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Ma X, Han X, Zhang L. An Improved k-Nearest Neighbor Algorithm for Recognition and Classification of Thyroid Nodules. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024; 43:1025-1036. [PMID: 38400537 DOI: 10.1002/jum.16429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 01/17/2024] [Accepted: 01/28/2024] [Indexed: 02/25/2024]
Abstract
OBJECTIVES To complete the task of automatic recognition and classification of thyroid nodules and solve the problem of high classification error rates when the samples are imbalanced. METHODS An improved k-nearest neighbor (KNN) algorithm is proposed and a method for automatic thyroid nodule classification based on the improved KNN algorithm is established. In the improved KNN algorithm, we consider not only the number of class labels for various classes of data in KNNs, but also the corresponding weights. And we use the Minkowski distance measure instead of the Euclidean distance measure. RESULTS A total of 508 ultrasound images of thyroid nodules, including 415 benign nodules and 93 malignant nodules, were used in the paper. Experimental results show the improved KNN has 0.872549 accuracy, 0.867347 precision, 1 recall, and 0.928962 F1-score. At the same time, we also considered the influence of different distance weights, the value of k, different distance measures on the classification results. CONCLUSIONS A comparison result shows that our method has a better performance than the traditional KNN and other classical machine learning methods.
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Affiliation(s)
- Xuesi Ma
- School of Mathematics and Information Science, Henan Polytechnic University, Jiaozuo, China
| | - Xiang Han
- School of Mathematics and Information Science, Henan Polytechnic University, Jiaozuo, China
| | - Lina Zhang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China
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2
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Chen J, Ye D, Lv S, Li X, Ye F, Huang Y, Su Z, Lin Y, Xie T, Wen X. Benign thyroid nodules classified as ACR TI-RADS 4 or 5: Imaging and histological features. Eur J Radiol 2023; 175:111261. [PMID: 38493559 DOI: 10.1016/j.ejrad.2023.111261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/15/2023] [Accepted: 12/09/2023] [Indexed: 03/19/2024]
Abstract
BACKGROUND American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) being most widely applied in clinical practice, there is an overlap in US imaging manifestations between benign and malignant thyroid nodules. OBJECTIVES To analyze the imaging and histological characteristics of pathological benign thyroid nodules categorized as American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) 4 or 5, and to explore the correlation between the suspicious sonographic signs resulting in the misdiagnoses and the histopathological features. MATERIALS AND METHODS Overall, 227 benign thyroid nodules (215 patients) in ACR TI-RADS 4 or 5 sampled through surgical excision were analyzed between December 2020 and August 2022. We retrospectively reread the ultrasound (US) images of the pathological discordant cases, after which we performed a systematic analysis focusing on the histopathological characteristics of thyroid lesions and recorded the findings. Qualitative US features and pathological significance of the thyroid nodules were analyzed using the chi-square and Fisher's exact tests. RESULTS The pathological type of 227 thyroid nodules (n = 103 in ACR TI-RADS 4 and n = 124 in ACR TI-RADS 5) was nodular goiter together with other histopathological features, namely, fibrosis (n = 103, 45.4 %), calcification (n = 70, 30.8 %), adenomatous hyperplasia (n = 31, 13.7 %), follicular epithelial hyperplasia (n = 23, 10.1 %), Hashimoto's thyroiditis (n = 18, 7.9 %), and cystic degeneration (n = 16, 7.1 %). Fibrosis was the most common histopathological feature in both ACR TI-RADS 4 (n = 42, 40.8 %) and 5 (n = 61, 49.2 %) categories of benign thyroid nodules. Thyroid nodules with fibrosis demonstrated sonographic features of "taller than wide" (p < 0.05), while lesions with follicular epithelial hyperplasia were likely to be detected with irregular and/or lobulated margins and very hypoechoic on US (p < 0.05 for both). CONCLUSION Benign thyroid nodules with histopathological findings such as fibrosis are associated with suspicious US features, which may give inappropriately higher TIRADS stratification.
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Affiliation(s)
- Jiamin Chen
- The Fifth Affiliated Hospital Sun Yat-Sen University, Zhuhai 519000, China.
| | - Dalin Ye
- The Fifth Affiliated Hospital Sun Yat-Sen University, Zhuhai 519000, China
| | - Shuhui Lv
- The Fifth Affiliated Hospital Sun Yat-Sen University, Zhuhai 519000, China
| | - Xuefeng Li
- The Fifth Affiliated Hospital Sun Yat-Sen University, Zhuhai 519000, China
| | - Feile Ye
- The Fifth Affiliated Hospital Sun Yat-Sen University, Zhuhai 519000, China
| | - Yongquan Huang
- The Fifth Affiliated Hospital Sun Yat-Sen University, Zhuhai 519000, China.
| | - Zhongzhen Su
- The Fifth Affiliated Hospital Sun Yat-Sen University, Zhuhai 519000, China.
| | - Yuhong Lin
- The Fifth Affiliated Hospital Sun Yat-Sen University, Zhuhai 519000, China.
| | - Ting Xie
- The Fifth Affiliated Hospital Sun Yat-Sen University, Zhuhai 519000, China.
| | - Xin Wen
- The Fifth Affiliated Hospital Sun Yat-Sen University, Zhuhai 519000, China.
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Liu N, Fenster A, Tessier D, Chun J, Gou S, Chong J. Self-supervised enhanced thyroid nodule detection in ultrasound examination video sequences with multi-perspective evaluation. Phys Med Biol 2023; 68:235007. [PMID: 37918343 DOI: 10.1088/1361-6560/ad092a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 11/02/2023] [Indexed: 11/04/2023]
Abstract
Objective.Ultrasound is the most commonly used examination for the detection and identification of thyroid nodules. Since manual detection is time-consuming and subjective, attempts to introduce machine learning into this process are ongoing. However, the performance of these methods is limited by the low signal-to-noise ratio and tissue contrast of ultrasound images. To address these challenges, we extend thyroid nodule detection from image-based to video-based using the temporal context information in ultrasound videos.Approach.We propose a video-based deep learning model with adjacent frame perception (AFP) for accurate and real-time thyroid nodule detection. Compared to image-based methods, AFP can aggregate semantically similar contextual features in the video. Furthermore, considering the cost of medical image annotation for video-based models, a patch scale self-supervised model (PASS) is proposed. PASS is trained on unlabeled datasets to improve the performance of the AFP model without additional labelling costs.Main results.The PASS model is trained by 92 videos containing 23 773 frames, of which 60 annotated videos containing 16 694 frames were used to train and evaluate the AFP model. The evaluation is performed from the video, frame, nodule, and localization perspectives. In the evaluation of the localization perspective, we used the average precision metric with the intersection-over-union threshold set to 50% (AP@50), which is the area under the smoothed Precision-Recall curve. Our proposed AFP improved AP@50 from 0.256 to 0.390, while the PASS-enhanced AFP further improved the AP@50 to 0.425. AFP and PASS also improve the performance in the valuations of other perspectives based on the localization results.Significance.Our video-based model can mitigate the effects of low signal-to-noise ratio and tissue contrast in ultrasound images and enable the accurate detection of thyroid nodules in real-time. The evaluation from multiple perspectives of the ablation experiments demonstrates the effectiveness of our proposed AFP and PASS models.
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Affiliation(s)
- Ningtao Liu
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi'an, 710126, People's Republic of China
- Robarts Research Institute, Western University, London, ON, N6A 5B7, Canada
| | - Aaron Fenster
- Robarts Research Institute, Western University, London, ON, N6A 5B7, Canada
- Department of Medical Imaging, Western University, London, ON, N6A 5A5, Canada
- Department of Medical Biophysics, Western University, London, ON, N6A 5C1, Canada
| | - David Tessier
- Robarts Research Institute, Western University, London, ON, N6A 5B7, Canada
| | - Jin Chun
- Schulich School of Medicine, Western University, London, ON, N6A 5C1, Canada
| | - Shuiping Gou
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi'an, 710126, People's Republic of China
| | - Jaron Chong
- Department of Medical Imaging, Western University, London, ON, N6A 5A5, Canada
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Cao CL, Li QL, Tong J, Shi LN, Li WX, Xu Y, Cheng J, Du TT, Li J, Cui XW. Artificial intelligence in thyroid ultrasound. Front Oncol 2023; 13:1060702. [PMID: 37251934 PMCID: PMC10213248 DOI: 10.3389/fonc.2023.1060702] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 04/07/2023] [Indexed: 05/31/2023] Open
Abstract
Artificial intelligence (AI), particularly deep learning (DL) algorithms, has demonstrated remarkable progress in image-recognition tasks, enabling the automatic quantitative assessment of complex medical images with increased accuracy and efficiency. AI is widely used and is becoming increasingly popular in the field of ultrasound. The rising incidence of thyroid cancer and the workload of physicians have driven the need to utilize AI to efficiently process thyroid ultrasound images. Therefore, leveraging AI in thyroid cancer ultrasound screening and diagnosis cannot only help radiologists achieve more accurate and efficient imaging diagnosis but also reduce their workload. In this paper, we aim to present a comprehensive overview of the technical knowledge of AI with a focus on traditional machine learning (ML) algorithms and DL algorithms. We will also discuss their clinical applications in the ultrasound imaging of thyroid diseases, particularly in differentiating between benign and malignant nodules and predicting cervical lymph node metastasis in thyroid cancer. Finally, we will conclude that AI technology holds great promise for improving the accuracy of thyroid disease ultrasound diagnosis and discuss the potential prospects of AI in this field.
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Affiliation(s)
- Chun-Li Cao
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Qiao-Li Li
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Jin Tong
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
| | - Li-Nan Shi
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Wen-Xiao Li
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Ya Xu
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
| | - Jing Cheng
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
| | - Ting-Ting Du
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
| | - Jun Li
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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Kim JS, Kim BG, Stybayeva G, Hwang SH. Diagnostic Performance of Various Ultrasound Risk Stratification Systems for Benign and Malignant Thyroid Nodules: A Meta-Analysis. Cancers (Basel) 2023; 15:cancers15020424. [PMID: 36672373 PMCID: PMC9857194 DOI: 10.3390/cancers15020424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/31/2022] [Accepted: 01/04/2023] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND To evaluate the diagnostic performance of ultrasound risk-stratification systems for the discrimination of benign and malignant thyroid nodules and to determine the optimal cutoff values of individual risk-stratification systems. METHODS PubMed, Embase, SCOPUS, Web of Science, and Cochrane library databases were searched up to August 2022. Sensitivity and specificity data were collected along with the characteristics of each study related to ultrasound risk stratification systems. RESULTS Sixty-seven studies involving 76,512 thyroid nodules were included in this research. The sensitivity, specificity, diagnostic odds ratios, and area under the curves by K-TIRADS (4), ACR-TIRADS (TR5), ATA (high suspicion), EU-TIRADS (5), and Kwak-TIRADS (4b) for malignancy risk stratification of thyroid nodules were 92.5%, 63.5%, 69.8%, 70.6%, and 95.8%, respectively; 62.8%, 89.6%, 87.2%, 83.9%, and 63.8%, respectively; 20.7111, 16.8442, 15.7398, 12.2986, and 38.0578, respectively; and 0.792, 0.882, 0.859, 0.843, and 0.929, respectively. CONCLUSION All ultrasound-based risk-stratification systems had good diagnostic performance. Although this study determined the best cutoff values in individual risk-stratification systems based on statistical assessment, clinicians could adjust or alter cutoff values based on the clinical purpose of the ultrasound and the reciprocal changes in sensitivity and specificity.
