Nomogram Combining Radiomics With the American College of Radiology Thyroid Imaging Reporting and Data System Can Improve Predictive Performance for Malignant Thyroid Nodules

Xingzhi Huang; Zhenghua Wu; Aiyun Zhou; Xiang Min; Qi Qi; Cheng Zhang; Songli Chen; Pan Xu

doi:10.3389/fonc.2021.737847

Nomogram Combining Radiomics With the American College of Radiology Thyroid Imaging Reporting and Data System Can Improve Predictive Performance for Malignant Thyroid Nodules

Front Oncol. 2021 Oct 13:11:737847. doi: 10.3389/fonc.2021.737847. eCollection 2021.

Authors

Xingzhi Huang¹, Zhenghua Wu¹, Aiyun Zhou¹, Xiang Min², Qi Qi¹, Cheng Zhang¹, Songli Chen¹, Pan Xu¹

Affiliations

¹ Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
² Department of Head and Neck Otolaryngology, The First Affiliated Hospital of Nanchang University, Nanchang, China.

Abstract

Purpose: To develop and validate a nomogram combining radiomics of B-mode ultrasound (BMUS) images and the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) for predicting malignant thyroid nodules and improving the performance of the guideline.

Method: A total of 451 thyroid nodules referred for surgery and proven pathologically at an academic referral center from January 2019 to September 2020 were retrospectively collected and randomly assigned to training and validation cohorts (7:3 ratio). A nomogram was developed through combining the BMUS radiomics score (Rad-Score) with ACR TI-RADS score (ACR-Score) in the training cohort; the performance of the nomogram was assessed with respect to discrimination, calibration, and clinical application in the validation and entire cohorts.

Results: The ACR-Rad nomogram showed good calibration and yielded an AUC of 0.877 (95% CI 0.836-0.919) in the training cohort and 0.864 (95% CI 0.799-0.931) in the validation cohort, which were significantly better than the ACR-Score model (p < 0.001 and 0.031, respectively). The significantly improved AUC, net reclassification index (NRI), and integrated discriminatory improvement (IDI) of the nomogram were found for both senior and junior radiologists (all p < 0.001). Decision curve analysis indicated that the nomogram was clinically useful. When cutoff values for 50% predicted malignancy risk (ACR-Rad_50%) were applied, the nomogram showed increased specificity, accuracy and positive predictive value (PPV), and decreased unnecessary fine-needle aspiration (FNA) rates in comparison to ACR TI-RADS.

Conclusion: The ACR-Rad nomogram has favorable value in predicting malignant thyroid nodules and improving performance of the ACR TI-RADS for senior and junior radiologists.

Keywords: nomogram; prediction; radiomics; thyroid nodule; ultrasound.