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Wei Y, Yang M, Zhang M, Gao F, Zhang N, Hu F, Zhang X, Zhang S, Huang Z, Xu L, Zhang F, Liu M, Deng J, Cheng X, Xie T, Wang X, Liu N, Gong H, Zhu S, Song B, Liu M. Focal liver lesion diagnosis with deep learning and multistage CT imaging. Nat Commun 2024; 15:7040. [PMID: 39147767 DOI: 10.1038/s41467-024-51260-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 08/02/2024] [Indexed: 08/17/2024] Open
Abstract
Diagnosing liver lesions is crucial for treatment choices and patient outcomes. This study develops an automatic diagnosis system for liver lesions using multiphase enhanced computed tomography (CT). A total of 4039 patients from six data centers are enrolled to develop Liver Lesion Network (LiLNet). LiLNet identifies focal liver lesions, including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), metastatic tumors (MET), focal nodular hyperplasia (FNH), hemangioma (HEM), and cysts (CYST). Validated in four external centers and clinically verified in two hospitals, LiLNet achieves an accuracy (ACC) of 94.7% and an area under the curve (AUC) of 97.2% for benign and malignant tumors. For HCC, ICC, and MET, the ACC is 88.7% with an AUC of 95.6%. For FNH, HEM, and CYST, the ACC is 88.6% with an AUC of 95.9%. LiLNet can aid in clinical diagnosis, especially in regions with a shortage of radiologists.
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Affiliation(s)
- Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Meiyi Yang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Meng Zhang
- Department of Radiology, Sanya People's Hospital, Sanya, Hainan, China
| | - Feifei Gao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ning Zhang
- Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, Henan, China
| | - Fubi Hu
- Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China
| | - Xiao Zhang
- Department of Radiology, Leshan People's Hospital, Leshan, Sichuan, China
| | - Shasha Zhang
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
| | - Zixing Huang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Lifeng Xu
- Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, Zhejiang, China
| | - Feng Zhang
- Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, Zhejiang, China
| | - Minghui Liu
- Yangtze Delta Region Institute(Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
| | - Jiali Deng
- Yangtze Delta Region Institute(Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
| | - Xuan Cheng
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Tianshu Xie
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Xiaomin Wang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Nianbo Liu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Haigang Gong
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Shaocheng Zhu
- Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, Henan, China.
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
- Department of Radiology, Sanya People's Hospital, Sanya, Hainan, China.
| | - Ming Liu
- Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, Zhejiang, China.
- Yangtze Delta Region Institute(Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China.
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Shah AA, Daud A, Bukhari A, Alshemaimri B, Ahsan M, Younis R. DEL-Thyroid: deep ensemble learning framework for detection of thyroid cancer progression through genomic mutation. BMC Med Inform Decis Mak 2024; 24:198. [PMID: 39039464 DOI: 10.1186/s12911-024-02604-1] [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: 03/26/2024] [Accepted: 07/10/2024] [Indexed: 07/24/2024] Open
Abstract
Genes, expressed as sequences of nucleotides, are susceptible to mutations, some of which can lead to cancer. Machine learning and deep learning methods have emerged as vital tools in identifying mutations associated with cancer. Thyroid cancer ranks as the 5th most prevalent cancer in the USA, with thousands diagnosed annually. This paper presents an ensemble learning model leveraging deep learning techniques such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and Bi-directional LSTM (Bi-LSTM) to detect thyroid cancer mutations early. The model is trained on a dataset sourced from asia.ensembl.org and IntOGen.org, consisting of 633 samples with 969 mutations across 41 genes, collected from individuals of various demographics. Feature extraction encompasses techniques including Hahn moments, central moments, raw moments, and various matrix-based methods. Evaluation employs three testing methods: self-consistency test (SCT), independent set test (IST), and 10-fold cross-validation test (10-FCVT). The proposed ensemble learning model demonstrates promising performance, achieving 96% accuracy in the independent set test (IST). Statistical measures such as training accuracy, testing accuracy, recall, sensitivity, specificity, Mathew's Correlation Coefficient (MCC), loss, training accuracy, F1 Score, and Cohen's kappa are utilized for comprehensive evaluation.
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Affiliation(s)
- Asghar Ali Shah
- Center of Excellence in Artificial Intelligence (CoE-AI), Department of Computer Science, Bahria University, Islamabad, 04408, Pakistan
| | - Ali Daud
- Faculty of Resilience, Rabdan Academy, Abu Dhabi, United Arab Emirates.
| | - Amal Bukhari
- Department of Information Systems and Technology, Collage of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Bader Alshemaimri
- Software Engineering Department, College of Computing and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Muhammad Ahsan
- Department of Computer Science, University of Alabama at Birmingham, 1402 10th Avenue S, Birmingham, AL, 35294, USA
| | - Rehmana Younis
- College of Letters and Sciences, Graduate Student of Robotics Engineering, Columbus State University, Columbus, USA
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3
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Maksim R, Buczyńska A, Sidorkiewicz I, Krętowski AJ, Sierko E. Imaging and Metabolic Diagnostic Methods in the Stage Assessment of Rectal Cancer. Cancers (Basel) 2024; 16:2553. [PMID: 39061192 PMCID: PMC11275086 DOI: 10.3390/cancers16142553] [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: 06/10/2024] [Revised: 07/04/2024] [Accepted: 07/12/2024] [Indexed: 07/28/2024] Open
Abstract
Rectal cancer (RC) is a prevalent malignancy with significant morbidity and mortality rates. The accurate staging of RC is crucial for optimal treatment planning and patient outcomes. This review aims to summarize the current literature on imaging and metabolic diagnostic methods used in the stage assessment of RC. Various imaging modalities play a pivotal role in the initial evaluation and staging of RC. These include magnetic resonance imaging (MRI), computed tomography (CT), and endorectal ultrasound (ERUS). MRI has emerged as the gold standard for local staging due to its superior soft tissue resolution and ability to assess tumor invasion depth, lymph node involvement, and the presence of extramural vascular invasion. CT imaging provides valuable information about distant metastases and helps determine the feasibility of surgical resection. ERUS aids in assessing tumor depth, perirectal lymph nodes, and sphincter involvement. Understanding the strengths and limitations of each diagnostic modality is essential for accurate staging and treatment decisions in RC. Furthermore, the integration of multiple imaging and metabolic methods, such as PET/CT or PET/MRI, can enhance diagnostic accuracy and provide valuable prognostic information. Thus, a literature review was conducted to investigate and assess the effectiveness and accuracy of diagnostic methods, both imaging and metabolic, in the stage assessment of RC.
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Affiliation(s)
- Rafał Maksim
- Department of Radiotherapy, Maria Skłodowska-Curie Białystok Oncology Center, 15-027 Bialystok, Poland;
| | - Angelika Buczyńska
- Clinical Research Centre, Medical University of Bialystok, 15-276 Bialystok, Poland; (A.B.); (A.J.K.)
| | - Iwona Sidorkiewicz
- Clinical Research Support Centre, Medical University of Bialystok, 15-276 Bialystok, Poland;
| | - Adam Jacek Krętowski
- Clinical Research Centre, Medical University of Bialystok, 15-276 Bialystok, Poland; (A.B.); (A.J.K.)
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, 15-276 Bialystok, Poland
| | - Ewa Sierko
- Department of Oncology, Medical University of Bialystok, 15-276 Bialystok, Poland
- Department of Radiotherapy I, Maria Sklodowska-Curie Bialystok Oncology Centre, 15-027 Bialystok, Poland
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Xu D, Sui L, Zhang C, Xiong J, Wang VY, Zhou Y, Zhu X, Chen C, Zhao Y, Xie Y, Kong W, Yao J, Xu L, Zhai Y, Wang L. The clinical value of artificial intelligence in assisting junior radiologists in thyroid ultrasound: a multicenter prospective study from real clinical practice. BMC Med 2024; 22:293. [PMID: 38992655 PMCID: PMC11241898 DOI: 10.1186/s12916-024-03510-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: 01/21/2024] [Accepted: 07/01/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND This study is to propose a clinically applicable 2-echelon (2e) diagnostic criteria for the analysis of thyroid nodules such that low-risk nodules are screened off while only suspicious or indeterminate ones are further examined by histopathology, and to explore whether artificial intelligence (AI) can provide precise assistance for clinical decision-making in the real-world prospective scenario. METHODS In this prospective study, we enrolled 1036 patients with a total of 2296 thyroid nodules from three medical centers. The diagnostic performance of the AI system, radiologists with different levels of experience, and AI-assisted radiologists with different levels of experience in diagnosing thyroid nodules were evaluated against our proposed 2e diagnostic criteria, with the first being an arbitration committee consisting of 3 senior specialists and the second being cyto- or histopathology. RESULTS According to the 2e diagnostic criteria, 1543 nodules were classified by the arbitration committee, and the benign and malignant nature of 753 nodules was determined by pathological examinations. Taking pathological results as the evaluation standard, the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) of the AI systems were 0.826, 0.815, 0.821, and 0.821. For those cases where diagnosis by the Arbitration Committee were taken as the evaluation standard, the sensitivity, specificity, accuracy, and AUC of the AI system were 0.946, 0.966, 0.964, and 0.956. Taking the global 2e diagnostic criteria as the gold standard, the sensitivity, specificity, accuracy, and AUC of the AI system were 0.868, 0.934, 0.917, and 0.901, respectively. Under different criteria, AI was comparable to the diagnostic performance of senior radiologists and outperformed junior radiologists (all P < 0.05). Furthermore, AI assistance significantly improved the performance of junior radiologists in the diagnosis of thyroid nodules, and their diagnostic performance was comparable to that of senior radiologists when pathological results were taken as the gold standard (all p > 0.05). CONCLUSIONS The proposed 2e diagnostic criteria are consistent with real-world clinical evaluations and affirm the applicability of the AI system. Under the 2e criteria, the diagnostic performance of the AI system is comparable to that of senior radiologists and significantly improves the diagnostic capabilities of junior radiologists. This has the potential to reduce unnecessary invasive diagnostic procedures in real-world clinical practice.
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Affiliation(s)
- Dong Xu
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China
- Wenling Institute of Big Data and Artificial Intelligence in Medicine, Taizhou, 317502, China
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, 310022, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), Taizhou, 317502, China
| | - Lin Sui
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China
- Wenling Institute of Big Data and Artificial Intelligence in Medicine, Taizhou, 317502, China
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, 310022, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), Taizhou, 317502, China
| | - Chunquan Zhang
- Department of Ultrasound, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China
| | - Jing Xiong
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Vicky Yang Wang
- Wenling Institute of Big Data and Artificial Intelligence in Medicine, Taizhou, 317502, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), Taizhou, 317502, China
| | - Yahan Zhou
- Wenling Institute of Big Data and Artificial Intelligence in Medicine, Taizhou, 317502, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), Taizhou, 317502, China
| | - Xinying Zhu
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, 310022, China
| | - Chen Chen
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China
- Wenling Institute of Big Data and Artificial Intelligence in Medicine, Taizhou, 317502, China
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, 310022, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), Taizhou, 317502, China
| | - Yu Zhao
- Department of Ultrasound, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China
| | - Yiting Xie
- Demetics Medical Technology Co. Ltd., Hangzhou, 310022, China
| | - Weizhen Kong
- Department of Mathematics, The University of Hong Kong, Hong Kong, 999077, China
| | - Jincao Yao
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, 310022, China
| | - Lei Xu
- Zhejiang Qiushi Institute for Mathematical Medicine, Hangzhou, 310022, China.
- Present address: Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden.
| | - Yuxia Zhai
- The Second Affiliated Hospital of Shantou University Medical College, Guangdong, 515041, China.
| | - Liping Wang
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China.
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, 310022, China.
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Wang R, Huang S, Wang P, Shi X, Li S, Ye Y, Zhang W, Shi L, Zhou X, Tang X. Bibliometric analysis of the application of deep learning in cancer from 2015 to 2023. Cancer Imaging 2024; 24:85. [PMID: 38965599 PMCID: PMC11223420 DOI: 10.1186/s40644-024-00737-0] [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: 02/21/2024] [Accepted: 06/27/2024] [Indexed: 07/06/2024] Open
Abstract
BACKGROUND Recently, the application of deep learning (DL) has made great progress in various fields, especially in cancer research. However, to date, the bibliometric analysis of the application of DL in cancer is scarce. Therefore, this study aimed to explore the research status and hotspots of the application of DL in cancer. METHODS We retrieved all articles on the application of DL in cancer from the Web of Science database Core Collection database. Biblioshiny, VOSviewer and CiteSpace were used to perform the bibliometric analysis through analyzing the numbers, citations, countries, institutions, authors, journals, references, and keywords. RESULTS We found 6,016 original articles on the application of DL in cancer. The number of annual publications and total citations were uptrend in general. China published the greatest number of articles, USA had the highest total citations, and Saudi Arabia had the highest centrality. Chinese Academy of Sciences was the most productive institution. Tian, Jie published the greatest number of articles, while He Kaiming was the most co-cited author. IEEE Access was the most popular journal. The analysis of references and keywords showed that DL was mainly used for the prediction, detection, classification and diagnosis of breast cancer, lung cancer, and skin cancer. CONCLUSIONS Overall, the number of articles on the application of DL in cancer is gradually increasing. In the future, further expanding and improving the application scope and accuracy of DL applications, and integrating DL with protein prediction, genomics and cancer research may be the research trends.
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Affiliation(s)
- Ruiyu Wang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province, 646099, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China
| | - Shu Huang
- Department of Gastroenterology, Lianshui County People' Hospital, Huaian, China
- Department of Gastroenterology, Lianshui People' Hospital of Kangda CollegeAffiliated to, Nanjing Medical University , Huaian, China
| | - Ping Wang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province, 646099, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China
| | - Xiaomin Shi
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province, 646099, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China
| | - Shiqi Li
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province, 646099, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China
| | - Yusong Ye
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province, 646099, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China
| | - Wei Zhang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province, 646099, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China
| | - Lei Shi
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province, 646099, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China
| | - Xian Zhou
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province, 646099, China.
