A Local and Global Feature Disentangled Network: Toward Classification of Benign-malignant Thyroid Nodules from Ultrasound Image

2022 ◽  
pp. 1-1
Author(s):  
Shi-Xuan Zhao ◽  
Yang Chen ◽  
Kai-Fu Yang ◽  
Kai-Fu Yang ◽  
Yan Luo ◽  
...  
Keyword(s):  
Thyroid Nodules ◽  
Ultrasound Image ◽  
Global Feature

The Accuracy of ACR TI-RADS Classification of Neck Ultrasound as a First-Line Diagnostic Approach for Thyroid Neoplasms in Pediatric Patients: A Retrospective Study

2018 ◽  
Vol 5 (1) ◽  
pp. 13-23
Author(s):  
Nikolai S. Grachev ◽  
Elena V. Feoktistova ◽  
Igor N. Vorozhtsov ◽  
Natalia V. Babaskina ◽  
Ekaterina Yu. Iaremenko ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Predictive Value ◽  
Thyroid Nodules ◽  
Pediatric Patients ◽  
Thyroid Neoplasms ◽  
Diagnostic Methods ◽  
Diagnostic Approach ◽  
First Line ◽  
Neck Ultrasound

Background.Ultrasound (US)-guided fine-needle aspiration biopsy (FNAB) is the gold standard in diagnosing the pathological nature of undetermined thyroid nodules. However, in some instances limitations and shortcomings arise, making it insufficient for determining a specific diagnosis.Objective.Our aim was to evaluate the effectiveness of ACR TI-RADS classification of neck ultrasound as a first-line diagnostic approach for thyroid neoplasms in pediatric patients.Methods.A retrospective analysis was made of FNA and US protocols in 70 patients who underwent the examination and treatment at Dmitry Rogachev National Research Center between January 2012 and August 2017. In the retrospective series 70% (49/70) of patients undergone FNA and 43% (30/70) of them undergone repeated FNA. All US protocols were interpreted according to ACR TI-RADS system by the two independent experts. The clinical judgment was assessed using the concordance test and the reliability of preoperative diagnostic methods was analized.Results.According to histologic examination protocols, benign nodules reported greater multimorbidity 29% (20/70), compared with thyroid cancer 17% (12/70), complicating FNA procedure. A statistically significant predictor of thyroid cancer with a tumor size ACR TI-RADS showed a significant advantage of ACR TI-RADS due to higher sensitivity (97.6 vs 60%), specificity (78.6 vs 53.8%), positive predictive value (87.2 vs 71.4%), and negative predictive value (95.7 vs 41.2%). Concordance on the interpreted US protocols according to ACR TI-RADS classification between two experts was high, excluding accidental coincidence.Conclusion.The data support the feasibility of US corresponding to the ACR TI-RADS classification as a first-line diagnostic approach for thyroid neoplasm reducing the number of unnecessary biopsies for thyroid nodules.

On the Classification of TI-RADS and Stratification of Signs of Thyroid Cancer According to Ultrasound Data

2017 ◽  
pp. 29-38 ◽  
Author(s):  
E. P. Fisenko ◽  
J. P. Sich ◽  
N. N. Vetsheva
Keyword(s):  
Thyroid Cancer ◽  
Retrospective Analysis ◽  
Thyroid Nodules ◽  
Thyroid Ultrasound ◽  
High Reproducibility ◽  
System 1

Objective:a comparative “blind” assessment of the thyroid nodules identified by ultrasound, according to the TI-RADS scale in various modifications.Materials and methods.Retrospective analysis of 149 echograms  of thyroid nodules by three independent experts was performed (the  experience of ultrasound of thyroid ultrasound for more than 7 years).Results. In solid nodules, high-specific large (more than 94%) and  small (more than 90%) ultrasound signs of thyroid cancer have been identified. The nodes are stratified according to the TI-RADS system: 1 – in the modification J.Y. Kwak et al. (2011), 2 – according to the  proposed system, taking into account small ultrasound signs of  thyroid cancer. High reproducibility of both systems are obtained. In the first system 13.7% of cancer nodes fell into the category of TI- RADS 3 (benign formations), in the second system only 5% of  cancers fell into the category of TI-RADS 3, which is important for  biopsy selection. The sensitivity of the first system was TI-RADS  82.05%, of the second system – 94.87%.Conclusions.Classification of TI-RADS can be used to interpret the  ultrasound results of thyroid nodules, taking into account both the  main large and small ultrasound signs of cancer. For its validation in  our country, it is necessary to further broad discussion of the proposed TI-RADS system.

