1
|
Palomba G, Fernicola A, Corte MD, Capuano M, De Palma GD, Aprea G. Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review. AIMS Public Health 2024; 11:557-576. [PMID: 39027395 PMCID: PMC11252578 DOI: 10.3934/publichealth.2024028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/29/2024] [Accepted: 04/02/2024] [Indexed: 07/20/2024] Open
Abstract
Artificial intelligence (AI) is playing an increasing role in several fields of medicine. It is also gaining popularity among surgeons as a valuable screening and diagnostic tool for many conditions such as benign and malignant colorectal, gastric, thyroid, parathyroid, and breast disorders. In the literature, there is no review that groups together the various application domains of AI when it comes to the screening and diagnosis of main surgical diseases. The aim of this review is to describe the use of AI in these settings. We performed a literature review by searching PubMed, Web of Science, Scopus, and Embase for all studies investigating the role of AI in the surgical setting, published between January 01, 2000, and June 30, 2023. Our focus was on randomized controlled trials (RCTs), meta-analysis, systematic reviews, and observational studies, dealing with large cohorts of patients. We then gathered further relevant studies from the reference list of the selected publications. Based on the studies reviewed, it emerges that AI could strongly enhance the screening efficiency, clinical ability, and diagnostic accuracy for several surgical conditions. Some of the future advantages of this technology include implementing, speeding up, and improving the automaticity with which AI recognizes, differentiates, and classifies the various conditions.
Collapse
Affiliation(s)
- Giuseppe Palomba
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Agostino Fernicola
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Marcello Della Corte
- Azienda Ospedaliera Universitaria San Giovanni di Dio e Ruggi d'Aragona - OO. RR. Scuola Medica Salernitana, Salerno, Italy
| | - Marianna Capuano
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Giovanni Domenico De Palma
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Giovanni Aprea
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| |
Collapse
|
2
|
Li W, Chen J, Ye F, Xu D, Fan X, Yang C. The diagnostic value of ultrasound on different-sized thyroid nodules based on ACR TI-RADS. Endocrine 2023; 82:569-579. [PMID: 37656349 DOI: 10.1007/s12020-023-03438-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 06/20/2023] [Indexed: 09/02/2023]
Abstract
OBJECTIVES The thyroid nodule is one of the most common endocrine system diseases. Risk classification models based on ultrasonic features have been created by multiple professional societies, including the American College of Radiology (ACR), which published the Thyroid Imaging Reporting and Data System (TI-RADS) in 2017. The effect of the size in the diagnostic value of ultrasound remains not well defined. The purposes of our study aims to explore diagnostic value of the ACR TI-RADS on different-sized thyroid nodules. METHODS A total of 1183 thyroid nodules were selected from 952 patients with thyroid nodules confirmed by surgical pathology from January 2021 to October 2022. Based on the maximum diameters of the nodules, they were stratified into groups A ( ≤ 10 mm), B ( > 10 mm, < 20 mm) and C ( ≥ 20 mm). The ultrasonic features of the thyroid nodules in each group were evaluated and scored based on ACR TI-RADS, and the receiver operating characteristic curve (ROC) was plotted to determine the optimal cut-off value for the ACR TI-RADS scores and categories in each group. Finally, the diagnostic efficacy of ACR TI-RADS on different-sized thyroid nodules was analyzed. RESULTS Among the 1183 thyroid nodules, 340 were benign, 10 were low-risk and 833 were malignant. For the convenience of statistical analysis, low-risk thyroid nodules were classified as malignant in this study. The ACR TI-RADS scores and categorical levels of malignant thyroid nodules in each group were higher than those of benign ones (p < 0.05). The areas under the ROCs (AUCs) plotted based on scores were 0.741, 0.907, and 0.904 respectively in the three groups, and the corresponding optimal cut-off values were > 6 points, > 5 points and > 4 points respectively. While the AUCs of the ACR TI-RADS categories were 0.668, 0.855, and 0.887 respectively in each group, with the optimal cut-off values were all > TR4. Besides, for thyroid nodules of larger sizes, ACR TI-RADS exhibited weaker sensitivity with lower positive prediction value (PPV), but the specificity and negative prediction value (NPV) were both higher, presenting with statistically significant differences (p < 0.05). CONCLUSION For thyroid nodules of different sizes, the diagnostic efficacy of ACR TI-RADS varies as well. The system shows better diagnostic efficacy on thyroid nodules of > 10 mm than on those ≤ 10 mm. Considering the favorable prognosis of thyroid microcarcinoma and the low diagnostic efficacy of ACR TI-RADS on it, the scoring and classification of thyroid micro-nodules can be left out in appropriate cases, so as to avoid the over-diagnosis and over-treatment of thyroid microcarcinoma to a certain extent.
