Multiple Level CT Radiomics Features Preoperatively Predict Lymph Node Metastasis in Esophageal Cancer: A Multicentre Retrospective Study

Lei Wu; Xiaojun Yang; Wuteng Cao; Ke Zhao; Wenli Li; Weitao Ye; Xin Chen; Zhiyang Zhou; Zaiyi Liu; Changhong Liang

doi:10.3389/fonc.2019.01548

Multiple Level CT Radiomics Features Preoperatively Predict Lymph Node Metastasis in Esophageal Cancer: A Multicentre Retrospective Study

Front Oncol. 2020 Jan 21:9:1548. doi: 10.3389/fonc.2019.01548. eCollection 2019.

Authors

Lei Wu^{1

2}, Xiaojun Yang^{1

2}, Wuteng Cao^{1

3}, Ke Zhao^{1

2}, Wenli Li³, Weitao Ye², Xin Chen², Zhiyang Zhou³, Zaiyi Liu^{1

2}, Changhong Liang^{1

2}

Affiliations

¹ School of Medicine, South China University of Technology, Guangzhou, China.
² Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
³ Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

Abstract

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.

Keywords: computer vision; deep learning; esophageal squamous cell carcinoma; lymph node metastasis; radiomics.