A Radiomic Nomogram for the Ultrasound-Based Evaluation of Extrathyroidal Extension in Papillary Thyroid Carcinoma

Xian Wang; Enock Adjei Agyekum; Yongzhen Ren; Jin Zhang; Qing Zhang; Hui Sun; Guoliang Zhang; Feiju Xu; Xiangshu Bo; Wenzhi Lv; Shudong Hu; Xiaoqin Qian

doi:10.3389/fonc.2021.625646

A Radiomic Nomogram for the Ultrasound-Based Evaluation of Extrathyroidal Extension in Papillary Thyroid Carcinoma

Front Oncol. 2021 Mar 4:11:625646. doi: 10.3389/fonc.2021.625646. eCollection 2021.

Authors

Xian Wang¹, Enock Adjei Agyekum², Yongzhen Ren², Jin Zhang¹, Qing Zhang¹, Hui Sun³, Guoliang Zhang⁴, Feiju Xu¹, Xiangshu Bo¹, Wenzhi Lv⁵, Shudong Hu⁶, Xiaoqin Qian¹

Affiliations

¹ Department of Ultrasound, Affiliated People's Hospital of Jiangsu University, Zhenjiang, China.
² School of Medicine, Jiangsu University, Zhenjiang, China.
³ Department of Pathology, Affiliated People's Hospital of Jiangsu University, Zhenjiang, China.
⁴ Department of General Surgery, Affiliated People's Hospital of Jiangsu University, Zhenjiang, China.
⁵ Department of Artificial Intelligence, Julei Technology Company, Wuhan, China.
⁶ Department of Radiology, The Affiliated Hospital, Jiangnan University, Wuxi, China.

Abstract

Purpose: To construct a sequence diagram based on radiological and clinical factors for the evaluation of extrathyroidal extension (ETE) in patients with papillary thyroid carcinoma (PTC).

Materials and methods: Between January 2016 and January 2020, 161 patients with PTC who underwent preoperative ultrasound examination in the Affiliated People's Hospital of Jiangsu University were enrolled in this retrospective study. According to the pathology results, the enrolled patients were divided into a non-ETE group and an ETE group. All patients were randomly divided into a training cohort (n = 97) and a validation cohort (n = 64). A total of 479 image features of lesion areas in ultrasonic images were extracted. The radiomic signature was developed using least absolute shrinkage and selection operator algorithms after feature selection using the minimum redundancy maximum relevance method. The radiomic nomogram model was established by multivariable logistic regression analysis based on the radiomic signature and clinical risk factors. The discrimination, calibration, and clinical usefulness of the nomogram model were evaluated in the training and validation cohorts.

Results: The radiomic signature consisted of six radiomic features determined in ultrasound images. The radiomic nomogram included the parameters tumor location, radiological ETE diagnosis, and the radiomic signature. Area under the curve (AUC) values confirmed good discrimination of this nomogram in the training cohort [AUC, 0.837; 95% confidence interval (CI), 0.756-0.919] and the validation cohort (AUC, 0.824; 95% CI, 0.723-0.925). The decision curve analysis showed that the radiomic nomogram has good clinical application value.

Conclusion: The newly developed radiomic nomogram model is a noninvasive and reliable tool with high accuracy to predict ETE in patients with PTC.

Keywords: extrathyroidal extension; nomogram; papillary thyroid carcinoma; thyroid neoplasms; ultrasonography; ultrasound radiomics.