scholarly journals Reliability of a computer-aided system in the evaluation of indeterminate ultrasound images of thyroid nodules

2022 ◽  
Vol 11 (1) ◽  
Author(s):  
J L Reverter ◽  
L Ferrer-Estopiñan ◽  
F Vázquez ◽  
S Ballesta ◽  
S Batule ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Risk Stratification ◽  
Real World ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Ultrasound Images ◽  
Cad System ◽  
The Us ◽  
Computer Aided

Introduction Computer-aided diagnostic (CAD) programs for malignancy risk stratification from ultrasound (US) imaging of thyroid nodules are being validated both experimentally and in real-world practice. However, they have not been tested for reliability in analyzing difficult or unclear images. Methods US images with indeterminate characteristics were evaluated by five observers with different experience in US examination and by a commercial CAD program. The nodules, on which the observers widely agreed, were considered concordant and, if there was little agreement, not concordant or difficult to assess. The diagnostic performance of the readers and the CAD program was calculated and compared in both groups of nodule images. Results In the group of concordant thyroid nodules (n = 37), the clinicians and the CAD system obtained similar levels of accuracy (77.0% vs 74.2%, respectively; P = 0.7) and no differences were found in sensitivity (SEN) (95.0% vs 87.5%, P = 0.2), specificity (SPE) (45.5 vs 49.4, respectively; P = 0.7), positive predictive value (PPV) (75.2% vs 77.7%, respectively; P = 0.8), nor negative predictive value (NPV) (85.6 vs 77.7, respectively; P = 0.3). When analyzing the non-concordant nodules (n = 43), the CAD system presented a decrease in accuracy of 4.2%, which was significantly lower than that observed by the experts (19.9%, P = 0.02). Conclusions Clinical observers are similar to the CAD system in the US assessment of the risk of thyroid nodules. However, the AI system for thyroid nodules AmCAD-UT® showed more reliability in the analysis of unclear or misleading images.

scholarly journals A Beneficial Role of Computer-aided Diagnosis System for Less Experienced Physicians in the Diagnosis of Thyroid Nodule on Ultrasound

2021 ◽  
Author(s):  
Sunyoung Kang ◽  
Eunjung Lee ◽  
Chae Won Chung ◽  
Han Na Jang ◽  
Joon Ho Moon ◽  
...  
Keyword(s):  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Needle Aspiration ◽  
Computer Aided Diagnosis ◽  
Ultrasound Images ◽  
Cad System ◽  
Computer Aided ◽  
Before And After ◽  
Needle Aspiration Cytology ◽  
Aided Diagnosis

Abstract Ultrasonography is the primary diagnostic tool for thyroid nodules, while the accuracy is highly operator-dependent. The aim of this study was to investigate whether ultrasonography with computer-aided diagnosis (CAD) has assisting roles to physicians in the diagnosis of thyroid nodules. 451 thyroid nodules (³ 1 cm) evaluated by fine-needle aspiration cytology following surgery were included. 300 (66.5%) of them were diagnosed as malignancy. Thirteen physicians who had 0 months (E0, n=8), 1 year (E1, n=2), or more than 5 years (E5, n=3) of experience in ultrasonography reviewed the prepared ultrasound images of thyroid nodules before and after CAD assistance. The diagnostic performance of CAD was comparable to that of the E5 group, and better than those of the E0 and E1 groups. The AUC of the CAD for conventional PTC was higher than that for FTC and follicular variant PTC (0.925 vs. 0.499), independent of tumor size. CAD assistance significantly improved diagnostic performance in E0 group, but not in the E1 and E5 groups. In conclusion, the CAD system showed good performance in the diagnosis of conventional PTC. CAD assistance improved the diagnostic performance of physicians with less experience in ultrasonography, especially in the diagnosis of conventional PTC.

scholarly journals Diagnostic performance evaluation of different TI-RADS using ultrasound computer-aided diagnosis of thyroid nodules: An experience with adjusted settings

