The analysis of differential diagnosis of benign and malignant thyroid nodules based on ultrasound reports

Miao, Shumei; Mang, Jing; Sheng, Rongrong; Cui, Dai; Lu, Shan; Zhang, Xin; Jing, Shenqi; Zhang, Xiaoliang; Shan, Tao; Shan, Hong‐Wei; Xu, Tingyu; Wang, Bing; Wang, Zhongmin; Liu, Yun

doi:10.21037/gs.2020.04.03

Cited by 13 publications

(9 citation statements)

References 22 publications

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“…The tendency to score most nodules in the higher categories transmits most of the diagnostic differentiation to cytological evaluation, leaving US without significant contribution in the clinical decision-making. A downstaging of the TIRADS scores would probably not give a better selection but could result in low NPV consistent with other reports ( 19 ). We believe that the role of TIRADS is outside thyroid competence centers, where it can be very useful for the selection of patients that need referral or FNC.…”

Section: Discussionsupporting

confidence: 89%

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Prediction of thyroid nodule histopathology by expert ultrasound evaluation

Håskjold

Foshaug

Iversen

et al. 2021

Endocrine Connections

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Objective: The basis of thyroid nodule diagnostics is ultrasound guided fine needle biopsy with cytological evaluation (FNC), if US appearance is not clearly benign. The aim of this study was to investigate the predictive potential of dedicated, expert high resolution ultrasound, to see if histopathological entities of thyroid nodules can be diagnosed without invasive FNC biopsies. Design: Prospective case cohort study. Methods: 180 patients with 221 thyroid nodules were examined with ultrasound and prospectively assigned to the expected histopathological diagnosis: colloid nodule, adenomatoid colloid nodule, follicular adenoma, follicular carcinoma, follicular variant of papillary thyroid carcinoma, papillary thyroid carcinoma, or other thyroid cancer. In 101 of these, we later obtained histopathological reports for comparison. Results: Overall accuracy for classification into discrete histopathological categories by expert ultrasound was 71.3% and Cohen’s Kappa was 0.62. The sensitivity and specificity for detecting malignancy was 97.3% and 78.1%. The diagnostic accuracy for malignancy was 85.1%. ACR-TIRADS scores for the same nodules had a sensitivity of 97.3%, specificity of 26.6%, and accuracy of 52.5%. Conclusion: Dedicated expert high-resolution ultrasound without FNC can reliably distinguish benign versus malignant nodules, but also differentiate between several histopathological entities in thyroid nodules. There is potential for a reduction in the number of invasive FNC biopsies and diagnostic operations.

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Section: Discussionsupporting

confidence: 89%

“…Copyright © 2019 the authors downstaging of the TIRADS scores would probably not give better selection but could result in low NPV consistent with other reports (19). We believe that the role of TIRADS is outside thyroid competence centers, where it can be very useful for selection of patients that need referral or FNC.…”

Section: Discussionsupporting

confidence: 72%

Prediction of thyroid nodule histopathology by expert ultrasound evaluation

Håskjold

Foshaug

Iversen

et al. 2021

Endocrine Connections

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“…All patients did not receive preoperative radiotherapy or chemotherapy prior to the study. Benign or malignant thyroid nodules were diagnosed based on pathological features by the pathologists as aspiration of thyroid nodules was used for primary diagnosis on TC, which was confirmed in a previous study (17). Patients with Tc were divided into the high-ccAT2 group and low-ccAT2 group according to the median level of tissue ccAT2 (n=15 per group).…”

Section: Methodsmentioning

confidence: 96%

Knockdown of long non‑coding RNA CCAT2 suppresses the progression of thyroid cancer by inhibiting the Wnt/β‑catenin pathway

