A CT-based radiomics nomogram for predicting local tumor progression of colorectal cancer lung metastases treated with radiofrequency ablation

Haozhe HUANG; Hong CHEN; Dezhong ZHENG; Chao CHEN; Ying WANG; Lichao XU; Yaohui WANG; Xinhong HE; Yuanyuan YANG; Wentao LI

doi:10.19401/j.cnki.1007-3639.2024.09.006

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A CT-based radiomics nomogram for predicting local tumor progression of colorectal cancer lung metastases treated with radiofrequency ablation

Article | 更新时间：2025-12-31

- A CT-based radiomics nomogram for predicting local tumor progression of colorectal cancer lung metastases treated with radiofrequency ablation
- China Oncology Vol. 34, Issue 9, Pages: 857-872(2024)
- 作者机构：
  
  1. 复旦大学附属肿瘤医院介入治疗科，复旦大学上海医学院肿瘤学系，上海 200032
  2. 上海交通大学医学院附属第六人民医院核医学科，上海 200233
  3. 中国科学院上海技术物理研究所医学影像实验室，上海 200083
- 作者简介：
  
  LI Wentao
- 基金信息：
- DOI：10.19401/j.cnki.1007-3639.2024.09.006
  CLC： R735.3+5；R735.3+7
- Received：07 May 2024，
  
  Revised：2024-06-13，
  
  Published：30 September 2024
- 稿件说明：
移动端阅览
Haozhe HUANG, Hong CHEN, Dezhong ZHENG, et al. A CT-based radiomics nomogram for predicting local tumor progression of colorectal cancer lung metastases treated with radiofrequency ablation[J]. China Oncology, 2024, 34(9): 857-872.
DOI：

Haozhe HUANG, Hong CHEN, Dezhong ZHENG, et al. A CT-based radiomics nomogram for predicting local tumor progression of colorectal cancer lung metastases treated with radiofrequency ablation[J]. China Oncology, 2024, 34(9): 857-872. DOI： 10.19401/j.cnki.1007-3639.2024.09.006.

摘要

背景与目的：

早期预测结直肠癌（colorectal cancer，CRC）肺转移瘤经过射频消融（radiofrequency ablation，RFA）治疗后的局部肿瘤无进展生存期（local tumor progression-free survival，LTPFS）具有重要的临床意义。影像组学在肿瘤预后预测方面已有一定的探索。本研究旨在构建基于影像组学的诺模图预测CRC肺转移患者RFA治疗后的LTPFS。

方法：

本研究回顾性分析2016年8月—2019年1月复旦大学附属肿瘤医院介入治疗科收治的172例伴有401个肺转移病灶的CRC患者。本研究通过复旦大学附属肿瘤医院医学伦理委员会的审查（伦理编号：2402291-24）。将患者的RFA术前及术后即刻计算机体层成像（computed tomography，CT）图像通过数据增强后，人工分割靶病灶和RFA后区域后提取影像组学特征。采用最大相关最小冗余算法（maximum relevance and minimum redundancy algorithm，MRMRA）和最小绝对值收敛（least absolute shrinkage and selection operator，LASSO）回归模型进行特征筛选。基于筛选后的影像组学特征和多因素分析筛选的临床变量，分别构建临床模型、影像组学模型和融合模型。计算一致性指数（concordance index，C-index）和受试者工作特征（receiver operating characteristic，ROC）曲线的曲线下面积（area under curve，AUC）来评估3种模型的预测性能，最后绘制最佳模型对应的诺模图。

结果：

在所有肺转移瘤中，最终复发的病灶有102个（25.4%），完全缓解（complete response，CR）的病灶有299个（74.6%）。中位随访时间为21个月（95% CI：19.466 ~ 22.534），RFA术后的1、2和3年的LTPFS率分别为76.5%（95% CI：72.0 ~ 80.4）、72.1%（95% CI：66.6 ~ 76.9）和69.9%（95% CI：64.0 ~ 75.1）。无论在训练集还是测试集中，基于LASSO回归模型最终筛选的12个影像组学特征和多因素分析筛选的4个临床变量而构建的融合模型预测LTPFS的AUC最高，C-index分别为0.890（95% CI：0.854 ~ 0.927）和0.843（95% CI：0.768 ~ 0.916）。

结论：

基于影像组学特征和临床变量的融合模型预测CRC肺转移患者RFA治疗后的LTPFS是可行的，并且其性能优于单一影像组学模型和临床模型。同时，融合模型的诺模图可以更直观地预测CRC肺转移患者RFA治疗的预后，协助临床医师为患者制订个体化的随访复查方案，灵活地调整治疗策略。

Abstract

Background and Purpose:

The early prediction of local tumor progression-free survival (LTPFS) after radiofrequency ablation (RFA) for colorectal cancer (CRC) lung metastases has significant clinical importance. The application of radiomics in the prediction of tumor prognosis has been explored. This study aimed to construct a radiomics-based nomogram for predicting LTPFS after RFA in CRC patients with lung metastases.

Methods:

This study retrospectively analyzed 172 CRC patients with 401 lung metastases admitted to Department of Interventional Radiology

Fudan University Shanghai Cancer Center from August 2016 to January 2019. This stu

dy was reviewed by the medical ethics committee of Fudan University Shanghai Cancer Center (ethics number: 2402291-24). After augmentation of pre-ablation and immediate post-ablation computed tomography (CT) images

the target metastases and ablation regions were segmented manually to extract the radiomic features. Maximum relevance and minimum redundancy algorithm (MRMRA) and least absolute shrinkage and selection operator (LASSO) regression models were applied for feature selection. The clinical model

the radiomics model

and the fusion model were constructed based on the selected radiomic features and clinical variables screened by the multivariate analysis. The Harrell concordance index (C-index) and area under receiver operating characteristic (ROC) curves (AUC) were calculated to evaluate the prediction performance. Finally

the corresponding nomogram of the best model was drawn.

Results:

Among all the lung metastases

102 (25.4%) had final recurrence

and 299 (74.6%) had complete response (CR). The median follow-up time was 21 months (95% CI: 19.466-22.534)

and the LTPFS rates at 1

and 3 years after RFA were 76.5% (95% CI: 72.0-80.4)

72.1% (95% CI: 66.6-76.9) and 69.9% (95% CI: 64.0-75.1). In both the training and test dataset

the fusion model based on the final 12 radiomic features through the LASSO regression and 4 clinical variables screened by multivariate analysis achieved the highest AUC values for LTPFS

with C-index values of 0.890 (95% CI: 0.854-0.927) and 0.843 (95% CI: 0.768-0.916)

respectively.

Conclusion:

The fusion model based on radiomic features and clinical variables is feasible for predicting LTPFS after RFA of CRC patients with lung metastases

whose performance is superior to the single radiomic and clinical model. At the same time

the nomogram of the fusion model can intuitively predict the prognosis of CRC patients with lung metastases after RFA

thus assisting clinicians in developing individualized follow-up review plans for patients and adj

usting treatment strategies flexibly.

关键词

Keywords

references

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