Multiple Level CT Radiomics Features Preoperatively Predict Lymph Node Metastasis in Esophageal Cancer: A Multicentre Retrospective Study
Multiple Level CT Radiomics Features Preoperatively Predict Lymph Node Metastasis in Esophageal Cancer: A Multicentre Retrospective Study
复制标题
多水平 CT 放射组学特征术前预测食管癌淋巴结转移：一项多中心回顾性研究
DOI：
10.3389/fonc.2019.01548
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发表时间：
2020-01-21
影响因子：
4.7
通讯作者：
Liang, Changhong
中科院分区：
文献类型：
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作者：
Wu, Lei;Yang, Xiaojun;Liang, Changhong
背景：淋巴结转移是影响食管鳞癌预后的重要因素。传统的临床因素和现有的基于CT影像的诊断方法在诊断淋巴结转移方面存在不足。一个更有效的方法来预测LN状态的基础上CT images.Methods：在这个多中心的回顾性研究，411例经病理证实的食管鳞癌患者登记从两家医院。从每位患者的术前动脉期CT图像中提取定量图像特征，包括手工制作、计算机视觉（CV-）和深度特征。分别构建了手工制作的、CV和深度放射组学签名。然后，通过将独立的临床危险因素合并到放射组学特征中，构建多个放射组学模型。模型的性能进行了评价方面的歧视，校准和临床实用性。最后，一个独立的外部验证队列被用来验证模型的预测performance.Results：五，七，九个功能被选中用于构建手工制作，CV，和深放射组学签名提取的功能，分别。在所有组群中，LN阳性和LN阴性患者之间的这些特征具有统计学显著性差异（p < 0.001）。所开发的多水平CT放射组学模型将多个放射组学特征与临床风险因素相结合，上级传统的临床因素和现有方法报告的结果，并获得了令人满意的区分性能，其C-统计量在开发队列中为0.875，在内部验证队列中为0.874，在独立外部验证队列中为0.840。诺模图和决策曲线分析（DCA）进一步证实了我们的方法可以作为一个有效的工具，为临床医生评估LN转移的风险，在ESCC患者，并进一步选择treatments.Conclusions：提出的多层CT放射组学模型，集成多层次的放射组学特征的临床危险因素，可用于术前预测LN转移的ESCC患者。
Background: Lymph node (LN) metastasis is the most important prognostic factor in esophageal squamous cell carcinoma (ESCC). Traditional clinical factor and existing methods based on CT images are insufficiently effective in diagnosing LN metastasis. A more efficient method to predict LN status based on CT image is needed.Methods: In this multicenter retrospective study, 411 patients with pathologically confirmed ESCC were registered from two hospitals. Quantitative image features including handcrafted-, computer vision-(CV-), and deep-features were extracted from preoperative arterial phase CT images for each patient. A handcrafted-, CV-, and deep-radiomics signature were built, respectively. Then, multiple radiomics models were constructed by merging independent clinical risk factor into radiomics signatures. The performance of models were evaluated with respect to the discrimination, calibration, and clinical usefulness. Finally, an independent external validation cohort was used to validate the model's predictive performance.Results: Five, seven, and nine features were selected for building handcrafted-, CV-, and deep-radiomics signatures from extracted features, respectively. Those signatures were statistically significant different between LN-positive and LN-negative patients in all cohorts (p < 0.001). The developed multiple level CT radiomics model that integrates multiple radiomics signatures with clinical risk factor, was superior to traditional clinical factors and the results reported by existing methods, and achieved satisfactory discrimination performance with C-statistic of 0.875 in development cohort, 0.874 in internal validation cohort and 0.840 in independent external validation cohort. Nomogram and decision curve analysis (DCA) further confirmed our method may serve as an effective tool for clinicians to evaluate the risk of LN metastasis in patients with ESCC and further choose treatment strategy.Conclusions: The proposed multiple level CT radiomics model which integrate multiple level radiomics features into clinical risk factor can be used for preoperative predicting LN metastasis of patients with ESCC.