The diagnostic value of the ultrasound gray scale ratio for different sizes of thyroid nodules

Nomogram model of serum thymidine kinase 1 combined with ultrasonography for prediction of central lymph node metastasis risk in patients with papillary thyroid carcinoma pre-surgery

Objective

The aim of this study was to develop a nomogram, using serum thymidine kinase 1 protein (STK1p) combined with ultrasonography parameters, to early predict central lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) pre-surgery.

Methods

Patients with PTC pre-surgery in January 2021 to February 2023 were divided into three cohorts: the observation cohort (CLNM, n = 140), the control cohort (NCLNM, n = 128), and the external verification cohort (CLNM, n = 50; NCLNM, n = 50). STK1p was detected by an enzyme immunodot-blot chemiluminescence analyzer and clinical parameters were evaluated by ultrasonography.

Results

A suitable risk threshold value for STK1p of 1.7 pmol/L was selected for predicting CLNM risk by receiver operating characteristic (ROC) curve analysis. Multivariate analysis identified the following six independent risk factors for CLNM: maximum tumor size >1 cm [odds ratio (OR) = 2.406, 95% confidence interval (CI) (1.279-4.526), p = 0.006]; capsule invasion [OR = 2.664, 95% CI (1.324-5.360), p = 0.006]; irregular margin [OR = 2.922; 95% CI (1.397-6.111), p = 0.004]; CLN flow signal [OR = 3.618, 95% CI (1.631-8.027), p = 0.002]; tumor-foci number ≥2 [OR = 4.064, 95% CI (2.102-7.859), p < 0.001]; and STK1p ≥1.7 pmol/L [OR = 7.514, 95% CI (3.852-14.660), p < 0.001]. The constructed nomogram showed that the area under the ROC curve for the main dataset was 0.867 and that for the validation dataset was 0.830, exhibiting effectivity, and was recalculated to a total score of approximately 383. Through monitoring the response post-surgery, all patients were assessed as tumor-free at 12 months post-surgery, which was significantly associated with a reduction in STK1p to disease-free levels.

Conclusion

We demonstrate for the first time that a novel nomogram including STK1p combined with ultrasonography can assist in the clinical prevention of CLNM, by facilitating timely, individualized prophylactic CLNM dissection, thereby reducing the risk of secondary surgery and the probability of recurrence.

The predictive value of the thyroid nodule benign and malignant based on the ultrasound nodule-to-muscle gray-scale ratio

OBJECTIVE

To investigate the efficacy of the ultrasonic nodule to muscle gray scale ratio as a predictive tool for distinguishing between benign and malignant thyroid nodules.

METHODS

A retrospective study was undertaken at the First People's Hospital of Hangzhou, affiliated with the Zhejiang University School of Medicine, analyzing ultrasound and pathological data of patients with thyroid nodules between May 2020 and December 2022. The study extracted ultrasound features of nodules and employed univariate and multivariate logistic regression analyses to identify independent risk factors for malignant tumors in the nodules. Subsequently, a predictive model for distinguishing benign and malignant thyroid nodules was developed.

RESULTS

A total of 466 patients were included in this retrospective study, of which 275 cases were malignant tumors. Univariate and multivariate logistic regression analyses showed that the nodular-muscle gray-scale ratio, nodule diameter, margin status, aspect ratio, and calcification were closely related to thyroid malignant tumors. The area under the curve (AUC) of training group was 0.832, with a sensitivity, specificity, and accuracy of 85.5%, 67.4%, and 76.6%, respectively. The AUC of the external validation group was 0.819, with a sensitivity, specificity, and accuracy of 76.4%, 74.5%, and 75.7%, respectively. The calibration and decision curves showed that the model had good diagnostic value.

CONCLUSION

The research findings indicate that ratio is significantly associated with the malignant nature of thyroid nodules. The application of a line chart model based on these parameters exhibits a high level of predictive performance.