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245617
Author(s):  
Nonhlanhla Chambara ◽  
Shirley Y. W. Liu ◽  
Xina Lo ◽  
Michael Ying
Keyword(s):  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Computer Aided Diagnosis ◽  
Ultrasound Images ◽  
American Thyroid Association ◽  
Korean Society ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Default Setting

Background Thyroid cancer diagnosis has evolved to include computer-aided diagnosis (CAD) approaches to overcome the limitations of human ultrasound feature assessment. This study aimed to evaluate the diagnostic performance of a CAD system in thyroid nodule differentiation using varied settings. Methods Ultrasound images of 205 thyroid nodules from 198 patients were analysed in this retrospective study. AmCAD-UT software was used at default settings and 3 adjusted settings to diagnose the nodules. Six risk-stratification systems in the software were used to classify the thyroid nodules: The American Thyroid Association (ATA), American College of Radiology Thyroid Imaging, Reporting, and Data System (ACR-TIRADS), British Thyroid Association (BTA), European Union (EU-TIRADS), Kwak (2011) and the Korean Society of Thyroid Radiology (KSThR). The diagnostic performance of CAD was determined relative to the histopathology and/or cytology diagnosis of each nodule. Results At the default setting, EU-TIRADS yielded the highest sensitivity, 82.6% and lowest specificity, 42.1% while the ATA-TIRADS yielded the highest specificity, 66.4%. Kwak had the highest AUROC (0.74) which was comparable to that of ACR, ATA, and KSThR TIRADS (0.72, 0.73, and 0.70 respectively). At a hyperechoic foci setting of 3.5 with other settings at median values; ATA had the best-balanced sensitivity, specificity and good AUROC (70.4%; 67.3% and 0.71 respectively). Conclusion The default setting achieved the best diagnostic performance with all TIRADS and was best for maximizing the sensitivity of EU-TIRADS. Adjusting the settings by only reducing the sensitivity to echogenic foci may be most helpful for improving specificity with minimal change in sensitivity.

scholarly journals A beneficial role of computer-aided diagnosis system for less experienced physicians in the diagnosis of thyroid nodule on ultrasound

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sunyoung Kang ◽  
Eunjung Lee ◽  
Chae Won Chung ◽  
Han Na Jang ◽  
Joon Ho Moon ◽  
...  
Keyword(s):  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Needle Aspiration ◽  
Computer Aided Diagnosis ◽  
Cad System ◽  
The Us ◽  
Computer Aided ◽  
Needle Aspiration Cytology ◽  
Aided Diagnosis

AbstractUltrasonography (US) is the primary diagnostic tool for thyroid nodules, while the accuracy is operator-dependent. It is widely used not only by radiologists but also by physicians with different levels of experience. The aim of this study was to investigate whether US with computer-aided diagnosis (CAD) has assisting roles to physicians in the diagnosis of thyroid nodules. 451 thyroid nodules evaluated by fine-needle aspiration cytology following surgery were included. 300 (66.5%) of them were diagnosed as malignancy. Physicians with US experience less than 1 year (inexperienced, n = 10), or more than 5 years (experienced, n = 3) reviewed the US images of thyroid nodules with or without CAD assistance. The diagnostic performance of CAD was comparable to that of the experienced group, and better than those of the inexperienced group. The AUC of the CAD for conventional PTC was higher than that for FTC and follicular variant PTC (0.925 vs. 0.499), independent of tumor size. CAD assistance significantly improved diagnostic performance in the inexperienced group, but not in the experienced groups. In conclusion, the CAD system showed good performance in the diagnosis of conventional PTC. CAD assistance improved the diagnostic performance of less experienced physicians in US, especially in diagnosis of conventional PTC.

scholarly journals Diagnostic Performance of Different Thyroid Imaging Reporting and Data Systems (Kwak-TIRADS, EU-TIRADS and ACR TI-RADS) for Risk Stratification of Small Thyroid Nodules (≤10 mm)