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Affiliation(s)
- Ji-Sun Kim
- Department of Otolaryngology-Head and Neck Surgery, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Byung Guk Kim
- Department of Otolaryngology-Head and Neck Surgery, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Gulnaz Stybayeva
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55902, USA
| | - Se Hwan Hwang
- Department of Otolaryngology-Head and Neck Surgery, Bucheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
- Correspondence: ; Tel.: +82-32-340-7044
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Wang D, Zhao CK, Wang HX, Lu F, Li XL, Guo LH, Sun LP, Fu HJ, Zhang YF, Xu HX. Ultrasound-based computer-aided diagnosis for cytologically indeterminate thyroid nodules with different radiologists. Clin Hemorheol Microcirc 2022; 82:217-230. [PMID: 35848013 DOI: 10.3233/ch-221423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
PURPOSE To evaluate a computer-aided diagnosis (CAD) technique in predicting malignancy for cytologically indeterminate thyroid nodules (TNs) as compared with different experienced radiologists. METHOD 436 patients with 436 cytologically indeterminate TNs on fine-needle aspiration cytology (FNAC) were included and all were confirmed by surgical pathology. They were retrospectively analyzed with respect to ultrasound (US) characteristics using a commercially available CAD system (AmCAD-UT; AmCad BioMed, Taiwan, China) and reviewed by one junior and one senior radiologists.The CAD system and different experienced radiologists stratified the risk of malignancy using ACR TI-RADS category. The diagnostic performance by different experienced radiologists independently and after consulting the CAD (different experienced radiologists + CAD) and by the CAD alone were compared. RESULTS The different experienced radiologists showed significantly higher specificities than the CAD system alone. The combination of radiologist and CAD system showed improved diagnostic performance with an AUC (Area under the curve) of 0.740 in the senior radiologist and 0.677 in the junior radiologist, as compared with CAD (AUC: 0.585) alone (all P < 0.05). The combination of senior radiologist and CAD system had the highest diagnostic performance (AUC: 0.740) and specificity (68.9%) compared to the others (all P < 0.05). CONCLUSION The CAD system may play the potential role as a decision-making assistant alongside radiologists for differential diagnosis of TNs with indeterminate cytology.
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Affiliation(s)
- Dan Wang
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.,Ultrasound Research and Education Institute, Clinical Research Center for Interventional Medicine, School of Medicine, Tongji University, Shanghai, China.,Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China
| | - Chong-Ke Zhao
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Han-Xiang Wang
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.,Ultrasound Research and Education Institute, Clinical Research Center for Interventional Medicine, School of Medicine, Tongji University, Shanghai, China.,Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China
| | - Feng Lu
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.,Ultrasound Research and Education Institute, Clinical Research Center for Interventional Medicine, School of Medicine, Tongji University, Shanghai, China.,Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China
| | - Xiao-Long Li
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.,Ultrasound Research and Education Institute, Clinical Research Center for Interventional Medicine, School of Medicine, Tongji University, Shanghai, China.,Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China
| | - Le-Hang Guo
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.,Ultrasound Research and Education Institute, Clinical Research Center for Interventional Medicine, School of Medicine, Tongji University, Shanghai, China.,Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China
| | - Li-Ping Sun
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.,Ultrasound Research and Education Institute, Clinical Research Center for Interventional Medicine, School of Medicine, Tongji University, Shanghai, China.,Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China
| | - Hui-Jun Fu
- Department of Pathology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yi-Feng Zhang
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.,Ultrasound Research and Education Institute, Clinical Research Center for Interventional Medicine, School of Medicine, Tongji University, Shanghai, China.,Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
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Abstract
OBJECTIVES This meta-analysis aimed to evaluate the value of ultrasonic S-Detect mode for the evaluation of thyroid nodules. METHODS We searched PubMed, Cochrane Library, and Chinese biomedical databases from inception to August 31, 2021. Meta-analysis was conducted using STATA version 14.0 and Meta-Disc version 1.4 software. We calculated the summary statistics for sensitivity (Sen), specificity (Spe), summary receiver operating characteristic curve, and the area under the curve, and compared the area under the curve between ultrasonic S-Detect mode and thyroid imaging report and data system (TI-RADS) for the diagnosis of thyroid nodules. As a systematic review summarizing the results of previous studies, this study does not need the informed consent of patients or the approval of the ethics review committee. RESULTS Fifteen studies that met all inclusion criteria were included in this meta-analysis. A total of 924 thyroid malignant nodules and 1228 thyroid benign nodules were assessed. All thyroid nodules were histologically confirmed after examination. The pooled Sen and Spe of TI-RADS were 0.89 (95% confidence interval [CI] = 0.85-0.91) and 0.85 (95% CI = 0.78-0.90), respectively; the pooled Sen and Spe of S-Detect were 0.88 (95% CI = 0.85-0.90) and 0.73 (95% CI = 0.63-0.81), respectively. The areas under the summary receiver operating characteristic curve of TI-RADS and S-Detect were 0.9370 (standard error [SE] = 0.0110) and 0.9128 (SE = 0.0147), respectively, between which there was no significant difference (Z = 1.318; SE = 0.0184; P = .1875). We found no evidence of publication bias (t = 0.36, P = .72). CONCLUSIONS Our meta-analysis indicates that ultrasonic S-Detect mode may have high diagnostic accuracy and may have certain clinical application value, especially for young doctors.
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Affiliation(s)
- Jinyi Bian
- Ultrasound Department, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Ruyue Wang
- Dalian Medical University, Dalian, China
| | - Mingxin Lin
- Ultrasound Department, The First Affiliated Hospital of Dalian Medical University, Dalian, China
- *Correspondence: Mingxin Lin, Ultrasound Department, The First Affiliated Hospital of Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian City, Liaoning Province 116011, China (e-mail: )
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A comparison of artificial intelligence versus radiologists in the diagnosis of thyroid nodules using ultrasonography: a systematic review and meta-analysis. Eur Arch Otorhinolaryngol 2022; 279:5363-5373. [PMID: 35767056 DOI: 10.1007/s00405-022-07436-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 05/06/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Thyroid nodules are common. Ultrasonography (US) is the first investigation for thyroid nodules. Artificial Intelligence (AI) is widely integrated into medical diagnosis to provide additional information. The primary objective of this study was to accumulate the pooled sensitivity and specificity between all available AI and radiologists using thyroid US imaging. The secondary objective was to compare AI's diagnostic performance to that of radiologists. MATERIALS AND METHODS A systematic review meta-analysis. PubMed, Scopus, Web of Science, and Cochrane Library data were searched for studies from inception until June 11, 2020. RESULTS Twenty five studies were included in this meta-analysis. The pooled sensitivity and specificity of AI were 0.86 (95% CI 0.81-0.91) and 0.78 (95% CI 0.73-0.83), respectively. The pooled sensitivity and specificity of radiologists were 0.85 (95% CI 0.80-0.89) and 0.82 (95% CI 0.77-0.86), respectively. The accuracy of AI and radiologists is equivalent in terms of AUC [AI 0.89 (95% CI 0.86-0.92), radiologist 0.91 (95% CI 0.88-0.93)]. The diagnostic odd ratio (DOR) between AI 23.10 (95% CI 14.20-37.58) and radiologists 27.12 (95% CI 17.45-42.16) had no statistically significant difference (P = 0.56). Meta-regression analysis revealed that Deep Learning AI had significantly greater sensitivity and specificity than classic machine learning AI (P < 0.001). CONCLUSION AI demonstrated comparable performance to radiologists in diagnosing benign and malignant thyroid nodules using ultrasonography. Additional research to establish its equivalency should be conducted.
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Hu M, Zong S, Xu N, Li J, Xia C, Yu F, Zhu Q, Zhao H. The Value of Thyroid Ultrasound Computer-Aided Diagnosis System in the Evaluation of Thyroid Nodules With Concurrent Hashimoto's Thyroiditis. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2022; 41:1117-1124. [PMID: 34382688 DOI: 10.1002/jum.15801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 06/07/2021] [Accepted: 07/11/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVE To investigate the value of computer-aided diagnosis (CAD) system in assessing thyroid nodules concurrent with Hashimoto's thyroiditis (HT). METHODS Totally 148 patients with 193 thyroid nodules were enrolled. A radiologist assessed the nodules using a thyroid ultrasound CAD system. Additionally, the nodules were evaluated by one experienced radiologist alone, and one training radiologist without and with CAD assistance. The diagnostic performance was compared between the CAD system and the experienced radiologist, and the training radiologist without and with CAD assistance. RESULTS The CAD system demonstrated a similar sensitivity to that of the experienced radiologist in diagnosing thyroid cancers (89.8% versus 92.4%, P > .05). The specificity and accuracy of the CAD system were lower than that of the experienced radiologist in assessing the nodules with diffusedly altered glands (specificity, 60.0% versus 81.7%, P = .007; accuracy, 77.5% versus 88.1%, P = .011). With CAD assistance, the training radiologist had improved sensitivity and accuracy that increased to 87.9% and 86.8% in classifying nodules with sonographically evident HT (both P = .012). CONCLUSION The CAD system has comparable sensitivity, but lower specificity compared with the experienced radiologist in diagnosing thyroid malignancies concurrent with HT. For a radiologist with less experience, the CAD system can help improve the diagnostic performance by increasing sensitivity and accuracy in assessing thyroid nodules with diffusely altered parenchyma.
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Affiliation(s)
- Minxia Hu
- Department of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, PR China
| | - Suting Zong
- Department of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, PR China
| | - Ning Xu
- Department of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, PR China
| | - Jinzhen Li
- Department of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, PR China
| | - Chunxia Xia
- Department of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, PR China
| | - Fengxia Yu
- Department of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, PR China
| | - Qiang Zhu
- Department of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, PR China
| | - Hanxue Zhao
- Department of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, PR China
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Belovarac B, Zhou F, Modi L, Sun W, Shafizadeh N, Negron R, Yee-Chang M, Szeto O, Simsir A, Sheth S, Brandler TC. Evaluation of ACR TI-RADS cytologically indeterminate thyroid nodules and molecular profiles: a single-institutional experience. J Am Soc Cytopathol 2022; 11:165-172. [PMID: 35181254 DOI: 10.1016/j.jasc.2022.01.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/03/2022] [Accepted: 01/15/2022] [Indexed: 06/14/2023]
Abstract
INTRODUCTION The American College of Radiology (ACR) Thyroid Imaging Reporting and Data Systems (TI-RADS) was developed to standardize thyroid ultrasound reports and predict the likelihood of malignancy. In our study, we aimed to correlate indeterminate thyroid fine needle aspiration cytology cases with preceding ultrasound (US) ACR TI-RADS scores and concurrent molecular testing results to examine how well the use of the ACR TI-RADS in our institution predicted which patients with indeterminate cytology might harbor molecular alterations. MATERIALS AND METHODS We performed a retrospective review of thyroid nodules. Patients with US reports that included TI-RADS scores, fine needle aspiration specimens with indeterminate cytology (Bethesda class III-V), and molecular testing results were included. RESULTS A total of 46 indeterminate cytology cases had had preceding US reports with TI-RADS scores and molecular testing (Bethesda class III, n = 37; Bethesda class IV, n = 6; Bethesda class V, n = 3). Most of the indeterminate cases had had a TI-RADS score of TR4 (31 of 46; 67.39%) or TR5 (9 of 46; 19.57%). RAS mutations were the most common alteration (n = 12). Of the 46 cases, 22 (47.85%) showed no alterations. Ten cases proceeded to surgery, of which seven displayed malignancies. CONCLUSIONS Molecular testing in cytologically indeterminate thyroid nodules provided valuable information for TR4 and TR5 lesions; however, the TR2 and TR3 lesions often had no molecular alterations. These findings highlight the potential value of including US imaging features when assessing the significance of indeterminate cytology findings.