The diagnostic value of ultrasound on different-sized thyroid nodules based on ACR TI-RADS

OBJECTIVES

The thyroid nodule is one of the most common endocrine system diseases. Risk classification models based on ultrasonic features have been created by multiple professional societies, including the American College of Radiology (ACR), which published the Thyroid Imaging Reporting and Data System (TI-RADS) in 2017. The effect of the size in the diagnostic value of ultrasound remains not well defined. The purposes of our study aims to explore diagnostic value of the ACR TI-RADS on different-sized thyroid nodules.

METHODS

A total of 1183 thyroid nodules were selected from 952 patients with thyroid nodules confirmed by surgical pathology from January 2021 to October 2022. Based on the maximum diameters of the nodules, they were stratified into groups A ( ≤ 10 mm), B ( > 10 mm, < 20 mm) and C ( ≥ 20 mm). The ultrasonic features of the thyroid nodules in each group were evaluated and scored based on ACR TI-RADS, and the receiver operating characteristic curve (ROC) was plotted to determine the optimal cut-off value for the ACR TI-RADS scores and categories in each group. Finally, the diagnostic efficacy of ACR TI-RADS on different-sized thyroid nodules was analyzed.

RESULTS

Among the 1183 thyroid nodules, 340 were benign, 10 were low-risk and 833 were malignant. For the convenience of statistical analysis, low-risk thyroid nodules were classified as malignant in this study. The ACR TI-RADS scores and categorical levels of malignant thyroid nodules in each group were higher than those of benign ones (p < 0.05). The areas under the ROCs (AUCs) plotted based on scores were 0.741, 0.907, and 0.904 respectively in the three groups, and the corresponding optimal cut-off values were > 6 points, > 5 points and > 4 points respectively. While the AUCs of the ACR TI-RADS categories were 0.668, 0.855, and 0.887 respectively in each group, with the optimal cut-off values were all > TR4. Besides, for thyroid nodules of larger sizes, ACR TI-RADS exhibited weaker sensitivity with lower positive prediction value (PPV), but the specificity and negative prediction value (NPV) were both higher, presenting with statistically significant differences (p < 0.05).

CONCLUSION

For thyroid nodules of different sizes, the diagnostic efficacy of ACR TI-RADS varies as well. The system shows better diagnostic efficacy on thyroid nodules of > 10 mm than on those ≤ 10 mm. Considering the favorable prognosis of thyroid microcarcinoma and the low diagnostic efficacy of ACR TI-RADS on it, the scoring and classification of thyroid micro-nodules can be left out in appropriate cases, so as to avoid the over-diagnosis and over-treatment of thyroid microcarcinoma to a certain extent.

Comparing Diagnostic Efficacy of C-TIRADS Positive Features on Different Sizes of Thyroid Nodules

Purpose

To explore the diagnostic value of positive features in the Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) for thyroid nodules of different sizes.

Patients and Methods

A total of 1864 patients with 2347 thyroid nodules were selected from January 2021 to December 2022 and assessed according to C-TIRADS. According to the maximum diameter, nodules were divided into the A1 group (≤10 mm), A2 group (>10 mm,<20 mm), and A3 group (≥20 mm). With surgical pathology as the golden standard, the receiver operating characteristic curves (ROC) were constructed, and each group's area under the curve (AUC) was calculated. The diagnostic value of positive features in C-TIRADS for different sizes of thyroid nodules was analyzed.

Results

In all groups, malignant thyroid nodules had a higher incidence of positive features than benign nodules (P < 0.05). In A1 group, the diagnostic efficiency of C-TIRADS positive features for thyroid nodules was vertical orientation> ill-defined/irregular margin or extrathyroidal extension> solid composition> markedly hypoechoic> microcalcifications. The AUCs were 0.718, 0.675, 0.609, 0.558, and 0.581, respectively. In A2 group, the diagnostic efficacy of each positive features for thyroid nodules was ill-defined/irregular margins or extra-thyroid invasion> solid composition> microcalcifications> markedly hypoechoic> vertical orientation. The AUCs were 0.854, 0.730, 0.719, 0.670, and 0.609, respectively. In A3 group, the diagnostic efficacy of each positive features for thyroid nodules was ill-defined/irregular margin or extrathyroidal extension> microcalcifications> solid composition> vertical orientation> markedly hypoechoic. The AUCs were 0.847, 0.778, 0.767, 0.584, and 0.560, respectively.