Collapse
Affiliation(s)
- WeiMin Li
- Departments of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, 214000, Jiangsu, PR China
| | - JunMin Chen
- Department of Ultrasonography, Hangzhou Linping District Traditional Chinese Medicine Hospital, Hangzhou, 311199, Zhejiang, PR China
| | - Feng Ye
- School of nursing, Wuxi Medical College of Jiangnan University, Wuxi, 214000, Jiangsu, PR China
| | - Dong Xu
- Department of Ultrasonography, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, PR China
| | - XiaoFang Fan
- Departments of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, 214000, Jiangsu, PR China
| | - Chen Yang
- Department of Ultrasonography, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, PR China.
| |
Collapse
|
3
|
Huang H, Zhu MJ, Gao Q, Huang YL, Li WM. Comparison of Diagnostic Values of ACR TI-RADS versus C-TIRADS Scoring and Classification Systems for the Elderly Thyroid Cancers. Int J Gen Med 2023; 16:4441-4451. [PMID: 37795310 PMCID: PMC10546997 DOI: 10.2147/ijgm.s429681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 09/15/2023] [Indexed: 10/06/2023] Open
Abstract
Purpose To compare the diagnostic value of the Thyroid Imaging Reporting and Data System (TI-RADS) of the American College of Radiology (ACR) versus the Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) scoring and classification system for elderly thyroid cancers. Patients and Methods A total of 512 nodules from 465 patients aged ≥60 with surgical pathology-proven thyroid nodules were enrolled in our study. The ultrasound features of thyroid nodules were independently evaluated by the ACR TI-RADS and C-TIRADS classification systems, and the receiver operating characteristic curve (ROC) was plotted. The optimal cut-off values of the ACR TI-RADS and C-TIRADS scoring and classification systems for diagnosing elderly thyroid nodules were estimated, and the diagnostic efficacy was analyzed. Results The ACR TI-RADS and C-TIRADS scores and classifications of thyroid cancers were both higher than benign nodules (both P < 0.05). The area under the curve (AUC) of ACR TI-RADS and C-TIRADS scoring and classification systems were 0.861, 0.897, 0.879, and 0.900, respectively, and the AUC of the scoring system was greater than the classification system for both criteria. When the Youden index was the highest, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the ACR TI-RADS scoring and classification systems were consistent, ie, they were 89.66%, 41.70%, 89.93%, and 59.00%, respectively; the sensitivity, specificity, PPV, and NPV of the C-TIRADS scoring and classification systems were also consistent, ie, they were 88.71%, 44.26%, 90.23%, 59.69%, respectively. The diagnostic efficacy between the two systems was not statistically significant. Conclusion ACR TI-RADS and C-TIRADS systems had relatively high diagnostic efficacy for elderly thyroid cancer. The diagnostic efficiency of the scoring systems of ACR TI-RADS and C-TIRADS were higher than the classification systems. In addition, the two systems had high clinical practical values, while there is still a significant risk of missed diagnosis.