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China.
| | - Xiaowei Tang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province, 646099, China.
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China.
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Wang M, Chen C, Xu Z, Xu L, Zhan W, Xiao J, Hou Y, Huang B, Huang L, Li S. An interpretable two-branch bi-coordinate network based on multi-grained domain knowledge for classification of thyroid nodules in ultrasound images. Med Image Anal 2024; 97:103255. [PMID: 39013206 DOI: 10.1016/j.media.2024.103255] [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: 12/01/2022] [Revised: 07/22/2023] [Accepted: 06/24/2024] [Indexed: 07/18/2024]
Abstract
Computer-aided diagnosis (CAD) for thyroid nodules has been studied for years, yet there are still reliability and interpretability challenges due to the lack of clinically-relevant evidence. To address this issue, inspired by Thyroid Imaging Reporting and Data System (TI-RADS), we propose a novel interpretable two-branch bi-coordinate network based on multi-grained domain knowledge. First, we transform the two types of domain knowledge provided by TI-RADS, namely region-based and boundary-based knowledge, into labels at multi-grained levels: coarse-grained classification labels, and fine-grained region segmentation masks and boundary localization vectors. We combine these two labels to form the Multi-grained Domain Knowledge Representation (MG-DKR) of TI-RADS. Then we design a Two-branch Bi-coordinate network (TB2C-net) which utilizes two branches to predict MG-DKR from both Cartesian and polar images, and uses an attention-based integration module to integrate the features of the two branches for benign-malignant classification. We validated our method on a large cohort containing 3245 patients (with 3558 nodules and 6466 ultrasound images). Results show that our method achieves competitive performance with AUC of 0.93 and ACC of 0.87 compared with other state-of-the-art methods. Ablation experiment results demonstrate the effectiveness of the TB2C-net and MG-DKR, and the knowledge attention map from the integration module provides the interpretability for benign-malignant classification.
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Affiliation(s)
- Mingyu Wang
- PingAn Technology (Shenzhen) Co., Ltd, 20 Keji South 12th Rd., Shenzhen, 518057, China; Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518061, China
| | - Chao Chen
- PingAn Technology (Shenzhen) Co., Ltd, 20 Keji South 12th Rd., Shenzhen, 518057, China
| | - Ziyue Xu
- Nvidia Corporation, Bethesda, MD 20814, USA
| | - Lang Xu
- PingAn Technology (Shenzhen) Co., Ltd, 20 Keji South 12th Rd., Shenzhen, 518057, China
| | - Weiwei Zhan
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jing Xiao
- PingAn Technology (Shenzhen) Co., Ltd, 20 Keji South 12th Rd., Shenzhen, 518057, China
| | - Yiqing Hou
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518061, China.
| | - Lingyun Huang
- PingAn Technology (Shenzhen) Co., Ltd, 20 Keji South 12th Rd., Shenzhen, 518057, China.
| | - Shuo Li
- Department of Biomedical Engineering, and Computer and Data Science, Case Western Reserve University, OH, USA
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Liu X, Qin X, Luo Q, Qiao J, Xiao W, Zhu Q, Liu J, Zhang C. A Transvaginal Ultrasound-Based Deep Learning Model for the Noninvasive Diagnosis of Myometrial Invasion in Patients with Endometrial Cancer: Comparison with Radiologists. Acad Radiol 2024; 31:2818-2826. [PMID: 38182443 DOI: 10.1016/j.acra.2023.12.035] [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: 11/09/2023] [Revised: 12/19/2023] [Accepted: 12/20/2023] [Indexed: 01/07/2024]
Abstract
RATIONALE AND OBJECTIVES This study aimed to determine the feasibility of using the deep learning (DL) method to determine the degree (whether myometrial invasion [MI] >50%) of MI in patients with endometrial cancer (EC) based on ultrasound (US) images. MATERIALS AND METHODS From September 2017 to April 2023, 1289 US images of 604 patients with EC who underwent surgical resection at center 1, center 2 or center 3 were obtained and divided into a training set and an internal validation set. Ninety-five patients from center 4 and center 5 were randomly selected as the external testing set according to the same criteria as those for the primary cohort. This study evaluated three DL models trained on the training set and tested them on the validation and testing sets. The models' performance was analyzed based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), and the performance of the models was subsequently compared with that of 15 radiologists. RESULTS In the final clinical diagnosis of MI in patients with EC, EfficientNet-B6 showed the best performance in the testing set in terms of area under the curve (AUC) [0.814, 95% CI (0.746-0.882]; accuracy [0.802, 95% CI (0.733-0.855]; sensitivity [0.623]; specificity [0.879]; positive likelihood ratio (PLR) [6.750]; and negative likelihood ratio (NLR) [0.389]. The diagnostic efficacy of EfficientNet-B6 was significantly better than that of the 15 radiologists, with an average diagnostic accuracy of 0.681, average AUC of 0.678, AUC of the best performance of 0.739, accuracy of 0.716, sensitivity of 0.806, specificity 0.672, PLR2.457, and NLR 0.289. CONCLUSION Based on the preoperative US images of patients with EC, the DL model can accurately determine the degree of endometrial MI; the performance of this model is significantly better than that of radiologists, and it can effectively assist in clinical treatment decisions.
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Affiliation(s)
- Xiaoling Liu
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Rd, Shushan District, Hefei, 230022, Anhui, China (X.L., X.Q., Q.L., Q.Z., C.Z.); Department of Ultrasound, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University), Nanchong, Sichuan, China (X.L., X.Q.)
| | - Xiachuan Qin
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Rd, Shushan District, Hefei, 230022, Anhui, China (X.L., X.Q., Q.L., Q.Z., C.Z.); Department of Ultrasound, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University), Nanchong, Sichuan, China (X.L., X.Q.)
| | - Qi Luo
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Rd, Shushan District, Hefei, 230022, Anhui, China (X.L., X.Q., Q.L., Q.Z., C.Z.)
| | - Jing Qiao
- Department of Ultrasound, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China (J.Q.)
| | - Weihan Xiao
- North Sichuan Medical College, Nanchong, China (W.X.)
| | - Qiwei Zhu
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Rd, Shushan District, Hefei, 230022, Anhui, China (X.L., X.Q., Q.L., Q.Z., C.Z.)
| | - Jian Liu
- Department of Ultrasound, The First Affiliated Hospital of Chengdu Medical College, Chengdu, China (J.L.)
| | - Chaoxue Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Rd, Shushan District, Hefei, 230022, Anhui, China (X.L., X.Q., Q.L., Q.Z., C.Z.).
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Teodoriu L, Ungureanu MC, Matei M, Grierosu I, Saviuc AI, Wael J, Ivanov I, Dragos L, Danila R, Cristian V, Costandache MA, Iftene A, Preda C, Stefanescu C. BRAF Detection in FNAC Combined with Semi-Quantitative 99mTc-MIBI Technique and AI Model, an Economic and Efficient Predicting Tool for Malignancy in Thyroid Nodules. Diagnostics (Basel) 2024; 14:1398. [PMID: 39001288 PMCID: PMC11241294 DOI: 10.3390/diagnostics14131398] [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: 06/07/2024] [Revised: 06/22/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024] Open
Abstract
BACKGROUND Technology allows us to predict a histopathological diagnosis, but the high costs prevent the large-scale use of these possibilities. The current liberal indication for surgery in benign thyroid conditions led to a rising frequency of incidental thyroid carcinoma, especially low-risk papillary micro-carcinomas. METHODS We selected a cohort of 148 patients with thyroid nodules by ultrasound characteristics and investigated them by fine needle aspiration cytology (FNAC)and prospective BRAF collection for 70 patients. Also, we selected 44 patients with thyroid nodules using semi-quantitative functional imaging with an oncological, 99mTc-methoxy-isobutyl-isonitrile (99mTc-MIBI) radiotracer. RESULTS Following a correlation with final histopathological reports in patients who underwent thyroidectomy, we introduced the results in a machine learning program (AI) in order to obtain a pattern. For semi-quantitative functional visual pattern imaging, we found a sensitivity of 33%, a specificity of 66.67%, an accuracy of 60% and a negative predicting value (NPV) of 88.6%. For the wash-out index (WOind), we found a sensitivity of 57.14%, a specificity of 50%, an accuracy of 70% and an NPV of 90.06%.The results of BRAF in FNAC included 87.50% sensitivity, 75.00% specificity, 83.33% accuracy, 75.00% NPV and 87.50% PPV. The prevalence of malignancy in our small cohort was 11.4%. CONCLUSIONS We intend to continue combining preoperative investigations such as molecular detection in FNAC, 99mTc-MIBI scanning and AI training with the obtained results on a larger cohort. The combination of these investigations may generate an efficient and cost-effective diagnostic tool, but confirmation of the results on a larger scale is necessary.
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Affiliation(s)
- Laura Teodoriu
- Endocrinology Department, "Grigore T. Popa" University of Medicine and Pharmacy, 700111 Iasi, Romania
| | - Maria-Christina Ungureanu
- Endocrinology Department, "Grigore T. Popa" University of Medicine and Pharmacy, 700111 Iasi, Romania
| | - Mioara Matei
- Preventive Medicine and Interdisciplinarity Department, "Grigore T. Popa" University of Medicine and Pharmacy, 700111 Iasi, Romania
| | - Irena Grierosu
- Biophysics and Medical Physics-Nuclear Medicine Department, "Grigore T. Popa" University of Medicine and Pharmacy, 700111 Iasi, Romania
| | - Alexandra Iuliana Saviuc
- Biophysics and Medical Physics-Nuclear Medicine Department, "Grigore T. Popa" University of Medicine and Pharmacy, 700111 Iasi, Romania
| | - Jalloul Wael
- Biophysics and Medical Physics-Nuclear Medicine Department, "Grigore T. Popa" University of Medicine and Pharmacy, 700111 Iasi, Romania
| | - Iuliu Ivanov
- Center of Fundamental Research and Experimental Development in Translational Medicine, Regional Institute of Oncology, 700483 Iasi, Romania
| | - Loredana Dragos
- Center of Fundamental Research and Experimental Development in Translational Medicine, Regional Institute of Oncology, 700483 Iasi, Romania
| | - Radu Danila
- Department of Surgery, Faculty of Medicine, "Grigore T. Popa" University of Medicine and Pharmacy, 700111 Iasi, Romania
| | - Velicescu Cristian
- Department of Surgery, Faculty of Medicine, "Grigore T. Popa" University of Medicine and Pharmacy, 700111 Iasi, Romania
| | | | - Adrian Iftene
- Faculty of Computer Science, "Alexandru Ioan Cuza" University, 700506 Iasi, Romania
| | - Cristina Preda
- Endocrinology Department, "Grigore T. Popa" University of Medicine and Pharmacy, 700111 Iasi, Romania
| | - Cipriana Stefanescu
- Biophysics and Medical Physics-Nuclear Medicine Department, "Grigore T. Popa" University of Medicine and Pharmacy, 700111 Iasi, Romania
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Sant VR, Radhachandran A, Ivezic V, Lee DT, Livhits MJ, Wu JX, Masamed R, Arnold CW, Yeh MW, Speier W. From Bench-to-Bedside: How Artificial Intelligence is Changing Thyroid Nodule Diagnostics, a Systematic Review. J Clin Endocrinol Metab 2024; 109:1684-1693. [PMID: 38679750 PMCID: PMC11180510 DOI: 10.1210/clinem/dgae277] [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: 01/30/2024] [Revised: 04/04/2024] [Accepted: 04/16/2024] [Indexed: 05/01/2024]
Abstract
CONTEXT Use of artificial intelligence (AI) to predict clinical outcomes in thyroid nodule diagnostics has grown exponentially over the past decade. The greatest challenge is in understanding the best model to apply to one's own patient population, and how to operationalize such a model in practice. EVIDENCE ACQUISITION A literature search of PubMed and IEEE Xplore was conducted for English-language publications between January 1, 2015 and January 1, 2023, studying diagnostic tests on suspected thyroid nodules that used AI. We excluded articles without prospective or external validation, nonprimary literature, duplicates, focused on nonnodular thyroid conditions, not using AI, and those incidentally using AI in support of an experimental diagnostic outside standard clinical practice. Quality was graded by Oxford level of evidence. EVIDENCE SYNTHESIS A total of 61 studies were identified; all performed external validation, 16 studies were prospective, and 33 compared a model to physician prediction of ground truth. Statistical validation was reported in 50 papers. A diagnostic pipeline was abstracted, yielding 5 high-level outcomes: (1) nodule localization, (2) ultrasound (US) risk score, (3) molecular status, (4) malignancy, and (5) long-term prognosis. Seven prospective studies validated a single commercial AI; strengths included automating nodule feature assessment from US and assisting the physician in predicting malignancy risk, while weaknesses included automated margin prediction and interobserver variability. CONCLUSION Models predominantly used US images to predict malignancy. Of 4 Food and Drug Administration-approved products, only S-Detect was extensively validated. Implementing an AI model locally requires data sanitization and revalidation to ensure appropriate clinical performance.
