The New Era of TIRADSs to Stratify the Risk of Malignancy of Thyroid Nodules: Strengths, Weaknesses and Pitfalls

Russ, Gilles; Trimboli, Pierpaolo; Buffet, C

doi:10.3390/cancers13174316

Cited by 30 publications

(33 citation statements)

References 72 publications

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“…Our findings suggest that the four US-based RSSs (i.e., ACR-TIRADS, EU-TIRADS, K-TIRADS, and ATA US RSS) have suboptimal performance in managing pediatric patients with thyroid nodules, with one-half of cancers being without indication for FNA according to their recommendations. All thyroidologists, endocrinologists, and radiologists, as well as panelists of later TIRADSs, should be aware of these findings [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

Exploring the Performance of Ultrasound Risk Stratification Systems in Thyroid Nodules of Pediatric Patients

Scappaticcio

Maiorino

Iorio

et al. 2021

Cancers

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Neck ultrasound (nUS) is the cornerstone of clinical management of thyroid nodules in pediatric patients, as well as adults. The current study was carried out to explore and compare the diagnostic performance of the main US-based risk stratification systems (RSSs) (i.e., the American College of Radiology (ACR), European (EU), Korean (K) TI-RADSs and ATA US RSS criteria) for detecting malignant thyroid lesions in pediatric patients. We conducted a retrospective analysis of consecutive children and adolescents who received a diagnosis of thyroid nodule. We included subjects with age <19 years having thyroid nodules with benign cytology/histology or final histological diagnosis. We excluded subjects with (a) a previous malignancy, (b) a history of radiation exposure, (c) cancer genetic susceptibility syndromes, (d) lymph nodes suspicious for metastases of thyroid cancer at nUS, (e) a family history of thyroid cancer, or (f) cytologically indeterminate nodules without histology and nodules with inadequate cytology. We included 41 nodules in 36 patients with median age 15 years (11–17 years). Of the 41 thyroid nodules, 29 (70.7%) were benign and 12 (29.3%) were malignant. For both ACR TI-RADS and EU-TIRADS, we found a sensitivity of 41.7%. Instead, for both K-TIRADS and ATA US RSS, we found a sensitivity of 50%. The missed malignancy rate for ACR-TIRADS and EU-TIRADS was 58.3%, while that for K-TIRADS and ATA US RSS was 50%. The unnecessary FNA prevalence for ACR TI-RADS and EU-TIRADS was 58.3%, while that for K-TIRADS and ATA US RSS was 76%. Our findings suggest that the four US-based RSSs (i.e., ACR-TIRADS, EU-TIRADS, K-TIRADS, and ATA US RSS) have suboptimal performance in managing pediatric patients with thyroid nodules, with one-half of cancers without indication for FNA according to their recommendations.

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Section: Discussionmentioning

confidence: 99%

Exploring the Performance of Ultrasound Risk Stratification Systems in Thyroid Nodules of Pediatric Patients

Scappaticcio

Maiorino

Iorio

et al. 2021

Cancers

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“…This worldwide diffused approach is based on the high sensitivity and specificity of US in discriminating malignant from benign thyroid lesions. Further improvement of US performance by AI remains however desirable [ 77 , 78 ]. In addition, a not negligible number of thyroid goiters are incidentally discovered during other imaging evaluations (i.e., CT, MR, PET/CT) of patients with non-thyroid indication [ 79 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Thyroid Field—A Comprehensive Review

Bini

Pica

Azzimonti

et al. 2021

Cancers

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Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient.

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“…For the malignancy risk categorization, the interpretations were scored based on the EU TIRADS with the assistance of an online calculator for computation (www.gap.pe.kr/thyroidnodule.php (accessed on 17 October 2021)). Like most TIRADS, the common features that are indicative of high malignancy suspicion in EU TIRADS are irregular margins, marked hypoechogenicity, taller than wide shape, and microcalcifications [25]. However, for the classification of the high-risk/suspicion category, EU TIRADS requires just the presence of any of the common suspicious features or marked hypoechogenicity in a solid nodule [10,26].…”

Section: Grey Scale Ultrasound Feature Assessmentmentioning

confidence: 99%

Diagnostic Value of AngioPLUS Microvascular Imaging in Thyroid Nodule Diagnosis Using Quantitative and Qualitative Vascularity Grading

Chambara

Liu

Lo³

et al. 2022

Biomedicines

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This study investigated the diagnostic value of the Angio Planewave Ultrasensitive (AngioPLUS) Doppler ultrasound in improving the efficacy of grey scale ultrasound in thyroid nodule diagnosis. The EU TIRADS was used for the grey scale ultrasound assessment of 94 thyroid nodules. conventional Doppler and AngioPLUS Doppler ultrasound images were evaluated using qualitative vascularity grading, where predominant central vascularity indicated malignancy-suspicion, and quantitative regional vascularity assessment, where predominant peripheral vascularity using a ratio vascularity index (RVI) of > 1 indicated benign disease. Diagnostic performance outcomes of sole and combination approaches were calculated based on final pathologic results. Using sole EU TIRADS and AngioPLUS + power Doppler imaging (APDI) based on qualitative vascularity and RVI, the results were a sensitivity of 83.3% vs. 83.3 vs. 66.7% and a specificity of 50% vs. 81.3% vs. 73.4, respectively. EU TIRADS combined with APDI significantly improved the specificity using both qualitative vascularity and RVI assessment approaches (84.4% and 81%, respectively, p < 0.05); and slightly reduced the sensitivity (76.7% and 58.1%). For cytologically-equivocal thyroid nodules, the combination approach using qualitative vascularity assessment outperformed the EU TIRADS (sensitivity: both were 88.9%; specificity: 77.4% vs. 38.7%, p < 0.05; and AUROC: 0.83 vs. 0.62, p < 0.05). APDI combined with EU TIRADS is diagnostically efficient in stratifying thyroid nodules, particularly cytologically-equivocal nodules.

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The New Era of TIRADSs to Stratify the Risk of Malignancy of Thyroid Nodules: Strengths, Weaknesses and Pitfalls

Cited by 30 publications

References 72 publications

Exploring the Performance of Ultrasound Risk Stratification Systems in Thyroid Nodules of Pediatric Patients

Exploring the Performance of Ultrasound Risk Stratification Systems in Thyroid Nodules of Pediatric Patients

Artificial Intelligence in Thyroid Field—A Comprehensive Review

Diagnostic Value of AngioPLUS Microvascular Imaging in Thyroid Nodule Diagnosis Using Quantitative and Qualitative Vascularity Grading

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