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Affiliation(s)
- Brendan Belovarac
- Department of Pathology, New York University Langone Health, New York, New York
| | - Fang Zhou
- Department of Pathology, New York University Langone Health, New York, New York
| | - Lopa Modi
- Department of Pathology, New York University Langone Health, New York, New York; Department of Pathology, Englewood Health, Englewood, New Jersey
| | - Wei Sun
- Department of Pathology, New York University Langone Health, New York, New York
| | - Negin Shafizadeh
- Department of Pathology, New York University Langone Health, New York, New York
| | - Raquel Negron
- Department of Pathology, New York University Langone Health, New York, New York
| | - Melissa Yee-Chang
- Department of Pathology, New York University Langone Health, New York, New York
| | - Oliver Szeto
- Department of Pathology, New York University Langone Health, New York, New York
| | - Aylin Simsir
- Department of Pathology, New York University Langone Health, New York, New York
| | - Sheila Sheth
- Department of Radiology, New York University Langone Health, New York, New York
| | - Tamar C Brandler
- Department of Pathology, New York University Langone Health, New York, New York.
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11
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Artificial Intelligence (AI) Tools for Thyroid Nodules on Ultrasound, From the AJR Special Series on AI Applications. AJR Am J Roentgenol 2022; 219:1-8. [PMID: 35383487 DOI: 10.2214/ajr.22.27430] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Artificial intelligence (AI) methods for evaluating thyroid nodules on ultrasound have been widely described in the literature, with reported performance of AI tools matching or in some instances surpassing radiologists. As these data have accumulated, products for classification and risk stratification of thyroid nodules on ultrasound have become commercially available. This article reviews FDA-approved products currently on the market, with a focus on product features, reported performance, and considerations for implementation. The products perform risk stratification primarily using the Thyroid Imaging Reporting and Data System (TI-RADS), though may provide additional prediction tools independent of TI-RADS. Key issues in implementation include integration with radiologist interpretation, impact on workflow and efficiency, and performance monitoring. AI applications beyond nodule classification, including report construction and incidental findings follow-up, are also described. Anticipated future directions of research and development in AI tools for thyroid nodules are highlighted.
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12
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Cleere EF, Davey MG, O’Neill S, Corbett M, O’Donnell JP, Hacking S, Keogh IJ, Lowery AJ, Kerin MJ. Radiomic Detection of Malignancy within Thyroid Nodules Using Ultrasonography-A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2022; 12:diagnostics12040794. [PMID: 35453841 PMCID: PMC9027085 DOI: 10.3390/diagnostics12040794] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/22/2022] [Accepted: 03/22/2022] [Indexed: 02/04/2023] Open
Abstract
Background: Despite investigation, 95% of thyroid nodules are ultimately benign. Radiomics is a field that uses radiological features to inform individualized patient care. We aimed to evaluate the diagnostic utility of radiomics in classifying undetermined thyroid nodules into benign and malignant using ultrasonography (US). Methods: A diagnostic test accuracy systematic review and meta-analysis was performed in accordance with PRISMA guidelines. Sensitivity, specificity, and area under curve (AUC) delineating benign and malignant lesions were recorded. Results: Seventy-five studies including 26,373 patients and 46,175 thyroid nodules met inclusion criteria. Males accounted for 24.6% of patients, while 75.4% of patients were female. Radiomics provided a pooled sensitivity of 0.87 (95% CI: 0.86−0.87) and a pooled specificity of 0.84 (95% CI: 0.84−0.85) for characterizing benign and malignant lesions. Using convolutional neural network (CNN) methods, pooled sensitivity was 0.85 (95% CI: 0.84−0.86) and pooled specificity was 0.82 (95% CI: 0.82−0.83); significantly lower than studies using non-CNN: sensitivity 0.90 (95% CI: 0.89−0.90) and specificity 0.88 (95% CI: 0.87−0.89) (p < 0.05). The diagnostic ability of radiologists and radiomics were comparable for both sensitivity (OR 0.98) and specificity (OR 0.95). Conclusions: Radiomic analysis using US provides a reproducible, reliable evaluation of undetermined thyroid nodules when compared to current best practice.
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Affiliation(s)
- Eoin F. Cleere
- The Lambe Institute for Translational Research, National University of Ireland, H91 YR71 Galway, Ireland; (M.G.D.); (A.J.L.); (M.J.K.)
- Department of Otolaryngology, Galway University Hospitals, H91 YR71 Galway, Ireland; (M.C.); (I.J.K.)
- Correspondence:
| | - Matthew G. Davey
- The Lambe Institute for Translational Research, National University of Ireland, H91 YR71 Galway, Ireland; (M.G.D.); (A.J.L.); (M.J.K.)
| | - Shane O’Neill
- Department of Breast and Endocrine Surgery, Galway University Hospitals, H91 YR71 Galway, Ireland;
| | - Mel Corbett
- Department of Otolaryngology, Galway University Hospitals, H91 YR71 Galway, Ireland; (M.C.); (I.J.K.)
| | - John P O’Donnell
- Department of Radiology, Galway University Hospitals, H91 YR71 Galway, Ireland;
| | - Sean Hacking
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI 02903, USA;
| | - Ivan J. Keogh
- Department of Otolaryngology, Galway University Hospitals, H91 YR71 Galway, Ireland; (M.C.); (I.J.K.)
| | - Aoife J. Lowery
- The Lambe Institute for Translational Research, National University of Ireland, H91 YR71 Galway, Ireland; (M.G.D.); (A.J.L.); (M.J.K.)
- Department of Breast and Endocrine Surgery, Galway University Hospitals, H91 YR71 Galway, Ireland;
| | - Michael J. Kerin
- The Lambe Institute for Translational Research, National University of Ireland, H91 YR71 Galway, Ireland; (M.G.D.); (A.J.L.); (M.J.K.)
- Department of Breast and Endocrine Surgery, Galway University Hospitals, H91 YR71 Galway, Ireland;
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13
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Zhang X, Lee VCS, Rong J, Liu F, Kong H. Multi-channel convolutional neural network architectures for thyroid cancer detection. PLoS One 2022; 17:e0262128. [PMID: 35061759 PMCID: PMC8782508 DOI: 10.1371/journal.pone.0262128] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 12/17/2021] [Indexed: 02/05/2023] Open
Abstract
Early detection of malignant thyroid nodules leading to patient-specific treatments can reduce morbidity and mortality rates. Currently, thyroid specialists use medical images to diagnose then follow the treatment protocols, which have limitations due to unreliable human false-positive diagnostic rates. With the emergence of deep learning, advances in computer-aided diagnosis techniques have yielded promising earlier detection and prediction accuracy; however, clinicians' adoption is far lacking. The present study adopts Xception neural network as the base structure and designs a practical framework, which comprises three adaptable multi-channel architectures that were positively evaluated using real-world data sets. The proposed architectures outperform existing statistical and machine learning techniques and reached a diagnostic accuracy rate of 0.989 with ultrasound images and 0.975 with computed tomography scans through the single input dual-channel architecture. Moreover, the patient-specific design was implemented for thyroid cancer detection and has obtained an accuracy of 0.95 for double inputs dual-channel architecture and 0.94 for four-channel architecture. Our evaluation suggests that ultrasound images and computed tomography (CT) scans yield comparable diagnostic results through computer-aided diagnosis applications. With ultrasound images obtained slightly higher results, CT, on the other hand, can achieve the patient-specific diagnostic design. Besides, with the proposed framework, clinicians can select the best fitting architecture when making decisions regarding a thyroid cancer diagnosis. The proposed framework also incorporates interpretable results as evidence, which potentially improves clinicians' trust and hence their adoption of the computer-aided diagnosis techniques proposed with increased efficiency and accuracy.
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Affiliation(s)
- Xinyu Zhang
- Department of Data Science and AI/Faculty of IT, Monash University, Melbourne, Victoria, Australia
| | - Vincent C. S. Lee
- Department of Data Science and AI/Faculty of IT, Monash University, Melbourne, Victoria, Australia
| | - Jia Rong
- Department of Data Science and AI/Faculty of IT, Monash University, Melbourne, Victoria, Australia
| | - Feng Liu
- West China Hospital of Sichuan University, Chengdu City, Sichuan Province, China
| | - Haoyu Kong
- Department of Human-Centred Computing/Faculty of IT, Monash University, Melbourne, Victoria, Australia
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Reverter JL, Ferrer-Estopiñan L, Vázquez F, Ballesta S, Batule S, Perez-Montes de Oca A, Puig-Jové C, Puig-Domingo M. Reliability of a computer-aided system in the evaluation of indeterminate ultrasound images of thyroid nodules. Eur Thyroid J 2022; 11:e210023. [PMID: 34981749 PMCID: PMC9142810 DOI: 10.1530/etj-21-0023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 09/07/2021] [Indexed: 12/03/2022] Open
Abstract
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.
