Construction of a risk prediction model for postoperative low anterior resection syndrome in patients with rectal cancer: Based on a random forest model_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

ZHANG Chun ¹ , LIN Yuxin ² , LI Lin ³ , LI Li ^4,5 ,  HUANG Mingjun ⁶

1. Department of Anesthesiology, West China Hospital, Sichuan University / West China School of Nursing, Sichuan University, Chengdu 610041, P. R. China;
2. West China School of Medicine, Sichuan University, Chengdu 610041, P. R. China;
3. Center of Biostatistics, Design, Measurement and Evaluation (CBDME), Department of Clinical Research Management, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
4. Department of General Surgery, Shangjin Nanfu Hospital, Chengdu 611700, P. R. China;
5. Center of Colorectal Cancer, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
6. Day Surgery Center, General Practice Medical Center, West China Hospital, Sichuan University / West China School of Nursing, Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

HUANG Mingjun, Email: huangmingiun@wchscu.cn

Keywords：

rectal cancer; low anterior resection syndrome; random forest; risk prediction; postoperative complications

DOI：

10.7507/1007-9424.202504144

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To investigate the key risk factors for low anterior resection syndrome (LARS) within 6 months after rectal cancer surgery and to construct a risk prediction model based on the random forest algorithm, providing a reference for early clinical intervention. Methods A retrospective study was conducted on patients who underwent rectal cancer surgery at West China Hospital of Sichuan University between January 2020 and August 2021. A total of 394 patients were included. A prediction model for the occurrence of LARS within 6 months after rectal cancer surgery was constructed using the random forest algorithm. The dataset was divided into a training set and a test set in an 8:2 ratio. Model performance was evaluated by accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), Brier score, and decision curve analysis (DCA). The SHAP (Shapley Additive Explanations) method was used to interpret the contribution of each variable. Results Among the 394 patients, 106 developed LARS within 6 months after surgery, with an incidence rate of 26.9%. According to the importance ranking in the random forest algorithm, the key predictors were: distance from the inferior tumor margin to the dentate line, body mass index (BMI), tumor size, time to postoperative flatus, operation time, age, neoadjuvant therapy, and TNM stage. The prediction model built using these key factors achieved an accuracy of 73.4%, sensitivity of 75.0%, specificity of 72.7%, AUC (95% confidence interval) of 0.801 (0.685, 0.916), and a Brier score of 0.198. DCA showed that the model provided favorable clinical benefit when the threshold probability was between 25% and 64%. Conclusion The results of this study suggest that patients with a shorter distance from the tumor to the dentate line, higher BMI, larger tumor size, and those receiving neoadjuvant therapy are at higher risk of developing LARS. The risk prediction model constructed in this study demonstrated good predictive performance and may provide a useful reference for early identification of high-risk patients after rectal cancer surgery.