Collapse
Affiliation(s)
- Hu Huang
- Department of Thyroid and Breast Surgery, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, People’s Republic of China
| | - Ming-Jie Zhu
- Department of Thyroid and Breast Surgery, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, People’s Republic of China
| | - Qi Gao
- Department of Ultrasonography, Southeast University Affiliated Zhongda Hospital, Nanjing, Jiangsu, People’s Republic of China
| | - Yan-Li Huang
- Department of Special Clinic, General Hospital of Eastern Theater Command, PLA, Nanjing, Jiangsu, People’s Republic of China
| | - Wei-Min Li
- Department of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, People’s Republic of China
| |
Collapse
|
4
|
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.
Collapse
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;
| |
Collapse
|
5
|
Li LR, Du B, Liu HQ, Chen C. Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives. Front Oncol 2021; 10:604051. [PMID: 33634025 PMCID: PMC7899964 DOI: 10.3389/fonc.2020.604051] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 12/21/2020] [Indexed: 12/12/2022] Open
Abstract
Thyroid cancers (TC) have increasingly been detected following advances in diagnostic methods. Risk stratification guided by refined information becomes a crucial step toward the goal of personalized medicine. The diagnosis of TC mainly relies on imaging analysis, but visual examination may not reveal much information and not enable comprehensive analysis. Artificial intelligence (AI) is a technology used to extract and quantify key image information by simulating complex human functions. This latent, precise information contributes to stratify TC on the distinct risk and drives tailored management to transit from the surface (population-based) to a point (individual-based). In this review, we started with several challenges regarding personalized care in TC, for example, inconsistent rating ability of ultrasound physicians, uncertainty in cytopathological diagnosis, difficulty in discriminating follicular neoplasms, and inaccurate prognostication. We then analyzed and summarized the advances of AI to extract and analyze morphological, textural, and molecular features to reveal the ground truth of TC. Consequently, their combination with AI technology will make individual medical strategies possible.
Collapse
Affiliation(s)
- Ling-Rui Li
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Bo Du
- School of Computer Science, Wuhan University, Wuhan, China.,Institute of Artificial Intelligence, Wuhan University, Wuhan, China
| | - Han-Qing Liu
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chuang Chen
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| |
Collapse
|
6
|
Nguyen DT, Kang JK, Pham TD, Batchuluun G, Park KR. Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence. SENSORS (BASEL, SWITZERLAND) 2020; 20:E1822. [PMID: 32218230 PMCID: PMC7180806 DOI: 10.3390/s20071822] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 03/09/2020] [Accepted: 03/23/2020] [Indexed: 12/14/2022]
Abstract
Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly based on the experiences of doctors. Therefore, the performance of such systems plays an important role in enhancing the quality of a diagnosing task. Although there have been the state-of-the art studies regarding this problem, which are based on handcrafted features, deep features, or the combination of the two, their performances are still limited. To overcome these problems, we propose an ultrasound image-based diagnosis of the malignant thyroid nodule method using artificial intelligence based on the analysis in both spatial and frequency domains. Additionally, we propose the use of weighted binary cross-entropy loss function for the training of deep convolutional neural networks to reduce the effects of unbalanced training samples of the target classes in the training data. Through our experiments with a popular open dataset, namely the thyroid digital image database (TDID), we confirm the superiority of our method compared to the state-of-the-art methods.
Collapse
Affiliation(s)
| | | | - Tuyen Danh Pham
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea; (D.T.N.); (J.K.K.); (G.B.); (K.R.P.)
