErDepartment of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Centre for Cancer, Tianjin, China.
Yuwen WangDepartment of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Centre for Cancer, Tianjin, China.
Jinqiang YouDepartment of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Centre for Cancer, Tianjin, China.
Ping WangDepartment of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Centre for Cancer, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo establish a nomogram for predicting the outcome of EGFR-mutated lung adenocarcinoma patients with brain metastases (BMs) and to estimate the efficacy of different therapeutic strategies.

methodsThe data of 129 cases with BM from the period between January 1st 2011 and December 31st 2014 were collected, and all of the cases were pathologically confirmed to be lung adenocarcinoma, stages I-IV and with 19 and/or 21 exon mutations of EGFR. Cox regression analysis and log-rank test were used for data analysis. The nomogram was used to establish the progression models.

resultsIn the univariate analysis, the stage, ECOG score, interval between the diagnosis of lung cancer and BM, the number of brain metastatic lesions, and the diameter of the maximal brain metastatic lesion correlated well with overall survival (OS). In multivariate Cox proportional hazard analysis, the ECOG score, interval between the diagnosis of lung cancer and BM, and the number of brain metastatic lesions correlated well with the OS. Patients were divided into the poor prognostic group and the good prognostic group based on the nomogram prognostic model score. Subgroup analysis showed that in the poor prognostic group, the OS of patients who received radiotherapy was better than that of the patients who did not receive radiotherapy as the first-line treatment (30

conclusionsWe established an effective nomogram model to predict the progression of EGFR-mutated lung adenocarcinoma patients with BM and the therapeutic effect of the individual treatments. Radiotherapy was beneficial for the patients of both the poor and good prognostic groups, but TKI may be better suited for treating the patients with good prognosis.

Indexed as

brain metastasis (BM)EGFR mutationnomogramNon-small cell lung cancer (NSCLC)prognosistreatment

Identifiers

PMID33717561
PMCPMC7947515

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