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Affiliation(s)
- J L Reverter
- Endocrinology and Nutrition Service, Germans Trias i Pujol Hospital and Research Institute, Badalona, Spain
- Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
- Correspondence should be addressed to J L Reverter:
| | - L Ferrer-Estopiñan
- Endocrinology and Nutrition Service, Germans Trias i Pujol Hospital and Research Institute, Badalona, Spain
- Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - F Vázquez
- Endocrinology and Nutrition Service, Germans Trias i Pujol Hospital and Research Institute, Badalona, Spain
- Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - S Ballesta
- Endocrinology and Nutrition Service, Germans Trias i Pujol Hospital and Research Institute, Badalona, Spain
- Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - S Batule
- Endocrinology and Nutrition Service, Germans Trias i Pujol Hospital and Research Institute, Badalona, Spain
- Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - A Perez-Montes de Oca
- Endocrinology and Nutrition Service, Germans Trias i Pujol Hospital and Research Institute, Badalona, Spain
- Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - C Puig-Jové
- Endocrinology and Nutrition Service, Germans Trias i Pujol Hospital and Research Institute, Badalona, Spain
- Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - M Puig-Domingo
- Endocrinology and Nutrition Service, Germans Trias i Pujol Hospital and Research Institute, Badalona, Spain
- Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
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15
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Xia R, Sun W, Yee J, Sheth S, Slywotzky C, Hodak S, Brandler TC. Do ACR TI-RADS scores demonstrate unique thyroid molecular profiles? Ultrasonography 2021; 41:480-492. [PMID: 35189676 PMCID: PMC9262667 DOI: 10.14366/usg.21130] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 12/20/2021] [Indexed: 11/09/2022] Open
Abstract
Purpose The present study aimed to examine the molecular profiles of cytologically indeterminate thyroid nodules stratified by American College of Radiology Thyroid Imaging Reporting and Data System (TI-RADS) categories and to determine whether certain ultrasonographic features display particular molecular alterations. Methods A retrospective review was conducted of cases from January 1, 2016 to April 1, 2018. Cases with in-house ultrasonography, fine-needle aspiration Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) diagnoses, molecular testing, and surgery were included. All cases were diagnosed as TBSRTC indeterminate categories. The ultrasound studies were retrospectively reviewed and assigned TI-RADS scores (TR1-TR5) by board-certified radiologists. The final diagnoses were determined based on the surgical resection pathology. Binary logistic regression analysis was used to study whether demographic characteristics, TI-RADS levels, and TBSRTC diagnoses were associated with ThyroSeq molecular results. Results Eighty-one cases met the inclusion criteria. RAS mutations were the most common alteration across all TI-RADS categories (TR2 2/2; TR3 10/19, TR4 13/44, and TR5 8/16), and did not stratify with any particular TI-RADS category. Only TR4 and TR5 categories displayed more aggressive mutations such as BRAFV600E; and TERT. ThyroSeq results were positively correlated with thyroid malignancy when non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) was categorized in the malignant category (odds ratio [OR], 6.859; P<0.01), but not when NIFTP was removed from the malignancy category. Echogenicity scores were found to be negatively correlated with ThyroSeq results in thyroid nodules (OR, 0.162; P<0.01). Conclusion Higher-risk molecular alterations tended to stratify with the higher TI-RADS categories.
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Affiliation(s)
- Rong Xia
- Department of Pathology, NYU Langone Health, New York, USA
| | - Wei Sun
- Department of Pathology, NYU Langone Health, New York, USA
| | - Joseph Yee
- Department of Radiology, NYU Langone Health, New York, USA
| | - Sheila Sheth
- Department of Radiology, NYU Langone Health, New York, USA
| | | | - Steven Hodak
- Department of Medicine, Division of Endocrinology, NYU Langone Health, New York, USA
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Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review. Diagnostics (Basel) 2021; 11:diagnostics11101796. [PMID: 34679494 PMCID: PMC8534713 DOI: 10.3390/diagnostics11101796] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 09/15/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022] Open
Abstract
The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.
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17
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Fresilli D, David E, Pacini P, Del Gaudio G, Dolcetti V, Lucarelli GT, Di Leo N, Bellini MI, D’Andrea V, Sorrenti S, Mascagni D, Biffoni M, Durante C, Grani G, De Vincentis G, Cantisani V. Thyroid Nodule Characterization: How to Assess the Malignancy Risk. Update of the Literature. Diagnostics (Basel) 2021; 11:diagnostics11081374. [PMID: 34441308 PMCID: PMC8391491 DOI: 10.3390/diagnostics11081374] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/19/2021] [Accepted: 07/27/2021] [Indexed: 12/17/2022] Open
Abstract
Ultrasound (US) is the first imaging modality for thyroid parenchyma evaluation. In the last decades, the role of ultrasound has been improved with the introduction of new US software, such as contrast-enhanced ultrasound (CEUS) and US-elastography (USE). USE is nowadays recognized as an essential part of the multiparametric ultrasound (MPUS) examination, in particular for the indeterminate thyroid nodule with possible fine-needle aspiration cytology (FNAC) number reduction; even if further and larger studies are needed to validate it. More controversial is the role of CEUS in thyroid evaluation, due to its high variability in sensitivity and specificity. Semi-automatic US systems based on the computer-aided diagnosis (CAD) system are producing interesting results, especially as an aid to less experienced operators. New knowledge on the molecular mechanisms involved in thyroid cancer is allowing practitioners to identify new genomic thyroid markers that could reduce the number of "diagnostic" thyroidectomies. We have therefore drawn up an updated representation of the current evidence in the literature for thyroid nodule multiparametric ultrasound (MPUS) evaluation with particular regard to USE, the US CAD system and CEUS.
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Affiliation(s)
- Daniele Fresilli
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy; (D.F.); (P.P.); (G.D.G.); (V.D.); (G.T.L.); (N.D.L.); (G.D.V.)
| | - Emanuele David
- Radiological Sciences, Radiology Unit, Papardo-Hospital, 98158 Messina, Italy;
| | - Patrizia Pacini
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy; (D.F.); (P.P.); (G.D.G.); (V.D.); (G.T.L.); (N.D.L.); (G.D.V.)
| | - Giovanni Del Gaudio
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy; (D.F.); (P.P.); (G.D.G.); (V.D.); (G.T.L.); (N.D.L.); (G.D.V.)
| | - Vincenzo Dolcetti
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy; (D.F.); (P.P.); (G.D.G.); (V.D.); (G.T.L.); (N.D.L.); (G.D.V.)
| | - Giuseppe Tiziano Lucarelli
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy; (D.F.); (P.P.); (G.D.G.); (V.D.); (G.T.L.); (N.D.L.); (G.D.V.)
| | - Nicola Di Leo
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy; (D.F.); (P.P.); (G.D.G.); (V.D.); (G.T.L.); (N.D.L.); (G.D.V.)
| | - Maria Irene Bellini
- Department of Surgical Sciences, Sapienza University, 00161 Rome, Italy; (M.I.B.); (V.D.); (S.S.); (D.M.); (M.B.)
| | - Vito D’Andrea
- Department of Surgical Sciences, Sapienza University, 00161 Rome, Italy; (M.I.B.); (V.D.); (S.S.); (D.M.); (M.B.)
| | - Salvatore Sorrenti
- Department of Surgical Sciences, Sapienza University, 00161 Rome, Italy; (M.I.B.); (V.D.); (S.S.); (D.M.); (M.B.)
| | - Domenico Mascagni
- Department of Surgical Sciences, Sapienza University, 00161 Rome, Italy; (M.I.B.); (V.D.); (S.S.); (D.M.); (M.B.)
| | - Marco Biffoni
- Department of Surgical Sciences, Sapienza University, 00161 Rome, Italy; (M.I.B.); (V.D.); (S.S.); (D.M.); (M.B.)
| | - Cosimo Durante
- Department of Translational and Precision Medicine, Sapienza University of Rome, 00185 Rome, Italy; (C.D.); (G.G.)
| | - Giorgio Grani
- Department of Translational and Precision Medicine, Sapienza University of Rome, 00185 Rome, Italy; (C.D.); (G.G.)
| | - Giuseppe De Vincentis
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy; (D.F.); (P.P.); (G.D.G.); (V.D.); (G.T.L.); (N.D.L.); (G.D.V.)
| | - Vito Cantisani
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy; (D.F.); (P.P.); (G.D.G.); (V.D.); (G.T.L.); (N.D.L.); (G.D.V.)
- Correspondence: author:
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TIRADS, SRE and SWE in INDETERMINATE thyroid nodule characterization: Which has better diagnostic performance? Radiol Med 2021; 126:1189-1200. [PMID: 34129178 PMCID: PMC8370962 DOI: 10.1007/s11547-021-01349-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 03/20/2021] [Indexed: 12/16/2022]
Abstract
Purpose To assess Strain Ratio (SRE) and Shear Wave Elastography (SWE) accuracy alone and with TIRADS classification, for the risk stratification of indeterminate thyroid nodules. Materials and methods 128 Patients with 128 indeterminate nodules candidates for thyroidectomy underwent preoperative staging neck ultrasound and were classified according to K-TIRADS score. After TIRADS evaluation, semi-quantitative (SRE) and quantitative (SWE expressed in kPa) elastosonography were performed and relative diagnostic performances, alone and in combination, were compared through ROC curves analysis. In order to maximize the SRE and SWE sensitivity and specificity, their cut-off values were calculated using the Liu test. Bonferroni test was used to evaluate statistically significant differences with a p value < 0.05. Results Sensitivity, specificity, PPV and NPV were, respectively, 71.4%, 82.4%, 62.5%, 87.5% for K-TIRADS baseline US, 85.7%, 94.1%, 85.7%, 94.1% for SRE and 57.1%, 79.4%, 53.3%, 81.8% for SWE (kPa expressed). SRE evaluation showed the best diagnostic accuracy compared to the SWE (kPa expressed) (p < 0.05) and to the K-TIRADS (p > 0.05). The association of SRE with conventional ultrasound with K-TIRADS score increased sensitivity (92.9% vs 71.4%) but decreased the specificity than conventional US alone (76.5% vs 82.4%). Conclusion Strain Elastosonography can be associated with K-TIRADS US examination in the thyroid nodule characterization with indeterminate cytology; in fact, adding the SRE to K-TIRADS assessment significantly increases its sensitivity and negative predictive value. However, further multicenter studies on larger population are warranted.
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The value of the Demetics ultrasound-assisted diagnosis system in the differential diagnosis of benign from malignant thyroid nodules and analysis of the influencing factors. Eur Radiol 2021; 31:7936-7944. [PMID: 33856523 DOI: 10.1007/s00330-021-07884-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 02/18/2021] [Accepted: 03/15/2021] [Indexed: 12/12/2022]
Abstract
OBJECTIVES To evaluate the value of Demetics and to explore whether Demetics can help radiologists with varying years of experience in the differential diagnosis of benign from malignant thyroid nodules. METHODS The clinical application value of Demetics was assessed by comparing the diagnostic accuracy of radiologists before and after applying Demetics. This retrospective analysis included 284 thyroid nodules that underwent pathological examinations. Two different combined methods were applied. Using method 1: the original TI-RADS classification was forcibly upgraded or downgraded by one level when Demetics classified the thyroid nodules as malignant or benign. Using method 2: the TI-RADS and benign or malignant classification of the thyroid nodules were flexibly adjusted after the physician learned the Demetics' results. RESULTS Demetics exhibited a higher sensitivity than did junior radiologist 1 (pD1 = 0.029) and was similar in sensitivity to the two senior radiologists. Demetics had a higher AUC than both junior radiologists (pD1 = 0.042, pD2 = 0.038) and an AUC similar to that of the senior radiologists. The sensitivity (p = 0.035) and AUC (p = 0.031) of junior radiologist 1 and the specificity (p < 0.001) and AUC (p = 0.026) of junior radiologist 2 improved with combined method 1. The AUC of junior radiologist 2 improved with combined method 2 (p = 0.045). The factors influencing the diagnostic results of Demetics include sonographic signs (echogenicity and echogenic foci), contrast of the image, and nodule size. CONCLUSION Demetics exhibited high sensitivity and accuracy in the differential diagnosis of benign from malignant thyroid nodules. Demetics could improve the diagnostic accuracy of junior radiologists. KEY POINTS • Demetics exhibited a high sensitivity and accuracy in the differential diagnosis of benign from malignant thyroid nodules. • Demetics could improve the diagnostic accuracy of junior radiologists in the differential diagnosis of benign from malignant thyroid nodules. • Factors influencing the diagnostic results of Demetics include the sonographic signs (echogenicity and echogenic foci), contrast of the image, and nodule size.
