Analysis and Comparison of the Malignant Thyroid Nodules Not Recommended for Biopsy in <scp>ACR TIRADS</scp> and <scp>AI TIRADS</scp> With a Large Sample of Surgical Series

Cui, Yiyang; Fu, Chao; Si, Caifeng; Li, Jing; Kang, Y.; Huang, Yuanjing; Cui, Kefei

doi:10.1002/jum.16132

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…In recent years, AI, especially radiomic features, has demonstrated promising potential for evaluating the characteristics of thyroid nodules ( 47 , 48 ). Radiomics has also been used to diagnose cytologically uncertain nodules ( 49 – 51 ), lymph node metastases ( 52 , 53 ), and extrathyroidal extension ( 54 ). Many studies employing AI for evaluating the thyroid are mainly based on 2D-US images ( 48 , 55 , 56 ).…”

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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Simplifying risk stratification for thyroid nodules on ultrasound: validation and performance of an artificial intelligence thyroid imaging reporting and data system

Wildman-Tobriner,

Yang,

Allen

et al. 2024

Current Problems in Diagnostic Radiology

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From Bench-to-Bedside: How Artificial Intelligence is Changing Thyroid Nodule Diagnostics, a Systematic Review

Sant,

Radhachandran,

Ivezic

et al. 2024

The Journal of Clinical Endocrinology &Amp; Metabolism

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Context Use of artificial intelligence (AI) to predict clinical outcomes in thyroid nodule diagnostics has grown exponentially over the past decade. The greatest challenge is in understanding the best model to apply to one’s own patient population, and how to operationalize such a model in practice. Evidence Acquisition A literature search of PubMed and IEEE Xplore was conducted for English language publications between January 1, 2015 and January 1, 2023 studying diagnostic tests on suspected thyroid nodules that utilized AI. We excluded articles without prospective or external validation, non-primary literature, duplicates, focused on non-nodular thyroid conditions, not using AI, and those incidentally utilizing AI in support of an experimental diagnostic outside standard clinical practice. Quality was graded by Oxford level of evidence. Evidence Synthesis A total of 61 studies were identified; all performed external validation, sixteen studies were prospective, and 33 compared a model to physician prediction of ground truth. Statistical validation was reported in 50 papers. A diagnostic pipeline was abstracted, yielding five high-level outcomes: (1) nodule localization, (2) ultrasound risk score, (3) molecular status, (4) malignancy, and (5) long-term prognosis. Seven prospective studies validated a single commercial AI; strengths included automating nodule feature assessment from ultrasound and assisting the physician in predicting malignancy risk, while weaknesses included automated margin prediction and inter-observer variability. Conclusions Models predominantly used ultrasound images to predict malignancy. Of four FDA-approved products, only S-Detect was extensively validated. Implementing an AI model locally requires data sanitization and re-validation to ensure appropriate clinical performance.

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Analysis and Comparison of the Malignant Thyroid Nodules Not Recommended for Biopsy in ACR TIRADS and AI TIRADS With a Large Sample of Surgical Series

Cited by 4 publications

References 24 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

Simplifying risk stratification for thyroid nodules on ultrasound: validation and performance of an artificial intelligence thyroid imaging reporting and data system

From Bench-to-Bedside: How Artificial Intelligence is Changing Thyroid Nodule Diagnostics, a Systematic Review

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