Dual-modal radiomics for predicting cervical lymph node metastasis in papillary thyroid carcinoma

Article type: Research Article

Authors: Ren, Yongzhen^{a; b; 1} | Lu, Siyuan^{b; c; 1} | Zhang, Dongmei^{a; 1} | Wang, Xian^a | Agyekum, Enock Adjei^{a; b} | Zhang, Jin^a | Zhang, Qing^a | Xu, Feiju^a | Zhang, Guoliang^d | Chen, Yu^e | Shen, Xiangjun^f | Zhang, Xuelin^a | Wu, Ting^g | Hu, Hui^c | Shan, Xiuhong^{c; *} | Wang, Jun^{h; *} | Qian, Xiaoqin^{a; *}

Affiliations: [a] Department of Medical Ultrasound, Jiangsu University Affiliated People’s Hospital, Zhenjiang, Jiangsu Province, China | [b] School of Medicine, Jiangsu University, Zhenjiang, Jiangsu Province, China | [c] Department of Radiology, Jiangsu University Affiliated People’s Hospital, Zhenjiang, Jiangsu Province, China | [d] Department of General Surgery, Jiangsu University Affiliated People’s Hospital, Zhenjiang, Jiangsu Province, China | [e] Materdicine Lab, School of Life Sciences, Shanghai University, Shanghai, China | [f] School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu Province, China | [g] Department of Pathology, Jiangsu University Affiliated People’s Hospital, Zhenjiang, Jiangsu Province, China | [h] School of Communication and Information Engineering, Shanghai University, Shanghai, China

Correspondence: [*] Corresponding author: Xiaoqin Qian, Department of Medical Ultrasound, Jiangsu University Affiliated People’s Hospital, Zhenjiang, No. 8, Dianli Road, Zhenjiang 212000, Jiangsu Province, China. E-mail: yz_tyz1030@126.com; and Jun Wang, School of Communication and Information Engineering, Shanghai University, No. 99, Shangda Road, Shanghai 200444, China. E-mail: wangjun_shu@shu.edu.cn; and Xiuhong Shan, Department of Radiology, Jiangsu University Affiliated People’s Hospital, Zhenjiang, No. 8, Dianli Road, Zhenjiang 212000, Jiangsu Province, China. E-mail: 13913433095@163.com.

Note: [1] Yongzhen Ren, Siyuan Lu and Dongmei Zhang should be considered joint first author.

Abstract: BACKGROUND:Preoperative prediction of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) is significant for surgical decision-making. OBJECTIVE:This study aims to develop a dual-modal radiomics (DMR) model based on grayscale ultrasound (GSUS) and dual-energy computed tomography (DECT) for non-invasive CLNM in PTC. METHODS:In this study, 348 patients with pathologically confirmed PTC at Jiangsu University Affiliated People’s Hospital who completed preoperative ultrasound (US) and DECT examinations were enrolled and randomly assigned to training (n = 261) and test (n = 87) cohorts. The enrolled patients were divided into two groups based on pathology findings namely, CLNM (n = 179) and CLNM-Free (n = 169). Radiomics features were extracted from GSUS images (464 features) and DECT images (960 features), respectively. Pearson correlation coefficient (PCC) and the least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation were then used to select CLNM-related features. Based on the selected features, GSUS, DECT, and GSUS combined DECT radiomics models were constructed by using a Support Vector Machine (SVM) classifier. RESULTS:Three predictive models based on GSUS, DECT, and a combination of GSUS and DECT, yielded performance of areas under the curve (AUC) = 0.700 [95% confidence interval (CI), 0.662–0.706], 0.721 [95% CI, 0.683–0.727], and 0.760 [95% CI, 0.728–0.762] in the training dataset, and AUC = 0.643 [95% CI, 0.582–0.734], 0.680 [95% CI, 0.623–0.772], and 0.744 [95% CI, 0.686–0.784] in the test dataset, respectively. It shows that the predictive model combined GSUS and DECT outperforms both models using GSUS and DECT only. CONCLUSIONS:The newly developed combined radiomics model could more accurately predict CLNM in PTC patients and aid in better surgical planning.

Keywords: Grayscale ultrasound, dual-energy computed tomography, radiomics, papillary thyroid carcinoma, cervical lymph node metastasis

DOI: 10.3233/XST-230091

Journal: Journal of X-Ray Science and Technology, vol. 31, no. 6, pp. 1263-1280, 2023