1.	黄理宾, 黄秋实, 杨烈. 全球及中国的结直肠癌流行病学特征及防治: 2022《全球癌症统计报告》解读. 中国普外基础与临床杂志, 2024, 31(5): 530-537.
2.	姚一菲, 孙可欣, 郑荣寿. 《2022全球癌症统计报告》解读: 中国与全球对比. 中国普外基础与临床杂志, 2024, 31(7): 769-780.
3.	梁国刚, 蔡克, 马琳, 等. 重度LARS的影响因素分析. 结直肠肛门外科, 2020, 26(5): 561-565.
4.	Nguyen TH, Chokshi RV. Low anterior resection syndrome. Curr Gastroenterol Rep, 2020, 22(10): 48. doi: 10.1007/s11894-020-00785-z.
5.	Huang MJ, Ye L, Yu KX, et al. Development of prediction model of low anterior resection syndrome for colorectal cancer patients after surgery based on machine-learning technique. Cancer Med, 2023, 12(2): 1501-1519.
6.	Pieniowski EHA, Palmer GJ, Juul T, et al. Low anterior resection syndrome and quality of life after sphincter-sparing rectal cancer surgery: A long-term longitudinal follow-up. Dis Colon Rectum, 2019, 62(1): 14-20.
7.	胡海燕, 孙佳男, 张晴, 等. 直肠癌低位前切除综合征发展轨迹的潜在类别及预测因素分析. 中国护理管理, 2024, 24(6): 819-824.
8.	Keane C, Wells C, O'Grady G, et al. Defining low anterior resection syndrome: a systematic review of the literature. Colorectal Dis, 2017, 19(8): 713-722.
9.	王璋, 邵胜利, 刘鹭, 等. 腹腔镜直肠癌前切除术后低位前切除综合征的发生率和症状学分析. 中华胃肠外科杂志, 2024, 27(1): 69-74.
10.	Al-Rashid F, Robitaille S, Liberman AS, et al. Trajectory of change of low anterior resection syndrome over time after restorative proctectomy for rectal adenocarcinoma. Tech Coloproctol, 2022, 26(3): 195-203.
11.	卜旻淳. 直肠癌保肛根治术后低位前切除综合征危险因素分析及列线图预测模型构建. 合肥: 安徽医科大学外科学(普外), 2022.
12.	Morgan RD, Youssi BW, Cacao R, et al. Random forest prognostication of survival and 6-month outcome in pediatric patients following decompressive craniectomy for traumatic brain injury. World Neurosurg, 2025 Jan: 193: 861-867.193-861.
13.	冯清, 贺培凤. 基于随机森林模型的耐碳青霉烯类鲍曼不动杆菌性呼吸机相关性肺炎风险预测模型的构建. 护理研究, 2024, 38(19): 3410-3416.
14.	Wang Z, Shao SL, Liu L, et al. Machine learning model for prediction of low anterior resection syndrome following laparoscopic anterior resection of rectal cancer: A multicenter study. World J Gastroenterol, 2023, 29(19): 2979-2991.
15.	汪晓东, 李立. 数据库建设第一部分: 个人数据的标签与结构化. 中国普外基础与临床杂志, 2019, 26(3): 335-342.
16.	汪晓东, 由屹先, 余其澳, 等. 数据库建设第九部分: 结直肠癌新辅助治疗的标签与结构化. 中国普外基础与临床杂志, 2021, 28(6): 799-804.
17.	汪晓东, 林容若, 刘健博, 等. 数据库建设第七部分: 结直肠癌手术特征的标签与结构化(一). 中国普外基础与临床杂志, 2020, 27(9): 1150-1152.
18.	汪晓东, 刘健博, 李立. 数据库建设第五部分: 结直肠癌的肿瘤特征—模块的设计(一). 中国普外基础与临床杂志, 2020, 27(3): 345-349.
19.	张峻岭, 董洁晶, 吴涛, 等. 直肠癌保肛根治术后低位前切除综合征危险因素的列线图分析. 中华普通外科杂志, 2021, 36(2): 81-85.
20.	Yan M, Lin Z, Wu Z, et al. A predictive nomogram model for low anterior resection syndrome after rectal cancer resection. ANZ J Surg, 2022, 92(12): 3224-3231.
21.	Emmertsen KJ, Laurberg S. Low anterior resection syndrome score: development and validation of a symptom-based scoring system for bowel dysfunction after low anterior resection for rectal cancer. Ann Surg, 2012, 255(5): 922-928.
22.	曹兰玉, 魏力, 王春梅. 直肠癌前切除综合征评分量表的汉化及信效度评价. 中国实用护理杂志, 2013, 29(27): 69-72.