| | | | | |
Collapse
|
7
|
Zhang L, Chen K, Han L, Zhuang Y, Hua Z, Li C, Lin J. Recognition of calcifications in thyroid nodules based on attention-gated collaborative supervision network of ultrasound images. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2020; 28:1123-1139. [PMID: 32804114 DOI: 10.3233/xst-200740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
BACKGROUND Calcification is an important criterion for classification between benign and malignant thyroid nodules. Deep learning provides an important means for automatic calcification recognition, but it is tedious to annotate pixel-level labels for calcifications with various morphologies. OBJECTIVE This study aims to improve accuracy of calcification recognition and prediction of its location, as well as to reduce the number of pixel-level labels in model training. METHODS We proposed a collaborative supervision network based on attention gating (CS-AGnet), which was composed of two branches: a segmentation network and a classification network. The reorganized two-stage collaborative semi-supervised model was trained under the supervision of all image-level labels and few pixel-level labels. RESULTS The results show that although our semi-supervised network used only 30% (289 cases) of pixel-level labels for training, the accuracy of calcification recognition reaches 92.1%, which is very close to 92.9% of deep supervision with 100% (966 cases) pixel-level labels. The CS-AGnet enables to focus the model's attention on calcification objects. Thus, it achieves higher accuracy than other deep learning methods. CONCLUSIONS Our collaborative semi-supervised model has a preferable performance in calcification recognition, and it reduces the number of manual annotations of pixel-level labels. Moreover, it may be of great reference for the object recognition of medical dataset with few labels.
Collapse
Affiliation(s)
- Liqun Zhang
- Department of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Ke Chen
- Department of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Lin Han
- Department of Biomedical Engineering, Sichuan University, Chengdu, China
- Highong Intellimage Medical Technology (Tianjin) Co., Ltd, Tianjin, China
| | - Yan Zhuang
- Department of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Zhan Hua
- China-Japan Friendship Hospital, Beijing, China
| | - Cheng Li
- China-Japan Friendship Hospital, Beijing, China
| | - Jiangli Lin
- Department of Biomedical Engineering, Sichuan University, Chengdu, China
| |
Collapse
|
8
|
Kil J, Kim KG, Kim YJ, Koo HR, Park JS. Deep Learning in Thyroid Ultrasonography to Predict Tumor Recurrence in Thyroid Cancers. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2020; 81:1164-1174. [PMID: 36238043 PMCID: PMC9431857 DOI: 10.3348/jksr.2019.0147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 09/24/2019] [Accepted: 10/08/2019] [Indexed: 11/15/2022]
Abstract
Purpose Materials and Methods Results Conclusion
Collapse
Affiliation(s)
- Jieun Kil
- Department of Radiology, Hanyang University College of Medicine, Seoul, Korea
| | - Kwang Gi Kim
- Department of Biomedical Engineering, College of Medicine, Gachon University, Incheon, Korea
| | - Young Jae Kim
- Department of Biomedical Engineering, College of Medicine, Gachon University, Incheon, Korea
| | - Hye Ryoung Koo
- Department of Radiology, Hanyang University College of Medicine, Seoul, Korea
| | - Jeong Seon Park
- Department of Radiology, Hanyang University College of Medicine, Seoul, Korea
| |
Collapse
|
9
|
Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains. J Clin Med 2019; 8:jcm8111976. [PMID: 31739517 PMCID: PMC6912332 DOI: 10.3390/jcm8111976] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 11/08/2019] [Accepted: 11/10/2019] [Indexed: 12/25/2022] Open
Abstract
Image-based computer-aided diagnosis (CAD) systems have been developed to assist doctors in the diagnosis of thyroid cancer using ultrasound thyroid images. However, the performance of these systems is strongly dependent on the selection of detection and classification methods. Although there are previous researches on this topic, there is still room for enhancement of the classification accuracy of the existing methods. To address this issue, we propose an artificial intelligence-based method for enhancing the performance of the thyroid nodule classification system. Thus, we extract image features from ultrasound thyroid images in two domains: spatial domain based on deep learning, and frequency domain based on Fast Fourier transform (FFT). Using the extracted features, we perform a cascade classifier scheme for classifying the input thyroid images into either benign (negative) or malign (positive) cases. Through expensive experiments using a public dataset, the thyroid digital image database (TDID) dataset, we show that our proposed method outperforms the state-of-the-art methods and produces up-to-date classification results for the thyroid nodule classification problem.
Collapse
|
10
|
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:E1759. [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] [Grants] [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.
Collapse
Affiliation(s)
| | - Michael Ying
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, SAR, China;
| |
Collapse
|
11
|
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.
