Does a normal thyroid ultrasound image always accompany normal thyroid functions test results?

Abstract. When discovered by neonatal screening, a thyroid dyshormonogenesis is usually not recognized as a goitre. Especially a total iodide transport defect can easily be misclassified as thyroid agenesis, since radionuclide imaging cannot visualize the thyroid. We present the only iodide transport defect ever discovered in the Netherlands, the 35th reported in the literature, and the first one found exclusively as a result of neonatal screening. We demonstrate that iodide transport defects, in common with organification and deiodinase defects, can be distinguished from thyroid dysgenesis by demonstrating a normal or enlarged thyroid ultrasound image, and especially by measuring very high serum thyroglobulin levels (above 1000 pmol/l). In the presented case, an iodide-123 saliva-to-serum ratio near unity completed the etiologic classification. Measurement of serum thyroglobulin levels, in combination with thyroid ultrasound imaging, will improve the early identification of hereditary types of congenital hypothyroidism, and especially iodide transport defects, in patients found by neonatal thyroid screening.

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Management of Thyrotoxicosis Induced by PD1 or PD-L1 Blockade

Journal of the Endocrine Society ◽

10.1210/jendso/bvab048.1716 ◽

2021 ◽

Vol 5 (Supplement_1) ◽

pp. A841-A841

Author(s):

Alessandro Brancatella ◽

Isabella Lupi ◽

Lucia Montanelli ◽

Debora Ricci ◽

Nicola Viola ◽

...

Keyword(s):

Thyroid Function ◽

Checkpoint Inhibitors ◽

Thyroid Volume ◽

Clinical Severity ◽

Response To Treatment ◽

Thyroid Function Tests ◽

Thyroid Ultrasound ◽

Normal Thyroid ◽

Normal Thyroid Function

Abstract Context: Thyrotoxicosis is a common immune-related adverse event in patients treated with PD1 or PD-L1 checkpoint inhibitors. A detailed endocrinological assessment, including thyroid ultrasound and scintigraphy is missing, as are data on response to treatment and follow-up. Objectives: To better characterize the thyrotoxicosis secondary to immune checkpoint inhibitors, gaining insights into pathogenesis and informing management. Methods: We conducted a prospective cohort study of 20 consecutive patients who had normal thyroid function before starting immunotherapy and then experienced thyrotoxicosis upon PD1 or PD-L1 blockade. Clinical assessment was combined with thyroid ultrasound, scintigraphy, and longitudinal thyroid function tests. Results: Five patients had normal scintigraphic uptake (Sci+), no serum antibodies against the TSH receptor, and remained hyperthyroid throughout follow-up. The other 15 patients had no scintigraphic uptake (Sci-) and experienced destructive thyrotoxicosis followed by hypothyroidism (N= 9) or euthyroidism (N= 6). Hypothyroidism was more readily seen in those with normal thyroid volume than in those with goiter (P= 0.04). Among Sci- subjects, a larger thyroid volume was associated to a longer time to remission (P<0.05). Methimazole (MMI) was effective only in Sci+ subjects (P<0.05). Conclusions: Administration of PD1 or PD-L1 blocking antibodies may induce two different forms of thyrotoxicosis that appear similar in clinical severity at onset: a type 1 characterized by persistent hyperthyroidism that requires treatment with MMI, and a type 2 characterized by destructive and transient thyrotoxicosis that evolves to hypo- or eu-thyroidism. Thyroid scintigraphy and ultrasound help differentiating and managing these two forms of iatrogenic thyrotoxicosis

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Prediction and Value of Ultrasound Image in Diagnosis of Fetal Central Nervous System Malformation under Deep Learning Algorithm

Scientific Programming ◽

10.1155/2021/6246274 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Yuehong Zhou

Keyword(s):

Neural Network ◽

Central Nervous System ◽

Nervous System ◽

Deep Learning ◽

Ultrasound Examination ◽

Ultrasound Image ◽

Control Group ◽

Test Results ◽

Experimental Group ◽

Central Nervous System Malformation

This study was to explore the application of deep learning neural network (DLNN) algorithms to identify and optimize the ultrasound image so as to analyze the effect and value in diagnosis of fetal central nervous system malformation (CNSM). 63 pregnant women who were gated in the hospital were suspected of being fetal CNSM and were selected as the research objects. The ultrasound images were reserved in duplicate, and one group was defined as the control group without any processing, and images in the experimental group were processed with the convolutional neural network (CNN) algorithm to identify and optimize. The ultrasound examination results and the pathological test results before, during, and after the pregnancy were observed and compared. The results showed that the test results in the experimental group were closer to the postpartum ultrasound and the results of the pathological result, but the results in both groups showed no statistical difference in contrast to the postpartum results in terms of similarity ( P > 0.05 ). In the same pregnancy stage, the ultrasound examination results of the experimental group were higher than those in the control group, and the contrast was statistically significant ( P < 0.05 ); in the different pregnancy stages, the ultrasound examination results in the second trimester were more close to the postpartum examination results, showing statistically obvious difference ( P < 0.05 ). In conclusion, ultrasonic image based on deep learning was higher in CNSM inspection; and ultrasonic technology had to be improved for the examination in different pregnancy stages, and the accuracy of the examination results is improved. However, the amount of data in this study was too small, so the representative was not high enough, which would be improved.

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A New Classification Method in Ultrasound Images of Benign and Malignant Thyroid Nodules Based on Transfer Learning and Deep Convolutional Neural Network

Complexity ◽

10.1155/2021/6296811 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Weibin Chen ◽

Zhiyang Gu ◽

Zhimin Liu ◽

Yaoyao Fu ◽

Zhipeng Ye ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Thyroid Nodules ◽

Ultrasound Image ◽

Image Data ◽

Data Sets ◽

Thyroid Ultrasound ◽

Joint Training ◽

Secondary Transfer

Thyroid nodule is a clinical disorder with a high incidence rate, with large number of cases being detected every year globally. Early analysis of a benign or malignant thyroid nodule using ultrasound imaging is of great importance in the diagnosis of thyroid cancer. Although the b-mode ultrasound can be used to find the presence of a nodule in the thyroid, there is no existing method for an accurate and automatic diagnosis of the ultrasound image. In this pursuit, the present study envisaged the development of an ultrasound diagnosis method for the accurate and efficient identification of thyroid nodules, based on transfer learning and deep convolutional neural network. Initially, the Total Variation- (TV-) based self-adaptive image restoration method was adopted to preprocess the thyroid ultrasound image and remove the boarder and marks. With data augmentation as a training set, transfer learning with the trained GoogLeNet convolutional neural network was performed to extract image features. Finally, joint training and secondary transfer learning were performed to improve the classification accuracy, based on the thyroid images from open source data sets and the thyroid images collected from local hospitals. The GoogLeNet model was established for the experiments on thyroid ultrasound image data sets. Compared with the network established with LeNet5, VGG16, GoogLeNet, and GoogLeNet (Improved), the results showed that using GoogLeNet (Improved) model enhanced the accuracy for the nodule classification. The joint training of different data sets and the secondary transfer learning further improved its accuracy. The results of experiments on the medical image data sets of various types of diseased and normal thyroids showed that the accuracy rate of classification and diagnosis of this method was 96.04%, with a significant clinical application value.

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