Development of a nomogram for predicting pathological complete response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer
10.3760/cma.j.cn441530-20250106-00012
- VernacularTitle:局部进展期直肠癌患者新辅助放化疗后病理完全缓解的列线图预测模型
- Author:
Ruoxi TIAN
1
;
Xuhua HU
;
Hengchang LIU
;
Pu CHENG
;
Jiyun LI
;
Mandula BAO
;
Liming ZHAO
;
Zhaoxu ZHENG
Author Information
1. 国家癌症中心 国家肿瘤临床医学研究中心 中国医学科学院北京协和医学院肿瘤医院结直肠外科，北京 100021
- Publication Type:Journal Article
- Keywords:
Rectal neoplasms, locally advanced;
Neoadjuvant chemoradiotherapy;
Pathological complete response;
Nomogram
- From:
Chinese Journal of Gastrointestinal Surgery
2025;28(3):304-313
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct and validate a predictive model for pathological complete response (pCR) in patients with locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy.Methods:This retrospective observational study included 595 patients with stage T2-4 and (or) N+M0 LARC diagnosed in the Cancer Hospital of Chinese Academy of Medical Sciences and the Fourth Hospital of Hebei Medical University who had no metastases, tolerated neoadjuvant therapy, completed neoadjuvant therapy, and had undergone radical surgery after neoadjuvant therapy. The training set comprised 299 patients admitted to the Cancer Hospital of Chinese Academy of Medical Sciences from 2013 to 2018, the internal validation set 155 patients admitted from 2019 to 2023, and the external validation set 141 patients admitted to the Fourth Hospital of Hebei Medical University from 2013 to 2021. They were divided into pCR group and non-pCR groups according to postoperative pathology. Among the 299 patients in the training set, 247 were in the non-PCR and 52 in the pCR group; among the 155 patients verified internally, 113 were in the non-PCR and 42 in the pCR group; and among the 141 patients validated externally, 132 were in the non-pCR and nine in the pCR group. Logistic regression was used for univariate and multifactorial analysis to explore the factors associated with pCR and construct a nomogram prediction model. Receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA) were used to validate the performance of the predictive model.Results:Univariate and multivariate logistic regression analysis showed that carbohydrate antigen 19-9 ( P=0.040, OR=0.97, 95%CI: 0.93-0.99), neutrophil count ( P<0.001, OR=0.66, 95%CI: 0.52-0.84), tumor T stage: Stage IV ( P=0.011, OR=0.22, 95%CI: 0.07-0.70), tumor N stage: Stage I ( P=0.003, OR=0.22,95%CI:0.08-0.60), Stage II ( P<0.001, OR=0.03, 95%CI: 0.01-0.09) and involvement of mesorectal fascia ( P=0.004, OR=0.09, 95%CI: 0.02-0.47) were independent predictors of pCR. In the training set, the area under the receiver operating characteristic curve of the model was 0.92 (95%CI: 0.87-0.96), whereas in the internal and external validation sets, the AUCs were 0.78 and 0.81, respectively. The calibration curve showed that the prediction model had good prediction efficiency in both the training and verification sets. Decision curve analysis showed that the net benefit of the model was largest when the threshold probability was in the range of 5.2% to 89.7% (in the internal and external validation sets, the threshold probabilities were in the range of 15.7% to 92.3% and 2.2% to 84.1%, respectively). Conclusion:The nomogram model constructed in this study showed efficacy in predicting whether patients with LARC will achieve pCR after receiving neoadjuvant chemoradiotherapy.