Collapse
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
| |
Collapse
|
12
|
Chen BD, Xu HX, Zhang YF, Liu BJ, Guo LH, Li DD, Zhao CK, Li XL, Wang D, Zhao SS. The diagnostic performances of conventional strain elastography (SE), acoustic radiation force impulse (ARFI) imaging and point shear-wave speed (pSWS) measurement for non-calcified thyroid nodules. Clin Hemorheol Microcirc 2017; 65:259-273. [PMID: 27567801 DOI: 10.3233/ch-16178] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
BACKGROUND Non-calcified thyroid nodules are relatively difficult to diagnose only relying on features of at conventional US images. OBJECTIVE To investigate the diagnostic performances of conventional strain elastography (SE), acoustic radiation force impulse (ARFI) SE and point shear-wave speed (pSWS) measurement for non-calcified thyroid nodules. METHODS A total of 201 non-calcified thyroid nodules in 195 patients were studied. They were examined with conventional ultrasound (US), conventional SE, ARFI SE and pSWS measurement. Their diagnostic performances and multivariable models were assessed with receiver operating characteristic (ROC) curve and logistic regression analyses respectively. RESULTS There were 156 benign and 45 malignant non-calcified nodules proven by histopathology or cystology. The mean diameters of the nodules were 21.2±10.8 mm. Areas under receiver operating characteristic curve (AUCs) of elastography features (ranged, 0.488-0.745) were all greater than that of US (ranged, 0.111-0.332). At multivariate analysis, there were three predictors of malignancy for non-calcified nodules, including pSWS of nodule (odds ratio [OR], 34.960; 95% CI, 11.582-105.529), marked hypoechogenicity (OR, 16.223; 95% CI, 1.761-149.454) and ARFI SE grade (OR, 10.900; 95% CI, 3.567-33.310). US+SE+pSWS owned the largest AUC (0.936; 95% CI, 0.887-0.985; P < 0.05), followed by US+pSWS (0.889; 95% CI, 0.823-0.955), and the poorest was US (0.727; 95% CI, 0.635-0.819). CONCLUSIONS ARFI SE and pSWS measurement had better diagnostic performances than conventional SE and US. When US combined with SE and pSWS measurement, it could achieve an excellent diagnostic performance and might contribute a better decision-making of FNA for non-calcified thyroid nodules.
Collapse
Affiliation(s)
- Bao-Ding Chen
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Nanjing Medical University, Shanghai, China.,Department of Medical Ultrasound, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Hui-Xiong Xu
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Nanjing Medical University, Shanghai, China.,Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Educational Institute, Tongji University School of Medicine, Shanghai, China.,Thyroid Institute, Tongji University School of Medicine, Shanghai, China.,Shanghai Research Center of Thyroid Diseases, Shanghai, China
| | - Yi-Feng Zhang
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Nanjing Medical University, Shanghai, China.,Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Educational Institute, Tongji University School of Medicine, Shanghai, China.,Thyroid Institute, Tongji University School of Medicine, Shanghai, China.,Shanghai Research Center of Thyroid Diseases, Shanghai, China
| | - Bo-Ji Liu
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Nanjing Medical University, Shanghai, China.,Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Educational Institute, Tongji University School of Medicine, Shanghai, China.,Thyroid Institute, Tongji University School of Medicine, Shanghai, China.,Shanghai Research Center of Thyroid Diseases, Shanghai, China
| | - Le-Hang Guo
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Nanjing Medical University, Shanghai, China.,Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Educational Institute, Tongji University School of Medicine, Shanghai, China.,Thyroid Institute, Tongji University School of Medicine, Shanghai, China.,Shanghai Research Center of Thyroid Diseases, Shanghai, China
| | - Dan-Dan Li
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Nanjing Medical University, Shanghai, China.,Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Educational Institute, Tongji University School of Medicine, Shanghai, China.,Thyroid Institute, Tongji University School of Medicine, Shanghai, China.,Shanghai Research Center of Thyroid Diseases, Shanghai, China
| | - Chong-Ke Zhao
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Nanjing Medical University, Shanghai, China.,Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Educational Institute, Tongji University School of Medicine, Shanghai, China.,Thyroid Institute, Tongji University School of Medicine, Shanghai, China.,Shanghai Research Center of Thyroid Diseases, Shanghai, China
