Using Deep Neural Network to Diagnose Thyroid Nodules on Ultrasound in Patients With Hashimoto’s Thyroiditis

Hou, Yiqing; Chen, Chao; Zhang, Lu; Zhou, Wei; Lu, Qinyang; Jia, Xiaohong; Zhang, Jingwen; Guo, Cen; Qin, Yuxiang; Zhu, Li; Zuo, Ming J.; Xiao, Jing; Huang, Lingyun; Zhan, Wei

doi:10.3389/fonc.2021.614172

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…Hou et al. used a deep learning-based CAD system to distinguish nodules in the context of HT, and the AUC was significantly higher than that of three groups of radiologists with different years of experience (all P < 0.05) ( 31 ). The above studies demonstrated the diagnostic feasibility of CAD systems based on deep learning for nodules in HT, but only static images of the largest section of the nodule were input into the CAD system.…”

Section: Discussionmentioning

confidence: 99%

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

Gong

Zhou

et al. 2023

Front. Oncol.

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ObjectivesThis 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.MethodsWe 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.ResultsThe 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.ConclusionsThe 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.

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Section: Discussionmentioning

confidence: 99%

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

Gong

Zhou

et al. 2023

Front. Oncol.

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“…As most of the nodules with benign features on grey‐scale ultrasound and follow‐up observation are not recommended for further CEUS and FNA, resulting in a high rate of malignancy in the included nodules. In addition, a previous study 21 suggested that the background of Hashimoto's thyroiditis may affect the diagnostic accuracy of AI, but this measure was not included in this study, and further studies will be conducted at a later stage.…”

Section: Discussionmentioning

confidence: 99%

Differential diagnosis of thyroid nodules by the Demetics ultrasound‐assisted diagnosis system and contrast‐enhanced ultrasound combined with thyroid image reporting and data systems

Wang

Nie

Wang

et al. 2022

Clinical Endocrinology

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Background: More and more new ultrasound techniques with their own characteristics are applied in the differential diagnosis of thyroid nodules. This study amied to assess and compare the diagnostic value of the Demetics ultrasound-assisted diagnosis system and contrast-enhanced ultrasound (CEUS) combined with the Thyroid Image Reporting and Data Systems (TI-RADS) for thyroid nodules. Design and Patients: A total of 600 thyroid nodules with pathological findings were retrospectively analysed. Demetics and CEUS were performed for all nodules. The diagnostic efficacy of Demetics and CEUS for nodules of different sizes was evaluated and compared in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+) and negative likelihood ratio (LR−). The characteristics of nodules diagnosed and misdiagnosed by Demetics were compared to analyse the factors affecting the diagnostic accuracy of Demetics. The necessity of CEUS for nodules that are prone to misdiagnosis in Demetics was assessed.Results: Both Demetics and CEUS can be used for the differential diagnosis of benign and malignant thyroid nodules of different sizes. The diagnostic agreement between Demetics and CEUS for thyroid nodules of different sizes was moderate, substantial and fair, respectively. The sensitivity and NPV of Demetics were higher than those of CEUS, and the specificity, PPV and LR+ of CEUS were higher than that of Demetics. The LR − of Demetics was lower than those of CEUS. There were significant differences in age, calcification and margin in analysing the factors affecting Demetics. CEUS correctly diagnosed 50 of the 101 nodules misdiagnosed by Demetics.Conclusions: Demetics showed high sensitivity in diagnosing thyroid nodules, while CEUS showed high specificity. In clinical practice, CEUS can further improve the diagnostic accuracy for nodules that are easily misdiagnosed by Demetics.

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“…However, HT is more difficult for radiologists to recognize compared with nodules. Additionally, HT is commonly combined with thyroid nodules, such as nodule goiter, adenoma, and cancer ( 31 ), whereas HT with thyroid nodules can induce significant interference in the US diagnosis of thyroid cancer ( 32 ). Thus, improving the accuracy of ultrasonic identification of HT can play a prominent role, which may not be limited to the early diagnosis of the disease only, and can also aid in the identification of thyroid nodules.…”

Section: Discussionmentioning

confidence: 99%

Convolutional Neural Network-Based Computer-Assisted Diagnosis of Hashimoto’s Thyroiditis on Ultrasound

Zhao

Kang

Qian

et al. 2021

The Journal of Clinical Endocrinology &Amp; Metabolism

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Purpose This study investigates the efficiency of deep learning models in the automated diagnosis of Hashimoto’s thyroiditis (HT) using real-world ultrasound data from ultrasound examinations by computer-assisted diagnosis (CAD) with artificial intelligence. Methods We retrospectively collected ultrasound images from patients with and without HT from two hospitals in China between September 2008 and February 2018. Images were divided into a training set (80%) and a validation set (20%). We ensembled nine convolutional neural networks (CNNs) as the final model (CAD-HT) for HT classification. The model’s diagnostic performance was validated and compared from two hospital validation sets. We also compared the accuracy of CAD-HT against seniors/junior radiologists. Subgroup analysis of CAD-HT performance in different thyroid hormone levels, such as hyperthyroidism, hypothyroidism, and euthyroidism, was also evaluated. Results 39280 ultrasound images from 21118 patients were included in this study. The accuracy, sensitivity, specificity of the ensemble HT-CAD model were 0.892, 0.890 and 0.895, respectively. HT-CAD performance between two hospitals was not significantly different. The HT-CAD model achieved a higher performance (P < 0.001) when compared to senior radiologists, with a nearly 9% accuracy improvement. HT-CAD had almost similar accuracy (from 0.871 to 0.894) among the three differences of thyroid hormone level subgroups. Conclusion The HT-CAD strategy based on CNN significantly improved the radiologists’ diagnostic accuracy on HT. Our model demonstrates good performance and robustness in different hospitals and for different thyroid hormone levels.

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Using Deep Neural Network to Diagnose Thyroid Nodules on Ultrasound in Patients With Hashimoto’s Thyroiditis

Cited by 6 publications

References 26 publications

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

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

Differential diagnosis of thyroid nodules by the Demetics ultrasound‐assisted diagnosis system and contrast‐enhanced ultrasound combined with thyroid image reporting and data systems

Convolutional Neural Network-Based Computer-Assisted Diagnosis of Hashimoto’s Thyroiditis on Ultrasound

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