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Affiliation(s)
- Vivek R Sant
- Division of Endocrine Surgery, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Ashwath Radhachandran
- Biomedical Artificial Intelligence Research Lab, UCLA Department of Bioengineering, Los Angeles, CA 90024, USA
| | - Vedrana Ivezic
- Biomedical Artificial Intelligence Research Lab, UCLA Department of Bioengineering, Los Angeles, CA 90024, USA
| | - Denise T Lee
- Department of Surgery, Icahn School of Medicine at Mount Sinai Hospital, New York, NY 10029, USA
| | - Masha J Livhits
- Section of Endocrine Surgery, UCLA David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - James X Wu
- Section of Endocrine Surgery, UCLA David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Rinat Masamed
- Department of Radiology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Corey W Arnold
- Biomedical Artificial Intelligence Research Lab, UCLA Department of Bioengineering, Los Angeles, CA 90024, USA
| | - Michael W Yeh
- Section of Endocrine Surgery, UCLA David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - William Speier
- Biomedical Artificial Intelligence Research Lab, UCLA Department of Bioengineering, Los Angeles, CA 90024, USA
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10
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Weng S, Ding C, Hu D, Chen J, Liu Y, Liu W, Chen Y, Guo X, Cao C, Yi Y, Yang Y, Peng D. Utilizing machine learning for early screening of thyroid nodules: a dual-center cross-sectional study in China. Front Endocrinol (Lausanne) 2024; 15:1385167. [PMID: 38948526 PMCID: PMC11211367 DOI: 10.3389/fendo.2024.1385167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 06/04/2024] [Indexed: 07/02/2024] Open
Abstract
Background Thyroid nodules, increasingly prevalent globally, pose a risk of malignant transformation. Early screening is crucial for management, yet current models focus mainly on ultrasound features. This study explores machine learning for screening using demographic and biochemical indicators. Methods Analyzing data from 6,102 individuals and 61 variables, we identified 17 key variables to construct models using six machine learning classifiers: Logistic Regression, SVM, Multilayer Perceptron, Random Forest, XGBoost, and LightGBM. Performance was evaluated by accuracy, precision, recall, F1 score, specificity, kappa statistic, and AUC, with internal and external validations assessing generalizability. Shapley values determined feature importance, and Decision Curve Analysis evaluated clinical benefits. Results Random Forest showed the highest internal validation accuracy (78.3%) and AUC (89.1%). LightGBM demonstrated robust external validation performance. Key factors included age, gender, and urinary iodine levels, with significant clinical benefits at various thresholds. Clinical benefits were observed across various risk thresholds, particularly in ensemble models. Conclusion Machine learning, particularly ensemble methods, accurately predicts thyroid nodule presence using demographic and biochemical data. This cost-effective strategy offers valuable insights for thyroid health management, aiding in early detection and potentially improving clinical outcomes. These findings enhance our understanding of the key predictors of thyroid nodules and underscore the potential of machine learning in public health applications for early disease screening and prevention.
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Affiliation(s)
- Shuwei Weng
- Department of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Research Institute of Blood Lipid and Atherosclerosis, Changsha, Hunan, China
| | - Chen Ding
- Department of Cardiology, The Fourth Affiliated Hospital of Soochow University, Suzhou Dushu Lake Hospital, Medical Center of Soochow University, Suzhou, Jiangsu, China
| | - Die Hu
- Department of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Research Institute of Blood Lipid and Atherosclerosis, Changsha, Hunan, China
| | - Jin Chen
- Department of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Research Institute of Blood Lipid and Atherosclerosis, Changsha, Hunan, China
| | - Yang Liu
- Department of Nephrology, The Key Laboratory for the Prevention and Treatment of Chronic Kidney Disease of Chongqing, Chongqing Clinical Research Center of Kidney and Urology Diseases, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Wenwu Liu
- Department of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Research Institute of Blood Lipid and Atherosclerosis, Changsha, Hunan, China
| | - Yang Chen
- Department of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Research Institute of Blood Lipid and Atherosclerosis, Changsha, Hunan, China
| | - Xin Guo
- Department of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Research Institute of Blood Lipid and Atherosclerosis, Changsha, Hunan, China
| | - Chenghui Cao
- Department of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Research Institute of Blood Lipid and Atherosclerosis, Changsha, Hunan, China
| | - Yuting Yi
- Department of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Research Institute of Blood Lipid and Atherosclerosis, Changsha, Hunan, China
| | - Yanyi Yang
- Health Management Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Hunan Provincial Clinical Medicine Research Center for Intelligent Management of Chronic Disease, Changsha, Hunan, China
| | - Daoquan Peng
- Department of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Research Institute of Blood Lipid and Atherosclerosis, Changsha, Hunan, China
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11
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Zhang J, Dawkins A. Artificial Intelligence in Ultrasound Imaging: Where Are We Now? Ultrasound Q 2024; 40:93-97. [PMID: 38842384 DOI: 10.1097/ruq.0000000000000680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Affiliation(s)
- Jie Zhang
- From the Department of Radiology, University of Kentucky, Lexington, KY
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12
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Weng L, Xu Y, Chen Y, Chen C, Qian Q, Pan J, Su H. Using Vision Transformer for high robustness and generalization in predicting EGFR mutation status in lung adenocarcinoma. Clin Transl Oncol 2024; 26:1438-1445. [PMID: 38194018 DOI: 10.1007/s12094-023-03366-4] [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: 10/17/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024]
Abstract
BACKGROUND Lung adenocarcinoma is a common cause of cancer-related deaths worldwide, and accurate EGFR genotyping is crucial for optimal treatment outcomes. Conventional methods for identifying the EGFR genotype have several limitations. Therefore, we proposed a deep learning model using non-invasive CT images to predict EGFR mutation status with robustness and generalizability. METHODS A total of 525 patients were enrolled at the local hospital to serve as the internal data set for model training and validation. In addition, a cohort of 30 patients from the publicly available Cancer Imaging Archive Data Set was selected for external testing. All patients underwent plain chest CT, and their EGFR mutation status labels were categorized as either mutant or wild type. The CT images were analyzed using a self-attention-based ViT-B/16 model to predict the EGFR mutation status, and the model's performance was evaluated. To produce an attention map indicating the suspicious locations of EGFR mutations, Grad-CAM was utilized. RESULTS The ViT deep learning model achieved impressive results, with an accuracy of 0.848, an AUC of 0.868, a sensitivity of 0.924, and a specificity of 0.718 on the validation cohort. Furthermore, in the external test cohort, the model achieved comparable performances, with an accuracy of 0.833, an AUC of 0.885, a sensitivity of 0.900, and a specificity of 0.800. CONCLUSIONS The ViT model demonstrates a high level of accuracy in predicting the EGFR mutation status of lung adenocarcinoma patients. Moreover, with the aid of attention maps, the model can assist clinicians in making informed clinical decisions.
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Affiliation(s)
- Luoqi Weng
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Yilun Xu
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Yuhan Chen
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Chengshui Chen
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Qinqing Qian
- Department of Respiratory Medicine, Shaoxing People's Hospital, Shaoxing, 312000, Zhejiang, China
| | - Jie Pan
- Department of Gastroenterology, Wenzhou Central Hospital, Wenzhou, 325000, Zhejiang, China
- Department of Gastroenterology, The Dingli Clinical College of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
- Department of Gastroenterology, The Second Affiliated Hospital of Shanghai University, Wenzhou, 325000, Zhejiang, China
| | - Huang Su
- Department of Gastroenterology, Wenzhou Central Hospital, Wenzhou, 325000, Zhejiang, China.
- Department of Gastroenterology, The Dingli Clinical College of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
- Department of Gastroenterology, The Second Affiliated Hospital of Shanghai University, Wenzhou, 325000, Zhejiang, China.
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13
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Xie H, Zhang Y, Dong L, Lv H, Li X, Zhao C, Tian Y, Xie L, Wu W, Yang Q, Liu L, Sun D, Qiu L, Shen L, Zhang Y. Deep learning driven diagnosis of malignant soft tissue tumors based on dual-modal ultrasound images and clinical indexes. Front Oncol 2024; 14:1361694. [PMID: 38846984 PMCID: PMC11153704 DOI: 10.3389/fonc.2024.1361694] [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: 12/26/2023] [Accepted: 05/08/2024] [Indexed: 06/09/2024] Open
Abstract
Background Soft tissue tumors (STTs) are benign or malignant superficial neoplasms arising from soft tissues throughout the body with versatile pathological types. Although Ultrasonography (US) is one of the most common imaging tools to diagnose malignant STTs, it still has several drawbacks in STT diagnosis that need improving. Objectives The study aims to establish this deep learning (DL) driven Artificial intelligence (AI) system for predicting malignant STTs based on US images and clinical indexes of the patients. Methods We retrospectively enrolled 271 malignant and 462 benign masses to build the AI system using 5-fold validation. A prospective dataset of 44 malignant masses and 101 benign masses was used to validate the accuracy of system. A multi-data fusion convolutional neural network, named ultrasound clinical soft tissue tumor net (UC-STTNet), was developed to combine gray scale and color Doppler US images and clinic features for malignant STTs diagnosis. Six radiologists (R1-R6) with three experience levels were invited for reader study. Results The AI system achieved an area under receiver operating curve (AUC) value of 0.89 in the retrospective dataset. The diagnostic performance of the AI system was higher than that of one of the senior radiologists (AUC of AI vs R2: 0.89 vs. 0.84, p=0.022) and all of the intermediate and junior radiologists (AUC of AI vs R3, R4, R5, R6: 0.89 vs 0.75, 0.81, 0.80, 0.63; p <0.01). The AI system also achieved an AUC of 0.85 in the prospective dataset. With the assistance of the system, the diagnostic performances and inter-observer agreement of the radiologists was improved (AUC of R3, R5, R6: 0.75 to 0.83, 0.80 to 0.85, 0.63 to 0.69; p<0.01). Conclusion The AI system could be a useful tool in diagnosing malignant STTs, and could also help radiologists improve diagnostic performance.
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Affiliation(s)
- Haiqin Xie
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Yudi Zhang
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China
| | - Licong Dong
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Heng Lv
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Xuechen Li
- National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China
| | - Chenyang Zhao
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Yun Tian
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Lu Xie
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Wangjie Wu
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Qi Yang
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Li Liu
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Desheng Sun
- Shenzhen Hospital, Peking University, Shenzhen, China
| | - Li Qiu
- West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Linlin Shen
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China
| | - Yusen Zhang
- Shenzhen Hospital, Peking University, Shenzhen, China
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14
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Zhang L, Wong C, Li Y, Huang T, Wang J, Lin C. Artificial intelligence assisted diagnosis of early tc markers and its application. Discov Oncol 2024; 15:172. [PMID: 38761260 PMCID: PMC11102422 DOI: 10.1007/s12672-024-01017-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 05/06/2024] [Indexed: 05/20/2024] Open
Abstract
Thyroid cancer (TC) is a common endocrine malignancy with an increasing incidence worldwide. Early diagnosis is particularly important for TC patients, because it allows patients to receive treatment as early as possible. Artificial intelligence (AI) provides great advantages for complex healthcare systems by analyzing big data based on machine learning. Nowadays, AI is widely used in the early diagnosis of cancer such as TC. Ultrasound detection and fine needle aspiration biopsy are the main methods for early diagnosis of TC. AI has been widely used in the detection of malignancy in thyroid nodules by ultrasound images, cytopathology images and molecular markers. It shows great potential in auxiliary medical diagnosis. The latest clinical trial has shown that the performance of AI models matches with the diagnostic efficiency of experienced clinicians, and more efficient AI tools will be developed in the future. Therefore, in this review, we summarized the recent advances in the application of AI algorithms in assessing the risk of malignancy in thyroid nodules. The objective of this review was to provide a data base for the clinical use of AI-assisted diagnosis in TC, as well as to provide new ideas for the next generation of AI-assisted diagnosis in TC.
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Affiliation(s)
- Laney Zhang
- Yale School of Public Health, New Haven, CT, USA
| | - Chinting Wong
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Yungeng Li
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
| | | | - Jiawen Wang
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Chenghe Lin
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, Jilin, China.
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15
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Liang Q, Qi Z, Li Y. Machine learning to predict the occurrence of thyroid nodules: towards a quantitative approach for judicious utilization of thyroid ultrasonography. Front Endocrinol (Lausanne) 2024; 15:1385836. [PMID: 38774231 PMCID: PMC11106422 DOI: 10.3389/fendo.2024.1385836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 04/15/2024] [Indexed: 05/24/2024] Open
Abstract
Introduction Ultrasound is instrumental in the early detection of thyroid nodules, which is crucial for appropriate management and favorable outcomes. However, there is a lack of clinical guidelines for the judicious use of thyroid ultrasonography in routine screening. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to leverage the ML approach in assessing the risk of thyroid nodules based on common clinical features. Methods Data were sourced from a Chinese cohort undergoing routine physical examinations including thyroid ultrasonography between 2013 and 2023. Models were established to predict the 3-year risk of thyroid nodules based on patients' baseline characteristics and laboratory tests. Four ML algorithms, including logistic regression, random forest, extreme gradient boosting, and light gradient boosting machine, were trained and tested using fivefold cross-validation. The importance of each feature was measured by the permutation score. A nomogram was established to facilitate risk assessment in the clinical settings. Results The final dataset comprised 4,386 eligible subjects. Thyroid nodules were detected in 54.8% (n=2,404) individuals within the 3-year observation period. All ML models significantly outperformed the baseline regression model, successfully predicting the occurrence of thyroid nodules in approximately two-thirds of individuals. Age, high-density lipoprotein, fasting blood glucose and creatinine levels exhibited the highest impact on the outcome in these models. The nomogram showed consistency and validity, providing greater net benefits for clinical decision-making than other strategies. Conclusion This study demonstrates the viability of an ML-based approach in predicting the occurrence of thyroid nodules. The findings highlight the potential of ML models in identifying high-risk individuals for personalized screening, thereby guiding the judicious use of ultrasound in this context.