2020 ◽  
Vol 9 (1) ◽  
pp. 236 ◽  
Author(s):  
Simone Schenke ◽  
Rigobert Klett ◽  
Philipp Seifert ◽  
Michael C. Kreissl ◽  
Rainer Görges ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Cohort Analysis ◽  
Medullary Carcinoma ◽  
Data Systems ◽  
Thyroid Imaging ◽  
Promising Tool ◽  
Risk Of Malignancy

Due to the widespread use of ultrasound, small thyroid nodules (TNs) ≤ 10 mm are common findings. Standardized approaches for the risk stratification of TNs with Thyroid Imaging Reporting and Data Systems (TIRADS) were evaluated for the clinical routine. With TIRADS, the risk of malignancy in TNs is calculated by scoring the number or combination of suspicious ultrasound features, leading to recommendations for further diagnostic steps. However, there are only scarce data on the performance of TIRADS for small TNs. The aim was to compare three different TIRADS for risk stratification of small TNs in routine clinical practice. We conducted a retrospective cohort analysis of TNs ≤ 10 mm and their available histology. Nodules were classified according to three different TIRADS. In the study, 140 patients (n = 113 female) with 145 thyroid nodules (n = 76 malignant) were included. Most of the malignant nodules were papillary carcinoma (97%), and the remaining 3% were medullary carcinoma. For all tested TIRADS, the prevalence of malignancy rose with increasing category levels. The highest negative predictive value was found for ACR TI-RADS and the highest positive predictive value for Kwak-TIRADS. All tested variants of TIRADS showed comparable diagnostic performance for the risk stratification of small TNs. TIRADS seems to be a promising tool to reliably assess the risk of malignancy of small TNs.

scholarly journals AIBx, artificial intelligence model to risk stratify thyroid nodules

2021 ◽  
Author(s):  
Johnson Thomas ◽  
Tracy Haertling
Keyword(s):  
Artificial Intelligence ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Thyroid Nodules ◽  
Classification Systems ◽  
Image Similarity ◽  
Ultrasound Images ◽  
Similarity Model ◽  
Sensitivity Specificity

AbstractBackgroundCurrent classification systems for thyroid nodules are very subjective. Artificial intelligence (AI) algorithms have been used to decrease subjectivity in medical image interpretation. 1 out of 2 women over the age of 50 may have a thyroid nodule and at present the only way to exclude malignancy is through invasive procedures. Hence, there exists a need for noninvasive objective classification of thyroid nodules. Some cancers have benign appearance on ultrasonogram. Hence, we decided to create an image similarity algorithm rather than image classification algorithm.MethodsUltrasound images of thyroid nodules from patients who underwent either biopsy or thyroid surgery from February of 2012 through February of 2017 in our institution were used to create AI models. Nodules were excluded if there was no definitive diagnosis of benignity or malignancy. 482 nodules met the inclusion criteria and all available images from these nodules were used to create the AI models. Later, these AI models were used to test 103 thyroid nodules which underwent biopsy or surgery from March of 2017 through July of 2018.ResultsNegative predictive value of the image similarity model was 93.2%. Sensitivity, specificity, positive predictive value and accuracy of the model was 87.8%, 78.5%, 65.9% and 81.5% respectively.ConclusionWhen compared to published results of ACR TIRADS and ATA classification system, our image similarity model had comparable negative predictive value with better sensitivity specificity and positive predictive value. By using image similarity AI models, we can eliminate subjectivity and decrease the number of unnecessary biopsies. Using image similarity AI model, we were able to create an explainable AI model which increases physician’s confidence in the predictions.

scholarly journals Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis

2019 ◽  
Vol 9 (4) ◽  
pp. 186-193
Author(s):  
Lei Xu ◽  
Junling Gao ◽  
Quan Wang ◽  
Jichao Yin ◽  
Pengfei Yu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Deep Learning ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Computer Aided Diagnosis ◽  
Cad Systems ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

