Can Nodular Hyperplasia of the Thyroid Gland be Differentiated From Follicular Adenoma and Follicular Carcinoma by Ultrasonography?

Expression data of FOS and JUN genes and FTIR spectra provide diagnosis of thyroid carcinoma

We explore the feasibility of using FOS and JUN gene expression and ATR-FTIR for diagnosis of thyroid cancer. For the study, 38 samples (6 non-neoplastic (NN), 10 papillary thyroid carcinoma (PTC), 7 follicular thyroid carcinoma (FTC), and 15 benign tumors (BT) were subjected to RNA extraction followed by quantitative real time PCR (qRT-PCR) and 30 samples (5 NN, 9 PTC, 5 FTC, and 11 BT) were used for Attenuated Total Reflectance - Fourier Transform Infrared (ATR-FTIR) followed by multivariate analysis. Of the above, 20 samples were used for both gene expression and ATR-FTIR studies. We found FOS and JUN expression in malignant tumor samples to be significantly lower than NN and benign. ATR-FIR after multivariate analysis could identify the difficult to diagnose FTC with 93 % efficiency. Overall, results suggest the diagnostic potential of molecular biology techniques combined with ATR-FTIR spectroscopy in differentiated thyroid carcinomas (PTC and FTC) and BT.

Visualizing Dipeptidyl Peptidase-IV with an Advanced Non-π-Conjugated Fluorescent Probe for Early Thyroid Disease Diagnosis

Early detection and effective treatment of thyroid cancer are vital due to the aggressiveness and high mortality rate of the cancer. Nevertheless, the exploration of dipeptidyl peptidase-IV (DPP-IV) as a biomarker for thyroid diseases has not been widely conducted. In this study, we developed a novel non-π-conjugated near-infrared fluorescent probe, MB-DPP4, specifically designed to visualize and detect endogenous DPP-IV. Traditional DPP-IV-specific fluorescent probes rely primarily on the intramolecular charge transfer mechanism. For this reason, these probes are often hampered by high background levels that can inhibit their ability to achieve a fluorescence turn-on effect. MB-DPP4 successfully surmounts several drawbacks of traditional DPP-IV probes, boasting unique features such as exceptional selectivity, ultrahigh sensitivity (0.29 ng/mL), innovative structure, low background, and long-wavelength fluorescence. MB-DPP4 is an "off-on" chemosensor that exhibits strong fluorescence at 715 nm and releases a methylene blue (MB) fluorophore upon interacting with DPP-IV, resulting in a visible color change from colorless to blue. Given these remarkable attributes, MB-DPP4 shows great promise as a versatile tool for advancing research on biological processes and for evaluating the physiological roles of DPP-IV in living systems. Finally, we conducted a comprehensive investigation of DPP-IV expression in human serum, urine, thyroid cells, and mouse thyroid tumor models. Our findings could potentially establish a foundation for the early diagnosis and treatment of thyroid diseases.

Ultrasound characteristics of follicular and parafollicular thyroid neoplasms: diagnostic performance of artificial neural network

BACKGROUND

Ultrasound is the first-line imaging modality for detection and classification of thyroid nodules. Certain features observable by ultrasound have recently been equated with potential malignancy. This retrospective cohort study was conducted to test the hypothesis that radiomics of the four categorical divisions (medullary [MTC], papillary [PTC], or follicular [FTC] carcinoma and follicular thyroid adenoma [FTA]) demonstrate distinctive sonographic characteristics. Using an artificial neural network model for proof of concept, these sonographic features served as input.

METHODS

A total of 148 patients were enrolled for study, all with confirmed thyroid pathology in one of the four named categories. Preoperative ultrasound profiles were obtained via standardized protocols. The neural network consisted of seven input neurons; three hidden layers with 50, 250, and 100 neurons, respectively; and one output layer.

RESULTS

Radiomics of contour, structure, and calcifications differed significantly according to nodule type (p = 0.025, p = 0.032, and p = 0.0002, respectively). Levels of accuracy shown by artificial neural network analysis in discriminating among categories ranged from 0.59 to 0.98 (95% confidence interval [CI]: 0.57-0.99), with positive and negative predictive ranges of 0.41-0.99 and 0.78-0.97, respectively.

CONCLUSIONS

Our data indicate that some MTCs, PTCs, FTCs, and FTAs have distinctive sonographic characteristics. However, a significant overlap of these characteristics may impede an explicit classification. Further prospective investigations involving larger patient and nodule numbers and multicenter access should be pursued to determine if neural networks of this sort are beneficial, helping to classify neoplasms of the thyroid gland.

Comparison of S-Detect and thyroid imaging reporting and data system classifications in the diagnosis of cytologically indeterminate thyroid nodules

Purpose

The aim of this study was to investigate the value of S-Detect for predicting the malignant risk of cytologically indeterminate thyroid nodules (CITNs).

