Evidence map›Paper›PMID 38877553›Full record

ArticleWorld journal of surgical oncology2024

The advanced lung cancer inflammation index (ALI) predicted the postoperative survival rate of patients with non-small cell lung cancer and the construction of a nomogram model.

Shixin Ma, Zongqi Li, Lunqing Wang

Abstract read
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Article in World journal of surgical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

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0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Shixin MaGraduate School, Dalian Medical University, Dalian, 116000, Liaoning, China.
Zongqi LiGraduate School, Dalian Medical University, Dalian, 116000, Liaoning, China.
Lunqing WangDepartment of Thoracic Surgery, Qingdao Municipal Hospital, No.5 Donghai Middle Road, Qingdao, 266071, Shandong, China. wanglunqing1973@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo investigate the prognostic significance of the advanced lung cancer inflammation index (ALI) in patients with operable non-small-cell lung carcinoma (NSCLC). By constructing the nomogram model, it can provide a reference for clinical work.

methodsA total of 899 patients with non-small cell lung cancer who underwent surgery in our hospital between January 2017 and June 2021 were retrospectively included. ALI was calculated by body mass index (BMI) × serum albumin/neutrophil to lymphocyte ratio (NLR). The optimal truncation value of ALI was obtained using the receiver operating characteristic (ROC) curve and divided into two groups. Survival analysis was represented by the Kaplan-Meier curve. The predictors of Overall survival (OS) were evaluated by the Cox proportional risk model using single factor and stepwise regression multifactor analysis. Based on the results of multi-factor Cox proportional risk regression analysis, a nomogram model was established using the R survival package. The bootstrap method (repeated sampling 1 000 times) was used for internal verification of the nomogram model. The concordance index (C-index) was used to represent the prediction performance of the nomogram model, and the calibration graph method was used to visually represent its prediction conformity. The application value of the model was evaluated by decision curve analysis (DCA).

resultsThe optimal cut-off value of ALI was 70.06, and the low ALI group (ALI < 70.06) showed a poor survival prognosis. In multivariate analyses, tumor location, pathological stage, neuroaggression, and ALI were independently associated with operable NSCLC-specific survival. The C index of OS predicted by the nomogram model was 0.928 (95% CI: 0.904-0.952). The bootstrap self-sampling method (B = 1000) was used for internal validation of the prediction model, and the calibration curve showed good agreement between the prediction and observation results of 1-year, 2-year, and 3-year OS. The ROC curves for 1-year, 2-year, and 3-year survival were plotted according to independent factors, and the AUC was 0.952 (95% CI: 0.925-0.979), 0.951 (95% CI: 0.916-0.985), and 0.939 (95% CI: 0.913-0.965), respectively. DCA shows that this model has good clinical application value.

conclusionALI can be used as a reliable indicator to evaluate the prognosis of patients with operable NSCLC, and through the construction of a nomogram model, it can facilitate better individualized treatment and prognosis assessment.

Indexed as

Carcinoma, Non-Small-Cell LungInflammationLung NeoplasmsNomogramsAgedFemaleFollow-Up StudiesHumansMaleMiddle AgedNeutrophilsPrognosisRetrospective StudiesROC CurveSurvival RateAdvanced lung cancer inflammation indexNomogramNon-small cell lung cancerPrognosis

Identifiers

PMID38877553
PMCPMC11177447

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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.