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

Abstract

Objectives: This study aims to investigate the efficacy of a computer-aided diagnosis (CAD) system in distinguishing between benign and malignant thyroid nodules in the context of Hashimoto's thyroiditis (HT) and to evaluate the role of the 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 ± 11.8 years) who were histopathologically diagnosed with HT. The two-dimensional ultrasound images and videos of all thyroid nodules were analyzed by the CAD system and two radiologists with different experiences according to ACR TI-RADS. The diagnostic cutoff values of ACR TI-RADS were divided into two categories (TR4 and TR5), and then the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of the CAD system and the 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 cutoff value, the AUCs of the CAD system and the 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 cutoff value, the AUCs of the CAD system and the junior and senior radiologists were 0.867, 0.654, and 0.812, respectively, and the CAD system had a higher AUC than the junior radiologist (P < 0.0001) but comparable to the 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.

Keywords: Hashimoto’s thyroiditis; computer-aided diagnosis; thyroid nodule; ultrasound; unnecessary biopsy.

Figure 1

(A) 2D ultrasound image of a malignant thyroid nodule with HT; (B) the corresponding diagnostic results of the CAD system. (C) 2D ultrasound image of a benign thyroid nodule with HT; (D) the corresponding diagnostic results of the CAD system. The numbers in the figure are the benign and malignant rates produced by the CAD system. As the numbers from this CAD system are all greater than 95%, these numbers are not significant in our study.

Figure 2

(A) A malignant thyroid nodule measuring 11 × 9 mm. The ACR TI-RADS of the nodule given by two radiologists were all TR4, and follow-up was recommended. (B) The CAD system indicated that the nodule was malignant, and the modified ACR TI-RADS was TR5, with a recommendation for FNA. (C) A benign thyroid nodule measuring 15 × 9 mm. The ACR TI-RADS of the nodule given by the junior radiologist was TR4, and FNA was recommended. (D) The CAD system indicated that the nodule was benign, and the modified ACR TI-RADS was TR3, with a recommendation for follow-up. The numbers in the figure are the benign and malignant rates produced by the CAD system. As the numbers from this CAD system are all greater than 95%, these numbers are not significant in our study.

Figure 3

Comparison of the ROC curves between CAD and the two radiologists when TR4 was used as the cutoff value.

Figure 4

Comparison of the ROC curves between CAD and the two radiologists when TR5 was used as the cutoff value.