基于CT的亚区域影像组学列线图预测接受根治性放化疗的食管鳞状细胞癌患者的无局部复发生存率：一项多中心研究

A CT-based subregional radiomics nomogram for predicting local recurrence-free survival in esophageal squamous cell cancer patients treated by definitive chemoradiotherapy: a multicenter study.

作者信息

Gong Jie, Lu Jianchao, Zhang Wencheng, Huang Wei, Li Jie, Yang Zhi, Meng Fan, Sun Hongfei, Zhao Lina

机构信息

Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an, China.

Department of Radiation Oncology, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital and Institution, University of Electronic Science and Technology of China, Chengdu, China.

出版信息

J Transl Med. 2024 Dec 5;22(1):1108. doi: 10.1186/s12967-024-05897-y.

DOI:10.1186/s12967-024-05897-y

PMID:39639328

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11619118/

Abstract

BACKGROUND

To develop and validate an online individualized model for predicting local recurrence-free survival (LRFS) in esophageal squamous cell carcinoma (ESCC) treated by definitive chemoradiotherapy (dCRT).

METHODS

ESCC patients from three hospitals were randomly stratified into the training set (715) and the internal testing set (179), and patients from the other hospital as the external testing set (120). The important radiomic features extracted from contrast-enhanced computed tomography (CECT)-based subregions clustered from the whole volume of tumor and peritumor were selected and used to construct the subregion-based radiomic signature by using COX proportional hazards model, which was compared with the tumor-based radiomic signature. The clinical model and the radiomics model combing the clinical factors and the radiomic signature were further constructed and compared, which were validated in two testing sets.

RESULTS

The subresion-based radiomic signature showed better prognostic performance than the tumor-based radiomic signature (training: 0.642 vs. 0.621, internal testing: 0.657 vs. 0.638, external testing: 0.636 vs. 0.612). Although the tumor-based radiomic signature, the subregion-based radiomic signature, the tumor-based radiomics model, and the subregion-based radiomics model had better performance compared to the clinical model, only the subregion-based radiomics model showed a significant advantage (p < 0.05; training: 0.666 vs. 0.616, internal testing: 0.689 vs. 0.649, external testing: 0.642 vs. 0.604). The clinical model and the subregion-based radiomics model were visualized as the nomograms, which are available online and could interactively calculate LRFS probability.

CONCLUSIONS

We established and validated a CECT-based online radiomics nomogram for predicting LRFS in ESCC received dCRT, which outperformed the clinical model and might serve as a powerful tool to facilitate individualized treatment.

TRIAL REGISTRATION

This retrospective study was approved by the ethics committee (KY20222145-C-1).

摘要

背景

开发并验证一种在线个体化模型，用于预测接受根治性放化疗（dCRT）的食管鳞状细胞癌（ESCC）患者的无局部复发生存期（LRFS）。

方法

将来自三家医院的ESCC患者随机分层为训练集（715例）和内部测试集（179例），将来自另一家医院的患者作为外部测试集（120例）。从基于对比增强计算机断层扫描（CECT）的肿瘤和瘤周全容积聚类的子区域中提取重要的放射组学特征，并使用COX比例风险模型构建基于子区域的放射组学特征，将其与基于肿瘤的放射组学特征进行比较。进一步构建并比较结合临床因素和放射组学特征的临床模型和放射组学模型，并在两个测试集中进行验证。

结果

基于子区域的放射组学特征显示出比基于肿瘤的放射组学特征更好的预后性能（训练集：0.642对0.621，内部测试集：0.657对0.638，外部测试集：0.636对0.612）。尽管与临床模型相比，基于肿瘤的放射组学特征、基于子区域的放射组学特征、基于肿瘤的放射组学模型和基于子区域的放射组学模型表现更好，但只有基于子区域的放射组学模型显示出显著优势（p < 0.05；训练集：0.666对0.616，内部测试集：0.689对0.649，外部测试集：0.642对0.604）。临床模型和基于子区域的放射组学模型被可视化为列线图，可在线获取并能交互式计算LRFS概率。