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Gentili F, Guerrini S, Mazzei FG, Monteleone I, Di Meglio N, Sansotta L, Perrella A, Puglisi S, De Filippo M, Gennaro P, Volterrani L, Castagna MG, Dotta F, Mazzei MA. Dual energy CT in gland tumors: a comprehensive narrative review and differential diagnosis. Gland Surg 2020; 9:2269-2282. [PMID: 33447579 DOI: 10.21037/gs-20-543] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Dual energy CT (DECT)with image acquisition at two different photon X-ray levels allows the characterization of a specific tissue or material/elements, the extrapolation of virtual unenhanced and monoenergetic images, and the quantification of iodine uptake; such special capabilities make the DECT the perfect technique to support oncological imaging for tumor detection and characterization and treatment monitoring, while concurrently reducing the dose of radiation and iodine and improving the metal artifact reduction. Even though its potential in the field of oncology has not been fully explored yet, DECT is already widely used today thanks to the availability of different CT technologies, such as dual-source, single-source rapid-switching, single-source sequential, single-source twin-beam and dual-layer technologies. Moreover DECT technology represents the future of the imaging innovation and it is subject to ongoing development that increase according its clinical potentiality, in particular in the field of oncology. This review points out recent state-of-the-art in DECT applications in gland tumors, with special focus on its potential uses in the field of oncological imaging of endocrine and exocrine glands.
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Affiliation(s)
- Francesco Gentili
- Unit of Diagnostic Imaging, Department of Radiological Sciences, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Susanna Guerrini
- Unit of Diagnostic Imaging, Department of Radiological Sciences, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Francesco Giuseppe Mazzei
- Unit of Diagnostic Imaging, Department of Radiological Sciences, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Ilaria Monteleone
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Nunzia Di Meglio
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Letizia Sansotta
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Armando Perrella
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Sara Puglisi
- Unit of Radiology, Department of Medicine and Surgery, Azienda Ospedaliero-Universitaria di Parma, Parma, Italy
| | - Massimo De Filippo
- Unit of Radiology, Department of Medicine and Surgery, Azienda Ospedaliero-Universitaria di Parma, Parma, Italy
| | - Paolo Gennaro
- Department of Maxillofacial Surgery, University of Siena, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Luca Volterrani
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Maria Grazia Castagna
- Unit of Endocrinology, Department of Medical, Surgical and Neuro Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Francesco Dotta
- Unit of Diabetology, Department of Medical, Surgical and Neuro Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Maria Antonietta Mazzei
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
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Martino M, Fodor D, Fresilli D, Guiban O, Rubini A, Cassoni A, Ralli M, De Vincentiis C, Arduini F, Celletti I, Pacini P, Polti G, Polito E, Greco A, Valentini V, Sorrenti S, D'Andrea V, Masciocchi C, Barile A, Cantisani V. Narrative review of multiparametric ultrasound in parotid gland evaluation. Gland Surg 2020; 9:2295-2311. [PMID: 33447581 DOI: 10.21037/gs-20-530] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Disorders affecting parotid gland represent a heterogeneous group comprising congenital, inflammatory and neoplastic diseases which show a focal or diffuse pattern of appearance. The differentiation of neoplastic from non-neoplastic conditions of parotid glands is pivotal for the diagnostic imaging. Frequently there is evidence of overlapping between the clinical and the imaging appearance of the various pathologies. The parotid gland is also often object of study with the combination of different techniques [ultrasound-computed tomography-magnetic resonance imaging (US-CT-MRI), ex.]. Compared to other dominant methods of medical imaging, US has several advantages providing images in real-time at lower cost, and without harmful use of ionizing radiation and of contrast enhancement. B-mode US, and the microvascular pattern color Doppler are usually used as first step evaluation of parotid lesions. Elastography and contrast-enhanced US (CEUS) has opened further possible perspectives to improve the differentiation between benign and malignant parotid lesions. The characterization of the parotid tumors plays a crucial role for their treatment planning and for the prediction of possible surgical complications. We present, here an updated review of the most recurrent pathologies of parotid gland focusing on the diagnostic power of multiparametric US including CEUS and ultrasound elastography (USE); limitations, advantages and the main key-points will be presented.
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Affiliation(s)
- Milvia Martino
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Daniela Fodor
- 2nd Internal Medicine Department, "Iuliu Hațieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Daniele Fresilli
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy
| | - Olga Guiban
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy
| | | | - Andrea Cassoni
- Department of Maxillofacial Surgery, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy
| | - Massimo Ralli
- Department of Sense Organs, Sapienza University of Rome, Rome, Italy
| | | | - Federico Arduini
- Department of Radiology, Ospedale Santa Maria del Carmine, Rovereto, Italy
| | - Ilaria Celletti
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy
| | - Patrizia Pacini
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy
| | - Giorgia Polti
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy
| | - Eleonora Polito
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy
| | - Antonio Greco
- Department of Sense Organs, Sapienza University of Rome, Rome, Italy
| | - Valentino Valentini
- Department of Maxillofacial Surgery, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy
| | - Salvatore Sorrenti
- Department of Surgical Sciences, "Sapienza" University of Rome, Rome, Italy
| | - Vito D'Andrea
- Department of Surgical Sciences, "Sapienza" University of Rome, Rome, Italy
| | - Carlo Masciocchi
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Antonio Barile
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Vito Cantisani
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I "Sapienza" University of Rome, Rome, Italy
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22
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Chung SR, Baek JH, Lee MK, Ahn Y, Choi YJ, Sung TY, Song DE, Kim TY, Lee JH. Computer-Aided Diagnosis System for the Evaluation of Thyroid Nodules on Ultrasonography: Prospective Non-Inferiority Study according to the Experience Level of Radiologists. Korean J Radiol 2020; 21:369-376. [PMID: 32090529 PMCID: PMC7039724 DOI: 10.3348/kjr.2019.0581] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 11/04/2019] [Indexed: 12/27/2022] Open
Abstract
Objective To determine whether a computer-aided diagnosis (CAD) system for the evaluation of thyroid nodules is non-inferior to radiologists with different levels of experience. Materials and Methods Patients with thyroid nodules with a decisive diagnosis of benign or malignant nodule were consecutively enrolled from November 2017 to September 2018. Three radiologists with different levels of experience (1 month, 4 years, and 7 years) in thyroid ultrasound (US) reviewed the thyroid US with and without using the CAD system. Statistical analyses included non-inferiority testing of the diagnostic accuracy for malignant thyroid nodules between the CAD system and the three radiologists with a non-inferiority margin of 10%, comparison of the diagnostic performance, and the added value of the CAD system to the radiologists. Results Altogether, 197 patients were included in the study cohort. The diagnostic accuracy of the CAD system (88.48%, 95% confidence interval [CI] = 82.65–92.53) was non-inferior to that of the radiologists with less experience (1 month and 4 year) of thyroid US (83.03%, 95% CI = 76.52–88.02; p < 0.001), whereas it was inferior to that of the experienced radiologist (7 years) (95.76%, 95% CI = 91.37–97.96; p = 0.138). The sensitivity and negative predictive value of the CAD system were significantly higher than those of the less-experienced radiologists were, whereas no significant difference was found with those of the experienced radiologist. A combination of US and the CAD system significantly improved sensitivity and negative predictive value, although the specificity and positive predictive value deteriorated for the less-experienced radiologists. Conclusion The CAD system may offer support for decision-making in the diagnosis of malignant thyroid nodules for operators who have less experience with thyroid US.
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Affiliation(s)
- Sae Rom Chung
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jung Hwan Baek
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
| | - Min Kyoung Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Yura Ahn
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Young Jun Choi
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Tae Yon Sung
- Department of Surgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Dong Eun Song
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Tae Yong Kim
- Department of Endocrinology and Metabolism, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jeong Hyun Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
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23
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Zhang Y, Wu Q, Chen Y, Wang Y. A Clinical Assessment of an Ultrasound Computer-Aided Diagnosis System in Differentiating Thyroid Nodules With Radiologists of Different Diagnostic Experience. Front Oncol 2020; 10:557169. [PMID: 33042840 PMCID: PMC7518212 DOI: 10.3389/fonc.2020.557169] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 08/17/2020] [Indexed: 01/18/2023] Open
Abstract
Introduction This study aimed to assess the diagnostic performance and the added value to radiologists of different levels of a computer-aided diagnosis (CAD) system for the detection of thyroid cancers. Methods 303 patients who underwent thyroidectomy from October 2018 to July 2019 were retrospectively reviewed. The diagnostic performance of the senior radiologist, the junior radiologist, and the CAD system were compared. The added value of the CAD system was assessed and subgroup analyses were performed according to the size of thyroid nodules. Results In total, 186 malignant thyroid nodules, and 179 benign thyroid nodules were included; 168 were papillary thyroid carcinoma (PTC), 7 were medullary thyroid carcinoma (MTC), 11 were follicular carcinoma (FTC), 127 were follicular adenoma (FA) and 52 were nodular goiters. The CAD system showed a comparable specificity as the senior radiologist (86.0% vs. 86.0%, p > 0.99), but a lower sensitivity and a lower area under the receiver operating characteristic (AUROC) curve (sensitivity: 71.5% vs. 95.2%, p < 0.001; AUROC: 0.788 vs. 0.906, p < 0.001). The CAD system improved the diagnostic sensitivities of both the senior and the junior radiologists (97.8% vs. 95.2%, p = 0.063; 88.2% vs. 75.3%, p < 0.001). Conclusion The use of the CAD system using artificial intelligence is a potential tool to distinguish malignant thyroid nodules and is preferable to serve as a second opinion for less experienced radiologists to improve their diagnosis performance.