| | - Xiao-Long Li
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Nanjing Medical University, Shanghai, China.,Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Educational Institute, Tongji University School of Medicine, Shanghai, China.,Thyroid Institute, Tongji University School of Medicine, Shanghai, China.,Shanghai Research Center of Thyroid Diseases, Shanghai, China
| | - Dan Wang
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Nanjing Medical University, Shanghai, China.,Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Educational Institute, Tongji University School of Medicine, Shanghai, China.,Thyroid Institute, Tongji University School of Medicine, Shanghai, China.,Shanghai Research Center of Thyroid Diseases, Shanghai, China
| | - Shuang-Shuang Zhao
- Department of Medical Ultrasound, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| |
Collapse
|
13
|
Macedo AA, Pessotti HC, Almansa LF, Felipe JC, Kimura ET. Morphometric information to reduce the semantic gap in the characterization of microscopic images of thyroid nodules. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 130:162-174. [PMID: 27208531 DOI: 10.1016/j.cmpb.2016.03.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2015] [Revised: 02/05/2016] [Accepted: 03/15/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND The analyses of several systems for medical-imaging processing typically support the extraction of image attributes, but do not comprise some information that characterizes images. For example, morphometry can be applied to find new information about the visual content of an image. The extension of information may result in knowledge. Subsequently, results of mappings can be applied to recognize exam patterns, thus improving the accuracy of image retrieval and allowing a better interpretation of exam results. Although successfully applied in breast lesion images, the morphometric approach is still poorly explored in thyroid lesions due to the high subjectivity thyroid examinations. OBJECTIVE This paper presents a theoretical-practical study, considering Computer Aided Diagnosis (CAD) and Morphometry, to reduce the semantic discontinuity between medical image features and human interpretation of image content. METHOD The proposed method aggregates the content of microscopic images characterized by morphometric information and other image attributes extracted by traditional object extraction algorithms. This method carries out segmentation, feature extraction, image labeling and classification. Morphometric analysis was included as an object extraction method in order to verify the improvement of its accuracy for automatic classification of microscopic images. RESULTS To validate this proposal and verify the utility of morphometric information to characterize thyroid images, a CAD system was created to classify real thyroid image-exams into Papillary Cancer, Goiter and Non-Cancer. Results showed that morphometric information can improve the accuracy and precision of image retrieval and the interpretation of results in computer-aided diagnosis. For example, in the scenario where all the extractors are combined with the morphometric information, the CAD system had its best performance (70% of precision in Papillary cases). CONCLUSION Results signalized a positive use of morphometric information from images to reduce semantic discontinuity between human interpretation and image characterization.
Collapse
Affiliation(s)
- Alessandra A Macedo
- University of São Paulo, Faculty of Philosophy, Sciences and Letters of Ribeirão Preto, Computer Science and Mathematics Department, Biomedical Informatics Group, Ribeirão Preto - SP, Brazil.
| | - Hugo C Pessotti
- University of São Paulo, Faculty of Philosophy, Sciences and Letters of Ribeirão Preto, Computer Science and Mathematics Department, Bioinformatics Graduate Program, Ribeirão Preto - SP, Brazil.
| | - Luciana F Almansa
- University of São Paulo, Faculty of Philosophy, Sciences and Letters of Ribeirão Preto, Computer Science and Mathematics Department, Bioinformatics Graduate Program, Ribeirão Preto - SP, Brazil.
| | - Joaquim C Felipe
- University of São Paulo, Faculty of Philosophy, Sciences and Letters of Ribeirão Preto, Computer Science and Mathematics Department, Biomedical Informatics Group, Ribeirão Preto - SP, Brazil.
| | - Edna T Kimura
- University of São Paulo, Biomedical Sciences Institute, Department of Cell and Developmental Biology, São Paulo - SP, Brazil.
| |
Collapse
|