Construction of a risk prediction model for postoperative low anterior resection syndrome in patients with rectal cancer: Based on a random forest algorithm_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 Ⅱ, Huaxi Shangjin Hospital, Sichuan University / Chengdu 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 model

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 the West China Hospital of Sichuan University from January 2020 to August 2021. 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. The model performance was evaluated by accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), Brier score, and decision curve analysis (DCA). Results A total of 394 patients were enrolled. 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 predictive factors were: distance from the inferior tumor margin to the dentate line, body mass index (BMI), tumor size, time to first postoperative flatus, operation time, age, neoadjuvant therapy, and TNM stage. The prediction model constructed using these key factors achieved the accuracy of 73.4%, sensitivity of 75.0%, specificity of 72.7%, AUC (95% confidence interval) of 0.801 (0.685, 0.916), and the Brier score of 0.198. DCA showed that the model provided favorable clinical benefit when the threshold probability was between 25% and 64%. Conclusions The results of this study suggest that patients with a shorter distance from the tumor to the dentate line, higher BMI, and larger tumor size are at higher risk of developing LARS. The risk prediction model constructed in this study demonstrates a good predictive performance and may provide a useful reference for early identification of high-risk patients after rectal cancer surgery.

Citation： ZHANG Chun, LIN Yuxin, LI Lin, LI Li, HUANG Mingjun. Construction of a risk prediction model for postoperative low anterior resection syndrome in patients with rectal cancer: Based on a random forest algorithm. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2025, 32(7): 801-807. doi: 10.7507/1007-9424.202504144 Copy

1.	黃理賓, 黃秋實, 楊烈. 全球及中國的結直腸癌流行病學特征及防治: 2022《全球癌癥統計報告》解讀. 中國普外基礎與臨床雜志, 2024, 31(5): 530-537.
2.	姚一菲, 孫可欣, 鄭榮壽. 《2022全球癌癥統計報告》解讀: 中國與全球對比. 中國普外基礎與臨床雜志, 2024, 31(7): 769-780.
3.	梁國剛, 蔡克, 馬琳, 等. 重度LARS的影響因素分析. 結直腸肛門外科, 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, 孫佳男, 張晴, 等. 直腸癌低位前切除綜合征發展軌跡的潛在類別及預測因素分析. 中國護理管理, 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, 邵勝利, 劉鷺, 等. 腹腔鏡直腸癌前切除術后低位前切除綜合征的發生率和癥狀學分析. 中華胃腸外科雜志, 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. 直腸癌保肛根治術后低位前切除綜合征危險因素分析及列線圖預測模型構建. 合肥: 安徽醫科大學外科學(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, 193: 861-867.
13.	馮清, 賀培鳳. 基于隨機森林模型的耐碳青霉烯類鮑曼不動桿菌性呼吸機相關性肺炎風險預測模型的構建. 護理研究, 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.	汪曉東, 李立. 數據庫建設第一部分: 個人數據的標簽與結構化. 中國普外基礎與臨床雜志, 2019, 26(3): 335-342.
16.	汪曉東, 由屹先, 余其澳, 等. 數據庫建設第九部分: 結直腸癌新輔助治療的標簽與結構化. 中國普外基礎與臨床雜志, 2021, 28(6): 799-804.
17.	汪曉東, 林容若, 劉健博, 等. 數據庫建設第七部分: 結直腸癌手術特征的標簽與結構化(一). 中國普外基礎與臨床雜志, 2020, 27(9): 1150-1152.
18.	汪曉東, 劉健博, 李立. 數據庫建設第五部分: 結直腸癌的腫瘤特征—模塊的設計(一). 中國普外基礎與臨床雜志, 2020, 27(3): 345-349.
19.	張峻嶺, 董潔晶, 吳濤, 等. 直腸癌保肛根治術后低位前切除綜合征危險因素的列線圖分析. 中華普通外科雜志, 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.	曹蘭玉, 魏力, 王春梅. 直腸癌前切除綜合征評分量表的漢化及信效度評價. 中國實用護理雜志, 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, 陳文生. 基于機器學習的早期胃癌淋巴結轉移預測模型構建與驗證. 陸軍軍醫大學學報, 2024, 46(21): 2432-2442.
25.	岳中屹, 李秀庚, 張敏, 等. 建立預測直腸癌術后前切除綜合征的列線圖模型. 現代腫瘤醫學, 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, 羅綽, 汪曉東, 等. 直腸癌術后肛門功能康復的研究進展. 中華結直腸疾病電子雜志, 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.	理紀, 周世云. 低位直腸癌患者腹腔鏡輔助經肛全直腸系膜切除術術后排便功能觀察及其相關影響因素分析. 實用癌癥雜志, 2022, 37(3): 471-473.
32.	宋美璇, 劉東, 徐林霞, 等. 腹腔鏡輔助經肛全直腸系膜切除術后患者低位前切除綜合征的發生率及影響因素的Meta分析. 胃腸病學和肝病學雜志, 2023, 32(5): 558-563.
33.	郭帆, 韓斌, 黃琳凱, 等. 腹腔鏡直腸癌保肛根治術后低位前切除綜合征的發生及影響因素分析. 華中科技大學學報(醫學版), 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.	Breiman L. Random forests. Machine Learning, 2001, 45(1): 5-32.
37.	韓玉, 施海龍, 曲波, 等. 隨機森林方法在醫學中的應用. 中國預防醫學雜志, 2014, 15(1): 79-81.
38.	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.
39.	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.
40.	海瑞, 王慧, 張蓉, 等. 基于機器學習的老年慢性心力衰竭病人衰弱風險預測模型的構建. 護理研究, 2024, 38(12): 2103-2109.
41.	楊芳, 郄淑文, 楊麗, 等. 基于機器學習的腹膜透析患者早發性腹膜透析相關性腹膜炎風險預測模型的構建. 中華現代護理雜志, 2025, 31(6): 778-783.

