AUTHOR=Gong Liu , Zhou Ping , Li Jia-Le , Liu Wen-Gang 

TITLE=Investigating the diagnostic efficiency of a computer-aided diagnosis system for thyroid nodules in the context of Hashimoto’s thyroiditis

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.941673

DOI=10.3389/fonc.2022.941673

ISSN=2234-943X

ABSTRACT=Objectives: The purpose of this study was to investigate the efficacy of the computer-aided diagnosis (CAD) system in distinguishing between benign and malignant thyroid nodules in the context of Hashimoto's thyroiditis (HT), and to evaluate role of CAD system in reducing unnecessary biopsies of benign lesions.
Methods: We included a total of 137 nodules from 137 consecutive patients (mean age, 43.5±11.8years) who were histopathologically diagnosed with HT. The two-dimensional ultrasound images and videos of all thyroid nodules were analyzed by CAD system and two radiologists with different experience according to ACR TI-RADS. The diagnostic cut-off values of ACR TI-RADS were divided into two categories (TR4 and TR5), then the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of the CAD system, junior and senior radiologists were compared in both cases. Moreover, ACR TI-RADS classification was revised according to the results of the CAD system, and the efficacy of recommended fine-needle aspiration (FNA) was evaluated by comparing the unnecessary biopsy rate and the malignant rate of punctured nodules.
Results: The accuracy, sensitivity, specificity, PPV and NPV of the CAD system were 0.876, 0.905, 0.830, 0.894, and 0.846, respectively. With TR4 as the cut-off value, the AUC of the CAD system, junior and senior radiologists were 0.867, 0.628, and 0.722, respectively, and the CAD system had the highest AUC (P < 0.0001). With TR5 as the cut-off value, the AUC of CAD system, junior and senior radiologists were 0.867, 0.654, and 0.812, respectively, CAD system performed higher AUC than junior radiologist (P<0.0001) but comparable to senior radiologist (P=0.0709). With the assistance of the CAD system, the number of TR4 nodules was decreased by both junior and senior radiologists, the malignant rate of punctured nodules increased by 30% and 22%, and the unnecessary biopsies of benign lesions were both reduced by nearly half.
Conclusions: The CAD system based on deep learning can improve the diagnostic performance of radiologists in identifying benign and malignant thyroid nodules in the context of Hashimoto's thyroiditis and can play a role in FNA recommendations to reduce unnecessary biopsy rates.