Evidence map›Paper›PMID 36776604›Full record

ArticleFrontiers in nutrition2023

Novel nutritional indicator as predictors among subtypes of lung cancer in diagnosis.

Haiyang Li, Zhangkai J Cheng, Zhiman Liang, Mingtao Liu, Li Liu, Zhenfeng Song, Chuanbo Xie, Junling Liu, Baoqing Sun

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Article in Frontiers in nutrition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

What it found

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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.

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Haiyang LiDepartment of Clinical Laboratory, National Clinical Research Center of Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China.
Zhangkai J ChengDepartment of Clinical Laboratory, National Clinical Research Center of Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China.
Zhiman LiangDepartment of Clinical Laboratory, National Clinical Research Center of Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China.
Mingtao LiuDepartment of Clinical Laboratory, National Clinical Research Center of Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China.
Li LiuDepartment of Clinical Laboratory, National Clinical Research Center of Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China.
Zhenfeng SongDepartment of Clinical Laboratory, National Clinical Research Center of Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China.
Chuanbo XieCancer Center, Sun Yat-sen University, Guangzhou, China.
Junling LiuCancer Center, Sun Yat-sen University, Guangzhou, China.
Baoqing SunDepartment of Clinical Laboratory, National Clinical Research Center of Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Lung cancer is a serious global health concern, and its subtypes are closely linked to lifestyle and dietary habits. Recent research has suggested that malnutrition, over-nutrition, electrolytes, and granulocytes have an effect on the development of cancer. This study investigated the impact of combining patient nutritional indicators, electrolytes, and granulocytes as comprehensive predictors for lung cancer treatment outcomes, and applied a machine learning algorithm to predict lung cancer. Methods: 6,336 blood samples were collected from lung cancer patients classified as lung squamous cell carcinoma (LUSC), lung adenocarcinoma (LUAD), and small cell lung cancer (SCLC). 2,191 healthy individuals were used as controls to compare the differences in nutritional indicators, electrolytes and granulocytes among different subtypes of lung cancer, respectively. Results: Our results demonstrated significant differences between men and women in healthy people and NSCLC, but no significant difference between men and women in SCLC patients. The relationship between indicators is basically that the range of indicators for cancer patients is wider, including healthy population indicators. In the process of predicting lung cancer through nutritional indicators by machine learning, the AUC of the random forest model was as high as 93.5%, with a sensitivity of 75.9% and specificity of 96.5%. Discussion: This study supports the feasibility and accuracy of nutritional indicators in predicting lung cancer through the random forest model. The successful implementation of this novel prediction method could guide clinicians in providing both effective diagnostics and treatment of lung cancers.

Indexed as

cancer predictionlung cancer subtypesmachine learningnutritional indicatorstumor nutrition

Identifiers

PMID36776604
PMCPMC9909296

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