Conclusion

C-TIRADS positive features exhibited different diagnostic efficacy for thyroid nodules of various sizes, especially for thyroid nodules ≤10 mm, for which all positive features had low diagnostic efficacy.

Musculoskeletal Ultrasound Image-Based Radiomics for the Diagnosis of Achilles Tendinopathy in Skiers

OBJECTIVES

Our study aimed to develop and validate an efficient ultrasound image-based radiomic model for determining the Achilles tendinopathy in skiers.

METHODS

A total of 88 feet of skiers clinically diagnosed with unilateral chronic Achilles tendinopathy and 51 healthy feet were included in our study. According to the time order of enrollment, the data were divided into a training set (n = 89) and a test set (n = 50). The regions of interest (ROIs) were segmented manually, and 833 radiomic features were extracted from red, green, blue color channels and grayscale of ROIs using Pyradiomics, respectively. Three feature selection and three machine learning modeling algorithms were implemented respectively, for determining the optimal radiomics pipeline. Finally, the area under the receiver operating characteristic curve (AUC), consistency analysis, and decision analysis were used to evaluate the diagnostic performance.

RESULTS

By comparing nine radiomics analysis strategies of three color channels and grayscale, the radiomic model under the green channel obtained the best diagnostic performance, using the Random Forest selection and Support Vector Machine modeling, which was selected as the final machine learning model. All the selected radiomic features were significantly associated with the Achilles tendinopathy (P < .05). The radiomic model had a training AUC of 0.98, a test AUC of 0.99, a sensitivity of 0.90, and a specificity of 1, which could bring sufficient clinical net benefits.

CONCLUSIONS

Ultrasound image-based radiomics achieved high diagnostic performance, which could be used as an intelligent auxiliary tool for the diagnosis of Achilles tendinopathy.

Ultrasound gray scale ratio for differential diagnosis of papillary thyroid microcarcinoma from benign micronodule in patients with Hashimoto's thyroiditis

BACKGROUND

To investigate the diagnostic value of ultrasound gray scale ratio (UGSR) in differentiating papillary thyroid microcarcinomas (PTMCs) from benign micronodules (BMNs) in patients with Hashimoto's thyroiditis (HT).

METHODS

The ultrasound images of 285 PTMCs (from 247 patients) and 173 BMNs (from 140 patients) in the HT group, as well as 461 PTMCs (from 417 patients) and 234 BMNs (from 197 patients) in the non-HT group were retrospectively analyzed. The diagnosis of all cases was confirmed by histopathological examinations. The gray scale values of the nodules and surrounding thyroid tissues were measured and subsequently the UGSRs were calculated. Receiver operating characteristic curve analysis was used to determine the area under the curve (AUC), optimal UGSR threshold, sensitivity and specificity in differentiating PTMCs and BMNs in the two groups.

RESULTS

The UGSR of PTMC and BMN was 0.52 ± 0.12 and 0.85 ± 0.24 in the HT group (P < 0.001), and 0.57 ± 0.13 and 0.87 ± 0.20 in the non-HT group (P < 0.001), respectively. The difference in PTMC-UGSR was significant between the two groups (P < 0.001), whereas BMN-UGSR did not differ between the two groups (P = 0.416). The AUC, optimal UGSR threshold, sensitivity and specificity of UGSR for differentiating PTMC and BMN in the HT and non-HT group were 0.890 versus 0.901, 0.68 versus 0.72, 91.23% versus 90.67%, and 77.46% versus 82.05%, respectively.

CONCLUSIONS

The USGR of the HT group was lower than that of the non-HT group. Moreover, UGSR exhibited important diagnostic value in differentiating PTMC from BMN in both HT and non-HT groups.

Artificial Intelligence for Thyroid Nodule Characterization: Where Are We Standing?

