Evidence mapPaperPMID 40859851Full record

Observational studyCancer medicine2025

Development and Validation of a Clinlabomics-Based Nomogram for Predicting the Prognosis of Small Cell Lung Cancer in China: A Multicenter, Retrospective Cohort Study.

Qi Peng, Fang Yang, Ke Xu, Wei Guo, Dongsheng Wang, Mingfei Xiang, Huaichao Luo

Abstract readMulticenter StudyObservational StudyValidation Study
In one paragraph

Observational study in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 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

7 authors.

Qi PengDepartment of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.ORCID https://orcid.org/0009-0002-0452-6985
Fang YangDepartment of Oncology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, China.
Ke XuDepartment of Oncology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, China.ORCID https://orcid.org/0000-0002-5520-5754
Wei GuoDepartment of Oncology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, China.
Dongsheng WangDepartment of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Mingfei XiangMedical Insurance Division, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Huaichao LuoDepartment of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.ORCID https://orcid.org/0000-0002-8632-5230

Funding

Natural Science Foundation of Sichuan Province 2024NSFSC0767Natural Science Foundation of Sichuan Province 2025ZNSFSC0003Natural Science Foundation of Sichuan Province 2025ZNSFSC0560
6 · The paper itself

Abstract

BACKGROUND AND

objectiveSmall cell lung cancer has a high incidence and mortality rate, frequently metastasizes, and is associated with a poor prognosis. However, traditional prognostic models based on stage alone cannot meet clinical needs. This study aims to establish a clinlabomics-based, highly accessible prognostic model for small cell lung cancer.

methodsWe conducted a multicenter observational retrospective study, enrolling clinical laboratory data of 276 small cell lung cancer patients. The cohort from Sichuan Cancer Hospital comprised a total of 196 samples. Of these, 88 samples were designated as the internal validation set, while 80 samples from an alternate institution were allocated as the external independent validation set. Utilizing the log-rank test, univariate and multivariate Cox regression analyses, six prognostic indicators were discerned. A nomogram was subsequently developed based on these identified indicators.

resultsThrough the log-rank test, univariate and multivariate Cox regression analyses, total protein (TP) (HR = 0.47, p < 0.001), aspartate aminotransferase (AST) (HR = 1.82, p < 0.001), and lymphocyte ratio (Lym ratio) (HR = 0.47, p = 0.005) were identified as laboratory biomarkers related to prognosis from 61 blood-related laboratory tests, covering routine blood, biochemical, coagulation, and infectious disease markers, while age, stage, and smoking were identified as clinical independent prognostic factors. A nomogram was developed based on these six indicators. The AUC of time-independent ROC for 2- and 3-year overall survival (OS) was 0.74, 0.74 in the training cohort, and 0.64, 0.74 in the validation cohort, respectively. The novel nomogram accurately predicted the prognosis for two independent cohorts with p values < 0.001 and performed risk adjustment, which classified patients with different OS at the same extensive stage (ES) or limited-stage (LS).

conclusionsThe clinlabomics-based nomogram helps to more effectively predict the prognosis of small cell lung cancer by leveraging blood laboratory data.

Indexed as

Lung NeoplasmsNomogramsSmall Cell Lung CarcinomaAdultAgedBiomarkers, TumorChinaFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesBiomarkers, TumorclinlabomicsmulticenterprognosisSCLC

Identifiers

PMID40859851
PMCPMC12381572

What Socratic holds

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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 Socratic graph.