CAD system based on B-mode and color Doppler sonographic features may predict if a thyroid nodule is hot or cold

Quantitative ultrasound imaging parameters in patients with cancerous thyroid nodules: development of a diagnostic model

OBJECTIVE

This study aimed to develop a diagnostic model utilizing quantitative ultrasound parameters to accurately differentiate benign from malignant thyroid nodules.

METHODS

A retrospective analysis of 194 patients with thyroid nodules, encompassing 65 malignant and 129 benign cases, was performed. Clinical data, ultrasound characteristics, and hemodynamic indicators were compared. Receiver operating characteristic (ROC) curves and logistic regression analysis identified independent diagnostic markers.

RESULTS

No significant differences in clinical data were observed between the groups (P>0.05). Malignant nodules, however, were more likely to exhibit solid composition, hypoechoicity, irregular shapes, calcifications, central blood flow, and unclear margins (P<0.05). Hemodynamic parameters showed that malignant nodules had lower end-diastolic volume (EDV) but higher peak systolic velocity (PSV), resistive index (RI), and vascularization flow index (VFI) (P<0.001). Independent diagnostic factors identified included calcification, margin definition, RI, and VFI. A risk prediction model was formulated, demonstrating significantly lower scores for benign nodules (P<0.0001), achieving an ROC area of 0.964.

CONCLUSION

Color Doppler ultrasound effectively distinguishes malignant from benign thyroid nodules. The diagnostic model emphasizes the importance of calcification, margin clarity, RI, and VFI as critical elements, enhancing the accuracy of thyroid nodule characterization and facilitating informed clinical decisions.

Multimodal fusion model for classifying placenta ultrasound imaging in pregnancies with hypertension disorders

BACKGROUND

A multimodal fusion model was proposed to assist the traditional visual diagnosis in evaluating the placental features of hypertension disorders of pregnancy (HDP).

OBJECTIVE

The aim of this study was to analyse and compare the placental features between normal and HDP pregnancies and propose a multimodal fusion deep learning model for differentiating and characterizing the placental features from HDP to normal pregnancy.

METHODS

This observational prospective study included 654 pregnant women, including 75 with HDPs. Grayscale ultrasound images (GSIs) and Microflow images (MFIs) of the placentas were collected from all patients during routine obstetric examinations. On the basis of intelligent extraction and features fusion, after quantities of training and optimization, the classification model named GMNet (the intelligent network based on GSIs and MFIs) was introduced for differentiating the placental features of normal and HDP pregnancies. The distributions of placental features extracted by the deep convolutional neural networks (DCNNs) were visualized by Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP). Metrics including sensitivity, specificity, accuracy, and the area under the curve (AUC) were used to score the model. Finally, placental tissue samples were randomly selected for microscopic analyses to prove the interpretability and effectiveness of the GMNet model.

RESULTS

Compared with the Normal group in ultrasonic images, the light spots were rougher and the parts with focal cystic or hypoechogenic lesions were increased in the HDP groups. The overall diagnostic performance of the GMNet model depending on the region of interest (ROI) was excellent (AUC: 97%), with a sensitivity of 90.0%, a specificity of 93.5%, and an accuracy of 93.1%. The fusion features of GSIs and MFIs in the placenta showed a higher discriminative power than single-mode features (fusion features vs GSI features vs MFI features, 97.0% vs 91.2% vs 94.8%). Furthermore, according to the microscopic analysis, unevenly distributed villi, increased syncyte nodules and aggregated intervillous cellulose deposition were particularly frequent in the HDP cases.

CONCLUSIONS

The GMNet model could sensitively identify abnormal changes in the placental microstructure in pregnancies with HDP.

Duplex Hemodynamic Parameters of Both Superior and Inferior Thyroid Arteries in Evaluation of Thyroid Hyperfunction Disorders

Background

Thyrotoxicosis may be caused by Graves’ disease or destructive thyroiditis. Differentiation between causes of thyrotoxicosis is crucial as management will differ. ^99mTechnetium (Tc)-pertechnetate thyroid scintigraphy is currently the gold standard for this purpose, however, is expensive and uses ionizing radiation.

Objective

To evaluate the role of color flow Doppler Ultrasound (CDU) of the superior thyroid (STA) and inferior thyroid arteries (ITA) as an inexpensive, non-invasive tool that can aid in differentiating between Graves’ disease and thyroiditis and compare it with thyroid scintigraphy.

Methods

Sixty-nine patients with newly-diagnosed thyrotoxicosis and 30 controls were enrolled. Thyroid functions, thyroid scintigraphy, and CDU of STA and ITA with measurements of peak systolic velocity (PSV) and end diastolic velocity (EDV), were performed. According to thyroid scintigraphy results, patients were divided into two groups: 42 patients with Graves’ disease and 27 patients with thyroiditis.

Results

PSV and EDV of both STA and ITA were significantly higher in patients with Graves’ disease than thyroiditis (p-values <0.001). The STA-PSV had an equal sensitivity and specificity of 66.7%; cut-off value 76.57 cm/s, while those of STA-EDV were 73.8%, and 77.8% respectively; cut-off value 28.22 cm/s. ITA-PSV had a sensitivity and specificity of 76.2% and 77.8%, respectively; cut-off value 62.12 cm/s), while those of ITA-EDV were 78.6% and 77.8%, respectively; cut-off value 5.22 cm/s.