Background: Computer-aided diagnosis (CAD) systems are being applied to the ultrasonographic diagnosis of malignant thyroid nodules, but it remains controversial whether the systems add any accuracy for radiologists. Objective: To determine the accuracy of CAD systems in diagnosing malignant thyroid nodules. Methods: PubMed, EMBASE, and the Cochrane Library were searched for studies on the diagnostic performance of CAD systems. The diagnostic performance was assessed by pooled sensitivity and specificity, and their accuracy was compared with that of radiologists. The present systematic review was registered in PROSPERO (CRD42019134460). Results: Nineteen studies with 4,781 thyroid nodules were included. Both the classic machine learning- and the deep learning-based CAD system had good performance in diagnosing malignant thyroid nodules (classic machine learning: sensitivity 0.86 [95% CI 0.79–0.92], specificity 0.85 [95% CI 0.77–0.91], diagnostic odds ratio (DOR) 37.41 [95% CI 24.91–56.20]; deep learning: sensitivity 0.89 [95% CI 0.81–0.93], specificity 0.84 [95% CI 0.75–0.90], DOR 40.87 [95% CI 18.13–92.13]). The diagnostic performance of the deep learning-based CAD system was comparable to that of the radiologists (sensitivity 0.87 [95% CI 0.78–0.93] vs. 0.87 [95% CI 0.85–0.89], specificity 0.85 [95% CI 0.76–0.91] vs. 0.87 [95% CI 0.81–0.91], DOR 40.12 [95% CI 15.58–103.33] vs. DOR 44.88 [95% CI 30.71–65.57]). Conclusions: The CAD systems demonstrated good performance in diagnosing malignant thyroid nodules. However, experienced radiologists may still have an advantage over CAD systems during real-time diagnosis.

scholarly journals The value of S-Detect in improving the diagnostic performance of radiologists for the differential diagnosis of thyroid nodules

2020 ◽  
Vol 22 (4) ◽  
pp. 415
Author(s):  
Qi Wei ◽  
Shu-E Zeng ◽  
Li-Ping Wang ◽  
Yu-Jing Yan ◽  
Ting Wang ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Characteristic Curve ◽  
Diagnostic Value ◽  
Ultrasound Images ◽  
Experienced Radiologist ◽  
Before And After ◽  
Sensitivity Specificity

Aims: To compare the diagnostic value of S-Detect (a computer aided diagnosis system using deep learning) in differentiating thyroid nodules in radiologists with different experience and to assess if S-Detect can improve the diagnostic performance of radiologists.Materials and methods: Between February 2018 and October 2019, 204 thyroid nodules in 181 patients were included. An experienced radiologist performed ultrasound for thyroid nodules and obtained the result of S-Detect. Four radiologists with different experience on thyroid ultrasound (Radiologist 1, 2, 3, 4 with 1, 4, 9, 20 years, respectively) analyzed the conventional ultrasound images of each thyroid nodule and made a diagnosis of “benign” or “malignant” based on the TI-RADS category. After referring to S-Detect results, they re-evaluated the diagnoses. The diagnostic performance of radiologists was analyzed before and after referring to the results of S-Detect.Results: The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of S-Detect were 77.0, 91.3, 65.2, 68.3 and 90.1%, respectively. In comparison with the less experienced radiologists (radiologist 1 and 2), S-Detect had a higher area under receiver operating characteristic curve (AUC), accuracy and specificity (p <0.05). In comparison with the most experienced radiologist, the diagnostic accuracy and AUC were lower (p<0.05). In the less experienced radiologists, the diagnostic accuracy, specificity and AUC were significantly improved when combined with S-Detect (p<0.05), but not for experienced radiologists (radiologist 3 and 4) (p>0.05).Conclusions: S-Detect may become an additional diagnostic method for the diagnosis of thyroid nodules and improve the diagnostic performance of less experienced radiologists. 

scholarly journals Qualitative analysis of contrast-enhanced ultrasound in the diagnosis of small, TR3–5 benign and malignant thyroid nodules measuring ≤1 cm