Suping

2020

Int J Mol Med

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Thyroid cancer (TC) is one of the most common malignancies with a high mortality rate. Long non-coding RNA CCAT2 (CCAT2) participates in the occurrence and development of certain human cancers; however, whether it is involved in TC remains unclear. Thus, the present study investigated the role of CCAT2 in TC and the underlying mechanism. CCAT2 expression in both TC tissues and cell lines was examined by reverse transcription-quantitative PCR. CCAT2 expression was silenced in TC cell lines by a specific small interfering (si)RNA against CCAT2 (si-CCAT2). The effects of CCAT2 silencing on TC cell proliferation were detected by CCK-8 and colony formation assays. Cell cycle and apoptosis of the treated TC cells were assessed by flow cytometry. Wound healing and Transwell assays were performed to detect the effects of si-CCAT2 on the migration and invasion of TC cells. Apoptosis-related proteins and Wnt/β-catenin cascade-associated agents were examined by western blotting. The interaction between CCAT2 and the Wnt/β-catenin pathway in the transfected cells was detected by performing a dual-luciferase reporter assay. CCAT2 expression was increased in TC tissue samples and cell lines compared with the controls. Tissue CCAT2 level was associated with T stage and tumor-node-metastasis stage of TC. Silencing CCAT2 inhibited TC cell proliferation, migration and invasion, and promoted TC cell cycle arrest and apoptosis. Furthermore, CCAT2 knockdown suppressed the activity of the Wnt/β-catenin cascade in TC cells treated with lithium chloride. In summary, the present study demonstrated that CCAT2 knockdown suppresses TC progression via inactivating the Wnt/β-catenin cascade, indicating that suppressing CCAT2 and the Wnt/β-catenin signaling pathway may be a promising therapeutic strategy for treating TC.

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“…In 2020, Miao et al used logistic regression to analyze the variables that significantly affected malignant nodules on the data of 811 patients with a total of 1290 pathologically confirmed nodules (506 benign and 784 malignant). The sensitivity and specificity of ultrasound thyroid imaging reporting and data system classification results for benign and malignant tumors were calculated [ 14 ]. In 2020, Ataide et al aimed to reduce subjectivity in the current diagnostic process by using geometric and morphological (G-M) features that represent the visual characteristics of thyroid nodules to provide physicians with decision support [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Thyroid Nodules Based on Image Enhancement and Deep Neural Networks

Zhang

2022

Computational Intelligence and Neuroscience

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The diagnosis of thyroid nodules at an early stage is a challenging task. Manual diagnosis of thyroid nodules is labor-intensive and time-consuming. Meanwhile, due to the difference of instruments and technical personnel, the original thyroid nodule ultrasound images collected are very different. In order to make better use of ultrasound image information of thyroid nodules, some image processing methods are indispensable. In this paper, we developed a method for automatic thyroid nodule classification based on image enhancement and deep neural networks. The selected image enhancement method is histogram equalization, and the neural networks have four-layer network nodes in our experiments. The dataset in this paper consists of thyroid nodule images of 508 patients. The data are divided into 80% training and 20% validation sets. A comparison result demonstrates that our method can achieve a better performance than other normal machine learning methods. The experimental results show that our method has achieved 0.901961 accuracy, 0.894737 precision, 1 recall, and 0.944444 F1-score. At the same time, we also considered the influence of network structure, activation function of network nodes, number of training iterations, and other factors on the classification results. The experimental results show that the optimal network structure is 2500-40-2-1, the optimal activation function is logistic function, and the best number of training iterations is 500.

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The analysis of differential diagnosis of benign and malignant thyroid nodules based on ultrasound reports

Cited by 13 publications

References 22 publications

Prediction of thyroid nodule histopathology by expert ultrasound evaluation

Prediction of thyroid nodule histopathology by expert ultrasound evaluation

Knockdown of long non‑coding RNA CCAT2 suppresses the progression of thyroid cancer by inhibiting the Wnt/β‑catenin pathway

Diagnosis of Thyroid Nodules Based on Image Enhancement and Deep Neural Networks

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