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Affiliation(s)
- Qijun Liang
- Health Management Center, Foshan Hospital of Traditional Chinese Medicine, Foshan, Guangdong, China
| | - Zhenhong Qi
- Health Management Center, Foshan Hospital of Traditional Chinese Medicine, Foshan, Guangdong, China
| | - Yike Li
- Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, United States
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16
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Zhou H, Hua Z, Gao J, Lin F, Chen Y, Zhang S, Zheng T, Wang Z, Shao H, Li W, Liu F, Li Q, Chen J, Wang X, Zhao F, Qu N, Xie H, Ma H, Zhang H, Mao N. Multitask Deep Learning-Based Whole-Process System for Automatic Diagnosis of Breast Lesions and Axillary Lymph Node Metastasis Discrimination from Dynamic Contrast-Enhanced-MRI: A Multicenter Study. J Magn Reson Imaging 2024; 59:1710-1722. [PMID: 37497811 DOI: 10.1002/jmri.28913] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/02/2023] [Accepted: 07/03/2023] [Indexed: 07/28/2023] Open
Abstract
BACKGROUND Accurate diagnosis of breast lesions and discrimination of axillary lymph node (ALN) metastases largely depend on radiologist experience. PURPOSE To develop a deep learning-based whole-process system (DLWPS) for segmentation and diagnosis of breast lesions and discrimination of ALN metastasis. STUDY TYPE Retrospective. POPULATION 1760 breast patients, who were divided into training and validation sets (1110 patients), internal (476 patients), and external (174 patients) test sets. FIELD STRENGTH/SEQUENCE 3.0T/dynamic contrast-enhanced (DCE)-MRI sequence. ASSESSMENT DLWPS was developed using segmentation and classification models. The DLWPS-based segmentation model was developed by the U-Net framework, which combined the attention module and the edge feature extraction module. The average score of the output scores of three networks was used as the result of the DLWPS-based classification model. Moreover, the radiologists' diagnosis without and with the DLWPS-assistance was explored. To reveal the underlying biological basis of DLWPS, genetic analysis was performed based on RNA-sequencing data. STATISTICAL TESTS Dice similarity coefficient (DI), area under receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and kappa value. RESULTS The segmentation model reached a DI of 0.828 and 0.813 in the internal and external test sets, respectively. Within the breast lesions diagnosis, the DLWPS achieved AUCs of 0.973 in internal test set and 0.936 in external test set. For ALN metastasis discrimination, the DLWPS achieved AUCs of 0.927 in internal test set and 0.917 in external test set. The agreement of radiologists improved with the DLWPS-assistance from 0.547 to 0.794, and from 0.848 to 0.892 in breast lesions diagnosis and ALN metastasis discrimination, respectively. Additionally, 10 breast cancers with ALN metastasis were associated with pathways of aerobic electron transport chain and cytoplasmic translation. DATA CONCLUSION The performance of DLWPS indicates that it can promote radiologists in the judgment of breast lesions and ALN metastasis and nonmetastasis. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY STAGE: 3.
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Affiliation(s)
- Heng Zhou
- School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, Shandong, China
| | - Zhen Hua
- School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, Shandong, China
| | - Jing Gao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Fan Lin
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Yuqian Chen
- School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, Shandong, China
| | - Shijie Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Tiantian Zheng
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Zhongyi Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Huafei Shao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Wenjuan Li
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Fengjie Liu
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Qin Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jingjing Chen
- Department of Radiology, Qingdao University Affiliated Hospital, Qingdao, Shandong, China
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital, Jinan, Shandong, China
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China
| | - Nina Qu
- Department of Ultrasound, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
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17
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Giorgini F, Di Dalmazi G, Diciotti S. Artificial intelligence in endocrinology: a comprehensive review. J Endocrinol Invest 2024; 47:1067-1082. [PMID: 37971630 PMCID: PMC11035463 DOI: 10.1007/s40618-023-02235-9] [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: 07/29/2023] [Accepted: 10/24/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND AND AIM Artificial intelligence (AI) has emerged as a promising technology in the field of endocrinology, offering significant potential to revolutionize the diagnosis, treatment, and management of endocrine disorders. This comprehensive review aims to provide a concise overview of the current landscape of AI applications in endocrinology and metabolism, focusing on the fundamental concepts of AI, including machine learning algorithms and deep learning models. METHODS The review explores various areas of endocrinology where AI has demonstrated its value, encompassing screening and diagnosis, risk prediction, translational research, and "pre-emptive medicine". Within each domain, relevant studies are discussed, offering insights into the methodology and main findings of AI in the treatment of different pathologies, such as diabetes mellitus and related disorders, thyroid disorders, adrenal tumors, and bone and mineral disorders. RESULTS Collectively, these studies show the valuable contributions of AI in optimizing healthcare outcomes and unveiling new understandings of the intricate mechanisms underlying endocrine disorders. Furthermore, AI-driven approaches facilitate the development of precision medicine strategies, enabling tailored interventions for patients based on their individual characteristics and needs. CONCLUSIONS By embracing AI in endocrinology, a future can be envisioned where medical professionals and AI systems synergistically collaborate, ultimately enhancing the lives of individuals affected by endocrine disorders.
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Affiliation(s)
- F Giorgini
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy
- Division of Endocrinology and Diabetes Prevention and Care, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - G Di Dalmazi
- Division of Endocrinology and Diabetes Prevention and Care, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Department of Medical and Surgical Sciences (DIMEC), Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - S Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy.
- Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy.
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18
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Liu D, Yang K, Zhang C, Xiao D, Zhao Y. Fully-Automatic Detection and Diagnosis System for Thyroid Nodules Based on Ultrasound Video Sequences by Artificial Intelligence. J Multidiscip Healthc 2024; 17:1641-1651. [PMID: 38646015 PMCID: PMC11027922 DOI: 10.2147/jmdh.s439629] [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: 09/10/2023] [Accepted: 04/08/2024] [Indexed: 04/23/2024] Open
Abstract
Background Interpretation of ultrasound findings of thyroid nodules is subjective and labor-intensive for radiologists. Artificial intelligence (AI) is a relatively objective and efficient technology. We aimed to establish a fully automatic detection and diagnosis system for thyroid nodules based on AI technology by analyzing ultrasound video sequences. Patients and Methods We prospectively acquired dynamic ultrasound videos of 1067 thyroid nodules (804 for training and 263 for validation) from December 2018 to January 2021. All the patients underwent hemithyroidectomy or total thyroidectomy. Dynamic ultrasound videos were used to develop an AI system consisting of two deep learning models that could automatically detect and diagnose thyroid nodules. Average precision (AP) was used to estimate the performance of the detection model. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of the diagnostic model. Results Location and shape were accurately detected with a high AP of 0.914 in the validation cohort. The AUC of the diagnostic model was 0.953 in the validation cohort. The sensitivity and specificity of junior and senior radiologists were 76.9% vs 78.3% and 68.4% vs 81.1%, respectively. The diagnostic performance of the AI diagnostic model was superior to that of junior radiologists (P = 0.016) and was not significantly different from that of senior radiologists (P = 0.281). Conclusion We established a fully automatic detection and diagnosis system for thyroid nodules based on ultrasound video using an AI approach that can be conveniently applied to optimize the management of patients with thyroid nodules.
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Affiliation(s)
- Dan Liu
- Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People’s Republic of China
| | - Ke Yang
- The First in-Patient Department, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, People’s Republic of China
| | - Chunquan Zhang
- Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People’s Republic of China
| | - Dandan Xiao
- Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People’s Republic of China
| | - Yu Zhao
- Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People’s Republic of China
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Zhang MB, Meng ZL, Mao Y, Jiang X, Xu N, Xu QH, Tian J, Luo YK, Wang K. Cervical lymph node metastasis prediction from papillary thyroid carcinoma US videos: a prospective multicenter study. BMC Med 2024; 22:153. [PMID: 38609953 PMCID: PMC11015607 DOI: 10.1186/s12916-024-03367-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 03/22/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Prediction of lymph node metastasis (LNM) is critical for individualized management of papillary thyroid carcinoma (PTC) patients to avoid unnecessary overtreatment as well as undesired under-treatment. Artificial intelligence (AI) trained by thyroid ultrasound (US) may improve prediction performance. METHODS From September 2017 to December 2018, patients with suspicious PTC from the first medical center of the Chinese PLA general hospital were retrospectively enrolled to pre-train the multi-scale, multi-frame, and dual-direction deep learning (MMD-DL) model. From January 2019 to July 2021, PTC patients from four different centers were prospectively enrolled to fine-tune and independently validate MMD-DL. Its diagnostic performance and auxiliary effect on radiologists were analyzed in terms of receiver operating characteristic (ROC) curves, areas under the ROC curve (AUC), accuracy, sensitivity, and specificity. RESULTS In total, 488 PTC patients were enrolled in the pre-training cohort, and 218 PTC patients were included for model fine-tuning (n = 109), internal test (n = 39), and external validation (n = 70). Diagnostic performances of MMD-DL achieved AUCs of 0.85 (95% CI: 0.73, 0.97) and 0.81 (95% CI: 0.73, 0.89) in the test and validation cohorts, respectively, and US radiologists significantly improved their average diagnostic accuracy (57% vs. 60%, P = 0.001) and sensitivity (62% vs. 65%, P < 0.001) by using the AI model for assistance. CONCLUSIONS The AI model using US videos can provide accurate and reproducible prediction of cervical lymph node metastasis in papillary thyroid carcinoma patients preoperatively, and it can be used as an effective assisting tool to improve diagnostic performance of US radiologists. TRIAL REGISTRATION We registered on the Chinese Clinical Trial Registry website with the number ChiCTR1900025592.
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Affiliation(s)
- Ming-Bo Zhang
- Department of Ultrasound, the First Medical Center, General Hospital of Chinese PLA, Beijing, China
| | - Zhe-Ling Meng
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yi Mao
- Department of Ultrasound, the First Medical Center, General Hospital of Chinese PLA, Beijing, China
| | - Xue Jiang
- Department of Ultrasound, the Fourth Medical Center, General Hospital of Chinese PLA, Beijing, China
| | - Ning Xu
- Department of Ultrasound, Beijing Tong Ren Hospital, Beijing, China
| | - Qing-Hua Xu
- Department of Ultrasound, China-Japan Friendship Hospital, Beijing, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yu-Kun Luo
- Department of Ultrasound, the First Medical Center, General Hospital of Chinese PLA, Beijing, China.
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
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20
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Fukuzawa F, Yanagita Y, Yokokawa D, Uchida S, Yamashita S, Li Y, Shikino K, Tsukamoto T, Noda K, Uehara T, Ikusaka M. Importance of Patient History in Artificial Intelligence-Assisted Medical Diagnosis: Comparison Study. JMIR MEDICAL EDUCATION 2024; 10:e52674. [PMID: 38602313 PMCID: PMC11024399 DOI: 10.2196/52674] [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: 09/12/2023] [Revised: 01/31/2024] [Accepted: 02/15/2024] [Indexed: 04/12/2024]
Abstract
Background Medical history contributes approximately 80% to a diagnosis, although physical examinations and laboratory investigations increase a physician's confidence in the medical diagnosis. The concept of artificial intelligence (AI) was first proposed more than 70 years ago. Recently, its role in various fields of medicine has grown remarkably. However, no studies have evaluated the importance of patient history in AI-assisted medical diagnosis. Objective This study explored the contribution of patient history to AI-assisted medical diagnoses and assessed the accuracy of ChatGPT in reaching a clinical diagnosis based on the medical history provided. Methods Using clinical vignettes of 30 cases identified in The BMJ, we evaluated the accuracy of diagnoses generated by ChatGPT. We compared the diagnoses made by ChatGPT based solely on medical history with the correct diagnoses. We also compared the diagnoses made by ChatGPT after incorporating additional physical examination findings and laboratory data alongside history with the correct diagnoses. Results ChatGPT accurately diagnosed 76.6% (23/30) of the cases with only the medical history, consistent with previous research targeting physicians. We also found that this rate was 93.3% (28/30) when additional information was included. Conclusions Although adding additional information improves diagnostic accuracy, patient history remains a significant factor in AI-assisted medical diagnosis. Thus, when using AI in medical diagnosis, it is crucial to include pertinent and correct patient histories for an accurate diagnosis. Our findings emphasize the continued significance of patient history in clinical diagnoses in this age and highlight the need for its integration into AI-assisted medical diagnosis systems.
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Affiliation(s)
- Fumitoshi Fukuzawa
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Yasutaka Yanagita
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Daiki Yokokawa
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Shun Uchida
- Uchida Internal Medicine Clinic, Saitama-shi, Japan
| | - Shiho Yamashita
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Yu Li
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Kiyoshi Shikino
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Tomoko Tsukamoto
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Kazutaka Noda
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Takanori Uehara
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
| | - Masatomi Ikusaka
- Department of General Medicine, Chiba University Hospital, Chiba-shi, Japan
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21
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Chen JH, Zhang YQ, Zhu TT, Zhang Q, Zhao AX, Huang Y. Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five contrast-enhanced ultrasound key frames. Front Endocrinol (Lausanne) 2024; 15:1299686. [PMID: 38633756 PMCID: PMC11021584 DOI: 10.3389/fendo.2024.1299686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 03/21/2024] [Indexed: 04/19/2024] Open
Abstract
Objectives To apply machine learning to extract radiomics features from thyroid two-dimensional ultrasound (2D-US) combined with contrast-enhanced ultrasound (CEUS) images to classify and predict benign and malignant thyroid nodules, classified according to the Chinese version of the thyroid imaging reporting and data system (C-TIRADS) as category 4. Materials and methods This retrospective study included 313 pathologically diagnosed thyroid nodules (203 malignant and 110 benign). Two 2D-US images and five CEUS key frames ("2nd second after the arrival time" frame, "time to peak" frame, "2nd second after peak" frame, "first-flash" frame, and "second-flash" frame) were selected to manually label the region of interest using the "Labelme" tool. A total of 7 images of each nodule and their annotates were imported into the Darwin Research Platform for radiomics analysis. The datasets were randomly split into training and test cohorts in a 9:1 ratio. Six classifiers, namely, support vector machine, logistic regression, decision tree, random forest (RF), gradient boosting decision tree and extreme gradient boosting, were used to construct and test the models. Performance was evaluated using a receiver operating characteristic curve analysis. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), and F1-score were calculated. One junior radiologist and one senior radiologist reviewed the 2D-US image and CEUS videos of each nodule and made a diagnosis. We then compared their AUC and ACC with those of our best model. Results The AUC of the diagnosis of US, CEUS and US combined CEUS by junior radiologist and senior radiologist were 0.755, 0.750, 0.784, 0.800, 0.873, 0.890, respectively. The RF classifier performed better than the other five, with an AUC of 1 for the training cohort and 0.94 (95% confidence interval 0.88-1) for the test cohort. The sensitivity, specificity, accuracy, PPV, NPV, and F1-score of the RF model in the test cohort were 0.82, 0.93, 0.90, 0.85, 0.92, and 0.84, respectively. The RF model with 2D-US combined with CEUS key frames achieved equivalent performance as the senior radiologist (AUC: 0.94 vs. 0.92, P = 0.798; ACC: 0.90 vs. 0.92) and outperformed the junior radiologist (AUC: 0.94 vs. 0.80, P = 0.039, ACC: 0.90 vs. 0.81) in the test cohort. Conclusions Our model, based on 2D-US and CEUS key frames radiomics features, had good diagnostic efficacy for thyroid nodules, which are classified as C-TIRADS 4. It shows promising potential in assisting less experienced junior radiologists.