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Affiliation(s)
- Yichun Zhang
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University, Shanghai, China.,Shanghai Institute of Ultrasound in Medicine, Shanghai, China
| | - Qiong Wu
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University, Shanghai, China.,Shanghai Institute of Ultrasound in Medicine, Shanghai, China
| | - Yutong Chen
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University, Shanghai, China.,Shanghai Institute of Ultrasound in Medicine, Shanghai, China
| | - Yan Wang
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University, Shanghai, China.,Shanghai Institute of Ultrasound in Medicine, Shanghai, China
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S-Detect Software vs. EU-TIRADS Classification: A Dual-Center Validation of Diagnostic Performance in Differentiation of Thyroid Nodules. J Clin Med 2020; 9:jcm9082495. [PMID: 32756510 PMCID: PMC7464710 DOI: 10.3390/jcm9082495] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 07/23/2020] [Accepted: 07/30/2020] [Indexed: 12/12/2022] Open
Abstract
Computer-aided diagnosis (CAD) and other risk stratification systems may improve ultrasound image interpretation. This prospective study aimed to compare the diagnostic performance of CAD and the European Thyroid Imaging Reporting and Data System (EU-TIRADS) classification applied by physicians with S-Detect 2 software CAD based on Korean Thyroid Imaging Reporting and Data System (K-TIRADS) and combinations of both methods (MODELs 1 to 5). In all, 133 nodules from 88 patients referred to thyroidectomy with available histopathology or with unambiguous results of cytology were included. The S-Detect system, EU-TIRADS, and mixed MODELs 1–5 for the diagnosis of thyroid cancer showed a sensitivity of 89.4%, 90.9%, 84.9%, 95.5%, 93.9%, 78.9% and 93.9%; a specificity of 80.6%, 61.2%, 88.1%, 53.7%, 73.1%, 89.6% and 80.6%; a positive predictive value of 81.9%, 69.8%, 87.5%, 67%, 77.5%, 88.1% and 82.7%; a negative predictive value of 88.5%, 87.2%, 85.5%, 92.3%, 92.5%, 81.1% and 93.1%; and an accuracy of 85%, 75.9%, 86.5%, 74.4%, 83.5%, 84.2%, and 87.2%, respectively. Comparison showed superiority of the similar MODELs 1 and 5 over other mixed models as well as EU-TIRADS and S-Detect used alone (p-value < 0.05). S-Detect software is characterized with high sensitivity and good specificity, whereas EU-TIRADS has high sensitivity, but rather low specificity. The best diagnostic performance in malignant thyroid nodule (TN) risk stratification was obtained for the combined model of S-Detect (“possibly malignant” nodule) and simultaneously obtaining 4 or 5 points (MODEL 1) or exactly 5 points (MODEL 5) on the EU-TIRADS scale.
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25
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Xu L, Gao J, Wang Q, Yin J, Yu P, Bai B, Pei R, Chen D, Yang G, Wang S, Wan M. Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis. Eur Thyroid J 2020; 9:186-193. [PMID: 32903956 PMCID: PMC7445671 DOI: 10.1159/000504390] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Revised: 10/25/2019] [Indexed: 12/16/2022] Open
Abstract
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.
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Affiliation(s)
- Lei Xu
- Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
- Xi'an Hospital of Traditional Chinese Medicine, Xi'an, China
| | - Junling Gao
- Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Quan Wang
- Laboratory of Surgical Oncology, Peking University People's Hospital, Peking University, Beijing, China
| | - Jichao Yin
- Xi'an Hospital of Traditional Chinese Medicine, Xi'an, China
| | - Pengfei Yu
- Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Bin Bai
- Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Ruixia Pei
- Xi'an Hospital of Traditional Chinese Medicine, Xi'an, China
| | - Dingzhang Chen
- Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Guochun Yang
- Xi'an Hospital of Traditional Chinese Medicine, Xi'an, China
| | - Shiqi Wang
- Xijing Hospital, Fourth Military Medical University, Xi'an, China
- **Shiqi Wang, Xijing Hospital, Fourth Military Medical University, Changlexi St. 127, Xi'an 710032 (China), E-Mail
| | - Mingxi Wan
- Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
- *Mingxi Wan, Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xianningxi St. 28, Xi'an 710049 (China), E-Mail
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False-Positive Malignant Diagnosis of Nodule Mimicking Lesions by Computer-Aided Thyroid Nodule Analysis in Clinical Ultrasonography Practice. Diagnostics (Basel) 2020; 10:diagnostics10060378. [PMID: 32517227 PMCID: PMC7345888 DOI: 10.3390/diagnostics10060378] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 06/03/2020] [Accepted: 06/04/2020] [Indexed: 11/17/2022] Open
Abstract
This study aims to test computer-aided diagnosis (CAD) for thyroid nodules in clinical ultrasonography (US) practice with a focus towards identifying thyroid entities associated with CAD system misdiagnoses. Two-hundred patients referred to thyroid US were prospectively enrolled. An experienced radiologist evaluated the thyroid nodules and saved axial images for further offline blinded analysis using a commercially available CAD system. To represent clinical practice, not only true nodules, but mimicking lesions were also included. Fine needle aspiration biopsy (FNAB) was performed according to present guidelines. US features and thyroid entities significantly associated with CAD system misdiagnosis were identified along with the diagnostic accuracy of the radiologist and the CAD system. Diagnostic specificity regarding the radiologist was significantly (p < 0.05) higher than when compared with the CAD system (88.1% vs. 40.5%) while no significant difference was found in the sensitivity (88.6% vs. 80%). Focal inhomogeneities and true nodules in thyroiditis, nodules with coarse calcification and inspissated colloid cystic nodules were significantly (p < 0.05) associated with CAD system misdiagnosis as false-positives. The commercially available CAD system is promising when used to exclude thyroid malignancies, however, it currently may not be able to reduce unnecessary FNABs, mainly due to the false-positive diagnoses of nodule mimicking lesions.
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Mauri G, Gitto S, Cantisani V, Vallone G, Schiavone C, Papini E, Sconfienza LM. Use of the Thyroid Imaging Reporting and Data System (TIRADS) in clinical practice: an Italian survey. Endocrine 2020; 68:329-335. [PMID: 31983030 DOI: 10.1007/s12020-020-02199-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 01/10/2020] [Indexed: 12/21/2022]
Abstract
PURPOSE To perform an online survey about the use of Thyroid Imaging Reporting and Data System (TIRADS) classification in Italy. METHODS An online questionnaire was administered to all members of the Italian Society of Medical and Interventional Radiology (Società Italiana di Radiologia Medica ed Interventistica, SIRM) and Italian Society of Ultrasound in Medicine and Biology (Società Italiana di Ultrasonologia in Medicina e Biologia, SIUMB). The survey consisted of 14 questions about demographics, knowledge, and the use of TIRADS classification, current job, expertize in thyroid ultrasound and fine needle aspiration biopsy, and work environment. Descriptive and nonparametric statistics were used, with P < 0.05 indicating statistical significance. RESULTS A total of 1544 answers (9.8% out of 15,836) were received. The participants were 45 (36-59) years old [median (25th-75th percentiles)] and mostly (53.6%) familiar with TIRADS classification. Structured reporting (P < 0.001), expertize in thyroid ultrasound (P = 0.005) and fine needle aspiration biopsy (P < 0.001), and work in a multidisciplinary team (P < 0.001) were associated with the use of TIRADS classification. Physicians working in other fields than radiology were more prone to using TIRADS classification than radiologists and radiologists-in-training (P < 0.001). CONCLUSION Most physicians adopt TIRADS classification when performing thyroid ultrasound. TIRADS classification provides recommendations for the management of thyroid nodules and its use has to be encouraged.
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Affiliation(s)
- Giovanni Mauri
- Divisione di Radiologia Interventistica, IRCCS Istituto Europeo di Oncologia, Milano, Italy
- Dipartimento di Oncologia ed Emato-Oncologia, Università degli Studi di Milano, Milano, Italy
| | - Salvatore Gitto
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milano, Italy.
| | - Vito Cantisani
- Unità operativa di Innovazioni Diagnostiche e Ultrasonografiche, Azienda Ospedaliera Universitaria Policlinico Umberto I, Roma, Italy
- Dipartimento di Scienze Radiologiche, Oncologiche e Anatomo-patologiche, Università degli Studi di Roma "La Sapienza", Roma, Italy
| | - Gianfranco Vallone
- Departimento Vita e Salute "V. Tiberio", Università degli Studi del Molise, Campobasso, Italy
| | - Cosima Schiavone
- Unità operativa di Ecografia Internistica, Policlinico SS. Annunziata, Chieti, Italy
- Dipartimento di Medicina e Scienze dell'Invecchiamento, Università degli Studi di Chieti e Pescara "G. D'Annunzio", Chieti, Italy
| | - Enrico Papini
- Dipartimento di Endocrinologia, Ospedale Regina Apostolorum, Albano Laziale, Italy
| | - Luca Maria Sconfienza
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milano, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milano, Italy
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Computer-aided diagnostic system for thyroid nodule sonographic evaluation outperforms the specificity of less experienced examiners. J Ultrasound 2020; 23:169-174. [PMID: 32246401 DOI: 10.1007/s40477-020-00453-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Accepted: 03/13/2020] [Indexed: 12/15/2022] Open
Abstract
PURPOSE Computer-aided diagnosis (CAD) may improve interobserver agreement in the risk stratification of thyroid nodules. This study aims to evaluate the performance of the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) classification as estimated by an expert radiologist, a senior resident, a medical student, and a CAD system, as well as the interobserver agreement among them. METHODS Between July 2016 and 2018, 107 nodules (size 5-40 mm, 27 malignant) were classified according to the K-TIRADS by an expert radiologist and CAD software. A third-year resident and a medical student with basic imaging training, both blinded to previous findings, retrospectively estimated the K-TIRADS classification. The diagnostic performance was calculated, including sensitivity, specificity, positive and negative predictive values, and the area under the receiver operating characteristic curve. RESULTS The CAD system and the expert achieved a sensitivity of 70.37% (95% CI 49.82-86.25%) and 81.48% (61.92-93.7%) and a specificity of 87.50% (78.21-93.84%) and 88.75% (79.72-94.72%), respectively. The specificity of the student was significantly lower (76.25% [65.42-85.05%], p = 0.02). CONCLUSION In our opinion, the CAD evaluation of thyroid nodules stratification risk has a potential role in a didactic field and does not play a real and effective role in the clinical field, where not only images but also specialistic medical practice is fundamental to achieve a diagnosis based on family history, genetics, lab tests, and so on. The CAD system may be useful for less experienced operators as its specificity was significantly higher.
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Barczyński M, Stopa-Barczyńska M, Wojtczak B, Czarniecka A, Konturek A. Clinical validation of S-Detect TM mode in semi-automated ultrasound classification of thyroid lesions in surgical office. Gland Surg 2020; 9:S77-S85. [PMID: 32175248 DOI: 10.21037/gs.2019.12.23] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background In recent years well-recognized scientific societies introduced guidelines for ultrasound (US) malignancy risk stratification of thyroid nodules. These guidelines categorize the risk of malignancy in relation to a combination of several US features. Based on these US image lexicons an US-based computer-aided diagnosis (CAD) systems were developed. Nevertheless, their clinical utility has not been evaluated in any study of surgeon-performed office US of the thyroid. Hence, the aim of this pilot study was to validate s-DetectTM mode in semi-automated US classification of thyroid lesions during surgeon-performed office US. Methods This is a prospective study of 50 patients who underwent surgeon-performed thyroid US (basic US skills without CAD vs. with CAD vs. expert US skills without CAD) in the out-patient office as part of the preoperative workup. The real-time CAD system software using artificial intelligence (S-DetectTM for Thyroid; Samsung Medison Co.) was integrated into the RS85 US system. Primary outcome was CAD system added-value to the surgeon-performed office US evaluation. Secondary outcomes were: diagnostic accuracy of CAD system, intra and interobserver variability in the US assessment of thyroid nodules. Surgical pathology report was used to validate the pre-surgical diagnosis. Results CAD system added-value to thyroid assessment by a surgeon with basic US skills was equal to 6% (overall accuracy of 82% for evaluation with CAD vs. 76% for evaluation without CAD system; P<0.001), and final diagnosis was different than predicted by US assessment in 3 patients (1 more true-positive and 2 more true-negative results). However, CAD system was inferior to thyroid assessment by a surgeon with expert US skills in 6 patients who had false-positive results (P<0.001). Conclusions The sensitivity and negative predictive value of CAD system for US classification of thyroid lesions were similar as surgeon with expert US skills whereas specificity and positive predictive value were significantly inferior but markedly better than judgement of a surgeon with basic US skills alone.