Ultrasound image classification of non-mass abnormalities of the breast

Choonpa Igaku ◽  
2018 ◽  
Vol 45 (4) ◽  
pp. 355-360
Author(s):  
Takanori WATANABE
Keyword(s):  
Image Classification ◽  
Ultrasound Image

scholarly journals Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques

Medicina ◽  
2021 ◽  
Vol 57 (6) ◽  
pp. 527
Author(s):  
Vijay Vyas Vadhiraj ◽  
Andrew Simpkin ◽  
James O’Connell ◽  
Naykky Singh Singh Ospina ◽  
Spyridoula Maraka ◽  
...  
Keyword(s):  
Decision Making ◽  
Sensitivity And Specificity ◽  
Thyroid Nodules ◽  
Median Filter ◽  
Ultrasound Image ◽  
Multiple Instance Learning ◽  
Image Features ◽  
Classification Model ◽  
Support Vector ◽  
Accuracy Score

Background and Objectives: Thyroid nodules are lumps of solid or liquid-filled tumors that form inside the thyroid gland, which can be malignant or benign. Our aim was to test whether the described features of the Thyroid Imaging Reporting and Data System (TI-RADS) could improve radiologists’ decision making when integrated into a computer system. In this study, we developed a computer-aided diagnosis system integrated into multiple-instance learning (MIL) that would focus on benign–malignant classification. Data were available from the Universidad Nacional de Colombia. Materials and Methods: There were 99 cases (33 Benign and 66 malignant). In this study, the median filter and image binarization were used for image pre-processing and segmentation. The grey level co-occurrence matrix (GLCM) was used to extract seven ultrasound image features. These data were divided into 87% training and 13% validation sets. We compared the support vector machine (SVM) and artificial neural network (ANN) classification algorithms based on their accuracy score, sensitivity, and specificity. The outcome measure was whether the thyroid nodule was benign or malignant. We also developed a graphic user interface (GUI) to display the image features that would help radiologists with decision making. Results: ANN and SVM achieved an accuracy of 75% and 96% respectively. SVM outperformed all the other models on all performance metrics, achieving higher accuracy, sensitivity, and specificity score. Conclusions: Our study suggests promising results from MIL in thyroid cancer detection. Further testing with external data is required before our classification model can be employed in practice.

scholarly journals Classification of Thyroid Nodules with Stacked Denoising Sparse Autoencoder

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Zexin Li ◽  
Kaiji Yang ◽  
Lili Zhang ◽  
Chiju Wei ◽  
Peixuan Yang ◽  
...  
Keyword(s):  
Likelihood Ratio ◽  
Predictive Value ◽  
Thyroid Nodules ◽  
Area Under The Curve ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Discriminative Ability ◽  
Sparse Autoencoder ◽  
Immune Related Genes

Purpose. Several commercial tests have been used for the classification of indeterminate thyroid nodules in cytology. However, the geographic inconvenience and high cost confine their widespread use. This study aims to develop a classifier for conveniently clinical utility. Methods. Gene expression data of thyroid nodule tissues were collected from three public databases. Immune-related genes were used to construct the classifier with stacked denoising sparse autoencoder. Results. The classifier performed well in discriminating malignant and benign thyroid nodules, with an area under the curve of 0.785 [0.638–0.931], accuracy of 92.9% [92.7–93.0%], sensitivity of 98.6% [95.9–101.3%], specificity of 58.3% [30.4–86.2%], positive likelihood ratio of 2.367 [1.211–4.625], and negative likelihood ratio of 0.024 [0.003–0.177]. In the cancer prevalence range of 20–40% for indeterminate thyroid nodules in cytology, the range of negative predictive value of this classifier was 37–61%, and the range of positive predictive value was 98–99%. Conclusion. The classifier developed in this study has the superb discriminative ability for thyroid nodules. However, it needs validation in cytologically indeterminate thyroid nodules before clinical use.

A Multi-Gene Test for Accurate Classification of Thyroid Nodules.

2010 ◽  
pp. P1-542-P1-542
Author(s):  
JI Wilde ◽  
N Rabbee ◽  
D Chudova ◽  
H Wang ◽  
C Friedlander ◽  
...  
Keyword(s):  
Thyroid Nodules ◽  
Gene Test

scholarly journals Segmentation of thyroid nodules using Improvised U-Net Architecture

2020 ◽  
Vol 9 (8) ◽  
pp. 56-60
Keyword(s):  
Thyroid Nodules ◽  
Performance Metrics ◽  
Automatic Segmentation ◽  
Research Work ◽  
Ultrasound Image ◽  
True Positive Rate ◽  
Medical Image Segmentation ◽  
Common Disease ◽  
Low Resolution ◽  
Feature Map

Thyroid nodules are considered as most common disease found in adults and thyroid cancer has increased over the years rapidly. Further automatic segmentation for ultrasound image is quite difficult due to the image poor quality, hence several researcher have focused and observed that U-Net achieves significant performance in medical image segmentation. However U-net faces the problem of low resolution which causes smoothness in image, hence in this research work we have proposed improvised U-Net which helps in achieving the better performance. The main aim of this research work is to achieve the probable Region of Interest through segmentation with better efficiency. In order to achieve that Improvised U-Net develops two distinctive feature map i.e. High level feature Map and low level feature map to avoid the problem of low resolution. Further proposed model is evaluated considering the standard dataset based on performance metrics such as Dice Coefficient and True positive Rate. Moreover our model achieves better performance than the existing model.

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