Influencing factors and establishment of a prediction model for the tumor regression after neoadjuvant chemoradiotherapy in locally advanced rectal cancer

Zhiyu LIU; Dong XU; Xihao CHEN; Jipeng LI

doi:10.19401/j.cnki.1007-3639.2024.02.007

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Influencing factors and establishment of a prediction model for the tumor regression after neoadjuvant chemoradiotherapy in locally advanced rectal cancer

Article | 更新时间：2025-12-31

- Influencing factors and establishment of a prediction model for the tumor regression after neoadjuvant chemoradiotherapy in locally advanced rectal cancer
- China Oncology Vol. 34, Issue 2, Pages: 191-200(2024)
- 作者机构：
  
  1. 西安医学院研究生处，陕西西安 710068
  2. 空军军医大学第一附属医院消化外科，陕西西安 710032
- 作者简介：
- 基金信息：
- DOI：10.19401/j.cnki.1007-3639.2024.02.007
  CLC： R735.3+7
- Received：15 March 2023，
  
  Revised：2023-12-15，
  
  Published：29 February 2024
- 稿件说明：
移动端阅览
Zhiyu LIU, Dong XU, Xihao CHEN, et al. Influencing factors and establishment of a prediction model for the tumor regression after neoadjuvant chemoradiotherapy in locally advanced rectal cancer[J]. China Oncology, 2024, 34(2): 191-200.
DOI：

Zhiyu LIU, Dong XU, Xihao CHEN, et al. Influencing factors and establishment of a prediction model for the tumor regression after neoadjuvant chemoradiotherapy in locally advanced rectal cancer[J]. China Oncology, 2024, 34(2): 191-200. DOI： 10.19401/j.cnki.1007-3639.2024.02.007.

摘要

背景与目的：

局部进展期直肠癌（locally advanced rectal cancer，LARC）的标准治疗策略是新辅助放化疗（neoadjuvant chemoradiotherapy，nCRT）后进行手术治疗，nCRT可以使肿块缩小，实现肿瘤降期，增加R0切除率。但直肠癌个体差异较大，有部分患者对nCRT反应较差，并不能从nCRT中获益。因此，采取有效的筛选措施，以识别nCRT效果不佳的患者很有必要。本研究旨在探讨临床基线指标对LARC nCRT后肿瘤退缩的预测价值并构建肿瘤退缩预测模型。

方法：

收集2016年1月—2020年12月在空军军医大学第一附属医院接受nCRT治疗且行全直肠系膜切除术的LARC患者，收集入组患者nCRT前的临床基线指标，包括实验室检查、肿瘤标志物和磁共振成像（magnetic resonance imaging，MRI）资料。根据nCRT前后的MRI报告的肿瘤大小，通过实体瘤疗效评价标准（Response Evaluation Criteria in Solid Tumors，RECIST）来评价LARC患者nCRT后肿瘤退缩程度。使用受试者工作特征（receiver operating characteristic，ROC）曲线对临床基线指标标准化处理后，进行多因素分析，筛选影响肿瘤退缩的因素，通过logistic回归构建肿瘤退缩预测模型，采用决策曲线分析（decision curve analysis，DCA）和校准曲线对模型的预测性能进行评估，并通过十折交叉验证来检测模型的准确度。

结果：

本回顾性队列研究共入组158例患者，其中98例患者nCRT后肿瘤退缩良好，达到完全缓解（complete response，CR）或部分缓解（partial response，PR），客观缓解率为62%；60例患者退缩不良，为疾病稳定（stable disease，SD）或疾病进展（progressive disease，PD）。多因素分析表明，治疗前肿瘤直径（

0.001）、nCRT后距离手术的时间（

= 0.006）、D-二聚体（

= 0.010）、预后营养指数（prognostic nutrition index，PNI）（

= 0.035）、癌胚抗原（carcinoembryonic antigen，CEA）（

= 0.004）、壁外血管侵犯（extramural vascular invasion，EMVI）（

= 0.026）与nCRT后肿瘤退缩显著相关。LARC nCRT后肿瘤退缩预测模型的ROC曲线的曲线下面积（area under curve，AUC）为0.84（95% CI：0.780 ~ 0.899），灵敏度为85.0%，特异度为72.4%。在校准曲线中，预测结果与实际结果吻合良好，具有良好的预测精准度。DCA表明，肿瘤退缩预测模型可以为诊断带来临床净收益。

结论：

治疗前肿瘤直径、nCRT后距离手术的时间、D-二聚体、PNI、CEA及EMVI是LARC nCRT后肿瘤退缩的独立风险因素，基于上述因素构建的肿瘤退缩预测模型对LARC患者nCRT后的肿瘤退缩具有较好的预测效能。

Abstract

Background and purpose:

The standard therapy for locally advanced rectal cancer (LARC) is neoadjuvant chemoradiotherapy (nCRT) followed by surgery. NCRT can make the tumor regress and downstage

and increase the R0 resection rate. However

individual differences in rectal cancer are large

and some patients respond poorly to nCRT and cannot benefit from nCRT. Therefore

it is necessary to establish effective screening measures to identify patients

with poor response to nCRT. This study aimed to analyze the influencing factors of nCRT for LARC and construct the tumor regression prediction model.

Methods:

Data of 158 LARC patients who underwent total mesenteric resection after receiving nCRT at the First Hospital Affiliated to Air Force Medical University from January 2016 to December 2020 were collected. Baseline clinical indicators before nCRT were collected

including laboratory examination

tumor markers and magnetic resonance imaging (MRI). According to the tumor size reported by MRI before and after nCRT

Response Evaluation Criteria in Solid Tumors (RECIST) was used to evaluate the extent of tumor regression after nCRT. After receiver operating characteristic (ROC) curve was used to standardize the clinical baseline indicators

logistic regression analysis was carried out to screen the factors affecting the tumor regression. The tumor regression prediction model was constructed by logistic regression

and the performance of the model was evaluated based on decision curve analysis (DCA) and the calibration curve. The accuracy of the model was tested by 10-fold cross-validation.

Results:

This retrospective cohort study enrolled 158 patients

in which

98 patients achieved complete response (CR) or partial response (PR). The objective response rate was 62%. Sixty patients had poor response to nCRT

either stable disease (SD) or progressive disease (PD). Multivariate logistic regression analysis showed that tumor diameter before treatment (

0.001)

time to surgery after nCRT (

= 0.006)

D-dimer (

= 0.010)

prognostic nutrition index (PNI) (

= 0.035)

carcinoembryonic antigen (CEA) (

= 0.004) and extramural vascular invasion (EMVI) (

= 0.026) were significantly related to tumor regression after nCRT. The area under ROC curve (AUC) of tumor regression after nCRT prediction model for LARC was 0.84 (95% CI: 0.780-0.899)

sen

sitivity was 85.0%

and specificity was 72.4%. In the calibration curve

the predicted results were in good agreement with the actual results

and the prediction accuracy was good. The DCA showed that the tumor regression prediction model could bring clinical net benefit to diagnosis.

Conclusion:

Tumor diameter before treatment

time to surgery after nCRT

D-dimer

PNI

CEA and EMVI are independent risk factors for the tumor regression after nCRT in LARC patients. The tumor regression prediction model based on the above factors has good predictive efficacy for the tumor regression after nCRT in LARC patients.

关键词

Keywords

references

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