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Affiliation(s)
- Marcin Barczyński
- Department of Endocrine Surgery, Third Chair of General Surgery, Jagiellonian University Medical College, Kraków, Poland
| | - Małgorzata Stopa-Barczyńska
- Clinical Ward of General Surgery and Oncology, Gabriel Narutowicz Memorial Municipal Hospital, Kraków, Poland
| | - Beata Wojtczak
- Department of General, Minimally Invasive and Endocrine Surgery, Wroclaw Medical University, Wroclaw, Poland
| | - Agnieszka Czarniecka
- Department of Oncological and Reconstructive Surgery, M. Sklodowska-Curie Institute - Oncology Centre, Gliwice, Poland
| | - Aleksander Konturek
- Department of Endocrine Surgery, Third Chair of General Surgery, Jagiellonian University Medical College, Kraków, Poland
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Jin Z, Zhu Y, Zhang S, Xie F, Zhang M, Zhang Y, Tian X, Zhang J, Luo Y, Cao J. Ultrasound Computer-Aided Diagnosis (CAD) Based on the Thyroid Imaging Reporting and Data System (TI-RADS) to Distinguish Benign from Malignant Thyroid Nodules and the Diagnostic Performance of Radiologists with Different Diagnostic Experience. Med Sci Monit 2020; 26:e918452. [PMID: 31929498 PMCID: PMC6977643 DOI: 10.12659/msm.918452] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
<strong>BACKGROUND</strong> The diagnosis of thyroid cancer and distinguishing benign from malignant thyroid nodules by junior radiologists can be challenging. This study aimed to develop a computer-aided diagnosis (CAD) system based on the Thyroid Imaging Reporting and Data System (TI-RADS) to distinguish benign from malignant thyroid nodules by analyzing ultrasound images to improve the diagnostic performance of junior radiologists. <strong>MATERIAL AND METHODS</strong> A modified TI-RADS based on a convolutional neural network (CNN) was used to develop the CAD system. This retrospective study reviewed 789 thyroid nodules from 695 patients and included radiologists with different diagnostic experience. Five study groups included the CAD group, the junior radiologist group, the intermediate-level radiologist group, the senior radiologist group, and the group in which the junior radiologist used the CAD system. The ultrasound findings were reviewed and compared with the histopathology diagnosis. <strong>RESULTS</strong> The CAD system for the diagnosis of thyroid cancer showed an accuracy of 80.35%, a sensitivity of 80.64%, a specificity of 80.13%, a positive predictive value (PPV) of 76.02%, a negative predictive value (NPV) of 84.12%, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.87. The accuracy of the junior radiologists in diagnosing thyroid cancer using CAD was similar to that of intermediate-level radiologists (79.21% <i>vs</i>. 77.57%; P=0.427). <strong>CONCLUSIONS</strong> The use of ultrasound CAD based on the TI-RADS showed potential for distinguishing between benign and malignant thyroid nodules and improved the diagnostic performance of junior radiologists.
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Affiliation(s)
- Zhuang Jin
- Department of Ultrasound, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China (mainland).,Department of Ultrasound, General Hospital of Northern Theater Command, Shenyang, Liaoning, China (mainland).,Medical School of Chinese People's Liberation Army (PLA), Beijing, China (mainland)
| | - Yaqiong Zhu
- Department of Ultrasound, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China (mainland).,Nankai University, Tianjin, China (mainland)
| | | | - Fang Xie
- Department of Ultrasound, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China (mainland)
| | - Mingbo Zhang
- Department of Ultrasound, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China (mainland)
| | - Ying Zhang
- Nankai University, Tianjin, China (mainland)
| | - Xiaoqi Tian
- Nankai University, Tianjin, China (mainland)
| | - Jue Zhang
- Peking University, Beijing, China (mainland)
| | - Yukun Luo
- Department of Ultrasound, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China (mainland).,Medical School of Chinese People's Liberation Army (PLA), Beijing, China (mainland)
| | - Junying Cao
- Department of Ultrasound, General Hospital of Northern Theater Command, Shenyang, Liaoning, China (mainland)
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31
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Seifert P, Görges R, Zimny M, Kreissl MC, Schenke S. Interobserver agreement and efficacy of consensus reading in Kwak-, EU-, and ACR-thyroid imaging recording and data systems and ATA guidelines for the ultrasound risk stratification of thyroid nodules. Endocrine 2020; 67:143-154. [PMID: 31741167 DOI: 10.1007/s12020-019-02134-1] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 11/07/2019] [Indexed: 02/07/2023]
Abstract
PURPOSE To investigate the interobserver agreement (IA) and the impact of consensus reading using four risk stratification systems for thyroid nodules (TN). METHODS Four experienced specialists independently rated US images of 80 TN according to the Kwak-TIRADS, EU-TIRADS, ACR TI-RADS, and ATA Guidelines. The cases were randomly extracted from a prospectively acquired database (n > 1500 TN). The observers were blinded to clinical data. This study was divided into two sessions (S1 and S2) with 40 image sets each. After every session, a consensus reading was carried out (C1, C2). Subsequently, the effect of C1 was tested in S2 with 40 new cases followed by C2. Fleiss' kappa (κ) was calculated for S1 and S2 to estimate the IA and learning curves. The results of C1 and C2 were used as reference for diagnostic accuracy calculations. RESULTS IA significantly increased (p < 0.01) after C1 with κ values of 0.375 (0.615), 0.411 (0.596), 0.321 (0.569), and 0.410 (0.583) for the Kwak-TIRADS, EU-TIRADS, ACR TI-RADS, and ATA Guidelines in S1 (S2), respectively. ROC analysis (C1 + C2) revealed similar areas under the curve (AUC) for the Kwak-TIRADS, EU-TIRADS, ACR TI-RADS, and ATA Guidelines (0.635, 0.675, 0.694, and 0.654, respectively, n.s.). AUC did not increase from C1 (0.677 ± 0.010) to C2 (0.632 ± 0.052, n.s.). ATA Guidelines were not applicable in five cases. CONCLUSIONS IA and diagnostic accuracy were very similar for the four investigated risk stratification systems. Consensus reading sessions significantly improved the IA but did not affect the diagnostic accuracy.
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Affiliation(s)
- Philipp Seifert
- Department of Nuclear Medicine, Jena University Hospital, Jena, Germany.
| | - Rainer Görges
- Department of Nuclear Medicine, Essen University Hospital, Essen, Germany
- Joint Practice for Nuclear Medicine, Duisburg/Moers, Duisburg, Germany
| | - Michael Zimny
- Institute for Nuclear Medicine Hanau, Giessen, Germany
| | - Michael C Kreissl
- Division of Nuclear Medicine, Department of Radiology and Nuclear Medicine, Magdeburg University Hospital, Magdeburg, Germany
| | - Simone Schenke
- Institute for Nuclear Medicine Hanau, Giessen, Germany
- Division of Nuclear Medicine, Department of Radiology and Nuclear Medicine, Magdeburg University Hospital, Magdeburg, Germany
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32
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Diagnosis of Thyroid Nodules: Performance of a Deep Learning Convolutional Neural Network Model vs. Radiologists. Sci Rep 2019; 9:17843. [PMID: 31780753 PMCID: PMC6882804 DOI: 10.1038/s41598-019-54434-1] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Accepted: 11/08/2019] [Indexed: 01/05/2023] Open
Abstract
Computer-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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33
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Chambara N, Ying M. The Diagnostic Efficiency of Ultrasound Computer-Aided Diagnosis in Differentiating Thyroid Nodules: A Systematic Review and Narrative Synthesis. Cancers (Basel) 2019; 11:cancers11111759. [PMID: 31717365 PMCID: PMC6896127 DOI: 10.3390/cancers11111759] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 11/03/2019] [Accepted: 11/06/2019] [Indexed: 12/20/2022] Open
Abstract
Computer-aided diagnosis (CAD) techniques have emerged to complement qualitative assessment in the diagnosis of benign and malignant thyroid nodules. The aim of this review was to summarize the current evidence on the diagnostic performance of various ultrasound CAD in characterizing thyroid nodules. PUBMED, EMBASE and Cochrane databases were searched for studies published until August 2019. The Quality Assessment of Studies of Diagnostic Accuracy included in Systematic Review 2 (QUADAS-2) tool was used to assess the methodological quality of the studies. Reported diagnostic performance data were analyzed and discussed. Fourteen studies with 2232 patients and 2675 thyroid nodules met the inclusion criteria. The study quality based on QUADAS-2 assessment was moderate. At best performance, grey scale CAD had a sensitivity of 96.7% while Doppler CAD was 90%. Combined techniques of qualitative grey scale features and Doppler CAD assessment resulted in overall increased sensitivity (92%) and optimal specificity (85.1%). The experience of the CAD user, nodule size and the thyroid malignancy risk stratification system used for interpretation were the main potential factors affecting diagnostic performance outcomes. The diagnostic performance of CAD of thyroid ultrasound is comparable to that of qualitative visual assessment; however, combined techniques have the potential for better optimized diagnostic accuracy.
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34
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Jin A, Li Y, Shen J, Zhang Y, Wang Y. Clinical Value of a Computer-Aided Diagnosis System in Thyroid Nodules: Analysis of a Reading Map Competition. ULTRASOUND IN MEDICINE & BIOLOGY 2019; 45:2666-2671. [PMID: 31281010 DOI: 10.1016/j.ultrasmedbio.2019.06.405] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Revised: 05/20/2019] [Accepted: 06/10/2019] [Indexed: 06/09/2023]
Abstract
We evaluated the accuracy of human and computer-aided diagnosis (CAD) in a reading map diagnosis competition for detection of thyroid cancers via ultrasonography (US). The competition comprised 33 thyroid nodule images randomly chosen between 2015 and 2017. One hundred seventy-seven contestants including one operator using CAD participated in the competition. The competition was separated into an online part and a live part. We compared the average accuracy of contestants and CAD in the detection of thyroid cancers. The accuracy of contestants and the CAD system was 60.3% and 84.8%, respectively. The accuracy of the CAD system was higher than that of the contestants with different technical titles. The areas under the curve for CAD and contestants were 0.985 (0.881-1.00) and 0.659 (0.645-0.673) (Z = 7.55, p < 0.01). The CAD system had high accuracy in this thyroid nodule reading map competition, and may be an adjuvant tool for radiologists.
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Affiliation(s)
- Anqi Jin
- Department of Ultrasound in Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China; Shanghai Institute of Ultrasound in Medicine, Shanghai, China
| | - Yi Li
- Department of Ultrasound in Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China; Shanghai Institute of Ultrasound in Medicine, Shanghai, China
| | - Jian Shen
- Department of Ultrasound in Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China; Shanghai Institute of Ultrasound in Medicine, Shanghai, China
| | - Yichun Zhang
- Department of Ultrasound in Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China; Shanghai Institute of Ultrasound in Medicine, Shanghai, China
| | - Yan Wang
- Department of Ultrasound in Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China; Shanghai Institute of Ultrasound in Medicine, Shanghai, China.