Simple Summary

In the present review, an up-to-date summary of the state of the art of artificial intelligence (AI) implementation for thyroid nodule characterization and cancer is provided. The opinion on the real effectiveness of AI systems remains controversial. Taking into consideration the largest and most scientifically valid studies, it is possible to state that AI provides results that are comparable or inferior to expert ultrasound specialists and radiologists. Promising data approve AI as a support tool and simultaneously highlight the need for a radiologist supervisory framework for AI provided results. Therefore, current solutions might be more suitable for educational purposes.

Abstract

Machine learning (ML) is an interdisciplinary sector in the subset of artificial intelligence (AI) that creates systems to set up logical connections using algorithms, and thus offers predictions for complex data analysis. In the present review, an up-to-date summary of the current state of the art regarding ML and AI implementation for thyroid nodule ultrasound characterization and cancer is provided, highlighting controversies over AI application as well as possible benefits of ML, such as, for example, training purposes. There is evidence that AI increases diagnostic accuracy and significantly limits inter-observer variability by using standardized mathematical algorithms. It could also be of aid in practice settings with limited sub-specialty expertise, offering a second opinion by means of radiomics and computer-assisted diagnosis. The introduction of AI represents a revolutionary event in thyroid nodule evaluation, but key issues for further implementation include integration with radiologist expertise, impact on workflow and efficiency, and performance monitoring.

Ultrasound grayscale ratio: a reliable parameter for differentiating between papillary thyroid microcarcinoma and micronodular goiter

BACKGROUND

The present study aimed to quantify and differentiate the echo levels of papillary thyroid microcarcinomas (PTMCs) and micronodular goiters (MNGs) using the ultrasound grayscale ratio (UGSR) and to investigate the repeatability of UGSR.

METHODS

The ultrasound (US) data of 241 patients with 265 PTMCs and 141 patients with 168 MNGs confirmed by surgery and pathology were retrospectively analyzed. All patients had received outpatient ultrasonic examination and preoperative ultrasonic positioning. The RADinfo radiograph reading system was used to measure the grayscales of PTMC, MNG, and thyroid tissues at the same gain level, and the UGSR values of the PTMC, MNG, and thyroid tissue were calculated. The patients were divided into outpatient examination, preoperative positioning, and mean value groups, and the receiver operating characteristic (ROC) curves were calculated to obtain the optimal UGSR threshold to distinguish PTMC from MNG. The interclass correlation coefficient (ICC) was used to assess the consistency of UGSR measured in three groups.

RESULTS

The UGSR values of the PTMC and MNG were 0.56 ± 0.14 and 0.80 ± 0.19 (t = 5.84, P < 0.001) in the outpatient examination group, 0.55 ± 0.14 and 0.80 ± 0.19 (t = 18.74, P < 0.001) in the preoperative positioning group, and 0.56 ± 0.12 and 0.80 ± 0.18 (t = 16.49, P < 0.001) in the mean value group. The areas under the ROC curves in the three groups were 0.860, 0.856, and 0.875, respectively. When the UGSR values for the outpatient examination, preoperative positioning, and mean value groups were 0.649, 0.646, and 0.657, respectively, each group obtained its largest Youden index. A reliable UGSR value was obtained between the outpatient examination and preoperative positioning groups (ICC = 0.79, P = 0.68).

CONCLUSION

UGSR is a simple and repeatable method to distinguish PTMC from MNG, and hence, can be widely applicable.

The value of ultrasound grayscale ratio in the diagnosis of papillary thyroid microcarcinomas and benign micronodules in patients with Hashimoto's thyroiditis: A two-center controlled study

OBJECTIVE

The value of ultrasound grayscale ratio (UGSR) in the diagnosis of papillary thyroid microcarcinomas (PTMCs) and benign micronodules (BMNs) has been recognized by some authors, but studies have not examined these aspects in patients with Hashimoto's thyroiditis (HT). This retrospective study investigated the value of UGSR in the diagnosis of PTMCs and BMNs in patients with HT using data from two medical centers.