23.	Le TT, Kim H, Kang H, et al. Classification and explanation for intrusion detection system based on ensemble trees and SHAP method. Sensors (Basel), 2022, 22(3): 1154. doi: 10.3390/s22031154.
24.	孟祥勇, 秦嘉怡, 陈文生. 基于机器学习的早期胃癌淋巴结转移预测模型构建与验证. 陆军军医大学学报, 2024, 46(21): 2432-2442.
25.	岳中屹, 李秀庚, 张敏, 等. 建立预测直肠癌术后前切除综合征的列线图模型. 现代肿瘤医学, 2021, 29(23): 4141-4145.
26.	Miyo M, Akizuki E, Okuya K, et al. Diagnosis and treatment of low anterior resection syndrome. J Anus Rectum Colon, 2025, 9(1): 1-9.
27.	Croese AD, Lonie JM, Trollope AF, et al. A meta-analysis of the prevalence of low anterior resection syndrome and systematic review of risk factors. Int J Surg, 2018, 56: 234-241.
28.	余可欣, 罗绰, 汪晓东, 等. 直肠癌术后肛门功能康复的研究进展. 中华结直肠疾病电子杂志, 2021, 10(3): 298-301.
29.	Sturiale A, Martellucci J, Zurli L, et al. Long-term functional follow-up after anterior rectal resection for cancer. Int J Colorectal Dis, 2017, 32(1): 83-88.
30.	Koda K, Yamazaki M, Shuto K, et al. Etiology and management of low anterior resection syndrome based on the normal defecation mechanism. Surg Today, 2019, 49(10): 803-808.
31.	理纪, 周世云. 低位直肠癌患者腹腔镜辅助经肛全直肠系膜切除术术后排便功能观察及其相关影响因素分析. 实用癌症杂志, 2022, 37(3): 471-473.
32.	宋美璇, 刘东, 徐林霞, 等. 腹腔镜辅助经肛全直肠系膜切除术后患者低位前切除综合征的发生率及影响因素的Meta分析. 胃肠病学和肝病学杂志, 2023, 32(5): 558-563.
33.	郭帆, 韩斌, 黄琳凯, 等. 腹腔镜直肠癌保肛根治术后低位前切除综合征的发生及影响因素分析. 华中科技大学学报(医学版), 2021, 50(2): 194-200.
34.	Nikolouzakis TK, Mariolis-Sapsakos T, Triantopoulou C, et al. Detailed and applied anatomy for improved rectal cancer treatment. Ann Gastroenterol, 2019, 32(5): 431-440.
35.	Bohlok A, Mercier C, Bouazza F, et al. The burden of low anterior resection syndrome on quality of life in patients with mid or low rectal cancer. Support Care Cancer, 2020, 28(3): 1199-1206.
36.	庞雪滢, 胡少华, 李慧, 等. 直肠癌患者保肛术1年后发生重度低位前切除综合征列线图预测模型的构建. 护理学报, 2022, 29(11): 5-10.
37.	Jimenez-Gomez LM, Espin-Basany E, Trenti L, et al. Factors associated with low anterior resection syndrome after surgical treatment of rectal cancer. Colorectal Dis. 2017 Sep 29. doi: 10.1111/codi.13901.
38.	吴晓华, 牛志新, 何峰, 等. 320例老年直肠癌保肛术后低位前切除综合征的危险因素分析. 临床消化病杂志, 2023, 35(4): 316-319.
39.	任洪波, 刘建, 胡涛, 等. 直肠癌患者术后1年内低位前切除综合征的变化特征分析及其对患者术后短期生存质量的影响. 川北医学院学报, 2020, 35(5): 853-857.
40.	Breiman L. Random forests. Machine Learning, 2001, 45(1): 5-32.
41.	韩玉, 施海龙, 曲波, 等. 随机森林方法在医学中的应用. 中国预防医学杂志, 2014, 15(1): 79-81.
42.	Hanko M, Grendár M, Snopko P, et al. Random forest-based prediction of outcome and mortality in patients with traumatic brain injury undergoing primary decompressive craniectomy. World Neurosurg, 2021, 148: e450-e458. doi: 10.1016/j.wneu.2021.01.002.
43.	Song Y, Yin Z, Zhang C, et al. Random forest classifier improving phenylketonuria screening performance in two Chinese populations. Front Mol Biosci, 2022, 9: 986556. doi: 10.3389/fmolb.2022.986556.
44.	海瑞, 王慧, 张蓉, 等. 基于机器学习的老年慢性心力衰竭病人衰弱风险预测模型的构建. 护理研究, 2024, 38(12): 2103-2109.
45.	杨芳, 郄淑文, 杨丽, 等. 基于机器学习的腹膜透析患者早发性腹膜透析相关性腹膜炎风险预测模型的构建. 中华现代护理杂志, 2025, 31(6): 778-783.