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Affiliation(s)
| | | | | | | | | | - Ying Huang
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China
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22
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Hu B, Shi Z, Lu L, Miao Z, Wang H, Zhou Z, Zhang F, Wang R, Luo X, Xu F, Li S, Fang X, Wang X, Yan G, Lv F, Zhang M, Sun Q, Cui G, Liu Y, Zhang S, Pan C, Hou Z, Liang H, Pan Y, Chen X, Li X, Zhou F, Schoepf UJ, Varga-Szemes A, Garrison Moore W, Yu Y, Hu C, Zhang LJ. A deep-learning model for intracranial aneurysm detection on CT angiography images in China: a stepwise, multicentre, early-stage clinical validation study. Lancet Digit Health 2024; 6:e261-e271. [PMID: 38519154 DOI: 10.1016/s2589-7500(23)00268-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 10/23/2023] [Accepted: 12/29/2023] [Indexed: 03/24/2024]
Abstract
BACKGROUND Artificial intelligence (AI) models in real-world implementation are scarce. Our study aimed to develop a CT angiography (CTA)-based AI model for intracranial aneurysm detection, assess how it helps clinicians improve diagnostic performance, and validate its application in real-world clinical implementation. METHODS We developed a deep-learning model using 16 546 head and neck CTA examination images from 14 517 patients at eight Chinese hospitals. Using an adapted, stepwise implementation and evaluation, 120 certified clinicians from 15 geographically different hospitals were recruited. Initially, the AI model was externally validated with images of 900 digital subtraction angiography-verified CTA cases (examinations) and compared with the performance of 24 clinicians who each viewed 300 of these cases (stage 1). Next, as a further external validation a multi-reader multi-case study enrolled 48 clinicians to individually review 298 digital subtraction angiography-verified CTA cases (stage 2). The clinicians reviewed each CTA examination twice (ie, with and without the AI model), separated by a 4-week washout period. Then, a randomised open-label comparison study enrolled 48 clinicians to assess the acceptance and performance of this AI model (stage 3). Finally, the model was prospectively deployed and validated in 1562 real-world clinical CTA cases. FINDINGS The AI model in the internal dataset achieved a patient-level diagnostic sensitivity of 0·957 (95% CI 0·939-0·971) and a higher patient-level diagnostic sensitivity than clinicians (0·943 [0·921-0·961] vs 0·658 [0·644-0·672]; p<0·0001) in the external dataset. In the multi-reader multi-case study, the AI-assisted strategy improved clinicians' diagnostic performance both on a per-patient basis (the area under the receiver operating characteristic curves [AUCs]; 0·795 [0·761-0·830] without AI vs 0·878 [0·850-0·906] with AI; p<0·0001) and a per-aneurysm basis (the area under the weighted alternative free-response receiver operating characteristic curves; 0·765 [0·732-0·799] vs 0·865 [0·839-0·891]; p<0·0001). Reading time decreased with the aid of the AI model (87·5 s vs 82·7 s, p<0·0001). In the randomised open-label comparison study, clinicians in the AI-assisted group had a high acceptance of the AI model (92·6% adoption rate), and a higher AUC when compared with the control group (0·858 [95% CI 0·850-0·866] vs 0·789 [0·780-0·799]; p<0·0001). In the prospective study, the AI model had a 0·51% (8/1570) error rate due to poor-quality CTA images and recognition failure. The model had a high negative predictive value of 0·998 (0·994-1·000) and significantly improved the diagnostic performance of clinicians; AUC improved from 0·787 (95% CI 0·766-0·808) to 0·909 (0·894-0·923; p<0·0001) and patient-level sensitivity improved from 0·590 (0·511-0·666) to 0·825 (0·759-0·880; p<0·0001). INTERPRETATION This AI model demonstrated strong clinical potential for intracranial aneurysm detection with improved clinician diagnostic performance, high acceptance, and practical implementation in real-world clinical cases. FUNDING National Natural Science Foundation of China. TRANSLATION For the Chinese translation of the abstract see Supplementary Materials section.
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Affiliation(s)
- Bin Hu
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zhao Shi
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Li Lu
- Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Zhongchang Miao
- Department of Medical Imaging, the First People's Hospital of Lianyungang, Lianyungang, Jiangsu, China
| | - Hao Wang
- Deepwise Artificial Intelligence (AI) Lab, Deepwise, Beijing, China
| | - Zhen Zhou
- Deepwise Artificial Intelligence (AI) Lab, Deepwise, Beijing, China
| | - Fandong Zhang
- Deepwise Artificial Intelligence (AI) Lab, Deepwise, Beijing, China
| | - Rongpin Wang
- Department of Medical Imaging, Guizhou Province People's Hospital, Guiyang, Guizhou, China
| | - Xiao Luo
- Department of Radiology, Ma'anshan People's Hospital, Ma'anshan, Anhui, China
| | - Feng Xu
- Department of Medical Imaging, the Affiliated Suqian First People's Hospital of Nanjing Medical University, Suqian, Jiangsu, China
| | - Sheng Li
- Department of Radiology, People's Hospital, Hubei University of Medicine, Shiyan, Hubei, China
| | - Xiangming Fang
- Department of Medical Imaging, the Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, Jiangsu, China
| | - Xiaodong Wang
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China
| | - Ge Yan
- Department of Medical Imaging, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Fajin Lv
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Meng Zhang
- Department of Radiology, People's Hospital of Sanya, Sanya, Hainan, China
| | - Qiu Sun
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China
| | - Guangbin Cui
- Department of Radiology, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi'an, Shaanxi, China
| | - Yubao Liu
- Medical Imaging Center, Shenzhen Hospital of Southern Medical University, Shenzhen, Guangdong, China
| | - Shu Zhang
- Deepwise Artificial Intelligence (AI) Lab, Deepwise, Beijing, China
| | - Chengwei Pan
- Institute of Artificial Intelligence, Beihang University, Beijing, China
| | - Zhibo Hou
- Department of Radiology, Medical Imaging Center, Peking University Shougang Hospital, Beijing, China
| | - Huiying Liang
- Medical Big Data Center, Guangdong Provincial People's Hospital, Guangzhou Guangdong, China
| | - Yuning Pan
- Department of Radiology, Ningbo First Hospital, Ningbo, Zhejiang, China
| | - Xiaoxia Chen
- Department of Radiology, Third Center Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xiaorong Li
- Department of Radiology, General Hospital of Southern Theater Command, PLA, Guangzhou, Guangdong, China
| | - Fei Zhou
- Department of Radiology, Central Hospital of Jilin City, Jilin, China
| | - U Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - Akos Varga-Szemes
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - W Garrison Moore
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - Yizhou Yu
- Department of Computer Science, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Chunfeng Hu
- Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Long Jiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
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Dimitri P, Savage MO. Artificial intelligence in paediatric endocrinology: conflict or cooperation. J Pediatr Endocrinol Metab 2024; 37:209-221. [PMID: 38183676 DOI: 10.1515/jpem-2023-0554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 12/18/2023] [Indexed: 01/08/2024]
Abstract
Artificial intelligence (AI) in medicine is transforming healthcare by automating system tasks, assisting in diagnostics, predicting patient outcomes and personalising patient care, founded on the ability to analyse vast datasets. In paediatric endocrinology, AI has been developed for diabetes, for insulin dose adjustment, detection of hypoglycaemia and retinopathy screening; bone age assessment and thyroid nodule screening; the identification of growth disorders; the diagnosis of precocious puberty; and the use of facial recognition algorithms in conditions such as Cushing syndrome, acromegaly, congenital adrenal hyperplasia and Turner syndrome. AI can also predict those most at risk from childhood obesity by stratifying future interventions to modify lifestyle. AI will facilitate personalised healthcare by integrating data from 'omics' analysis, lifestyle tracking, medical history, laboratory and imaging, therapy response and treatment adherence from multiple sources. As data acquisition and processing becomes fundamental, data privacy and protecting children's health data is crucial. Minimising algorithmic bias generated by AI analysis for rare conditions seen in paediatric endocrinology is an important determinant of AI validity in clinical practice. AI cannot create the patient-doctor relationship or assess the wider holistic determinants of care. Children have individual needs and vulnerabilities and are considered in the context of family relationships and dynamics. Importantly, whilst AI provides value through augmenting efficiency and accuracy, it must not be used to replace clinical skills.
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Affiliation(s)
- Paul Dimitri
- Department of Paediatric Endocrinology, Sheffield Children's NHS Foundation Trust, Sheffield, UK
| | - Martin O Savage
- Centre for Endocrinology, William Harvey Research Institute, Barts and the London School of Medicine & Dentistry, Queen Mary University of London, London, UK
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24
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Yim D, Khuntia J, Parameswaran V, Meyers A. Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review. JMIR Med Inform 2024; 12:e52073. [PMID: 38506918 PMCID: PMC10993141 DOI: 10.2196/52073] [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: 08/21/2023] [Revised: 10/12/2023] [Accepted: 01/30/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. OBJECTIVE This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. METHODS We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. RESULTS Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. CONCLUSIONS GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.
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Affiliation(s)
- Dobin Yim
- Loyola University, Maryland, MD, United States
| | - Jiban Khuntia
- University of Colorado Denver, Denver, CO, United States
| | | | - Arlen Meyers
- University of Colorado Denver, Denver, CO, United States
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Yao S, Dai F, Sun P, Zhang W, Qian B, Lu H. Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population. Nat Commun 2024; 15:1958. [PMID: 38438371 PMCID: PMC10912763 DOI: 10.1038/s41467-024-44906-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 01/09/2024] [Indexed: 03/06/2024] Open
Abstract
Artificial Intelligence (AI) models for medical diagnosis often face challenges of generalizability and fairness. We highlighted the algorithmic unfairness in a large thyroid ultrasound dataset with significant diagnostic performance disparities across subgroups linked causally to sample size imbalances. To address this, we introduced the Quasi-Pareto Improvement (QPI) approach and a deep learning implementation (QP-Net) combining multi-task learning and domain adaptation to improve model performance among disadvantaged subgroups without compromising overall population performance. On the thyroid ultrasound dataset, our method significantly mitigated the area under curve (AUC) disparity for three less-prevalent subgroups by 0.213, 0.112, and 0.173 while maintaining the AUC for dominant subgroups; we also further confirmed the generalizability of our approach on two public datasets: the ISIC2019 skin disease dataset and the CheXpert chest radiograph dataset. Here we show the QPI approach to be widely applicable in promoting AI for equitable healthcare outcomes.
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Affiliation(s)
- Siqiong Yao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, MoE Key Lab of Artificial Intelligence, AI Institute Shanghai Jiao Tong University, Shanghai, 200240, PR China
| | - Fang Dai
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China
| | - Peng Sun
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China
| | - Weituo Zhang
- Hongqiao International Institute of Medicine, Shanghai Tong Ren Hospital and School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, PR China.
| | - Biyun Qian
- Hongqiao International Institute of Medicine, Shanghai Tong Ren Hospital and School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, PR China.
| | - Hui Lu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China.
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, MoE Key Lab of Artificial Intelligence, AI Institute Shanghai Jiao Tong University, Shanghai, 200240, PR China.
- Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, NHC Key Laboratory of Medical Embryogenesis and Developmental Molecular Biology & Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai, 200020, PR China.
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Wentland AL. Beyond the AJR: Benefits of Artificial Intelligence-Based Thyroid Nodule Interpretation Depend on Radiologist Skill Level. AJR Am J Roentgenol 2024; 222:e2330001. [PMID: 37530397 DOI: 10.2214/ajr.23.30001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/03/2023]
Affiliation(s)
- Andrew L Wentland
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, 1111 Highland Ave, Madison, WI 53705
- Department of Medical Physics, University of Wisconsin School of Medicine & Public Health, Madison, WI
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI
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Taha A, Saad B, Taha-Mehlitz S, Ochs V, El-Awar J, Mourad MM, Neumann K, Glaser C, Rosenberg R, Cattin PC. Analysis of artificial intelligence in thyroid diagnostics and surgery: A scoping review. Am J Surg 2024; 229:57-64. [PMID: 38036334 DOI: 10.1016/j.amjsurg.2023.11.019] [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: 07/23/2023] [Revised: 10/07/2023] [Accepted: 11/15/2023] [Indexed: 12/02/2023]
Abstract
BACKGROUND Artificial Intelligence provides numerous applications in the healthcare sector. The main aim of this study is to evaluate the extent of the current application of artificial intelligence in thyroid diagnostics. METHODS Our protocol was based on the Scoping Reviews extension of the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA-ScR). Information was gathered from PubMed, Cochrane, and EMBASE databases and Google Scholar. Eligible studies were published between 2017 and 2022. RESULTS The search identified 133 records, after which 18 articles were included in the scoping review. All the publications were journal articles and discussed various ways that specialists in thyroid diagnostics and surgery have utilized artificial intelligence in their practice. CONCLUSIONS The development and incorporation of Artificial Intelligence applications in thyroid diagnostics and surgery has been moderate yet promising. However, applications are currently inconsistent and further research is needed to delineate the true benefit and limitations in this field.