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Gitto S, Bisdas S, Emili I, Nicosia L, Pescatori LC, Bhatia K, Lingam RK, Sardanelli F, Sconfienza LM, Mauri G. Clinical practice guidelines on ultrasound-guided fine needle aspiration biopsy of thyroid nodules: a critical appraisal using AGREE II. Endocrine 2019; 65:371-378. [PMID: 30903569 DOI: 10.1007/s12020-019-01898-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Accepted: 03/11/2019] [Indexed: 12/26/2022]
Abstract
PURPOSE To appraise the quality of current guidelines on fine needle aspiration biopsy (FNAB) of thyroid nodules for adults using the AGREE II quality assessment tool. METHODS We conducted an online search for guidelines on FNAB of thyroid nodules published between 2013 and October 2018. They were evaluated by four independent reviewers previously trained to apply the AGREE II instrument, which is organized into items and domains. A fifth independent reviewer calculated scores for each domain and guideline as well as inter-appraiser agreement. RESULTS Six sets of guidelines were included, respectively, provided by the American Thyroid Association (ATA), the American Association of Clinical Endocrinologists/American College of Endocrinology/Associazione Medici Endocrinologi (AACE/ACE/AME), the Korean Society of Thyroid Radiology (KSThR), the European Thyroid Association (ETA), the American College of Radiology (ACR) and the Korean Society of Radiology and National Evidence-Based Healthcare Collaborating Agency (KSR/NECA). Five out of the six guidelines (ATA, AACE/ACE/AME, ETA, ACR and KSR/NECA) reached a high level of overall quality, having at least five domain scores >60%. An average level of overall quality was achieved in one case (KSThR recommendations). Inter-appraiser agreement ranged from moderate to excellent. CONCLUSIONS Overall, the quality of guidelines on FNAB of thyroid nodules is satisfactory when evaluated using the AGREE II instrument.
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Affiliation(s)
- Salvatore Gitto
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Milano, Italy.
| | - Sotirios Bisdas
- Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Trust, London, UK
- Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, London, UK
| | - Ilaria Emili
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Milano, Italy
| | - Luca Nicosia
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Milano, Italy
| | | | - Kunwar Bhatia
- Department of Radiology, Imperial College Healthcare NHS Trust, London, UK
| | - Ravi K Lingam
- Department of Radiology, Northwick Park & Central Middlesex Hospitals, London North West University Healthcare NHS Trust, London, UK
| | - Francesco Sardanelli
- Servizio di Radiologia, IRCCS Policlinico San Donato, San Donato Milanese, Italy
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milano, Italy
| | - Luca Maria Sconfienza
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milano, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milano, Italy
| | - Giovanni Mauri
- Divisione di Radiologia Interventistica, IRCCS Istituto Europeo di Oncologia, Milano, Italy
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36
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Zhao WJ, Fu LR, Huang ZM, Zhu JQ, Ma BY. Effectiveness evaluation of computer-aided diagnosis system for the diagnosis of thyroid nodules on ultrasound: A systematic review and meta-analysis. Medicine (Baltimore) 2019; 98:e16379. [PMID: 31393347 PMCID: PMC6709241 DOI: 10.1097/md.0000000000016379] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND More and more automated efficient ultrasound image analysis techniques, such as ultrasound-based computer-aided diagnosis system (CAD), were developed to obtain accurate, reproducible, and more objective diagnosis results for thyroid nodules. So far, whether the diagnostic performance of existing CAD systems can reach the diagnostic level of experienced radiologists is still controversial. The aim of the meta-analysis was to evaluate the accuracy of CAD for thyroid nodules' diagnosis by reviewing current literatures and summarizing the research status. METHODS A detailed literature search on PubMed, Embase, and Cochrane Libraries for articles published until December 2018 was carried out. The diagnostic performances of CAD systems vs radiologist were evaluated by meta-analysis. We determined the sensitivity and the specificity across studies, calculated positive and negative likelihood ratios and constructed summary receiver-operating characteristic (SROC) curves. Meta-analysis of studies was performed using a mixed-effect, hierarchical logistic regression model. RESULTS Five studies with 536 patients and 723 thyroid nodules were included in this meta-analysis. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio (DOR) for CAD system were 0.87 (95% confidence interval [CI], 0.73-0.94), 0.79 (95% CI 0.63-0.89), 4.1 (95% CI 2.5-6.9), 0.17 (95% CI 0.09-0.32), and 25 (95% CI 15-42), respectively. The SROC curve indicated that the area under the curve was 0.90 (95% CI 0.87-0.92). The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and DOR for experienced radiologists were 0.82 (95% CI 0.69-0.91), 0.83 (95% CI 0.76-0.89), 4.9 (95% CI 3.4-7.0), 0.22 (95% CI 0.12-0.38), and 23 (95% CI 11-46), respectively. The SROC curve indicated that the area under the curve was 0.96 (95% CI 0.94-0.97). CONCLUSION The sensitivity of the CAD system in the diagnosis of thyroid nodules was similar to that of experienced radiologists. However, the CAD system had lower specificity and DOR than experienced radiologists. The CAD system may play the potential role as a decision-making assistant alongside radiologists in the thyroid nodules' diagnosis. Future technical improvements would be helpful to increase the accuracy as well as diagnostic efficiency.
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Affiliation(s)
- Wan-Jun Zhao
- Department of Thyroid & Parathyroid Surgery, West China Hospital
| | - Lin-Ru Fu
- West China School of Medicine, Sichuan University, Sichuan
| | - Zhi-Mian Huang
- Business College, New York University in Shanghai, Shanghai
| | - Jing-Qiang Zhu
- Department of Thyroid & Parathyroid Surgery, West China Hospital
| | - Bu-Yun Ma
- Department of Ultrasonography, West China Hospital, Sichuan University, Sichuan, China
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37
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Liu R, Li H, Liang F, Yao L, Liu J, Li M, Cao L, Song B. Diagnostic accuracy of different computer-aided diagnostic systems for malignant and benign thyroid nodules classification in ultrasound images: A systematic review and meta-analysis protocol. Medicine (Baltimore) 2019; 98:e16227. [PMID: 31335673 PMCID: PMC6709132 DOI: 10.1097/md.0000000000016227] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 06/06/2019] [Indexed: 02/01/2023] Open
Abstract
OBJECTIVE The aim of this study was to determine the diagnostic accuracy of different computer-aided diagnostic (CAD) systems for thyroid nodules classification. METHODS A systematic search of the literature was conducted from inception until March, 2019 using the PubMed, EMBASE, Web of science, and Cochrane library. Literature selection and data extraction were conducted by 2 independent reviewers. Numerical values for sensitivity and specificity were obtained from false negative (FN), false positive (FP), true negative (TN), and true positive (TP) rates, presented alongside graphical representations with boxes marking the values and horizontal lines showing the confidence intervals (CIs). Summary receiver operating characteristic (SROC) curves were applied to assess the performance of diagnostic tests. Data were processed using Review Manager 5.3 and Stata 15. The methodological quality of included studies was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. TRIAL REGISTRATION NUMBER PROSPERO CRD42019132540.
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Affiliation(s)
- Ruisheng Liu
- The First Hospital of Lanzhou University
- The First Clinical Medical College of Lanzhou University
| | - Huijuan Li
- School of Public Health, Evidence-based Social Science Research Center
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou
| | - Fuxiang Liang
- The First Hospital of Lanzhou University
- The First Clinical Medical College of Lanzhou University
| | - Liang Yao
- Chinese Medicine Faculty of Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Jieting Liu
- The First Clinical Medical College of Lanzhou University
- The Second hospital of Lanzhou University, Lanzhou, P.R. China
| | - Meixuan Li
- School of Public Health, Evidence-based Social Science Research Center
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou
| | - Liujiao Cao
- School of Public Health, Evidence-based Social Science Research Center
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou
| | - Bing Song
- The First Hospital of Lanzhou University
- The First Clinical Medical College of Lanzhou University
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Gitto S, Grassi G, De Angelis C, Monaco CG, Sdao S, Sardanelli F, Sconfienza LM, Mauri G. A computer-aided diagnosis system for the assessment and characterization of low-to-high suspicion thyroid nodules on ultrasound. Radiol Med 2018; 124:118-125. [PMID: 30244368 DOI: 10.1007/s11547-018-0942-z] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 09/11/2018] [Indexed: 12/25/2022]
Abstract
AIM OF THE STUDY To compare the diagnostic performance of a commercially available computer-aided diagnosis (CAD) system for thyroid ultrasound (US) with that of a non-computer-aided radiologist in the characterization of low-to-high suspicion thyroid nodules. METHODS This retrospective study included a consecutive series of adult patients referred for US-guided fine-needle aspiration biopsy (FNAB) of a thyroid nodule. All patients were eligible for thyroid nodule FNAB according to the current international guidelines. An interventional radiologist experienced in thyroid imaging acquired the US images subsequently used for post-processing, performed FNAB and provided the US features of each nodule. A radiology resident and an endocrinology resident in consensus performed post-processing using the CAD system to assess the same nodule characteristics. The diagnostic performance and agreement of US features between the CAD system and the radiologist were compared. RESULTS Sixty-two patients (50 F; age 60 ± 12 years) were enrolled: 77.4% (48/62) of thyroid nodules were benign, 22.6% (14/62) were undetermined to malignant and required follow-up or surgery. Interobserver agreement between the CAD system and the radiologist was substantial for orientation (K = 0.69), fair for composition (K = 0.36), echogenicity (K = 0.36), K-TIRADS (K = 0.29), and slight for margins (K = 0.03). The radiologist demonstrated a significantly higher sensitivity than the CAD system (78.6% vs. 21.4%; P = 0.008), while there was no statistical difference in specificity (66.7% vs. 81.3%; P = 0.065). CONCLUSION This CAD system is less sensitive than an experienced radiologist and showed slight-to-substantial agreement with the radiologist for the characterization of thyroid nodules. Although it is an innovative tool with good potential, additional efforts are needed to improve its diagnostic performance.
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Affiliation(s)
- Salvatore Gitto
- Scuola di Specializzazione in Radiodiagnostica, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy.
| | - Giorgia Grassi
- Scuola di Specializzazione in Endocrinologia e Malattie del Metabolismo, Università degli Studi di Milano, Milan, Italy
| | - Chiara De Angelis
- Scuola di Specializzazione in Radiodiagnostica, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy
| | - Cristian Giuseppe Monaco
- Scuola di Specializzazione in Radiodiagnostica, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy
| | - Silvana Sdao
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Francesco Sardanelli
- Servizio di Radiologia, IRCCS Policlinico San Donato, San Donato Milanese, Italy.,Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy
| | - Luca Maria Sconfienza
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy.,Unità Operativa di Radiologia Diagnostica ed Interventistica, IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Giovanni Mauri
- Divisione di Radiologia Interventistica, IEO, Istituto Europeo di Oncologia IRCCS, Milano, Italy
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