2020 ◽  
Vol 93 (1111) ◽  
pp. 20190923
Author(s):  
Xin Li ◽  
Feng Gao ◽  
Fan Li ◽  
Xiao-xia Han ◽  
Si-hui Shao ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Qualitative Analysis ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Area Under The Curve ◽  
Contrast Enhanced Ultrasound ◽  
Contrast Enhanced ◽  
Sensitivity Specificity

Objective: To evaluate the performance of contrast-enhanced ultrasound in the diagnosis of small, solid, TR3–5 benign and malignant thyroid nodules (≤1 cm). Methods: From January 2016 to March 2018, 185 thyroid nodules from 154 patients who underwent contrast enhanced ultrasound (CEUS) and fine-needle aspiration or thyroidectomy in Shanghai General Hospital were included. The χ2 test was used to compare the CEUS characteristics of benign and malignant thyroid nodules, and the CEUS features of malignant nodules assigned scores. The total score of the CEUS features and the scores of the above nodules were evaluated according to the latest 2017 version of the Thyroid Imaging Reporting and Data System (TI-RADS). The diagnostic performance of the two were compared based on the receiver operating characteristic curves generated for benign and malignant thyroid nodules. Results: The degree, enhancement patterns, boundary, shape, and homogeneity of enhancement in thyroid small solid nodules were significantly different (p＜0.05). No significant differences were seen between benign and malignant thyroid nodules regarding completeness of enhancement and size of enhanced lesions (p＞0.05). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the TI-RADS classification TR5 in diagnosis of malignant nodules were 90.10%, 55.95%, 74.59%, 72.22%, and 82.46%, respectively (area under the curve [AUC]=0.738; 95% confidence interval[CI], 0.663–0.813). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the total score of CEUS qualitative analysis indicators were 86.13%, 89.29%, 87.57%, 90.63%, and 84.27% respectively (AUC = 0.916; 95% CI, 0.871–0.961). Conclusion: CEUS qualitative analysis is superior to TI-RADS in evaluating the diagnostic performance of small, solid thyroid nodules. Qualitative analysis of CEUS has a significantly higher specificity for diagnosis of malignant thyroid nodules than TI-RADS. Advances in knowledge: The 2017 version of TI-RADS has recently suggested the malignant stratification of thyroid nodules by ultrasound. In this paper we applied this system and CEUS to evaluate 185 nodules and compare the results with pathological findings to access the diagnostic performance.

scholarly journals Diagnosis of Thyroid Nodules: Performance of a Deep Learning Convolutional Neural Network Model vs. Radiologists

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Vivian Y. Park ◽  
Kyunghwa Han ◽  
Yeong Kyeong Seong ◽  
Moon Ho Park ◽  
Eun-Kyung Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Estimating Equation ◽  
Support Vector ◽  
Generalized Estimating Equation ◽  
Cad System ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

AbstractComputer-aided diagnosis (CAD) systems hold potential to improve the diagnostic accuracy of thyroid ultrasound (US). We aimed to develop a deep learning-based US CAD system (dCAD) for the diagnosis of thyroid nodules and compare its performance with those of a support vector machine (SVM)-based US CAD system (sCAD) and radiologists. dCAD was developed by using US images of 4919 thyroid nodules from three institutions. Its diagnostic performance was prospectively evaluated between June 2016 and February 2017 in 286 nodules, and was compared with those of sCAD and radiologists, using logistic regression with the generalized estimating equation. Subgroup analyses were performed according to experience level and separately for small thyroid nodules 1–2 cm. There was no difference in overall sensitivity, specificity, positive predictive value (PPV), negative predictive value and accuracy (all p > 0.05) between radiologists and dCAD. Radiologists and dCAD showed higher specificity, PPV, and accuracy than sCAD (all p < 0.001). In small nodules, experienced radiologists showed higher specificity, PPV and accuracy than sCAD (all p < 0.05). In conclusion, dCAD showed overall comparable diagnostic performance with radiologists and assessed thyroid nodules more effectively than sCAD, without loss of sensitivity.

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