Methods

The preoperative prediction of 159 CITNs (Bethesda III, IV and V) were performed using S-Detect, Thyroid Imaging Reporting and Data System of American College of Radiology (ACR TI-RADS) and Chinese TI-RADS (C-TIRADS). First, Linear-by-Linear Association test and chi-square test were used to analyze the malignant risk of CITNs. McNemar's test and receiver operating characteristic curve were used to compare the diagnostic efficacy of S-Detect and the two TI-RADS classifications for CITNs. In addition, the McNemar's test was used to compare the diagnostic accuracy of the above three methods for different pathological types of nodules.

Results

The maximum diameter of the benign nodules was significantly larger than that of malignant nodules [0.88(0.57-1.42) vs 0.57(0.46-0.81), P=0.002]. The risk of malignant CITNs in Bethesda system and the two TI-RADS classifications increased with grade (all P for trend<0.001). In all the enrolled CITNs, the diagnostic results of S-Detect were significantly different from those of ACR TI-RADS and C-TIRADS, respectively (P=0.021 and P=0.007). The sensitivity and accuracy of S-Detect [95.9%(90.1%-98.5%) and 88.1%(81.7%-92.5%)] were higher than those of ACR TI-RADS [87.6%(80.1%-92.7%) and 81.8%(74.7%-87.3%)] (P=0.006 and P=0.021) and C-TIRADS [84.3%(76.3%-90.0%) and 78.6%(71.3%-84.5%)] (P=0.001 and P=0.001). Moreover, the negative predictive value and the area under curve value of S-Detect [82.8% (63.5%-93.5%) and 0.795%(0.724%-0.855%)] was higher than that of C-TIRADS [54.8%(38.8%-69.8%) and 0.724%(0.648%-0.792%] (P=0.024 and P=0.035). However, the specificity and positive predictive value of S-Detect were similar to those of ACR TI-RADS (P=1.000 and P=0.154) and C-TIRADS (P=1.000 and P=0.072). There was no significant difference in all the evaluated indicators between ACR TI-RADS and C-TIRADS (all P>0.05). The diagnostic accuracy of S-Detect (97.4%) for papillary thyroid carcinoma (PTC) was higher than that of ACR TI-RADS (90.4%) and C-TIRADS (87.8%) (P=0.021 and P=0.003).

Conclusion

The diagnostic performance of S-Detect in differentiating CITNs was similar to ACR TI-RADS and superior to C-TIRADS, especially for PTC.

ACR TI-RADS classification combined with number of nodules, halo features optimizes diagnosis and prediction of follicular thyroid cancer

OBJECTIVES

To investigate the application value of The American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) category combined with other ultrasound features of nodules in distinguishing follicular thyroid carcinoma (FTC) from thyroid follicular adenoma (FTA).

METHODS

We collected and retrospectively analyzed clinical and ultrasound data for 118 and 459 patients with FTCs and FTAs, respectively, at our hospital. Next, we used ACR TI-RADS classification combined with other ultrasound features of nodules to distinguish FTC from FTA. Multivariate Logistic regression was used to screen independent risk factors for FTC, which were subsequently used to construct a nomogram for predicting FTC.

RESULTS

ACR TI-RADS categories 4 and 5, unilateral multiple nodules, and halo thickness≥2 mm were independent risk factors for FTC. ACR TI-RADS category combined with number of nodules, halo features of the nodule was a significantly better prediction model for FTC diagnosis (AUC = 0.869) than that of ACR TI-RADS classification alone (AUC = 0.756).

CONCLUTIONS

Clinicians need to pay attention to the halo of nodules when distinguishing FTA from FTC. Notably, ACR TI-RADS combined with other nodule ultrasound features has superior predictive performance in diagnosis of FTC compared to ACR TI-RADS classification alone, thus can provide an important reference value for preoperative diagnosis of FTC.

Clinicoradiological Characteristics in the Differential Diagnosis of Follicular-Patterned Lesions of the Thyroid: A Multicenter Cohort Study

OBJECTIVE

Preoperative differential diagnosis of follicular-patterned lesions is challenging. This multicenter cohort study investigated the clinicoradiological characteristics relevant to the differential diagnosis of such lesions.

MATERIALS AND METHODS

From June to September 2015, 4787 thyroid nodules (≥ 1.0 cm) with a final diagnosis of benign follicular nodule (BN, n = 4461), follicular adenoma (FA, n = 136), follicular carcinoma (FC, n = 62), or follicular variant of papillary thyroid carcinoma (FVPTC, n = 128) collected from 26 institutions were analyzed. The clinicoradiological characteristics of the lesions were compared among the different histological types using multivariable logistic regression analyses. The relative importance of the characteristics that distinguished histological types was determined using a random forest algorithm.

