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基于三相增强计算机断层扫描影像组学特征预测结直肠癌微卫星不稳定状态:一项多中心研究

Predicting Microsatellite Instability Status in Colorectal Cancer Based on Triphasic Enhanced Computed Tomography Radiomics Signatures: A Multicenter Study.

作者信息

Cao Yuntai, Zhang Guojin, Zhang Jing, Yang Yingjie, Ren Jialiang, Yan Xiaohong, Wang Zhan, Zhao Zhiyong, Huang Xiaoyu, Bao Haihua, Zhou Junlin

机构信息

Department of Radiology, Affiliated Hospital of Qinghai University, Xining, China.

Second Clinical School, Lanzhou University, Lanzhou, China.

出版信息

Front Oncol. 2021 Jun 10;11:687771. doi: 10.3389/fonc.2021.687771. eCollection 2021.

Abstract

BACKGROUND

This study aimed to develop and validate a computed tomography (CT)-based radiomics model to predict microsatellite instability (MSI) status in colorectal cancer patients and to identify the radiomics signature with the most robust and high performance from one of the three phases of triphasic enhanced CT.

METHODS

In total, 502 colorectal cancer patients with preoperative contrast-enhanced CT images and available MSI status (441 in the training cohort and 61 in the external validation cohort) were enrolled from two centers in our retrospective study. Radiomics features of the entire primary tumor were extracted from arterial-, delayed-, and venous-phase CT images. The least absolute shrinkage and selection operator method was used to retain the features closely associated with MSI status. Radiomics, clinical, and combined Clinical Radiomics models were built to predict MSI status. Model performance was evaluated by receiver operating characteristic curve analysis.

RESULTS

Thirty-two radiomics features showed significant correlation with MSI status. Delayed-phase models showed superior predictive performance compared to arterial- or venous-phase models. Additionally, age, location, and carcinoembryonic antigen were considered useful predictors of MSI status. The Clinical Radiomics nomogram that incorporated both clinical risk factors and radiomics parameters showed excellent performance, with an AUC, accuracy, and sensitivity of 0.898, 0.837, and 0.821 in the training cohort and 0.964, 0.918, and 1.000 in the validation cohort, respectively.

CONCLUSIONS

The proposed CT-based radiomics signature has excellent performance in predicting MSI status and could potentially guide individualized therapy.

摘要

背景

本研究旨在开发并验证一种基于计算机断层扫描(CT)的放射组学模型,以预测结直肠癌患者的微卫星不稳定性(MSI)状态,并从三期增强CT的三个阶段之一中识别出性能最稳健且高效的放射组学特征。

方法

在我们的回顾性研究中,从两个中心招募了总共502例有术前对比增强CT图像且MSI状态可用的结直肠癌患者(训练队列441例,外部验证队列61例)。从动脉期、延迟期和静脉期CT图像中提取整个原发肿瘤的放射组学特征。采用最小绝对收缩和选择算子方法保留与MSI状态密切相关的特征。构建放射组学、临床和联合临床放射组学模型来预测MSI状态。通过受试者工作特征曲线分析评估模型性能。

结果

32个放射组学特征与MSI状态显示出显著相关性。与动脉期或静脉期模型相比,延迟期模型显示出更好的预测性能。此外,年龄、位置和癌胚抗原被认为是MSI状态的有用预测指标。结合临床危险因素和放射组学参数的临床放射组学列线图表现出色,在训练队列中的曲线下面积(AUC)、准确率和敏感性分别为0.898、0.837和0.821,在验证队列中分别为0.964、0.918和1.000。

结论

所提出的基于CT的放射组学特征在预测MSI状态方面具有出色性能,并可能指导个体化治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b7a/8222982/608144b1ea3f/fonc-11-687771-g001.jpg

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