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Affiliation(s)
- Anas Taha
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, 4123, Allschwil, Switzerland; Department of Surgery, Centre of Gastrointestinal Diseases, Cantonal Hospital Basel-land, Basel-Land, Switzerland.
| | - Baraa Saad
- Faculty of Medicine, St. George's University of London, London, UK
| | - Stephanie Taha-Mehlitz
- Clarunis, Department of Visceral Surgery, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital Basel, 4002, Basel, Switzerland
| | - Vincent Ochs
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, 4123, Allschwil, Switzerland
| | - Joelle El-Awar
- Faculty of Medicine, St. George's University of London, London, UK
| | | | - Katerina Neumann
- Department of Surgery, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Christine Glaser
- Department of Surgery, Centre of Gastrointestinal Diseases, Cantonal Hospital Basel-land, Basel-Land, Switzerland
| | - Robert Rosenberg
- Department of Surgery, Centre of Gastrointestinal Diseases, Cantonal Hospital Basel-land, Basel-Land, Switzerland
| | - Philippe C Cattin
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, 4123, Allschwil, Switzerland
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de Carlos J, Garcia J, Basterra FJ, Pineda JJ, Dolores Ollero M, Toni M, Munarriz P, Anda E. Interobserver variability in thyroid ultrasound. Endocrine 2024:10.1007/s12020-024-03731-5. [PMID: 38372907 DOI: 10.1007/s12020-024-03731-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 02/04/2024] [Indexed: 02/20/2024]
Abstract
PURPOSE Ultrasound evaluation of thyroid nodules is the preferred technique, but it is dependent on operator interpretation, leading to inter-observer variability. The current study aimed to determine the inter-physician consensus on nodular characteristics, risk categorization in the classification systems, and the need for fine needle aspiration puncture. METHODS Four endocrinologists from the same center blindly evaluated 100 ultrasound images of thyroid nodules from 100 different patients. The following ultrasound features were evaluated: composition, echogenicity, margins, calcifications, and microcalcifications. Nodules were also classified according to ATA, EU-TIRADS, K-TIRADS, and ACR-TIRADS classifications. Krippendorff's alpha test was used to assess interobserver agreement. RESULTS The interobserver agreement for ultrasound features was: Krippendorff's coefficient 0.80 (0.71-0.89) for composition, 0.59 (0.47-0.72) for echogenicity, 0.73 (0.57-0.88) for margins, 0.55 (0.40-0.69) for calcifications, and 0.50 (0.34-0.67) for microcalcifications. The concordance for the classification systems was 0.7 (0.61-0.80) for ATA, 0.63 (0.54-0.73) for EU-TIRADS, 0.64 (0.55-0.73) for K-TIRADS, and 0.68 (0.60-0.77) for K-TIRADS. The concordance in the indication of fine needle aspiration puncture (FNA) was 0.86 (0.71-1), 0.80 (0.71-0.88), 0.77 0.67-0.87), and 0.73 (0.64-0.83) for systems previously described respectively. CONCLUSIONS Interobserver agreement was acceptable for the identification of nodules requiring cytologic study using various classification systems. However, limited concordance was observed in risk stratification and many ultrasonographic characteristics of the nodules.
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Affiliation(s)
- Joaquín de Carlos
- Endocrinology Department, Hospital Universitario de Navarra, Pamplona, Navarra, Spain.
| | - Javier Garcia
- Endocrinology Department, Hospital Universitario de Navarra, Pamplona, Navarra, Spain
| | - Francisco Javier Basterra
- Endocrinology Department, Hospital Universitario de Navarra, Pamplona, Navarra, Spain
- Instituto de Investigación Sanitaria de Navarra, Pamplona, Navarra, Spain
- Universidad Pública de Navarra, Pamplona, Navarra, Spain
| | - Jose Javier Pineda
- Endocrinology Department, Hospital Universitario de Navarra, Pamplona, Navarra, Spain
| | - M Dolores Ollero
- Endocrinology Department, Hospital Universitario de Navarra, Pamplona, Navarra, Spain
| | - Marta Toni
- Endocrinology Department, Hospital Universitario de Navarra, Pamplona, Navarra, Spain
- Instituto de Investigación Sanitaria de Navarra, Pamplona, Navarra, Spain
- Universidad Pública de Navarra, Pamplona, Navarra, Spain
| | - Patricia Munarriz
- Endocrinology Department, Hospital Universitario de Navarra, Pamplona, Navarra, Spain
| | - Emma Anda
- Endocrinology Department, Hospital Universitario de Navarra, Pamplona, Navarra, Spain
- Instituto de Investigación Sanitaria de Navarra, Pamplona, Navarra, Spain
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Liu Q, Li Y, Hao Y, Fan W, Liu J, Li T, Liu L. Multi-modal ultrasound multistage classification of PTC cervical lymph node metastasis via DualSwinThyroid. Front Oncol 2024; 14:1349388. [PMID: 38434683 PMCID: PMC10906093 DOI: 10.3389/fonc.2024.1349388] [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: 12/04/2023] [Accepted: 01/29/2024] [Indexed: 03/05/2024] Open
Abstract
Objective This study aims to predict cervical lymph node metastasis in papillary thyroid carcinoma (PTC) patients with high accuracy. To achieve this, we introduce a novel deep learning model, DualSwinThyroid, leveraging multi-modal ultrasound imaging data for prediction. Materials and methods We assembled a substantial dataset consisting of 3652 multi-modal ultrasound images from 299 PTC patients in this retrospective study. The newly developed DualSwinThyroid model integrates various ultrasound modalities and clinical data. Following its creation, we rigorously assessed the model's performance against a separate testing set, comparing it with established machine learning models and previous deep learning approaches. Results Demonstrating remarkable precision, DualSwinThyroid achieved an AUC of 0.924 and an 96.3% accuracy on the test set. The model efficiently processed multi-modal data, pinpointing features indicative of lymph node metastasis in thyroid nodule ultrasound images. It offers a three-tier classification that aligns each level with a specific surgical strategy for PTC treatment. Conclusion DualSwinThyroid, a deep learning model designed with multi-modal ultrasound radiomics, effectively estimates the degree of cervical lymph node metastasis in PTC patients. In addition, it also provides early, precise identification and facilitation of interventions for high-risk groups, thereby enhancing the strategic selection of surgical approaches in managing PTC patients.
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Affiliation(s)
- Qiong Liu
- Department of Ultrasound, First Hospital of Shanxi Medical University, Taiyuan, China
- College of Medical Imaging, Shanxi Medical University, Taiyuan, China
| | - Yue Li
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanhong Hao
- Department of Ultrasound, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Wenwen Fan
- Department of Ultrasound, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Jingjing Liu
- Department of Ultrasound, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Ting Li
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Liping Liu
- Department of Ultrasound, First Hospital of Shanxi Medical University, Taiyuan, China
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Bojunga J, Trimboli P. Thyroid ultrasound and its ancillary techniques. Rev Endocr Metab Disord 2024; 25:161-173. [PMID: 37946091 DOI: 10.1007/s11154-023-09841-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/29/2023] [Indexed: 11/12/2023]
Abstract
Ultrasound (US) of the thyroid has been used as a diagnostic tool since the late 1960s. US is the most important imaging tool for diagnosing thyroid disease. In the majority of cases a correct diagnosis can already be made in synopsis of the sonographic together with clinical findings and basal thyroid hormone parameters. However, the characterization of thyroid nodules by US remains challenging. The introduction of Thyroid Imaging Reporting and Data Systems (TIRADSs) has improved diagnostic accuracy of thyroid cancer significantly. Newer techniques such as elastography, superb microvascular imaging (SMI), contrast enhanced ultrasound (CEUS) and multiparametric ultrasound (MPUS) expand diagnostic options and tools further. In addition, the use of artificial intelligence (AI) is a promising tool to improve and simplify diagnostics of thyroid nodules and there is evidence that AI can exceed the performance of humans. Combining different US techniques with the introduction of new software, the use of AI, FNB as well as molecular markers might pave the way for a completely new area of diagnostic accuracy in thyroid disease. Finally, interventional ultrasound using US-guided thermal ablation (TA) procedures are increasingly proposed as therapy options for benign as well as malignant thyroid diseases.
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Affiliation(s)
- Joerg Bojunga
- Department of Medicine I, Goethe University Hospital, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany.
| | - Pierpaolo Trimboli
- Servizio di Endocrinologia e Diabetologia, Ospedale Regionale di Lugano, Ente Ospedaliero Cantonale (EOC), Lugano, Switzerland
- Facoltà di Scienze Biomediche, Università della Svizzera Italiana (USI), Lugano, Switzerland
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31
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Zhou T, Xu L, Shi J, Zhang Y, Lin X, Wang Y, Hu T, Xu R, Xie L, Sun L, Li D, Zhang W, Chen C, Wang W, Xu C, Kong F, Xun Y, Yu L, Zhang S, Ding J, Wu F, Tang T, Zhan S, Zhang J, Wu G, Zheng H, Kong D, Luo D. US of thyroid nodules: can AI-assisted diagnostic system compete with fine needle aspiration? Eur Radiol 2024; 34:1324-1333. [PMID: 37615763 DOI: 10.1007/s00330-023-10132-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 06/20/2023] [Accepted: 07/04/2023] [Indexed: 08/25/2023]
Abstract
OBJECTIVES Artificial intelligence (AI) systems can diagnose thyroid nodules with similar or better performance than radiologists. Little is known about how this performance compares with that achieved through fine needle aspiration (FNA). This study aims to compare the diagnostic yields of FNA cytopathology alone and combined with BRAFV600E mutation analysis and an AI diagnostic system. METHODS The ultrasound images of 637 thyroid nodules were collected in three hospitals. The diagnostic efficacies of an AI diagnostic system, FNA-based cytopathology, and BRAFV600E mutation analysis were evaluated in terms of sensitivity, specificity, accuracy, and the κ coefficient with respect to the gold standard, defined by postsurgical pathology and consistent benign outcomes from two combined FNA and mutation analysis examinations performed with a half-year interval. RESULTS The malignancy threshold for the AI system was selected according to the Youden index from a retrospective cohort of 346 nodules and then applied to a prospective cohort of 291 nodules. The combination of FNA cytopathology according to the Bethesda criteria and BRAFV600E mutation analysis showed no significant difference from the AI system in terms of accuracy for either cohort in our multicenter study. In addition, for 45 included indeterminate Bethesda category III and IV nodules, the accuracy, sensitivity, and specificity of the AI system were 84.44%, 95.45%, and 73.91%, respectively. CONCLUSIONS The AI diagnostic system showed similar diagnostic performance to FNA cytopathology combined with BRAFV600E mutation analysis. Given its advantages in terms of operability, time efficiency, non-invasiveness, and the wide availability of ultrasonography, it provides a new alternative for thyroid nodule diagnosis. CLINICAL RELEVANCE STATEMENT Thyroid ultrasonic artificial intelligence shows statistically equivalent performance for thyroid nodule diagnosis to FNA cytopathology combined with BRAFV600E mutation analysis. It can be widely applied in hospitals and clinics to assist radiologists in thyroid nodule screening and is expected to reduce the need for relatively invasive FNA biopsies. KEY POINTS • In a retrospective cohort of 346 nodules, the evaluated artificial intelligence (AI) system did not significantly differ from fine needle aspiration (FNA) cytopathology alone and combined with gene mutation analysis in accuracy. • In a prospective multicenter cohort of 291 nodules, the accuracy of the AI diagnostic system was not significantly different from that of FNA cytopathology either alone or combined with gene mutation analysis. • For 45 indeterminate Bethesda category III and IV nodules, the AI system did not perform significantly differently from BRAFV600E mutation analysis.
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Affiliation(s)
- Tianhan Zhou
- Department of Surgical Oncology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- The Department of General Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Lei Xu
- Zhejiang Qiushi Institute for Mathematical Medicine, Hangzhou, China
| | - Jingjing Shi
- Department of Surgical Oncology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yu Zhang
- Department of Surgical Oncology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiangfeng Lin
- Department of Thyroid Surgery, Affiliated Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Yuanyuan Wang
- Department of Thyroid Surgery, The First Affiliated Hospital of Henan University, Zhengzhou, China
| | - Tao Hu
- The Fourth Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Rujun Xu
- Department of Pathology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lesi Xie
- Department of Pathology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lijuan Sun
- Department of Pathology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Dandan Li
- Department of Pathology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenhua Zhang
- Department of Pathology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chuanghua Chen
- Department of Ultrasonography, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wei Wang
- Department of Ultrasonography, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chenke Xu
- Department of Ultrasonography, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Fanlei Kong
- Department of Ultrasonography, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yanping Xun
- Department of Translational Medicine Research Center, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lingying Yu
- Department of Endocrinology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shirong Zhang
- Department of Translational Medicine Research Center, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jinwang Ding
- Department of Head and Neck Surgery, Cancer hospital of the University of Chinese Academy of Sciences, Hangzhou, China
| | - Fan Wu
- Department of Surgical Oncology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tian Tang
- The Fourth Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Siqi Zhan
- The Fourth Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Jiaoping Zhang
- Department of Surgical Oncology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Guoyang Wu
- Department of General Surgery, Affiliated Zhongshan Hospital, Xiamen University, Xiamen, China.
| | - Haitao Zheng
- Department of Thyroid Surgery, Affiliated Yantai Yuhuangding Hospital, Qingdao University, Yantai, China.
| | - Dexing Kong
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China.
- School of Mathematical Sciences, Zhejiang University, Hangzhou, China.
| | - Dingcun Luo
- Department of Surgical Oncology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- The Fourth Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China.
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Pace-Asciak P, Tufano RP. Future Directions in the Treatment of Thyroid and Parathyroid Disease. Otolaryngol Clin North Am 2024; 57:155-170. [PMID: 37634983 DOI: 10.1016/j.otc.2023.07.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
The surgical management of thyroid and parathyroid disease has evolved considerably since the era of Theodor Kocher. We review the current trends in thyroid and parathyroid surgery concerning robotic surgery for remote access, the use of parathyroid autofluorescence detection technology to aid in the prevention of hypocalcemia as well as the use of thermal ablation to target thyroid nodules in a minimally invasive way. We also discuss how artificial intelligence is being used to improve the workflow and diagnostics preoperatively as well as for intraoperative decision-making. We also discuss potential areas where future research may enhance outcomes.
