Comparison of Various Ultrasound-Based Malignant Risk Stratification Systems on an Occasion for Assessing Thyroid Nodules in Hashimoto’s Thyroiditis

Zhao, Tianxue; Zhang, Xianfeng; Xu, Chenke

doi:10.2147/ijgm.s398601

Cited by 3 publications

(2 citation statements)

References 42 publications

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“…Traditional diagnostic methods for thyroid nodules, such as 2D-US, color Doppler flow imaging (CDFI), elastography, and FNA, have many disadvantages ( 35 – 37 ); the main ones are severe overdiagnosis and overtreatment ( 38 , 39 ). For example, some Hashimoto’s nodules may show hypoechogenicity with blurred margins on 2D-US, which may be classified as TIRADS >4 and require unnecessary FNA according to the guidelines ( 7 , 40 , 41 ). CEUS, as a novel noninvasive microangiography technology, can reveal microvasculature with a smaller diameter (>40 µm) than that by CDFI (>100 µm) and is helpful in the detection of malignant thyroid nodules ( 42 , 43 ).…”

Section: Discussionmentioning

confidence: 99%

Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five contrast-enhanced ultrasound key frames

Chen,

Zhang,

Zhu

et al. 2024

Front. Endocrinol.

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ObjectivesTo apply machine learning to extract radiomics features from thyroid two-dimensional ultrasound (2D-US) combined with contrast-enhanced ultrasound (CEUS) images to classify and predict benign and malignant thyroid nodules, classified according to the Chinese version of the thyroid imaging reporting and data system (C-TIRADS) as category 4.Materials and methodsThis retrospective study included 313 pathologically diagnosed thyroid nodules (203 malignant and 110 benign). Two 2D-US images and five CEUS key frames (“2nd second after the arrival time” frame, “time to peak” frame, “2nd second after peak” frame, “first-flash” frame, and “second-flash” frame) were selected to manually label the region of interest using the “Labelme” tool. A total of 7 images of each nodule and their annotates were imported into the Darwin Research Platform for radiomics analysis. The datasets were randomly split into training and test cohorts in a 9:1 ratio. Six classifiers, namely, support vector machine, logistic regression, decision tree, random forest (RF), gradient boosting decision tree and extreme gradient boosting, were used to construct and test the models. Performance was evaluated using a receiver operating characteristic curve analysis. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), and F1-score were calculated. One junior radiologist and one senior radiologist reviewed the 2D-US image and CEUS videos of each nodule and made a diagnosis. We then compared their AUC and ACC with those of our best model.ResultsThe AUC of the diagnosis of US, CEUS and US combined CEUS by junior radiologist and senior radiologist were 0.755, 0.750, 0.784, 0.800, 0.873, 0.890, respectively. The RF classifier performed better than the other five, with an AUC of 1 for the training cohort and 0.94 (95% confidence interval 0.88–1) for the test cohort. The sensitivity, specificity, accuracy, PPV, NPV, and F1-score of the RF model in the test cohort were 0.82, 0.93, 0.90, 0.85, 0.92, and 0.84, respectively. The RF model with 2D-US combined with CEUS key frames achieved equivalent performance as the senior radiologist (AUC: 0.94 vs. 0.92, P = 0.798; ACC: 0.90 vs. 0.92) and outperformed the junior radiologist (AUC: 0.94 vs. 0.80, P = 0.039, ACC: 0.90 vs. 0.81) in the test cohort.ConclusionsOur model, based on 2D-US and CEUS key frames radiomics features, had good diagnostic efficacy for thyroid nodules, which are classified as C-TIRADS 4. It shows promising potential in assisting less experienced junior radiologists.

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Section: Discussionmentioning

confidence: 99%

Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five contrast-enhanced ultrasound key frames

Chen,

Zhang,

Zhu

et al. 2024

Front. Endocrinol.

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“…Its main pathological feature is that the normal thyroid follicular structure is widely replaced by lymphocytes, plasma cells, and lymphoid germinal centers, producing various antibodies such as antioxidant enzyme antibodies. [39,40] Different thyroid parenchyma backgrounds, thyroid blood flow, and hormone levels are different.…”

Section: Hashimoto Thyroiditis (Ht)mentioning

confidence: 99%

Advances in influence factors of ultrasound-guided percutaneous thermal ablations for benign thyroid nodules: A review

Wang,

Zheng

2024

Medicine

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Ultrasound-guided percutaneous thermal ablation is a safe and effective minimally invasive treatment for benign thyroid nodules, which is now widely used in the world. Studies have found that some preoperative factors played an important role in the outcome of thermal ablation. This paper mainly reviews the various factors affecting the efficacy of ultrasound-guided percutaneous thermal ablation in the treatment of benign thyroid nodules to provide a variety of perspectives for the clinical and to promote the postoperative outcome of patients.

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Association of autoimmune thyroid disease with type 1 diabetes mellitus and its ultrasonic diagnosis and management

Wang,

Wan,

Chang

et al. 2024

World J Diabetes

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As a common hyperglycemic disease, type 1 diabetes mellitus (T1DM) is a complicated disorder that requires a lifelong insulin supply due to the immune-mediated destruction of pancreatic β cells. Although it is an organ-specific autoimmune disorder, T1DM is often associated with multiple other autoimmune disorders. The most prevalent concomitant autoimmune disorder occurring in T1DM is autoimmune thyroid disease (AITD), which mainly exhibits two extremes of phenotypes: hyperthyroidism [Graves' disease (GD)] and hypo-thyroidism [Hashimoto's thyroiditis, (HT)]. However, the presence of comorbid AITD may negatively affect metabolic management in T1DM patients and thereby may increase the risk for potential diabetes-related complications. Thus, routine screening of thyroid function has been recommended when T1DM is diagnosed. Here, first, we summarize current knowledge regarding the etiology and pathogenesis mechanisms of both diseases. Subsequently, an updated review of the association between T1DM and AITD is offered. Finally, we provide a relatively detailed review focusing on the application of thyroid ultrasonography in diagnosing and managing HT and GD, suggesting its critical role in the timely and accurate diagnosis of AITD in T1DM.

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Comparison of Various Ultrasound-Based Malignant Risk Stratification Systems on an Occasion for Assessing Thyroid Nodules in Hashimoto’s Thyroiditis

Cited by 3 publications

References 42 publications

Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five contrast-enhanced ultrasound key frames

Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five contrast-enhanced ultrasound key frames

Advances in influence factors of ultrasound-guided percutaneous thermal ablations for benign thyroid nodules: A review

Association of autoimmune thyroid disease with type 1 diabetes mellitus and its ultrasonic diagnosis and management

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