1. 黃理賓, 黃秋實, 楊烈. 全球及中國的結直腸癌流行病學特征及防治: 2022《全球癌癥統計報告》解讀. 中國普外基礎與臨床雜志, 2024, 31(5): 530-537.
2. 姚一菲, 孫可欣, 鄭榮壽. 《2022全球癌癥統計報告》解讀: 中國與全球對比. 中國普外基礎與臨床雜志, 2024, 31(7): 769-780.
3. 梁國剛, 蔡克, 馬琳, 等. 重度LARS的影響因素分析. 結直腸肛門外科, 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, 孫佳男, 張晴, 等. 直腸癌低位前切除綜合征發展軌跡的潛在類別及預測因素分析. 中國護理管理, 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, 邵勝利, 劉鷺, 等. 腹腔鏡直腸癌前切除術后低位前切除綜合征的發生率和癥狀學分析. 中華胃腸外科雜志, 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. 直腸癌保肛根治術后低位前切除綜合征危險因素分析及列線圖預測模型構建. 合肥: 安徽醫科大學外科學(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, 193: 861-867.
13. 馮清, 賀培鳳. 基于隨機森林模型的耐碳青霉烯類鮑曼不動桿菌性呼吸機相關性肺炎風險預測模型的構建. 護理研究, 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. 汪曉東, 李立. 數據庫建設第一部分: 個人數據的標簽與結構化. 中國普外基礎與臨床雜志, 2019, 26(3): 335-342.
16. 汪曉東, 由屹先, 余其澳, 等. 數據庫建設第九部分: 結直腸癌新輔助治療的標簽與結構化. 中國普外基礎與臨床雜志, 2021, 28(6): 799-804.
17. 汪曉東, 林容若, 劉健博, 等. 數據庫建設第七部分: 結直腸癌手術特征的標簽與結構化(一). 中國普外基礎與臨床雜志, 2020, 27(9): 1150-1152.
18. 汪曉東, 劉健博, 李立. 數據庫建設第五部分: 結直腸癌的腫瘤特征—模塊的設計(一). 中國普外基礎與臨床雜志, 2020, 27(3): 345-349.
19. 張峻嶺, 董潔晶, 吳濤, 等. 直腸癌保肛根治術后低位前切除綜合征危險因素的列線圖分析. 中華普通外科雜志, 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. 曹蘭玉, 魏力, 王春梅. 直腸癌前切除綜合征評分量表的漢化及信效度評價. 中國實用護理雜志, 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, 陳文生. 基于機器學習的早期胃癌淋巴結轉移預測模型構建與驗證. 陸軍軍醫大學學報, 2024, 46(21): 2432-2442.
25. 岳中屹, 李秀庚, 張敏, 等. 建立預測直腸癌術后前切除綜合征的列線圖模型. 現代腫瘤醫學, 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, 羅綽, 汪曉東, 等. 直腸癌術后肛門功能康復的研究進展. 中華結直腸疾病電子雜志, 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. 理紀, 周世云. 低位直腸癌患者腹腔鏡輔助經肛全直腸系膜切除術術后排便功能觀察及其相關影響因素分析. 實用癌癥雜志, 2022, 37(3): 471-473.
32. 宋美璇, 劉東, 徐林霞, 等. 腹腔鏡輔助經肛全直腸系膜切除術后患者低位前切除綜合征的發生率及影響因素的Meta分析. 胃腸病學和肝病學雜志, 2023, 32(5): 558-563.
33. 郭帆, 韓斌, 黃琳凱, 等. 腹腔鏡直腸癌保肛根治術后低位前切除綜合征的發生及影響因素分析. 華中科技大學學報(醫學版), 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. Breiman L. Random forests. Machine Learning, 2001, 45(1): 5-32.
37. 韓玉, 施海龍, 曲波, 等. 隨機森林方法在醫學中的應用. 中國預防醫學雜志, 2014, 15(1): 79-81.
38. 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.
39. 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.
40. 海瑞, 王慧, 張蓉, 等. 基于機器學習的老年慢性心力衰竭病人衰弱風險預測模型的構建. 護理研究, 2024, 38(12): 2103-2109.
41. 楊芳, 郄淑文, 楊麗, 等. 基于機器學習的腹膜透析患者早發性腹膜透析相關性腹膜炎風險預測模型的構建. 中華現代護理雜志, 2025, 31(6): 778-783.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

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

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