Radiomics Nomogram for Identifying Sub-1 cm Benign and Malignant Thyroid Lesions

Xinxin Wu; Jingjing Li; Yakui Mou; Yao Yao; Jingjing Cui; Ning Mao; Xicheng Song

doi:10.3389/fonc.2021.580886

Radiomics Nomogram for Identifying Sub-1 cm Benign and Malignant Thyroid Lesions

Front Oncol. 2021 Jun 7:11:580886. doi: 10.3389/fonc.2021.580886. eCollection 2021.

Authors

Xinxin Wu¹, Jingjing Li^{1

2}, Yakui Mou¹, Yao Yao¹, Jingjing Cui³, Ning Mao⁴, Xicheng Song¹

Affiliations

¹ Department of Otorhinolaryngology-Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China.
² School of Clinical Medicine, Binzhou Medical University, Yantai, China.
³ Collaboration Department, Huiying Medical Technology Co., Ltd, Beijing, China.
⁴ Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China.

Abstract

Purpose: To develop and validate a radiomics nomogram for identifying sub-1 cm benign and malignant thyroid lesions.

Method: A total of 171 eligible patients with sub-1 cm thyroid lesions (56 benign and 115 malignant) who were treated in Yantai Yuhuangding Hospital between January and September 2019 were retrospectively collected and randomly divided into training (n = 136) and validation sets (n = 35). The radiomics features were extracted from unenhanced and arterial contrast-enhanced computed tomography images of each patient. In the training set, one-way analysis of variance and least absolute shrinkage and selection operator (LASSO) logistic regression were used to select the features related to benign and malignant lesions, and the LASSO algorithm was used to construct the radiomics signature. Combined with clinical independent predictive factors, a radiomics nomogram was constructed with a multivariate logistic regression model. The performance of the radiomics nomogram was evaluated by using the receiver operating characteristic (ROC) and calibration curves in the training and validation sets. The clinical usefulness was evaluated by using decision curve analysis (DCA).

Results: The radiomics signature consisting of 13 selected features achieved favorable prediction efficiency. The radiomics nomogram, which incorporated radiomics signature and clinical independent predictive factors including age and Thyroid Imaging Reporting and Data System category, showed good calibration and discrimination in the training (area under the ROC [AUC]: 0.853; 95% confidence interval [CI]: 0.797, 0.899) and validation sets (AUC: 0.851; 95% CI: 0.735, 0.931). DCA demonstrated that the nomogram was clinically useful.

Conclusion: As a noninvasive preoperative prediction tool, the radiomics nomogram incorporating radiomics signature and clinical predictive factors shows favorable predictive efficiency for identifying sub-1 cm benign and malignant thyroid lesions.

Keywords: computed tomography; nomogram; radiomics; thyroid imaging reporting and data system; thyroid lesions.