METHODS

Ultrasound images of 428 PTMCs in 368 patients with HT and 225 BMNs in 181 patients with HT in center A were retrospectively analyzed and compared to the ultrasound images of 412 PTMCs in 324 patients with HT and 315 BMNs in 229 patients with HT in medical center B. All of the cases were surgically confirmed. The UGSR was calculated as the ratio of the grayscale value of lesions to the surrounding normal thyroid tissues. The optimal UGSR thresholds for the PTMCs and BMNs in patients with HT from the two medical centers were determined using a receiver operating characteristic (ROC) curve. Furthermore, other statistics, including the area under the curve (AUC), the optimal UGSR threshold, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of the two medical centers, were pair analyzed in this study.

RESULTS

The UGSR of PTMCs and BMNs in patients with HT from medical center A were 0.513 (0.442, 0.592) and 0.857 (0.677, 0.977) (Z = -15.564, p = 0), and those from medical center B were 0.514 (0.431, 0.625) and 0.917 (0.705, 1.131) (Z = -15.564, p = 0). For both medical centers A and B, the AUC, optimal UGSR threshold, sensitivity, specificity, PPV, NPV, and diagnostic accuracy of the UGSR in differentiating between PTMCs and BMNs in patients with HT were 0.870 and 0.889, 0.68 and 0.70, 0.921 and 0.898, 0.747 and 0.759, 0.874 and 0.829, 0.832 and 0.848, and 0.861 and 0.836, respectively. There were no significant differences in the UGSR for the PTMCs between patients from the two medical centers (Z = -0.815, p = 0.415), while there was a significant difference in the UGSR of the BMNs between patients from the two medical centers (Z = -3.637, p = 0).

CONCLUSION

In the context of HT, UGSR still has high sensitivity, accuracy, and stability in differentiating between PTMCs and BMNs, making it a complementary differentiator of thyroid imaging reporting and data systems. However, due to its low specificity, a comprehensive analysis of other ultrasound signs is required.

Texture and shape analysis of diffusion-weighted imaging for thyroid nodules classification using machine learning

PURPOSE

To assess whether the integration between (a) functional imaging features that will be extracted from diffusion-weighted imaging (DWI); and (b) shape and texture imaging features as well as volumetric features that will be extracted from T2-weighted magnetic resonance imaging (MRI) can noninvasively improve the diagnostic accuracy of thyroid nodules classification.

PATIENTS AND METHODS

In a retrospective study of 55 patients with pathologically proven thyroid nodules, T2-weighted and diffusion-weighted MRI scans of the thyroid gland were acquired. Spatial maps of the apparent diffusion coefficient (ADC) were reconstructed in all cases. To quantify the nodules' morphology, we used spherical harmonics as a new parametric shape descriptor to describe the complexity of the thyroid nodules in addition to traditional volumetric descriptors (e.g., tumor volume and cuboidal volume). To capture the inhomogeneity of the texture of the thyroid nodules, we used the histogram-based statistics (e.g., kurtosis, entropy, skewness, etc.) of the T2-weighted signal. To achieve the main goal of this paper, a fusion system using an artificial neural network (NN) is proposed to integrate both the functional imaging features (ADC) with the structural morphology and texture features. This framework has been tested on 55 patients (20 patients with malignant nodules and 35 patients with benign nodules), using leave-one-subject-out (LOSO) for training/testing validation tests.

RESULTS

The functionality, morphology, and texture imaging features were estimated for 55 patients. The accuracy of the computer-aided diagnosis (CAD) system steadily improved as we integrate the proposed imaging features. The fusion system combining all biomarkers achieved a sensitivity, specificity, positive predictive value, negative predictive value, F1-score, and accuracy of 92.9 % (confidence interval [CI]: 78.9 % -- 99.5 % ), 95.8 % (CI: 87.4 % -- 99.7 % ), 93 % (CI: 80.7 % -- 99.5 % ), 96 % (CI: 88.8 % -- 99.7 % ), 92.8 % (CI: 83.5 % -- 98.5 % ), and 95.5 % (CI: 88.8 % -- 99.2 % ), respectively, using the LOSO cross-validation approach.

