Evidence map›Paper›PMID 38286746›Full record

ArticleThe clinical respiratory journal2024

Construction of brain metastasis prediction model in limited stage small cell lung cancer patients without prophylactic cranial irradiation.

Jiayi Guo, Jianjiang Liu, Wanli Ye, Jun Xu, Wangyan Zhong, Xiaoyu Zhang, Hang Yuan, Hao Shi, Ting Li, Yibing Xu and 3 more

Open access · goldAbstract read
In one paragraph

Article in The clinical respiratory journal, 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
0.6field-weighted citation impact, top 32% 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

13 authors at 2 institutions in 1 country.

Jiayi GuoSchool of Medicine, Shaoxing University, Shaoxing, Zhejiang, China.
Jianjiang LiuDepartment of Radiation Oncology, First Affiliated Hospital of Shaoxing University, Shaoxing, Zhejiang, China.
Wanli YeDepartment of Radiation Oncology, First Affiliated Hospital of Shaoxing University, Shaoxing, Zhejiang, China.
Jun XuDepartment of Radiation Oncology, First Affiliated Hospital of Shaoxing University, Shaoxing, Zhejiang, China.
Wangyan ZhongDepartment of Radiation Oncology, First Affiliated Hospital of Shaoxing University, Shaoxing, Zhejiang, China.
Xiaoyu ZhangDepartment of Radiation Oncology, First Affiliated Hospital of Shaoxing University, Shaoxing, Zhejiang, China.
Hang YuanDepartment of Radiation Oncology, First Affiliated Hospital of Shaoxing University, Shaoxing, Zhejiang, China.
Hao ShiDepartment of Radiation Oncology, First Affiliated Hospital of Shaoxing University, Shaoxing, Zhejiang, China.
Ting LiDepartment of Radiation Oncology, First Affiliated Hospital of Shaoxing University, Shaoxing, Zhejiang, China.
Yibing XuDepartment of Radiation Oncology, First Affiliated Hospital of Shaoxing University, Shaoxing, Zhejiang, China.
Jiwei MaoDepartment of Radiation Oncology, First Affiliated Hospital of Shaoxing University, Shaoxing, Zhejiang, China.
Bin ShenShaoxing People's Hospital, Shaoxing, Zhejiang, China.
Dongping WuDepartment of Radiation Oncology, First Affiliated Hospital of Shaoxing University, Shaoxing, Zhejiang, China.ORCID https://orcid.org/0000-0002-7338-3423
Shaoxing University · CNShaoxing People's Hospital · CN

Funding

Beijing Medical and Health Foundation TC2023EZD1001Health Science and Technology Plan Project of Zhejiang 2021KY1149Shaoxing Health Science and Technology Plan 2022KY027
6 · The paper itself

Abstract

introductionSmall cell lung cancer (SCLC) is a highly aggressive lung cancer variant known for its elevated risk of brain metastases (BM). While earlier meta-analyses supported the use of prophylactic cranial irradiation (PCI) to reduce BM incidence and enhance overall survival, modern MRI capabilities raise questions about PCI's universal benefit for limited-stage SCLC (LS-SCLC) patients. As a response, we have created a predictive model for BM, aiming to identify low-risk individuals who may not require PCI.

methodsA total of 194 LS-SCLC patients without PCI treated between 2009 and 2021 were included. We conducted both univariate and multivariate analyses to pinpoint the factors associated with the development of BM. A nomogram for predicting the 2- and 3-year probabilities of BM was then constructed.

resultsUnivariate and multivariate analyses revealed several significant independent risk factors for the development of BM. These factors include TNM stage, the number of chemotherapy (ChT) cycles, Ki-67 expression level, pretreatment serum lactate dehydrogenase (LDH) levels, and haemoglobin (HGB) levels. These findings underscore their respective roles as independent predictors of BM. Based on the results of the final multivariable analysis, a nomogram model was created. In the training cohort, the nomogram yielded an area under the receiver operating characteristic curve (AUC) of 0.870 at 2 years and 0.828 at 3 years. In the validation cohort, the AUC values were 0.897 at 2 years and 0.789 at 3 years. The calibration curve demonstrated good agreement between the predicted and observed probabilities of BM.

conclusionsA novel nomogram has been developed to forecast the likelihood of BM in patients diagnosed with LS-SCLC. This tool holds the potential to assist healthcare professionals in formulating more informed and tailored treatment plans.

Indexed as

Brain NeoplasmsLung NeoplasmsSmall Cell Lung CarcinomaCranial IrradiationHumansRisk Factorsbrain metastasislimited stagenomogramprophylactic cranial irradiationsmall cell lung cancer

Identifiers

PMID38286746
PMCPMC10790059
OpenAlexW4390919722

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.