1. 黄理宾, 黄秋实, 杨烈. 全球及中国的结直肠癌流行病学特征及防治: 2022《全球癌症统计报告》解读. 中国普外基础与临床杂志, 2024, 31(5): 530-537.
2. 姚一菲, 孙可欣, 郑荣寿. 《2022全球癌症统计报告》解读: 中国与全球对比. 中国普外基础与临床杂志, 2024, 31(7): 769-780.
3. 梁国刚, 蔡克, 马琳, 等. 重度LARS的影响因素分析. 结直肠肛门外科, 2020, 26(5): 561-565.
4. Nguyen TH, Chokshi RV. Low anterior resection syndrome. Curr Gastroenterol Rep, 2020, 22(10): 48. doi: 10.1007/s11894-020-00785-z.
5. Huang MJ, Ye L, Yu KX, et al. Development of prediction model of low anterior resection syndrome for colorectal cancer patients after surgery based on machine-learning technique. Cancer Med, 2023, 12(2): 1501-1519.
6. Pieniowski EHA, Palmer GJ, Juul T, et al. Low anterior resection syndrome and quality of life after sphincter-sparing rectal cancer surgery: A long-term longitudinal follow-up. Dis Colon Rectum, 2019, 62(1): 14-20.
7. 胡海燕, 孙佳男, 张晴, 等. 直肠癌低位前切除综合征发展轨迹的潜在类别及预测因素分析. 中国护理管理, 2024, 24(6): 819-824.
8. Keane C, Wells C, O'Grady G, et al. Defining low anterior resection syndrome: a systematic review of the literature. Colorectal Dis, 2017, 19(8): 713-722.
9. 王璋, 邵胜利, 刘鹭, 等. 腹腔镜直肠癌前切除术后低位前切除综合征的发生率和症状学分析. 中华胃肠外科杂志, 2024, 27(1): 69-74.
10. Al-Rashid F, Robitaille S, Liberman AS, et al. Trajectory of change of low anterior resection syndrome over time after restorative proctectomy for rectal adenocarcinoma. Tech Coloproctol, 2022, 26(3): 195-203.
11. 卜旻淳. 直肠癌保肛根治术后低位前切除综合征危险因素分析及列线图预测模型构建. 合肥: 安徽医科大学外科学(普外), 2022.
12. Morgan RD, Youssi BW, Cacao R, et al. Random forest prognostication of survival and 6-month outcome in pediatric patients following decompressive craniectomy for traumatic brain injury. World Neurosurg, 2025 Jan: 193: 861-867.193-861.
13. 冯清, 贺培凤. 基于随机森林模型的耐碳青霉烯类鲍曼不动杆菌性呼吸机相关性肺炎风险预测模型的构建. 护理研究, 2024, 38(19): 3410-3416.
14. Wang Z, Shao SL, Liu L, et al. Machine learning model for prediction of low anterior resection syndrome following laparoscopic anterior resection of rectal cancer: A multicenter study. World J Gastroenterol, 2023, 29(19): 2979-2991.
15. 汪晓东, 李立. 数据库建设第一部分: 个人数据的标签与结构化. 中国普外基础与临床杂志, 2019, 26(3): 335-342.
16. 汪晓东, 由屹先, 余其澳, 等. 数据库建设第九部分: 结直肠癌新辅助治疗的标签与结构化. 中国普外基础与临床杂志, 2021, 28(6): 799-804.
17. 汪晓东, 林容若, 刘健博, 等. 数据库建设第七部分: 结直肠癌手术特征的标签与结构化(一). 中国普外基础与临床杂志, 2020, 27(9): 1150-1152.
18. 汪晓东, 刘健博, 李立. 数据库建设第五部分: 结直肠癌的肿瘤特征—模块的设计(一). 中国普外基础与临床杂志, 2020, 27(3): 345-349.
19. 张峻岭, 董洁晶, 吴涛, 等. 直肠癌保肛根治术后低位前切除综合征危险因素的列线图分析. 中华普通外科杂志, 2021, 36(2): 81-85.
20. Yan M, Lin Z, Wu Z, et al. A predictive nomogram model for low anterior resection syndrome after rectal cancer resection. ANZ J Surg, 2022, 92(12): 3224-3231.
21. Emmertsen KJ, Laurberg S. Low anterior resection syndrome score: development and validation of a symptom-based scoring system for bowel dysfunction after low anterior resection for rectal cancer. Ann Surg, 2012, 255(5): 922-928.
22. 曹兰玉, 魏力, 王春梅. 直肠癌前切除综合征评分量表的汉化及信效度评价. 中国实用护理杂志, 2013, 29(27): 69-72.
23. Le TT, Kim H, Kang H, et al. Classification and explanation for intrusion detection system based on ensemble trees and SHAP method. Sensors (Basel), 2022, 22(3): 1154. doi: 10.3390/s22031154.