CONCLUSION

The results demonstrated in this paper show the promise that integrating the functional features with morphology as well as texture features by using the current state-of-the-art machine learning approaches will be extremely useful for identifying thyroid nodules as well as diagnosing their malignancy.

A Control Study on the Value of the Ultrasound Grayscale Ratio for the Differential Diagnosis of Thyroid Micropapillary Carcinoma and Micronodular Goiter in Two Medical Centers

Objective

To investigate the value of ultrasound gray-scale ratio (UGSR) for the differential diagnosis of papillary thyroid microcarcinoma (PTMC) and micronodular goiter (MNG) in two medical centers.

Methods

Ultrasound images of 881 PTMCs from 785 patients and 744 MNGs from 687 patients in center A were retrospectively analyzed and compared with 243 PTMCs from 203 patients and 251 MNGs from 198 patients in center B. All cases were confirmed by surgery and histology. The grayscale values of thyroid lesions and surrounding normal tissues were measured, and the UGSR was calculated. The optimal UGSR threshold for identifying PTMCs and MNGs in two medical centers was determined by receiver operating characteristic (ROC) curve, and the area under the curve (AUC), optimal UGSR threshold, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were compared between the two medical centers.

Results

The UGSR values of PTMCs and MNGs in medical center A were 0.5537 (0.4699, 0.6515) and 0.8708 (0.7616, 1.0123) (Z = -27.691, P = 0), respectively, whereas those in medical center B were 0.5517 (0.4698, 0.6377) and 0.8539 (0.7366, 0.9929) (Z = -16.057, P = 0), respectively. The UGSR of PTMCs and MNGs did not differ significantly between the two medical centers (Z = -0.609, P = 0.543 and Z = -1.394, P = 0.163, respectively). The AUC, optimal UGSR threshold, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the two medical centers were 0.898 vs. 0.918, 0.7214 vs. 0.6911, 0.881 vs. 0.868, 0.817 vs. 0.833, 0.851 vs. 0.834, 0.853 vs. 0.867, and 0.852 vs. 0.850, respectively.

Conclusions

UGSR can quantify the echo intensity of PTMCs and MNGs and is therefore valuable for the differential diagnosis of the two diseases. The diagnostic efficacy was consistent between the two medical centers. This method should be widely promoted and applied.

The diagnostic value of the ultrasound gray scale ratio for different sizes of thyroid nodules

At present, hypoechogenicity, as one of the clinically relevant features associated with suspicion of malignant thyroid disease, is affected by the variability of modules and the experience of sonographers, thus leading to unsatisfying results. We propose the ultrasound gray scale ratio (UGSR) to obtain an objective, numerical estimate of the echogenicity degree in different‐sized thyroid nodules, and we then evaluate its diagnostic efficacy in differentiating benign and malignant thyroid lesions. In total, 553 ultrasound images of thyroid nodules from one kind of ultrasonographic scanner were analyzed, among which 281 were papillary thyroid carcinomas (PTCs) and 272 were nodular goiters (NGs). The UGSR of the PTCs, NGs, and surrounding normal thyroid tissue was measured by image analysis software. The best cut‐off value for distinguishing various sizes of PTCs and NGs was determined by receiver operating characteristic (ROC) curve analysis. As the UGSR increased, the sensitivity of the diagnosing PTCs decreased, and the specificity increased. When the maximum Jordan index was 0.611, the best cut‐off value was 0.692, and the corresponding sensitivity and specificity of diagnosing PTCs were 87.9% and 73.2%, respectively. For the analysis of subgroups of different tumor sizes, as the size of thyroid nodules increased from 0.3 to 2 cm, the sensitivity of the diagnosis of PTCs decreased from 97.5% to 58.8%, and the specificity increased from 72.4% to 90.9%. These results strongly suggest that the UGSR is an appropriate objective, numerical method for estimating the echogenicity degree and has various diagnostic efficacies in different‐sized thyroid nodules. Thus, the UGSR can be used as an additional ultrasound parameter in the diagnosis of different‐sized PTCs and NGs.