The impact of dietary fiber on colorectal cancer patients based on machine learning
ObjectiveThis study aimed to evaluate the impact of enteral nutrition with dietary fiber on patients undergoing laparoscopic colorectal cancer (CRC) surgery.MethodsBetween January 2023 and August 2024, 164 CRC patients were randomly assigned to two groups at our hospital. The control group received...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Nutrition |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnut.2025.1508562/full |
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| author | Xinwei Ji Lixin Wang Pengbo Luan Jingru Liang Weicai Cheng |
| author_facet | Xinwei Ji Lixin Wang Pengbo Luan Jingru Liang Weicai Cheng |
| author_sort | Xinwei Ji |
| collection | DOAJ |
| description | ObjectiveThis study aimed to evaluate the impact of enteral nutrition with dietary fiber on patients undergoing laparoscopic colorectal cancer (CRC) surgery.MethodsBetween 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.ResultsIn the observation group, levels of procalcitonin (PCT), beta-endorphin (β-EP), C-reactive protein (CRP), interleukin-1 (IL-1), interleukin-8 (IL-8), and tumor necrosis factor-alpha (TNF-α) were significantly lower compared to the control group (p < 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 (p < 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.ConclusionPostoperative 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. |
| format | Article |
| id | doaj-art-02d55f8017d840a8825c2a711cc991bd |
| institution | Kabale University |
| issn | 2296-861X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Nutrition |
| spelling | doaj-art-02d55f8017d840a8825c2a711cc991bd2025-01-24T05:21:16ZengFrontiers Media S.A.Frontiers in Nutrition2296-861X2025-01-011210.3389/fnut.2025.15085621508562The impact of dietary fiber on colorectal cancer patients based on machine learningXinwei JiLixin WangPengbo LuanJingru LiangWeicai ChengObjectiveThis study aimed to evaluate the impact of enteral nutrition with dietary fiber on patients undergoing laparoscopic colorectal cancer (CRC) surgery.MethodsBetween 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.ResultsIn the observation group, levels of procalcitonin (PCT), beta-endorphin (β-EP), C-reactive protein (CRP), interleukin-1 (IL-1), interleukin-8 (IL-8), and tumor necrosis factor-alpha (TNF-α) were significantly lower compared to the control group (p < 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 (p < 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.ConclusionPostoperative 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.https://www.frontiersin.org/articles/10.3389/fnut.2025.1508562/fullcolorectal cancerdietary fiberenteral nutrition supportnutritional statusmachine learning |
| spellingShingle | Xinwei Ji Lixin Wang Pengbo Luan Jingru Liang Weicai Cheng The impact of dietary fiber on colorectal cancer patients based on machine learning Frontiers in Nutrition colorectal cancer dietary fiber enteral nutrition support nutritional status machine learning |
| title | The impact of dietary fiber on colorectal cancer patients based on machine learning |
| title_full | The impact of dietary fiber on colorectal cancer patients based on machine learning |
| title_fullStr | The impact of dietary fiber on colorectal cancer patients based on machine learning |
| title_full_unstemmed | The impact of dietary fiber on colorectal cancer patients based on machine learning |
| title_short | The impact of dietary fiber on colorectal cancer patients based on machine learning |
| title_sort | impact of dietary fiber on colorectal cancer patients based on machine learning |
| topic | colorectal cancer dietary fiber enteral nutrition support nutritional status machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fnut.2025.1508562/full |
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