Conclusion

CDU parameters of the STA and ITA could be used as an alternative to thyroid scintigraphy for discriminating between Graves’ disease and thyroiditis.

Differential diagnosis and feature visualization for thyroid nodules using computer-aided ultrasonic diagnosis system: initial clinical assessment

BACKGROUND

To assess the diagnostic efficacy of the computer-aided ultrasonic diagnosis system (CAD system) in differentiating benign and malignant thyroid nodules.

METHODS

The images of 296 thyroid nodules were included in validation sets. The diagnostic efficacy of the CAD system was compared with that of junior physicians and senior physicians, as well as that of the combination diagnosis of the CAD system with junior physicians. The diagnostic efficacy of the CAD system for different sizes of thyroid nodules was compared.

RESULTS

The diagnostic sensitivity and accuracy of the CAD system were higher than those of junior physicians (83.4% vs. 72.2%, 73.0% vs. 69.6%), but the diagnostic specificity of the CAD system was lower than that of junior physicians (62.1% vs. 66.9%). The diagnostic accuracy of the CAD system was lower than that of senior physicians (73.0% vs. 83.8%). However, the combination diagnosis of the CAD system with junior physicians had higher accuracy (81.8%) and AUC (0.842) than those of either the CAD system or junior physicians alone, and comparable diagnostic performance with those of senior physicians. The Kappa was 0.635 in the combination diagnosis of the CAD system with junior physicians, showing good consistency with the pathological results. The accuracy (76.4%) of the CAD system was the highest for nodules of 1-2 cm.

CONCLUSION

The CAD system can effectively assist physicians to identify malignant and benign thyroid nodules, reduce the overdiagnosis and overtreatment of thyroid nodules, avoid unnecessary invasive fine needle aspiration, and improve the diagnostic accuracy of junior physicians.

Machine Learning Assisted Doppler Features for Enhancing Thyroid Cancer Diagnosis: A Multi-Cohort Study

BACKGROUND

This pilot study aims at exploiting machine learning techniques to extract color Doppler ultrasound (CDUS) features and to build an artificial neural network (ANN) model based on these CDUS features for improving the diagnostic performance of thyroid cancer classification.

METHODS

A total of 674 patients with 712 thyroid nodules (TNs) (512 from internal dataset and 200 from external dataset) were randomly selected in this retrospective study. We used ANN to build a model (TDUS-Net) for classifying malignant and benign TNs using both the automatically extracted quantitative CDUS features (whole ratio, intranodular ratio, peripheral ratio, and number of vessels) and gray-scale ultrasound (US) features defined by the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS). Then, we compared the diagnostic performance of the model, the performance of another ANN model based on the gray-scale US features alone (TUS-Net), and that of radiologists.

RESULTS

The TDUS-Net (0.898, 95% CI: 0.868-0.922) achieved a higher area under the curve (AUC) than that of TUS-Net (0.881, 95% CI: 0.850-0.908) in the internal tests. Compared with radiologists, TDUS-Net (AUC: 0.925, 95% CI: 0.880-0.958) performed better than radiologists (AUC: 0.810, 95% CI: 0.749-0.862) in the external tests.

CONCLUSIONS

Applying a machine learning model by combining both gray-scale US features and CDUS features can achieve comparable or even higher performance than radiologists in classifying TNs.

The value of color Doppler ultrasound in the diagnosis of thyroid nodules: a systematic review and meta-analysis

BACKGROUND

This study aimed to analyze the value of color Doppler ultrasound in the diagnosis of thyroid nodules.

METHODS

We searched the PubMed, Web of Science, Embase, and Cochrane Library databases for randomized controlled trials (RCTs) on using color Doppler ultrasound, thyroid nodules, thyroid tumors, and Doppler ultrasound to diagnose the thyroid nodules. The outcome indicators in the articles had to include the numbers of true positives (TP), false positives (FP), false negatives (FN), and true negatives (TN). Subsequently, the Jadad tool was adopted to evaluate the quality of the included articles, and Review Manager 5.3 software was used to conduct a meta-analysis of the experimental data.

RESULTS

A total of eight suitable articles were selected. The results showed that the estimated sensitivity and specificity of color Doppler ultrasound for the diagnostic of thyroid nodules were 0.46-0.89 and 0.00-1.00, respectively. The pooled estimate of sensitivity for the different articles was 0.71 [95% confidence interval (CI): 0.46-0.89], and the pooled estimate of specificity was 0.77 (95% CI: 0.00-1.00). The area under the summary receiver operating characteristic (SROC) curve (AUC) was 0.917, which was larger than 0.9, signifying high diagnostic accuracy. This suggests that color doppler ultrasound can realize the clinical diagnosis of thyroid nodules.

DISCUSSION

In summary, the results of this study could provide a clinical data for the promotion and application of color Doppler ultrasound in the clinical diagnosis of thyroid nodules, as well as further reliable data for follow-up clinical research on the diagnosis and treatment of thyroid nodules.