Risk for malignancy of thyroid nodules: Comparative study between TIRADS and US based classification system

Ashamallah, Germeen Albair; EL-Adalany, Mohamed Ali

doi:10.1016/j.ejrnm.2016.08.021

Cited by 7 publications

(7 citation statements)

References 10 publications

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“…9 Horvath et al proposed TI-RADS for ultrasonographic evaluation and classification of patients with thyroid nodule prior to FNAC/FNA biopsy to safeguard them from unwanted complications. 10,11 Some studies found TI-RADS classification as a sensitive method for identifying patients with thyroid cancer, 12,13 while others advocated the need of cancer prediction with more sensitive gold-standard technique of FNAB. 13 This study was planned in the context of these controversies and unavailability of relevant data in local settings.…”

Section: Original Articlementioning

confidence: 99%

Comparison of TI-RADS classification with FNAC for the Diagnosis of Thyroid Nodules

Malik¹,

Rauf²,

Malik

2020

JIMDC

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Background: Thyroid nodules are very common and its prevalence is largely dependent on the identification techniques. Recently high-resolution ultrasound thyroid imaging has paved the way for significant transformation in clinical approach to thyroid nodule. This study aimed to determine the efficacy of TI-RADS classification and its association with FNAC findings in thyroid lesions. Material and Methods: This prospective study was carried out in the Department of Radiology of Islamabad Diagnostic Centre, Islamabad for a period of 6 months from 6th January 2018 to 6th July, 2018. All male and female patients presenting with thyroid nodules were selected for the study. Ultrasound neck was performed with high frequency linear probes. Ultrasonography findings were classified according to Thyroid Imaging Reporting and Data System (TI-RADS) classification, as defined by Horvath et al. Patients with TI-RADS II-V were scheduled for US-guided Fine Needle Aspiration (FNA). All the specimens were sent to pathology laboratory for cytology (FNAC). Results: Total 123 patients of thyroid nodules were studied. Mean age of the patients was 45.78 ± 13.11 years, with a female predominance (73.2%). A significant association was seen between TI-RADS classification of thyroid nodules and findings on cytology. Thyroid nodules with TI-RADS II, III and IV a classification showed benign cytological findings, while TI-RADS class V had a significant association with malignant findings on cytology (P=0.001). Conclusions: TI-RADS classification is a reliable modality in differentiating benign thyroid nodules from malignant ones and circumvent the need for FNAC in every case with a thyroid nodule.

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Section: Original Articlementioning

confidence: 99%

Comparison of TI-RADS classification with FNAC for the Diagnosis of Thyroid Nodules

Malik¹,

Rauf²,

Malik

2020

JIMDC

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“…None of the pre-operative investigations can confirm the diagnosis of FTC [ 89 , 102 , 103 ]. The majority of patients who have FTC are euthyroid, and the ultrasound examination is likely to show the same worrisome malignancy that is seen in PTC [ 104 , 105 , 106 , 107 , 108 ]. Among the worrisome features for malignancy on ultrasound of FTC is a solid hypoechoic lesion that is taller than wide and has increased intra-nodal vascularity, micro-calcifications, and an irregular border [ 104 , 105 , 106 , 107 , 109 , 110 ].…”

Section: Diagnosis and Management Of Follicular Carcinomamentioning

confidence: 99%

Multi-Omics and Management of Follicular Carcinoma of the Thyroid

et al. 2023

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Follicular thyroid carcinoma (FTC) is the second most common cancer of the thyroid gland, accounting for up to 20% of all primary malignant tumors in iodine-replete areas. The diagnostic work-up, staging, risk stratification, management, and follow-up strategies in patients who have FTC are modeled after those of papillary thyroid carcinoma (PTC), even though FTC is more aggressive. FTC has a greater propensity for haematogenous metastasis than PTC. Furthermore, FTC is a phenotypically and genotypically heterogeneous disease. The diagnosis and identification of markers of an aggressive FTC depend on the expertise and thoroughness of pathologists during histopathological analysis. An untreated or metastatic FTC is likely to de-differentiate and become poorly differentiated or undifferentiated and resistant to standard treatment. While thyroid lobectomy is adequate for the treatment of selected patients who have low-risk FTC, it is not advisable for patients whose tumor is larger than 4 cm in diameter or has extensive extra-thyroidal extension. Lobectomy is also not adequate for tumors that have aggressive mutations. Although the prognosis for over 80% of PTC and FTC is good, nearly 20% of the tumors behave aggressively. The introduction of radiomics, pathomics, genomics, transcriptomics, metabolomics, and liquid biopsy have led to improvements in the understanding of tumorigenesis, progression, treatment response, and prognostication of thyroid cancer. The article reviews the challenges that are encountered during the diagnostic work-up, staging, risk stratification, management, and follow-up of patients who have FTC. How the application of multi-omics can strengthen decision-making during the management of follicular carcinoma is also discussed.

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“…We use histopathological images and received 99.00% classification accuracy. A comparative study [32] was conducted to classify the thyroid nodules by using ultrasound. The nodules were classified in five categories as proposed by [30] and the best accuracy was recorded 89% for class five; whereas we propose a system, which is fully automated and micro architectural level features such as analysis of nucleus is considered as key component to predict the malignant nodules existing in the bunch of nuclei on a FNAB (Fine Needle Aspiration Cytology).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Most of times medullary thyroid cancer may palpable at advance stages and it may become very difficult to treat and cure it. In literature [26], [27], [28], [29], [30], [31], [32] some approaches are seen to classify the A medical images of ultrasonic and cytological material where above stated optimization may enhance the quality of CAD systems. This paper proposes a novel approach so called "Decision Support System for Classification of Thyroid Medullary Cancer", which offers a classification system to predict malignant behaviors of MTC and visualizes eccentric nuclear behaviors within cells of MTC.…”

Section: Introductionmentioning

confidence: 99%

Decision Support System for Classification Medullary Thyroid Cancer

2020

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Due to the complex, heterogeneous and mimic morphological features of medullary thyroid cancer (MTC). It becomes often difficult to diagnose MTC at early stage. Since histopathological complex patterns of cancerous cells and tissues requires a huge effort to classify. Therefore thyroid cancer classification has become one of the significant research area area(s) of Machine Learning. We propose a decision support system to classify initial variation of morphological appearance of nuclei by using Convolutional Neural Networks (CNNs). The system comprises over three major layers, where image preprocessing techniques are used at top layer along with feature selection techniques. Classification model constructed by using CNNs at the second layer and result visualization described at third layer. Due to the unavailability of datasets for medullary thyroid cancer in literature, this research uses real-world datasets consisting upon 20GB cytological medical images and the approximated classification accuracy is measured about 99.00%. Malignant and non-malignant cells are visualized to assist the doctors in better way.

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Risk for malignancy of thyroid nodules: Comparative study between TIRADS and US based classification system

Cited by 7 publications

References 10 publications

Comparison of TI-RADS classification with FNAC for the Diagnosis of Thyroid Nodules

Comparison of TI-RADS classification with FNAC for the Diagnosis of Thyroid Nodules

Multi-Omics and Management of Follicular Carcinoma of the Thyroid

Decision Support System for Classification Medullary Thyroid Cancer

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