Risk Stratification of Thyroid Nodules: From Ultrasound Features to TIRADS

Rago, Teresa; Vitti, Paolo

doi:10.3390/cancers14030717

Cited by 22 publications

(19 citation statements)

References 86 publications

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“…Several studies analyzed the US features and ROM for cystic lesions, combining them with the cytologic classification systems, mostly with the Bethesda thyroid system 30–33…”

Section: Cystic Lesions In Ultrasound Classsification Systemsmentioning

confidence: 99%

Approach to FNA of Thyroid Gland Cysts

Rossi¹,

Tralongo²,

Fiorentino³

et al. 2022

Advances in Anatomic Pathology

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Fine needle aspiration is a well-known procedure for the diagnosis and management of thyroid lesions, representing the first diagnostic tool for the definition of their nature. In clinical practice, a thyroid nodule can be classified as solid, cystic, and partially cystic based on its internal components. Different thyroid imaging reporting systems and cytologic diagnostic systems have focused their attention on solid nodules, which are more frequently linked with a malignant outcome. In fact, numerous papers demonstrated that nodules with microcalcifications, a taller-than-wide shape, hypoechogeneity, and irregular margins, are more likely to be malignant on histology. Nevertheless, according to the literature, the risk of malignancy in a partially cystic thyroid nodule ranges between 3.3 and 17-5%, including, for instance, the possible diagnosis of a cystic papillary thyroid carcinoma and other malignant entities. Therefore, in the current review article, we are going to discuss the approach to thyroid cystic lesions on fine needle aspiration cytology.

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“…Several studies analyzed the US features and ROM for cystic lesions, combining them with the cytologic classification systems, mostly with the Bethesda thyroid system 30–33…”

Section: Cystic Lesions In Ultrasound Classsification Systemsmentioning

confidence: 99%

Approach to FNA of Thyroid Gland Cysts

Rossi¹,

Tralongo²,

Fiorentino³

et al. 2022

Advances in Anatomic Pathology

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“…highly positive associations with malignancy, especially in combination with other suspicious features (3,5).…”

Section: Figurementioning

confidence: 99%

“…The incidence of sonographically detected thyroid nodules is increasing in individuals; approximately 50% to 68% can be detected in healthy individuals. Most of these nodules are benign and asymptomatic (1)(2)(3), and only about 8% to 16% are malignant nodules (4)(5)(6). Due to the complexity and diversity of thyroid nodules, it is challenging for doctors to distinguish which nodules harbor clinically relevant malignancies (7).…”

Section: Introductionmentioning

confidence: 99%

Incorporation of a machine learning pathological diagnosis algorithm into the thyroid ultrasound imaging data improves the diagnosis risk of malignant thyroid nodules

Hong

Fang

et al. 2022

Front. Oncol.

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ObjectiveThis study aimed at establishing a new model to predict malignant thyroid nodules using machine learning algorithms.MethodsA retrospective study was performed on 274 patients with thyroid nodules who underwent fine-needle aspiration (FNA) cytology or surgery from October 2018 to 2020 in Xianyang Central Hospital. The least absolute shrinkage and selection operator (lasso) regression analysis and logistic analysis were applied to screen and identified variables. Six machine learning algorithms, including Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Naive Bayes Classifier (NBC), Random Forest (RF), and Logistic Regression (LR), were employed and compared in constructing the predictive model, coupled with preoperative clinical characteristics and ultrasound features. Internal validation was performed by using 10-fold cross-validation. The performance of the model was measured by the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, F1 score, Shapley additive explanations (SHAP) plot, feature importance, and correlation of features. The best cutoff value for risk stratification was identified by probability density function (PDF) and clinical utility curve (CUC).ResultsThe malignant rate of thyroid nodules in the study cohort was 53.2%. The predictive models are constructed by age, margin, shape, echogenic foci, echogenicity, and lymph nodes. The XGBoost model was significantly superior to any one of the machine learning models, with an AUC value of 0.829. According to the PDF and CUC, we recommended that 51% probability be used as a threshold for determining the risk stratification of malignant nodules, where about 85.6% of patients with malignant nodules could be detected. Meanwhile, approximately 89.8% of unnecessary biopsy procedures would be saved. Finally, an online web risk calculator has been built to estimate the personal likelihood of malignant thyroid nodules based on the best-performing ML-ed model of XGBoost.ConclusionsCombining clinical characteristics and features of ultrasound images, ML algorithms can achieve reliable prediction of malignant thyroid nodules. The online web risk calculator based on the XGBoost model can easily identify in real-time the probability of malignant thyroid nodules, which can assist clinicians to formulate individualized management strategies for patients.

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“…In considering whether thyroid nodules should be subjected to FNA, a US-based RSS plays an essential role [2,16,17]. Until now, there has been not enough scienti c evidence supporting the size threshold of each guideline.…”

Section: Diagnostic Performances Of the Two Tirads And The Modi Ed C ...mentioning

confidence: 99%

comparison of the diagnostic performance of the ACR and C TIRADS applying the ACR TIRADS’ thresholds for FNAcomparison of the diagnostic performance of the ACR and C TIRADS applying the ACR TIRADS’ thresholds for FNA

Cui

et al. 2022

Preprint

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Objective To investigate the diagnostic performances and unnecessary fine needle aspiration (FNA) rates of two point-scale based TIRADS and compare them with a modified version using the ACR TIRADS’ size thresholds. Methods Our Institutional Review Board approved this retrospective study and waived the requirement for informed consent. A total of 2083 thyroid nodules 10 mm or larger in size in 1779 patients with definitive pathological findings were included. Ultrasonography categories were assigned according to each guideline. We applied the ACR TIRADS’ size thresholds for FNA to the C TIRADS and defined it as the modified C TIRADS (mC TIRADS). Diagnostic performances and unnecessary FNA rates were evaluated for the original and modified guidelines. Results Of the original guidelines, the ACR TIRADS had higher specificity, accuracy, and area under the receiver operating characteristic curve (AUC) (51.6%, 67.7%, and 0.723, respectively). When the size threshold of the ACR TIRADS was applied to the C TIRADS, the resultant mC TIRADS had higher specificity and accuracy than the ACR (56.1% vs 51.6%,68.7% vs 67.7%). The mC TIRADS had similar unnecessary FNA rate and AUC with the ACR TIRADS (43.7% and 45%, 0.722 vs 0.723，respectively). The false-negative rate of the C TIRADS was the lowest (2.0%) among all TIRADS. Conclusion The modified C TIRADS incorporating the size thresholds of the ACR showed higher diagnostic performance and a lower unnecessary FNA rate than the original point-scale based TIRADS.

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Risk Stratification of Thyroid Nodules: From Ultrasound Features to TIRADS

Cited by 22 publications

References 86 publications

Approach to FNA of Thyroid Gland Cysts

Approach to FNA of Thyroid Gland Cysts

Incorporation of a machine learning pathological diagnosis algorithm into the thyroid ultrasound imaging data improves the diagnosis risk of malignant thyroid nodules

comparison of the diagnostic performance of the ACR and C TIRADS applying the ACR TIRADS’ thresholds for FNAcomparison of the diagnostic performance of the ACR and C TIRADS applying the ACR TIRADS’ thresholds for FNA

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