MRI-Based Radiomics of Rectal Cancer: Assessment of the Local Recurrence at the Site of Anastomosis
MRI-Based Radiomics of Rectal Cancer: Assessment of the Local Recurrence at the Site of Anastomosis
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DOI：
10.1016/j.acra.2020.09.024
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发表时间：
2021-11-12
影响因子：
4.8
通讯作者：
Lu, Jianping
中科院分区：
文献类型：
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作者：
Chen, Fangying;Ma, Xiaolu;Lu, Jianping
理由和目的：探讨基于磁共振成像（MRI）的放射组学模型在直肠癌吻合口局部复发与非复发性病变鉴别诊断中的意义。材料与方法：共80例临床怀疑吻合口病变的患者接受了3.0T盆腔MRI，包括T2加权成像（T2WI），弥散加权成像（DWI），以及对比增强T1加权容积内插身体检查（VIBE）成像。放射组学特征从感兴趣体积（VOI）中提取，在多个MRI序列上手动描绘。随后，主成分分析分别降低了T2WI，DWI，VIBE和组合多序列的特征维数。在此基础上，训练极端梯度增强（XGBoost）分类器，构建ModelT2WI、ModelDWI、ModelVIBE和Modelcombination。结果：主成分分析分别选取8个、4个、7个和6个主成分构建T2WI、DWI、VIBE和联合多序列的放射组学模型。模型组合的受试者工作特征曲线下面积为0.864，验证集的灵敏度和特异性分别为81.82%和75.86%，与其他模型相比表现出更优的性能（p< 0.05）。决策曲线分析证实了临床实用性的model.Conclusion：这项研究表明，MRI为基础的放射组学是一个复杂的和非侵入性的工具，准确区分LR从非复发性病变的吻合部位。组合多个序列显著提高了其性能。
Rationale and Objective: To investigate the significance of magnetic resonance imaging (MRI)-based radiomics model in differentiating local recurrence of rectal cancer from nonrecurrence lesions at the site of anastomosis.Materials and Methods: A total of 80 patients with clinically suspected lesions of anastomosis underwent 3.0T pelvic MRI consisting of T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted volume interpolated body examination (VIBE) imaging. Radiomics features were extracted from volumes of interest (VOIs), delineated manually on multiple MRI sequences. Subsequently, principal component analysis reduced the dimensionality of features for T2WI, DWI, VIBE, and combined multisequences, respectively. On this basis, the extreme gradient boosting (XGBoost) classifier was trained to build ModelT2WI, ModelDWI, ModelVIBE, and Modelcombination. Receiver operating characteristic curves were generated to determine the diagnostic performance of various models.Results: Principal component analysis selected eight, four, seven, and six principal components to construct the radiomics model for T2WI, DWI, VIBE, and combined multisequences, respectively. Modelcombination had an area under the receiver operating characteristic curve of 0.864, with sensitivity and specificity of 81.82% and 75.86% in the validation set, demonstrating a more optimal performance compared to other models (p< 0.05). The decision curve analysis confirmed the clinical usefulness of the model.Conclusion: This study demonstrated that MRI-based radiomics is a sophisticated and noninvasive tool for accurately distinguishing LR from nonrecurrence lesions at the site of anastomosis. Combining multiple sequences significantly improves its performance.