24. 孟祥勇, 秦嘉怡, 陈文生. 基于机器学习的早期胃癌淋巴结转移预测模型构建与验证. 陆军军医大学学报, 2024, 46(21): 2432-2442.
25. 岳中屹, 李秀庚, 张敏, 等. 建立预测直肠癌术后前切除综合征的列线图模型. 现代肿瘤医学, 2021, 29(23): 4141-4145.
26. Miyo M, Akizuki E, Okuya K, et al. Diagnosis and treatment of low anterior resection syndrome. J Anus Rectum Colon, 2025, 9(1): 1-9.
27. Croese AD, Lonie JM, Trollope AF, et al. A meta-analysis of the prevalence of low anterior resection syndrome and systematic review of risk factors. Int J Surg, 2018, 56: 234-241.
28. 余可欣, 罗绰, 汪晓东, 等. 直肠癌术后肛门功能康复的研究进展. 中华结直肠疾病电子杂志, 2021, 10(3): 298-301.
29. Sturiale A, Martellucci J, Zurli L, et al. Long-term functional follow-up after anterior rectal resection for cancer. Int J Colorectal Dis, 2017, 32(1): 83-88.
30. Koda K, Yamazaki M, Shuto K, et al. Etiology and management of low anterior resection syndrome based on the normal defecation mechanism. Surg Today, 2019, 49(10): 803-808.
31. 理纪, 周世云. 低位直肠癌患者腹腔镜辅助经肛全直肠系膜切除术术后排便功能观察及其相关影响因素分析. 实用癌症杂志, 2022, 37(3): 471-473.
32. 宋美璇, 刘东, 徐林霞, 等. 腹腔镜辅助经肛全直肠系膜切除术后患者低位前切除综合征的发生率及影响因素的Meta分析. 胃肠病学和肝病学杂志, 2023, 32(5): 558-563.
33. 郭帆, 韩斌, 黄琳凯, 等. 腹腔镜直肠癌保肛根治术后低位前切除综合征的发生及影响因素分析. 华中科技大学学报(医学版), 2021, 50(2): 194-200.
34. Nikolouzakis TK, Mariolis-Sapsakos T, Triantopoulou C, et al. Detailed and applied anatomy for improved rectal cancer treatment. Ann Gastroenterol, 2019, 32(5): 431-440.
35. Bohlok A, Mercier C, Bouazza F, et al. The burden of low anterior resection syndrome on quality of life in patients with mid or low rectal cancer. Support Care Cancer, 2020, 28(3): 1199-1206.
36. 庞雪滢, 胡少华, 李慧, 等. 直肠癌患者保肛术1年后发生重度低位前切除综合征列线图预测模型的构建. 护理学报, 2022, 29(11): 5-10.
37. Jimenez-Gomez LM, Espin-Basany E, Trenti L, et al. Factors associated with low anterior resection syndrome after surgical treatment of rectal cancer. Colorectal Dis. 2017 Sep 29. doi: 10.1111/codi.13901.
38. 吴晓华, 牛志新, 何峰, 等. 320例老年直肠癌保肛术后低位前切除综合征的危险因素分析. 临床消化病杂志, 2023, 35(4): 316-319.
39. 任洪波, 刘建, 胡涛, 等. 直肠癌患者术后1年内低位前切除综合征的变化特征分析及其对患者术后短期生存质量的影响. 川北医学院学报, 2020, 35(5): 853-857.
40. Breiman L. Random forests. Machine Learning, 2001, 45(1): 5-32.
41. 韩玉, 施海龙, 曲波, 等. 随机森林方法在医学中的应用. 中国预防医学杂志, 2014, 15(1): 79-81.
42. Hanko M, Grendár M, Snopko P, et al. Random forest-based prediction of outcome and mortality in patients with traumatic brain injury undergoing primary decompressive craniectomy. World Neurosurg, 2021, 148: e450-e458. doi: 10.1016/j.wneu.2021.01.002.
43. Song Y, Yin Z, Zhang C, et al. Random forest classifier improving phenylketonuria screening performance in two Chinese populations. Front Mol Biosci, 2022, 9: 986556. doi: 10.3389/fmolb.2022.986556.
44. 海瑞, 王慧, 张蓉, 等. 基于机器学习的老年慢性心力衰竭病人衰弱风险预测模型的构建. 护理研究, 2024, 38(12): 2103-2109.
45. 杨芳, 郄淑文, 杨丽, 等. 基于机器学习的腹膜透析患者早发性腹膜透析相关性腹膜炎风险预测模型的构建. 中华现代护理杂志, 2025, 31(6): 778-783.

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Latest ArticlesConstruction of a risk prediction model for postoperative low anterior resection syndrome in patients with rectal cancer: Based on a random forest model

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