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用于预测胰腺导管腺癌预后的MRI影像组学列线图的开发与验证

Development and validation of an MRI-radiomics nomogram for the prognosis of pancreatic ductal adenocarcinoma.

作者信息

Xu Xinsen, Qu Jiaqi, Zhang Yijue, Qian Xiaohua, Chen Tao, Liu Yingbin

机构信息

Department of Biliary-Pancreatic Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.

School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.

出版信息

Front Oncol. 2023 Feb 24;13:1074445. doi: 10.3389/fonc.2023.1074445. eCollection 2023.

Abstract

OBJECTIVE

To develop and validate an MRI-radiomics nomogram for the prognosis of pancreatic ductal adenocarcinoma (PDAC).

BACKGROUND

"Radiomics" enables the investigation of huge amounts of radiological features in parallel by extracting high-throughput imaging data. MRI provides better tissue contrast with no ionizing radiation for PDAC.

METHODS

There were 78 PDAC patients enrolled in this study. In total, there were 386 radiomics features extracted from MRI scan, which were screened by the least absolute shrinkage and selection operator algorithm to develop a risk score. Cox multivariate regression analysis was applied to develop the radiomics-based nomogram. The performance was assessed by discrimination and calibration.

RESULTS

The radiomics-based risk-score was significantly associated with PDAC overall survival (OS) (P < 0.05). With respect to survival prediction, integrating the risk score, clinical data and TNM information into the nomogram exhibited better performance than the TNM staging system, radiomics model and clinical model. In addition, the nomogram showed fine discrimination and calibration.

CONCLUSIONS

The radiomics nomogram incorporating the radiomics data, clinical data and TNM information exhibited precise survival prediction for PDAC, which may help accelerate personalized precision treatment.

CLINICAL TRIAL REGISTRATION

clinicaltrials.gov, identifier NCT05313854.

摘要

目的

开发并验证一种用于预测胰腺导管腺癌(PDAC)预后的MRI影像组学列线图。

背景

“影像组学”通过提取高通量成像数据,能够同时研究大量的放射学特征。MRI为PDAC提供了更好的组织对比度,且无电离辐射。

方法

本研究纳入了78例PDAC患者。从MRI扫描中总共提取了386个影像组学特征,通过最小绝对收缩和选择算子算法进行筛选以建立风险评分。应用Cox多变量回归分析来构建基于影像组学的列线图。通过区分度和校准来评估其性能。

结果

基于影像组学的风险评分与PDAC的总生存期(OS)显著相关(P<0.05)。在生存预测方面,将风险评分、临床数据和TNM信息整合到列线图中,其表现优于TNM分期系统、影像组学模型和临床模型。此外,该列线图显示出良好的区分度和校准。

结论

结合影像组学数据、临床数据和TNM信息的影像组学列线图对PDAC具有精确的生存预测能力,这可能有助于加速个性化精准治疗。

临床试验注册

clinicaltrials.gov,标识符NCT05313854。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5890/9998897/e3718f20d082/fonc-13-1074445-g001.jpg

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