Evidence map›Paper›PMID 40394504›Full record

ArticleBMC cancer2025

Rapid identification of tumor patients with PG-SGA ≥ 4 based on machine learning: a prospective study.

Gui Qian, Huang Jiaxin, Cong Minghua, Liu Beijia, Li Yinfeng, Huang Guiyu, Yang Mingxue, Tang Xiaoli, Yan Hongyan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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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

9 authors.

Gui QianSchool of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Huang JiaxinDepartment of Comprehensive Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Cong MinghuaDepartment of Comprehensive Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Liu BeijiaSchool of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Li YinfengSichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital &Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Huang GuiyuDepartment of Chest Radiotherapy, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Yang MingxueSichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital &Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Tang XiaoliSichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital &Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China. 1585470513@qq.com.
Yan HongyanSichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital &Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China. 1752146434@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMalnutrition is common in cancer patients and worsens treatment and prognosis. The Patient-Generated Subjective Global Assessment (PG-SGA) is the best tool to evaluate malnutrition, but it is complicated has limited its routine clinical use.

methodsWe reviewed 798 records from 416 cancer patients treated at our hospital from July 2022 to March 2024. We used machine learning methods like XGBoost and Random Forest to find important factors linked to PG-SGA scores of 4 or higher. We confirmed the most important factors with logistic regression analysis.

resultsAmong all models, XGBoost and Random Forest models perform the best, with the area under the curve (AUC) reaching of 0.75 and 0.77. Multivariate logistic regression analysis identified body mass index (BMI) (OR = 0.82, 95%CI 0.66-0.99; P = 0.045), handgrip strength (HGS) (OR = 0.89, 95%CI 0.82-0.96; P = 0.004), fat-free mass index (FFMI) (OR = 1.36, 95%CI 1.01-1.88; P = 0.045), and bedridden status (OR = 3.16, 95%CI 1.17-9.14; P = 0.026) as key predictors for PG-SGA scores of ≥ 4.

conclusionBMI, HGS, FFMI, and bedridden status were identified as practical indicators to efficiently screen patients likely to have PG-SGA scores ≥ 4.

Indexed as

Machine LearningMalnutritionNeoplasmsNutrition AssessmentAdultAgedBody Mass IndexFemaleHand StrengthHumansMaleMiddle AgedPrognosisProspective StudiesCancerMachine LearningNutritional AssessmentPatient-Generated Subjective Global Assessment

Identifiers

PMID40394504
PMCPMC12093709

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