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Affiliation(s)
- Pia Pace-Asciak
- Department of Otolaryngology-Head and Neck Surgery, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada.
| | - Ralph P Tufano
- Sarasota Memorial Health Care System Multidisciplinary Thyroid and Parathyroid Center, Sarasota, FL, USA
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Pozdeyev N, Dighe M, Barrio M, Raeburn C, Smith H, Fisher M, Chavan S, Rafaels N, Shortt JA, Lin M, Leu MG, Clark T, Marshall C, Haugen BR, Subramanian D, Crooks K, Gignoux C, Cohen T. Thyroid Cancer Polygenic Risk Score Improves Classification of Thyroid Nodules as Benign or Malignant. J Clin Endocrinol Metab 2024; 109:402-412. [PMID: 37683082 DOI: 10.1210/clinem/dgad530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 08/29/2023] [Accepted: 09/05/2023] [Indexed: 09/10/2023]
Abstract
CONTEXT Thyroid nodule ultrasound-based risk stratification schemas rely on the presence of high-risk sonographic features. However, some malignant thyroid nodules have benign appearance on thyroid ultrasound. New methods for thyroid nodule risk assessment are needed. OBJECTIVE We investigated polygenic risk score (PRS) accounting for inherited thyroid cancer risk combined with ultrasound-based analysis for improved thyroid nodule risk assessment. METHODS The convolutional neural network classifier was trained on thyroid ultrasound still images and cine clips from 621 thyroid nodules. Phenome-wide association study (PheWAS) and PRS PheWAS were used to optimize PRS for distinguishing benign and malignant nodules. PRS was evaluated in 73 346 participants in the Colorado Center for Personalized Medicine Biobank. RESULTS When the deep learning model output was combined with thyroid cancer PRS and genetic ancestry estimates, the area under the receiver operating characteristic curve (AUROC) of the benign vs malignant thyroid nodule classifier increased from 0.83 to 0.89 (DeLong, P value = .007). The combined deep learning and genetic classifier achieved a clinically relevant sensitivity of 0.95, 95% CI [0.88-0.99], specificity of 0.63 [0.55-0.70], and positive and negative predictive values of 0.47 [0.41-0.58] and 0.97 [0.92-0.99], respectively. AUROC improvement was consistent in European ancestry-stratified analysis (0.83 and 0.87 for deep learning and deep learning combined with PRS classifiers, respectively). Elevated PRS was associated with a greater risk of thyroid cancer structural disease recurrence (ordinal logistic regression, P value = .002). CONCLUSION Augmenting ultrasound-based risk assessment with PRS improves diagnostic accuracy.
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Affiliation(s)
- Nikita Pozdeyev
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Division of Endocrinology Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- University of Colorado Cancer Center, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Manjiri Dighe
- Department of Radiology, University of Washington, Seattle, WA 98195, USA
| | - Martin Barrio
- Division of GI, Trauma, and Endocrine Surgery, Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Christopher Raeburn
- Division of GI, Trauma, and Endocrine Surgery, Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Harry Smith
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Matthew Fisher
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Sameer Chavan
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Nicholas Rafaels
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Jonathan A Shortt
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Meng Lin
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Michael G Leu
- Information Technology Services, UW Medicine, Seattle, WA 98195, USA
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA
- Department of Pediatrics, University of Washington, Seattle, WA 98105, USA
- Division of Hospital Medicine, Seattle Children's Hospital, Seattle, WA 98105, USA
| | - Toshimasa Clark
- Department of Radiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Carrie Marshall
- Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Bryan R Haugen
- Division of Endocrinology Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- University of Colorado Cancer Center, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | | | - Kristy Crooks
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Christopher Gignoux
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Trevor Cohen
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA
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Scott IA, Slavotinek J, Glasziou PP. First do no harm in responding to incidental imaging findings. Med J Aust 2024; 220:7-9. [PMID: 38009654 DOI: 10.5694/mja2.52177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 09/05/2023] [Indexed: 11/29/2023]
Affiliation(s)
- Ian A Scott
- Centre for Health Services Research, University of Queensland, Brisbane, QLD
- Princess Alexandra Hospital, Brisbane, QLD
| | | | - Paul P Glasziou
- Institute for Evidence-based Healthcare, Bond University, Gold Coast, QLD
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Sun S, Wang Y, Yu Q, Qu M, Li H, Yang J. STGA-MS: AI diagnosis model of regional wall motion abnormality based on 2D transthoracic echocardiography. Heliyon 2024; 10:e23224. [PMID: 38163158 PMCID: PMC10755298 DOI: 10.1016/j.heliyon.2023.e23224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 10/27/2023] [Accepted: 11/29/2023] [Indexed: 01/03/2024] Open
Abstract
Regional wall motion abnormality (RWMA) is a common manifestation of ischemic heart disease detected through echocardiography. Currently, RWMA diagnosis heavily relies on visual assessment by doctors, leading to limitations in experience-based dependence and suboptimal reproducibility among observers. Several RWMA diagnosis models were proposed, while RWMA diagnosis with more refined segments can provide more comprehensive wall motion information to better assist doctors in the diagnosis of ischemic heart disease. In this paper, we proposed the STGA-MS model which consists of three modules, the spatial-temporal grouping attention (STGA) module, the segment feature extraction module, and the multiscale downsampling module, for the diagnosis of RWMA for multiple myocardial segments. The STGA module captures global spatial and temporal information, enhancing the representation of myocardial motion characteristics. The segment feature extraction module focuses on specific segment regions, extracting relevant features. The multiscale downsampling module analyzes myocardial motion deformation across different receptive fields. Experimental results on a 2D transthoracic echocardiography dataset show that the proposed STGA-MS model achieves better performance compared to state-of-the-art models. It holds promise in improving the accuracy and reproducibility of RWMA diagnosis, assisting clinicians in diagnosing ischemic heart disease more reliably.
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Affiliation(s)
- Song Sun
- Computer Science and Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, China
| | - Yonghuai Wang
- Department of Cardiovascular Ultrasound, The First Hospital of China Medical University, China
| | - Qi Yu
- Computer Science and Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, China
| | - Mingjun Qu
- Computer Science and Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, China
| | - Honghe Li
- Computer Science and Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, China
| | - Jinzhu Yang
- Computer Science and Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, China
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Jiang Y, Sun X, Jiang M, Min H, Wang J, Fu X, Qi J, Yu Z, Zhu X, Wu Y. Impact of a mobile health intervention based on multi-theory model of health behavior change on self-management in patients with differentiated thyroid cancer: protocol for a randomized controlled trial. Front Public Health 2024; 12:1327442. [PMID: 38282759 PMCID: PMC10808536 DOI: 10.3389/fpubh.2024.1327442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 01/02/2024] [Indexed: 01/30/2024] Open
Abstract
Introduction Theoretical models of health behavior are important guides for disease prevention and detection, treatment and rehabilitation, and promotion and maintenance of physical and mental health, but there are no intervention studies related to differentiated thyroid cancer (DTC) that use theoretical models of health as a guide. In this study, we used a microblogging platform as an intervention vehicle and mobile patient-doctor interactive health education as a means of intervention, with the aim of improving the health behaviors of DTC patients as well as the corresponding clinical outcomes. Methods This research project is a quantitative methodological study, and the trial will be a single-blind, single-center randomized controlled trial conducted at the Fourth Hospital of Harbin Medical University in Harbin, Heilongjiang Province. The study subjects are patients over 18 years of age with differentiated thyroid cancer who were given radioactive iodine-131 therapy as well as endocrine therapy after radical surgery for thyroid cancer. The intervention group will receive MTM-mhealth, and the realization of health education will rely on the smart terminal WeChat platform. Routine discharge education will be given to the control group at discharge. The primary outcome will be change in thyroid-stimulating hormone (TSH) from baseline and at 3 and 6 months of follow-up, and secondary outcomes will include change in self-management behavior, social cognitive and psychological, and metabolic control. Discussion This study will explore a feasible mHealth intervention program applied to a population of DTC patients using the Multi-theory model of health behavior change (MTM) as a guide, with the aim of evaluating the MTM-based intervention program for clinical outcome improvement in DTC patients, as well as determining the effectiveness of the MTM-based intervention program in improving self-management skills in DTC patients. The results of this study will indicate the feasibility as well as the effectiveness of the application of health theoretical modeling combined with mHealth applications in disease prognostic health management models, and provide policy recommendations and technological translations for the development of mobility-based health management applications in the field of health management.
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Affiliation(s)
- Yang Jiang
- Jitang College, North China University of Science and Technology, Tangshan, China
| | - Xiangju Sun
- Clinical Pharmacy, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Maomin Jiang
- School of Public Affairs, Xiamen University, Xiamen, China
| | - Hewei Min
- School of Public Health, Peking University, Beijing, China
| | - Jing Wang
- School of Public Health, Peking University, Beijing, China
| | - Xinghua Fu
- The Fourth School of Clinical Medicine, Harbin Medical University, Harbin, China
| | - Jiale Qi
- School of Journalism and Communication, Zhengzhou University, Zhengzhou, China
| | - Zhenjie Yu
- School of Nursing, Tianjin Medical University, Tianjin, China
| | - Xiaomei Zhu
- Department of Pharmacy, Beidahuang Group General Hospital, Harbin, China
| | - Yibo Wu
- School of Public Health, Peking University, Beijing, China
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Zhou T, Hu T, Ni Z, Yao C, Xie Y, Jin H, Luo D, Huang H. Comparative analysis of machine learning-based ultrasound radiomics in predicting malignancy of partially cystic thyroid nodules. Endocrine 2024; 83:118-126. [PMID: 37542676 DOI: 10.1007/s12020-023-03461-0] [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: 05/03/2023] [Accepted: 07/18/2023] [Indexed: 08/07/2023]
Abstract
OBJECTIVE To investigate the application of machine learning (ML) model-based thyroid ultrasound radiomics in the evaluation of malignancy in partially cystic thyroid nodules (PCTNs). METHODS One hundred and ninety-two patients with 197 nodules PCTNs from January 2020 to December 2020 were retrospectively analyzed. Radiomics features were extracted based on hand-crafted features from the ultrasound images, and machine learning methods were used to build a classification model by radiomics features. The least absolute shrinkage and selection operator regression was applied to select the features of nonzero coefficients from radiomics features. The prediction performance of the established model was mainly evaluated by the area under the curve (AUC) and accuracy, sensitivity, and specificity. RESULTS Nineteen radiomics features were extracted from the original images for each nodule. Eight ML classifiers were able to differentiate malignancy in PCTNs. The AUC, accuracy, sensitivity, and specificity of k-Nearest Neighbor (KNN) model were 0.909, 82.95%, 83.33%, and 89.90%, respectively, on the test cohort. The comparative result showed statistically equivalent performance for thyroid nodule diagnosis based on image fusion and single image. In addition, the ML-Based ultrasound radiomics system showed a better AUC as compared with ACR TI-RADS model and the ultrasound features model. CONCLUSION The novel ultrasonic-based ML model has an important clinical value for predicting malignancy in PCTNs. It can provide clinicians with a preoperative non-invasive primary screening method for PCTN diagnosis to avoid unnecessary medical investment and improve treatment outcomes.
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Affiliation(s)
- Tianhan Zhou
- The Department of General Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Tao Hu
- The Fourth Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhongkai Ni
- The Department of General Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Chun Yao
- The Department of Ultrasound, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Yangyang Xie
- The Department of General Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Haimin Jin
- The Department of General Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Dingcun Luo
- Department of Surgical Oncology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Hai Huang
- The Department of General Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China.
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Yao W, Peng X, Guan Y, Du X, Xia C, Liu F. Thyroid Nodules: Emerging Trends in Detection and Visualization based on Citespace. Endocr Metab Immune Disord Drug Targets 2024; 24:130-141. [PMID: 37608676 PMCID: PMC10909822 DOI: 10.2174/1871530323666230822143549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 06/21/2023] [Accepted: 07/18/2023] [Indexed: 08/24/2023]
Abstract
BACKGROUND Thyroid nodule (TN) is a highly prevalent clinical endocrine disease. Many countries have formed guidelines on the prevention and treatment of TN based on extensive research. However, there is a scarcity of TN-related literature based on bibliometrics. OBJECTIVES This study aimed to evaluate the scientific achievements and progress of TN research from a global perspective by investigating the literature for 20 years through bibliometrics. METHODS We searched the literature on TN in the core collection of the Web of Science database from 2002 to 2021 and used the Citespace software to analyze the co-authorship, co-citation, and co-occurrence of countries, institutions, authors, keywords, and co-cited literature. RESULTS We retrieved 12319 documents related to TN. The literature on TN has been growing since 2002. The United States has contributed the largest proportion of TN papers (20.64%), followed by China, Italy, and South Korea. The United States ranked first in terms of centrality (0.38). Haugen BR, Gharib H, and Cibas ES are the top three most cited authors. The papers published in Thyroid were cited most frequently (7952 times). The most prominent keywords were management, cancer, fine needle aspiration, diagnosis, malignant tumor, thyroid cancer, ultrasound, biopsy, benign, surgery, ablation, and cytology. All keywords could be divided into three categories: diagnosis stratification, treatment, and cancer. As far as potential hot spots are concerned, the keywords that have recently burst strongly and are still continuing are: "Association Guideline" (2018-2021), "Radiofrequency Ablation" (2017-2021), "Classification" (2019-2021), and "Data System" (2017-2021). CONCLUSION Based on the current trends, the number of publications on TN will continue to increase. The United States is the most active contributor to research in this field. Previous literature focused on stratification, cancer, surgery, and ablation, and there were different opinions on the stratification of diagnosis. There were relatively few studies on pathogenesis and treatment using medicine. More focus will be placed on association guidelines, radiofrequency ablation, classification, and data system, which may be the next popular topics in TN research.
