Ji Xinwei, Wang Lixin, Luan Pengbo, Liang Jingru, Cheng Weicai
Department of Gastrointestinal Surgery, Yantaishan Hospital, Yantai, China.
Front Nutr. 2025 Jan 24;12:1508562. doi: 10.3389/fnut.2025.1508562. eCollection 2025.
This study aimed to evaluate the impact of enteral nutrition with dietary fiber on patients undergoing laparoscopic colorectal cancer (CRC) surgery.
Between January 2023 and August 2024, 164 CRC patients were randomly assigned to two groups at our hospital. The control group received standard nutritional intervention, while the observation group received enteral nutritional support containing dietary fiber. Both groups were subjected to intervention and continuously observed until the 14th postoperative day. An observational analysis assessed the impact of dietary fiber intake on postoperative nutritional status in CRC patients. The study compared infection stress index, inflammatory factors, nutritional status, intestinal function recovery, and complication incidence between groups. Additionally, four machine learning models-Logistic Regression (LR), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM)-were developed based on nutritional and clinical indicators.
In the observation group, levels of procalcitonin (PCT), beta-endorphin (-EP), C-reactive protein (CRP), interleukin-1 (IL-1), interleukin-8 (IL-8), and tumor necrosis factor-alpha (TNF-) were significantly lower compared to the control group ( < 0.01). Conversely, levels of albumin (ALB), hemoglobin (HB), transferrin (TRF), and prealbumin (PAB) in the observation group were significantly higher than those in the control group ( < 0.01). Furthermore, LR, RF, NN, and SVM models can effectively predict the effects of dietary fiber on the immune function and inflammatory response of postoperative CRC patients, with the NN model performing the best. Through the screening of machine learning models, four key predictors for CRC patients were identified: PCT, PAB, ALB, and IL-1.
Postoperative dietary fiber administration in colorectal cancer enhances immune function, reduces disease-related inflammation, and inhibits tumor proliferation. Machine learning-based CRC prediction models hold clinical value.
本研究旨在评估含膳食纤维的肠内营养对接受腹腔镜结直肠癌（CRC）手术患者的影响。
2023年1月至2024年8月期间，我院164例CRC患者被随机分为两组。对照组接受标准营养干预，而观察组接受含膳食纤维的肠内营养支持。两组均接受干预并持续观察至术后第14天。一项观察性分析评估了膳食纤维摄入对CRC患者术后营养状况的影响。该研究比较了两组之间的感染应激指数、炎症因子、营养状况、肠功能恢复情况及并发症发生率。此外，基于营养和临床指标开发了四个机器学习模型——逻辑回归（LR）、随机森林（RF）、神经网络（NN）和支持向量机（SVM）。
观察组中，降钙素原（PCT）、β-内啡肽（β-EP）、C反应蛋白（CRP）、白细胞介素-1（IL-1）、白细胞介素-8（IL-8）和肿瘤坏死因子-α（TNF-α）水平显著低于对照组（P<0.01）。相反，观察组中白蛋白（ALB）、血红蛋白（HB）、转铁蛋白（TRF）和前白蛋白（PAB）水平显著高于对照组（P<0.01）。此外，LR、RF、NN和SVM模型可以有效预测膳食纤维对CRC术后患者免疫功能和炎症反应的影响，其中NN模型表现最佳。通过机器学习模型筛选，确定了CRC患者的四个关键预测指标：PCT、PAB、ALB和IL-1。
结直肠癌术后给予膳食纤维可增强免疫功能，减轻疾病相关炎症，并抑制肿瘤增殖。基于机器学习的CRC预测模型具有临床价值。
