Evidence map›Paper›PMID 34970848›Full record

ArticleThoracic cancer2022

Clinical characteristics and prognostic model for extensive-stage small cell lung cancer: A retrospective study over an 8-year period.

Jun Ni, Xiaotong Zhang, Hanping Wang, Xiaoyan Si, Yan Xu, Jing Zhao, Minjiang Chen, Li Zhang, Mengzhao Wang

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Article in Thoracic cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  9. Corrigendum.Thoracic cancer · 2022
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Jun NiDepartment of Pulmonary and Critical Care Medicine, Peking Union Medical Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China.ORCID 0000-0002-5939-8856
Xiaotong ZhangDepartment of Pulmonary and Critical Care Medicine, Peking Union Medical Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China.ORCID 0000-0001-8937-820X
Hanping WangDepartment of Pulmonary and Critical Care Medicine, Peking Union Medical Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China.
Xiaoyan SiDepartment of Pulmonary and Critical Care Medicine, Peking Union Medical Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China.ORCID 0000-0003-2913-3045
Yan XuDepartment of Pulmonary and Critical Care Medicine, Peking Union Medical Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China.ORCID 0000-0002-2832-2664
Jing ZhaoDepartment of Pulmonary and Critical Care Medicine, Peking Union Medical Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China.
Minjiang ChenDepartment of Pulmonary and Critical Care Medicine, Peking Union Medical Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China.ORCID 0000-0002-4040-6115
Li ZhangDepartment of Pulmonary and Critical Care Medicine, Peking Union Medical Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China.ORCID 0000-0002-8101-672X
Mengzhao WangDepartment of Pulmonary and Critical Care Medicine, Peking Union Medical Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSmall cell lung cancer (SCLC) is a highly aggressive neuroendocrine tumor with a short replication time and a rapid growth rate. Prognostic factors for SCLC in clinical practice are scarce. Retrospective analysis of 8-year extensive-stage SCLC data from the Department Respiratory and Intensive Care Unit, Peking Union Medical College Hospital (Beijing, China) was performed to develop a risk prediction model that can facilitate the identification of extensive-stage SCLC with differing prognosis in clinical practice.

methodsA retrospective analysis of data from patients with extensive-stage SCLC at a single-center from January 2013 to January 2021, including age, sex, ECOG physical score, immunohistochemistry (CgA, Syn, CD56, TTF1, and Ki67), staging, treatment regimen, laboratory examinations, and survival period, was performed. Clinical variables with potential prognostic significance were screened by univariate Cox analysis. Next, multifactor Cox risk prediction regression analysis was performed to establish an extensive-stage SCLC risk prognostic model. Survival curves and ROC curves for high and low risk groups were plotted according to risk scores. Nomogram and calibration curves were developed to assess the accuracy of the risk prediction model.

resultsThis study included 300 patients who were diagnosed with extensive-stage SCLC at our center from January 2013 to January 2021. The most common first presentation was respiratory symptoms, especially cough (162, 54%). The most common extra-thoracic metastatic organs were bone (36.3%), liver (24.7%), brain (15.7%), and adrenal glands (15.7%). A total of 99% of patients received first-line systemic therapy, with 86.3% of patients treated with platinum-etoposide and 10.7% of patients treated with immune checkpoint inhibitor combined with platinum-etoposide backbone. First-line progression-free survival was up to 198 days, and the median OS was 439 days. After Cox regression screening and backward stepwise selection, "time from initial therapy to relapse or progression (PFS1), liver metastases, adrenal metastases, M stage and first-line treatment pattern" were retained to establish a prognostic model with an AUC value of 0.763. The prognostic model was shown as a nomogram with good agreement between predicted and observed outcomes.

conclusionsThe first-line treatment of SCLC patients admitted to our hospital in the past 8 years was relatively standardized, and the progression-free survival and OS were slightly longer than those reported in the literature. We developed a prognostic risk score model for extensive-stage SCLC to calculate individual survival in clinical practice.

Indexed as

Lung NeoplasmsSmall Cell Lung CarcinomaHumansNeoplasm Recurrence, LocalNeoplasm StagingPrognosisRetrospective Studiesclinical characteristicsCox regression analysisextensive-stage SCLCnomogramrisk factors

Identifiers

PMID34970848
PMCPMC8841711

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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.