Evidence map›Paper›PMID 40529760›Full record

ArticleJournal of thoracic disease2025

A SEER prognostic nomogram analysis for extensive-stage small cell lung cancer: comparing the immune checkpoint inhibitors (ICIs) and non-ICIs eras.

Xiaomei Jiang, Zhongqi Wang, Xiufeng Zhang, Liangyun Xie, Shanshan Ji, Qinqin Song, Jing Dong, Ye Jin, Shuangli Li, Zhi Zhang and 1 more

Abstract read
In one paragraph

Article in Journal of thoracic disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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

2 citing papers in PubMed.

  1. Article
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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

11 authors.

Xiaomei JiangDepartment of Medical Oncology, Affiliated Tangshan Gongren Hospital, North China University of Science and Technology, Tangshan, China.
Zhongqi WangDepartment of Medical Oncology, Affiliated Tangshan Gongren Hospital, North China University of Science and Technology, Tangshan, China.
Xiufeng ZhangCollege of Life Sciences, North China University of Science and Technology, Tangshan, China.
Liangyun XieDepartment of Medical Oncology, Affiliated Tangshan Gongren Hospital, North China University of Science and Technology, Tangshan, China.
Shanshan JiDepartment of Medical Oncology, Affiliated Tangshan Gongren Hospital, North China University of Science and Technology, Tangshan, China.
Qinqin SongDepartment of Medical Oncology, Affiliated Tangshan Gongren Hospital, North China University of Science and Technology, Tangshan, China.
Jing DongDepartment of Medical Oncology, Affiliated Tangshan Gongren Hospital, North China University of Science and Technology, Tangshan, China.
Ye JinCollege of Clinical Medicine, North China University of Science and Technology, Tangshan, China.
Shuangli LiDepartment of Medical Oncology, Affiliated Tangshan Gongren Hospital, North China University of Science and Technology, Tangshan, China.
Zhi ZhangDepartment of Medical Oncology, Affiliated Tangshan Gongren Hospital, North China University of Science and Technology, Tangshan, China.
Xuemei ZhangCollege of Life Sciences, North China University of Science and Technology, Tangshan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Extensive-stage small cell lung cancer (ES-SCLC) has a poor prognosis, and the advent of immune checkpoint inhibitors (ICIs) has introduced new possibilities for treatment. This study aimed to develop a novel nomogram to predict survival in ES-SCLC patients and identify key prognostic factors by comparing the ICIs era with the non-ICIs era. Methods: Data were extracted from the surveillance epidemiology and end results (SEER) database, including patients diagnosed with ES-SCLC between 2015 and 2020. After using propensity score matching (PSM), patients were categorized into the ICIs era group and the non-ICIs era group (1:1 matching). Univariable and multivariable Cox regression analyses identified significant prognostic factors for overall survival (OS) and cancer-specific survival (CSS), which were used to develop nomograms for predicting 12-, 24-, and 35-month survival rates. Kaplan-Meier analysis, Cox proportional hazards models, and subgroup analyses were conducted to evaluate OS and CSS. A nomogram was developed using multivariate Cox regression analysis and validated with calibration and decision curve analyses. Results: Immunotherapy significantly improved OS and CSS, with ICIs era patients showing a 14% reduction in OS risk [hazard ratio (HR) = 0.86, 95% confidence interval (CI): 0.82-0.90, P<0.001] and a 15% reduction in CSS risk (HR =0.85, 95% CI: 0.81-0.90, P<0.001) compared to the non-ICIs era. Significant prognostic factors included chemotherapy, liver metastasis, age, gender, marital status, income, N stage, and metastasis sites. Subgroup analyses indicated that the combination of immunotherapy and chemotherapy was particularly beneficial for female patients without liver metastases. The nomograms demonstrated higher predictive accuracy than the tumor, node, metastasis (TNM) staging system, with a Harrell's C-index of 0.691 compared to 0.514 for TNM staging. The receiver operating characteristic (ROC) curves and decision curve analysis (DCA) further confirmed the superior performance and clinical utility of the nomograms. Conclusions: Immunotherapy significantly enhances survival outcomes in ES-SCLC patients. The developed nomograms provide a more accurate and clinically useful tool for predicting patient survival compared to the traditional TNM staging system. These findings support the integration of immunotherapy and the use of nomograms in clinical decision-making for ES-SCLC patients.

Indexed as

Extensive-stage small cell lung cancer (ES-SCLC)immune checkpoint inhibitors (ICIs)prognostic nomogrampropensity score matching (PSM)Surveillance Epidemiology and End Results database (SEER database)

Identifiers

PMID40529760
PMCPMC12170010

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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.