Liu Xiangliang, Li Yuguang, Ji Wei, Zheng Kaiwen, Lu Jin, Zhao Yixin, Zhang Wenxin, Liu Mingyang, Cui Jiuwei, Li Wei
Cancer Center, The First Hospital of Jilin University, Changchun, Jilin, People's Republic of China.
College of Instrumentation and Electrical Engineering, Jilin University, Changchun, Jilin, People's Republic of China.
Cancer Manag Res. 2022 Apr 12;14:1431-1441. doi: 10.2147/CMAR.S342658. eCollection 2022.
Patient-Generated Subjective Global Assessment (PG-SGA) was a nutritional status assessment technique specifically tailored for patients with oncology. The goal of this study was to develop a machine learning (ML) prediction model for predicting PG-SGA categorization of patients with tumor.
From 2014 to 2020, patients at the First Hospital of Jilin University performed laboratory testing, bioelectrical impedance, physical measures, and the PG-SGA scale. A total of 8230 patients were involved in the study. Patients with missing or partial data were removed, leaving 7287 patients, of which 3743 were males and 3544 were females. ML was used to design a clinical prediction model for PG-SGA categories.
Through the least absolute shrinkage and selection operator (LASSO) and the correlation matrix, 135 variables were screened and 6 variables were retained; ML was performed among the remaining variables. The accuracy of neural network prediction models was 70.3% and 70.4% for males and females in the training cohort, respectively, and 74.4% and 73.2% for males and females in the validation cohort, respectively. The area under curve (AUC) of males was 0.87 for PG-SGA scores "0-3", 0.70 for PG-SGA scores "4-8" and 0.74 for PG-SGA scores ">8". As for females, the AUC was 0.85 for PG-SGA scores "0-3", 0.65 for PG-SGA scores "4-8" and 0.76 for PG-SGA scores ">8". The results of confusion matrix showed that the models were of good predictive validity. The prediction model was nearly 90% accurate for predictions that do not require nutritional support.
We demonstrated that neural network learning is the best clinical prediction model using ML. The model can work as a prediction for the PG-SGA classification of patients with cancer and can be promoted further in the clinic.
患者主观整体评定法（PG-SGA）是一种专门为肿瘤患者量身定制的营养状况评估技术。本研究的目的是开发一种机器学习（ML）预测模型，用于预测肿瘤患者的PG-SGA分类。
2014年至2020年，吉林大学第一医院的患者进行了实验室检测、生物电阻抗、身体测量以及PG-SGA量表评估。共有8230名患者参与了本研究。剔除数据缺失或不完整的患者，剩余7287名患者，其中男性3743名，女性3544名。使用机器学习设计PG-SGA分类的临床预测模型。
通过最小绝对收缩和选择算子（LASSO）及相关矩阵，筛选出135个变量，保留6个变量；在其余变量中进行机器学习。训练队列中男性和女性神经网络预测模型的准确率分别为70.3%和70.4%，验证队列中男性和女性的准确率分别为74.4%和73.2%。男性PG-SGA评分为“0 - 3”时曲线下面积（AUC）为0.87，评分为“4 - 8”时为0.70，评分为“>8”时为0.74。女性方面，PG-SGA评分为“0 - 3”时AUC为0.85，评分为“4 - 8”时为0.65，评分为“>8”时为0.76。混淆矩阵结果显示模型具有良好的预测效度。对于不需要营养支持的预测，该预测模型的准确率接近90%。
我们证明了神经网络学习是使用机器学习的最佳临床预测模型。该模型可用于预测癌症患者的PG-SGA分类，并可在临床上进一步推广。