RESULTS

Compared to BN (as the control group), the distinguishing features of follicular-patterned neoplasms (FA, FC, and FVPTC) were patient's age (odds ratio [OR], 0.969 per 1-year increase), lesion diameter (OR, 1.054 per 1-mm increase), presence of solid composition (OR, 2.255), presence of hypoechogenicity (OR, 2.181), and presence of halo (OR, 1.761) (all p < 0.05). Compared to FA (as the control), FC differed with respect to lesion diameter (OR, 1.040 per 1-mm increase) and rim calcifications (OR, 17.054), while FVPTC differed with respect to patient age (OR, 0.966 per 1-year increase), lesion diameter (OR, 0.975 per 1-mm increase), macrocalcifications (OR, 3.647), and non-smooth margins (OR, 2.538) (all p < 0.05). The five important features for the differential diagnosis of follicular-patterned neoplasms (FA, FC, and FVPTC) from BN are maximal lesion diameter, composition, echogenicity, orientation, and patient's age. The most important features distinguishing FC and FVPTC from FA are rim calcifications and macrocalcifications, respectively.

CONCLUSION

Although follicular-patterned lesions have overlapping clinical and radiological features, the distinguishing features identified in our large clinical cohort may provide valuable information for preoperative distinction between them and decision-making regarding their management.

Sonographic Features Differentiating Follicular Thyroid Cancer from Follicular Adenoma-A Meta-Analysis

Simple Summary

The risk of thyroid malignancy assessment may include certain ultrasound features. The analysis is lacking for the differentiation of follicular thyroid adenomas and cancers (FTAs and FTCs). Our meta-analysis aimed to identify sonographic features suggesting malignancy in the case of follicular lesions, potentially differentiating FTA and FTC. Based on twenty studies describing sonographic features of 10,215 nodules, we found that the most crucial feature associated with an increased risk of FTC were tumor protrusion (odds ratios—OR = 10.19), microcalcifications or mixed type of calcifications: 6.09, irregular margins: 5.11, marked hypoechogenicity: 4.59, and irregular shape: 3.6.

Abstract

Certain ultrasound features are associated with an increased risk of thyroid malignancy. However, they were studied mainly in papillary thyroid cancers (PTCs); these results cannot be simply extrapolated for the differentiation of follicular thyroid adenomas and cancers (FTAs and FTCs). The aim of our study was to perform a meta-analysis to identify sonographic features suggesting malignancy in the case of follicular lesions, potentially differentiating FTA and FTC. We searched thirteen databases from January 2006 to December 2020 to find all relevant, full-text journal articles written in English. Analyses assessed the accuracy of malignancy detection in case of follicular lesions, potentially differentiating FTA and FTC included the odds ratio (OR), sensitivity, specificity, positive and negative predictive values. A random-effects model was used to summarize collected data. Twenty studies describing sonographic features of 10,215 nodules met the inclusion criteria. The highest overall ORs to increase the risk of malignancy were calculated for tumor protrusion (OR = 10.19; 95% confidence interval: 2.62–39.71), microcalcifications or mixed type of calcifications (coexisting micro and macrocalcifications): 6.09 (3.22–11.50), irregular margins: 5.11 (2.90–8.99), marked hypoechogenicity: 4.59 (3.23–6.54), and irregular shape: 3.6 (1.19–10.92). The most crucial feature associated with an increased risk of FTC is capsule protrusion, followed by the presence of calcifications, irrespectively of their type.

A computer-aided diagnosing system in the evaluation of thyroid nodules-experience in a specialized thyroid center

Background

The evaluation of thyroid nodules with ultrasonography has created a large burden for radiologists. Artificial intelligence technology has been rapidly developed in recent years to reduce the cost of labor and improve the differentiation of thyroid malignancies. This study aimed to investigate the diagnostic performance of a novel computer-aided diagnosing system (CADs: S-detect) for the ultrasound (US) interpretation of thyroid nodule subtypes in a specialized thyroid center.

Methods

Our study prospectively included 180 thyroid nodules that underwent ultrasound interpretation. The CADs and radiologist assessed all nodules. The ultrasonographic features of different subtypes were analyzed, and the diagnostic performances of the CADs and radiologist were compared.

Results

There were seven subtypes of thyroid nodules, among which papillary thyroid cancer (PTC) accounted for 50.6% and follicular thyroid carcinoma (FTC) accounted for 2.2%. Among all thyroid nodules, the CADs presented a higher sensitivity and lower specificity than the radiologist (90.5% vs 81.1%; 41.2% vs 83.5%); the radiologist had a higher accuracy than the CADs (82.2% vs 67.2%) for diagnosing malignant thyroid nodules. The accuracy of the CADs was not as good as that of the radiologist in diagnosing PTCs (70.9% vs 82.1%). The CADs and radiologist presented accuracies of 43.8% and 60.9% in identifying FTCs, respectively.

Conclusions

The ultrasound CADs presented a higher sensitivity for identifying malignant thyroid nodules than experienced radiologists. The CADs was not as good as experienced radiologists in a specialized thyroid center in identifying PTCs. Radiologists maintained a higher specificity than the CADs for FTC detection.