作者
Qiyuan Qin,Binjie Huang,Aiwen Wu,Jiale Gao,Xinzhi Liu,Wuteng Cao,Tenghui Ma,Yukun Kuang,Jirui Guo,Qian Wu,Biyan Shao,Qi Guan,Hongwei Yao,Xiaoyan Zhang,Hui Wang,Feng Wang,Gang Ji,Yuxin Liu,Guoxin Li,Haijun Deng,Jian Wang,Jianguang Qiu,Jianjiang Lin,Jian‐Ping Wang,Jihong Liu,Jun Jiang,Kefeng Ding,Kewei Jiang,Lekun Fang,Ning Li,Pan Chi,Peng Guo,Ping Lan,Qian Liu,Qingchuan Zhao,Ren Zhao,Rui Zhang,Shan Wang,Shoumin Bai,Wei Zhang,Weitang Yuan,Xiang Wan,Xiaochun Meng,Xiaojian Wu,Xin Wang,Xinjuan Fan,Xinping Cao,Xinxiang Li,Sheng Wang,Yanbing Zhou,Yi Xiao,Yingjiang Ye,Yousheng Li,Zhe Sun,Zhen Zhang,Zheng Lou,Zhenjun Wang,Zhong‐Sheng Xia,Zhongtao Zhang,Ziqiang Wang
摘要
Background & AimsThe benefit of radiotherapy for rectal cancer is based largely on a balance between a decrease in local recurrence and an increase in bowel dysfunction. Predicting postoperative disability is helpful for recovery plans and early intervention. We aimed to develop and validate a risk model to improve the prediction of major bowel dysfunction after restorative rectal cancer resection with neoadjuvant radiotherapy using perioperative features.MethodsEligible patients more than 1 year after restorative resection following radiotherapy were invited to complete the low anterior resection syndrome (LARS) score at 3 national hospitals in China. Clinical characteristics and imaging parameters were assessed with machine learning algorithms. The post-radiotherapy LARS prediction model (PORTLARS) was constructed by means of logistic regression on the basis of key factors with proportional weighs. The accuracy of the model for major LARS prediction was internally and externally validated.ResultsA total of 868 patients reported a mean LARS score of 28.4 after an average time of 4.7 years since surgery. Key predictors for major LARS included the length of distal rectum, anastomotic leakage, proximal colon of neorectum, and pathologic nodal stage. PORTLARS had a favorable area under the curve for predicting major LARS in the internal dataset (0.835; 95% CI, 0.800–0.870, n = 521) and external dataset (0.884; 95% CI, 0.848–0.921, n = 347). The model achieved both sensitivity and specificity >0.83 in the external validation. In addition, PORTLARS outperformed the preoperative LARS score for prediction of major events.ConclusionsPORTLARS could predict major bowel dysfunction after rectal cancer resection following radiotherapy with high accuracy and robustness. It may serve as a useful tool to identify patients who need additional support for long-term dysfunction in the early stage. ClinicalTrials.gov, number NCT05129215. The benefit of radiotherapy for rectal cancer is based largely on a balance between a decrease in local recurrence and an increase in bowel dysfunction. Predicting postoperative disability is helpful for recovery plans and early intervention. We aimed to develop and validate a risk model to improve the prediction of major bowel dysfunction after restorative rectal cancer resection with neoadjuvant radiotherapy using perioperative features. Eligible patients more than 1 year after restorative resection following radiotherapy were invited to complete the low anterior resection syndrome (LARS) score at 3 national hospitals in China. Clinical characteristics and imaging parameters were assessed with machine learning algorithms. The post-radiotherapy LARS prediction model (PORTLARS) was constructed by means of logistic regression on the basis of key factors with proportional weighs. The accuracy of the model for major LARS prediction was internally and externally validated. A total of 868 patients reported a mean LARS score of 28.4 after an average time of 4.7 years since surgery. Key predictors for major LARS included the length of distal rectum, anastomotic leakage, proximal colon of neorectum, and pathologic nodal stage. PORTLARS had a favorable area under the curve for predicting major LARS in the internal dataset (0.835; 95% CI, 0.800–0.870, n = 521) and external dataset (0.884; 95% CI, 0.848–0.921, n = 347). The model achieved both sensitivity and specificity >0.83 in the external validation. In addition, PORTLARS outperformed the preoperative LARS score for prediction of major events. PORTLARS could predict major bowel dysfunction after rectal cancer resection following radiotherapy with high accuracy and robustness. It may serve as a useful tool to identify patients who need additional support for long-term dysfunction in the early stage. ClinicalTrials.gov, number NCT05129215.