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Affiliation(s)
- Wenyan Yao
- Shaanxi Institute of International Trade & Commerce, Xianyang, 712046, China
- College of Pharmacy, Dali University, Dali, 671000, China
| | - Xiujuan Peng
- Shaanxi Institute of International Trade & Commerce, Xianyang, 712046, China
| | - Yunhui Guan
- College of Pharmacy, Dali University, Dali, 671000, China
| | - Xia Du
- Shaanxi Academy of Traditional Chinese Medicine, Xi'an 710003, China
| | - Conglong Xia
- College of Pharmacy, Dali University, Dali, 671000, China
| | - Feng Liu
- Shaanxi Institute of International Trade & Commerce, Xianyang, 712046, China
- Shaanxi Buchang Pharmaceutical Co., Ltd., Xi'an, 710075, China
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Kikuchi T, Hanaoka S, Nakao T, Nomura Y, Yoshikawa T, Alam MA, Mori H, Hayashi N. Relationship between Thyroid CT Density, Volume, and Future TSH Elevation: A 5-Year Follow-Up Study. Life (Basel) 2023; 13:2303. [PMID: 38137904 PMCID: PMC10744487 DOI: 10.3390/life13122303] [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: 11/06/2023] [Revised: 11/30/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023] Open
Abstract
This study aimed to explore the relationship between thyroid-stimulating hormone (TSH) elevation and the baseline computed tomography (CT) density and volume of the thyroid. We examined 86 cases with new-onset hypothyroidism (TSH > 4.5 IU/mL) and 1071 controls from a medical check-up database over 5 years. A deep learning-based thyroid segmentation method was used to assess CT density and volume. Statistical tests and logistic regression were employed to determine differences and odds ratios. Initially, the case group showed a higher CT density (89.8 vs. 81.7 Hounsfield units (HUs)) and smaller volume (13.0 vs. 15.3 mL) than those in the control group. For every +10 HU in CT density and -3 mL in volume, the odds of developing hypothyroidism increased by 1.40 and 1.35, respectively. Over the course of the study, the case group showed a notable CT density reduction (median: -8.9 HU), whereas the control group had a minor decrease (-2.9 HU). Thyroid volume remained relatively stable for both groups. Higher CT density and smaller thyroid volume at baseline are correlated with future TSH elevation. Over time, there was a substantial and minor decrease in CT density in the case and control groups, respectively. Thyroid volumes remained consistent in both cohorts.
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Affiliation(s)
- Tomohiro Kikuchi
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo 113-8655, Japan (M.A.A.)
- Department of Radiology, School of Medicine, Jichi Medical University, Tochigi 329-0498, Japan
| | - Shouhei Hanaoka
- Department of Radiology, The University of Tokyo Hospital, Tokyo 113-8655, Japan
| | - Takahiro Nakao
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo 113-8655, Japan (M.A.A.)
| | - Yukihiro Nomura
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo 113-8655, Japan (M.A.A.)
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
| | - Takeharu Yoshikawa
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo 113-8655, Japan (M.A.A.)
| | - Md Ashraful Alam
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo 113-8655, Japan (M.A.A.)
| | - Harushi Mori
- Department of Radiology, School of Medicine, Jichi Medical University, Tochigi 329-0498, Japan
| | - Naoto Hayashi
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo 113-8655, Japan (M.A.A.)
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Wang J, Dong C, Zhang YZ, Wang L, Yuan X, He M, Xu S, Zhou Q, Jiang J. A novel approach to quantify calcifications of thyroid nodules in US images based on deep learning: predicting the risk of cervical lymph node metastasis in papillary thyroid cancer patients. Eur Radiol 2023; 33:9347-9356. [PMID: 37436509 DOI: 10.1007/s00330-023-09909-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 04/23/2023] [Accepted: 05/15/2023] [Indexed: 07/13/2023]
Abstract
OBJECTIVE Based on ultrasound (US) images, this study aimed to detect and quantify calcifications of thyroid nodules, which are regarded as one of the most important features in US diagnosis of thyroid cancer, and to further investigate the value of US calcifications in predicting the risk of lymph node metastasis (LNM) in papillary thyroid cancer (PTC). METHODS Based on the DeepLabv3+ networks, 2992 thyroid nodules in US images were used to train a model to detect thyroid nodules, of which 998 were used to train a model to detect and quantify calcifications. A total of 225 and 146 thyroid nodules obtained from two centers, respectively, were used to test the performance of these models. A logistic regression method was used to construct the predictive models for LNM in PTCs. RESULTS Calcifications detected by the network model and experienced radiologists had an agreement degree of above 90%. The novel quantitative parameters of US calcification defined in this study showed a significant difference between PTC patients with and without cervical LNM (p < 0.05). The calcification parameters were beneficial to predicting the LNM risk in PTC patients. The LNM prediction model using these calcification parameters combined with patient age and other US nodular features showed a higher specificity and accuracy than the calcification parameters alone. CONCLUSIONS Our models not only detect the calcifications automatically, but also have value in predicting cervical LNM risk of PTC patients, thereby making it possible to investigate the relationship between calcifications and highly invasive PTC in detail. CLINICAL RELEVANCE STATEMENT Due to the high association of US microcalcifications with thyroid cancers, our model will contribute to the differential diagnosis of thyroid nodules in daily practice. KEY POINTS • We developed an ML-based network model for automatically detecting and quantifying calcifications within thyroid nodules in US images. • Three novel parameters for quantifying US calcifications were defined and verified. • These US calcification parameters showed value in predicting the risk of cervical LNM in PTC patients.
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Affiliation(s)
- Juan Wang
- Department of Ultrasound, the Second Affiliated Hospital, Medical School of Xi'an Jiaotong University, Xi'an, 710004, China
| | - Caixia Dong
- Institute of Artificial Intelligence, the Second Affiliated Hospital, Medical School of Xi'an Jiaotong University, Xi'an, 710004, China
| | - Yao-Zhong Zhang
- The Institute of Medical Science, The University of Tokyo, Shirokanedai 4-6-1, Minato-ku, Tokyo, 108-8639, Japan
| | - Lirong Wang
- Department of Ultrasound, the Second Affiliated Hospital, Medical School of Xi'an Jiaotong University, Xi'an, 710004, China
| | - Xin Yuan
- Department of Ultrasound, the Second Affiliated Hospital, Medical School of Xi'an Jiaotong University, Xi'an, 710004, China
| | - Meiqing He
- Department of Ultrasound, Shaanxi Provincial People's Hospital, Xi'an, 710068, China
| | - Songhua Xu
- Institute of Artificial Intelligence, the Second Affiliated Hospital, Medical School of Xi'an Jiaotong University, Xi'an, 710004, China.
| | - Qi Zhou
- Department of Ultrasound, the Second Affiliated Hospital, Medical School of Xi'an Jiaotong University, Xi'an, 710004, China.
| | - Jue Jiang
- Department of Ultrasound, the Second Affiliated Hospital, Medical School of Xi'an Jiaotong University, Xi'an, 710004, China.
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Li M, Zhou H, Li X, Yan P, Jiang Y, Luo H, Zhou X, Yin S. SDA-Net: Self-distillation driven deformable attentive aggregation network for thyroid nodule identification in ultrasound images. Artif Intell Med 2023; 146:102699. [PMID: 38042598 DOI: 10.1016/j.artmed.2023.102699] [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: 03/10/2023] [Revised: 07/12/2023] [Accepted: 10/29/2023] [Indexed: 12/04/2023]
Abstract
Early detection and accurate identification of thyroid nodules are the major challenges in controlling and treating thyroid cancer that can be difficult even for expert physicians. Currently, many computer-aided diagnosis (CAD) systems have been developed to assist this clinical process. However, most of these systems are unable to well capture geometrically diverse thyroid nodule representations from ultrasound images with subtle and various characteristic differences, resulting in suboptimal diagnosis and lack of clinical interpretability, which may affect their credibility in the clinic. In this context, a novel end-to-end network equipped with a deformable attention network and a distillation-driven interaction aggregation module (DIAM) is developed for thyroid nodule identification. The deformable attention network learns to identify discriminative features of nodules under the guidance of the deformable attention module (DAM) and an online class activation mapping (CAM) mechanism and suggests the location of diagnostic features to provide interpretable predictions. DIAM is designed to take advantage of the complementarities of adjacent layers, thus enhancing the representation capabilities of aggregated features; driven by an efficient self-distillation mechanism, the identification process is complemented with more multi-scale semantic information to calibrate the diagnosis results. Experimental results on a large dataset with varying nodule appearances show that the proposed network can achieve competitive performance in nodule diagnosis and provide interpretability suitable for clinical needs.
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Affiliation(s)
- Minglei Li
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
| | - Hang Zhou
- Department of In-Patient Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China
| | - Xiang Li
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
| | - Pengfei Yan
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
| | - Yuchen Jiang
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
| | - Hao Luo
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China.
| | - Xianli Zhou
- Department of In-Patient Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China.
| | - Shen Yin
- Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway
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Qin X, Zhang C. Prediction of central lymph node metastasis based on deep learning models for patients with clinically node-negative papillary thyroid carcinoma. Asian J Surg 2023; 46:5763-5764. [PMID: 37648544 DOI: 10.1016/j.asjsur.2023.08.130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 08/22/2023] [Indexed: 09/01/2023] Open
Affiliation(s)
- Xiachuan Qin
- Department of Ultrasound, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College(University), 97 Renmin South Road, Shuqin 8 District, Nan Chong, Sichuan, 637000, China; Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei, Anhui, 230022, China
| | - Chaoxue Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei, Anhui, 230022, China.
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Miao K, Zhao N, Lv Q, He X, Xu M, Dong X, Li D, Shao X. Prediction of benign and malignant ovarian tumors using Resnet34 on ultrasound images. J Obstet Gynaecol Res 2023; 49:2910-2917. [PMID: 37696522 DOI: 10.1111/jog.15788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 08/24/2023] [Indexed: 09/13/2023]
Abstract
OBJECTIVE To develop deep learning (DL) prediction models using transvaginal ultrasound (TVS), transabdominal ultrasound (TAS), and color Doppler flow imaging (CDFI) of TVS (CDFI_TVS) to automatically predict benign or malignant ovarian tumors. METHODS This retrospective study included women with ovarian tumors who underwent ultrasound between August 2018 and October 2022. Histopathological analysis was used as a reference standard. The dataset was preprocessed by clipping, flipping, and rotating images to generate a larger, more complicated, and diverse dataset to improve accuracy and generalizability. The dataset was then divided into training (80%) and test (20%) sets. The weights of the models, modified from the residual network (ResNet) with the TVS, TAS, and CDFI_TVS images (hereafter, referred to as DLTVS , DLTAS , and DLCDFI_TVS , respectively) were developed. The area under the receiver operating characteristic curve (AUC) analysis in the test set was used to compare the predictive value of DL for malignancy. RESULTS A total of 2340 images from 1350 women with adnexal masses were included. DLTVS had an AUC of 0.95 (95% CI: 0.93-0.97) for classifying malignant and benign ovarian tumors, comparable with that of DLTAS (AUC, 0.95; 95% CI: 0.91-0.98; p = 0.96) and DLCDFI_TVS (AUC, 0.88; 95% CI: 0.84-0.93; p = 0.02). Decision curve analysis indicated that DLTVS performed better than DLTAS and DLCDFI_TVS . CONCLUSION We developed DL models based on TVS, TAS, and CDFI_TVS on ultrasound images to predict benign and malignant ovarian tumors with high diagnostic performance. The DLTVS model had the best prediction compared with the DLTAS and DLCDFI_TVS models.
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Affiliation(s)
- Kuo Miao
- Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Ning Zhao
- Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qian Lv
- Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xin He
- Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Mingda Xu
- Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xiaoqiu Dong
- Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Dandan Li
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China
| | - Xiaohui Shao
- Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
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Liao X, Yao C, Zhang J, Liu LZ. Recent advancement in integrating artificial intelligence and information technology with real-world data for clinical decision-making in China: A scoping review. J Evid Based Med 2023; 16:534-546. [PMID: 37772921 DOI: 10.1111/jebm.12549] [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: 05/24/2023] [Accepted: 08/31/2023] [Indexed: 09/30/2023]
Abstract
OBJECTIVE Striking innovations and advancements have been achieved with the use of artificial intelligence and healthcare information technology being integrated into clinical real-world data. The current scoping review aimed to provide an overview of the current status of artificial intelligence-/information technology-based clinical decision support tools in China. METHODS PubMed/MEDLINE, Embase, China National Knowledge Internet, and Wanfang data were searched for both English and Chinese literature. The gray literature search was conducted for commercially available tools. Original studies that focused on clinical decision support tools driven by artificial intelligence or information technology in China and were published between 2010 and February 2022 were included. Information extracted from each article was further synthesized by themes based on three types of clinical decision-making. RESULTS A total of 37 peer-reviewed publications and 13 commercially available tools were included in the final analysis. Among them, 32.0% were developed for disease diagnosis, 54.0% for risk prediction and classification, and 14.0% for disease management. Chronic diseases were the most popular therapeutic areas of exploration, with particular emphasis on cardiovascular and cerebrovascular diseases. Single-center electronic medical records were the mainstream data sources leveraged to inform clinical decision-making, with internal validation being predominately used for model evaluation. CONCLUSIONS To effectively promote the extensive use of real-world data and drive a paradigm shift in clinical decision-making in China, multidisciplinary collaboration of key stakeholders is urgently needed.
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Affiliation(s)
- Xiwen Liao
- Peking University Clinical Research Institute, Peking University First Hospital, Beijing, China
| | - Chen Yao
- Peking University Clinical Research Institute, Peking University First Hospital, Beijing, China
- Hainan Institute of Real World Data, Qionghai, Hainan, China
| | - Jun Zhang
- Center for Observational and Real-world Evidence (CORE), MSD R&D (China) Co., Ltd., Beijing, China
| | - Larry Z Liu
- Center for Observational and Real-world Evidence (CORE), Merck & Co Inc, Rahway, Rahway, New Jersey, USA
- Department of Population Health Sciences, Weill Cornell Medical College, New York City, New York, USA
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Yao J, Zhang Y, Shen J, Lei Z, Xiong J, Feng B, Li X, Li W, Ou D, Lu Y, Feng N, Yan M, Chen J, Chen L, Yang C, Wang L, Wang K, Zhou J, Liang P, Xu D. AI diagnosis of Bethesda category IV thyroid nodules. iScience 2023; 26:108114. [PMID: 37867955 PMCID: PMC10589877 DOI: 10.1016/j.isci.2023.108114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/20/2023] [Accepted: 09/29/2023] [Indexed: 10/24/2023] Open
Abstract
Thyroid nodules are a common disease, and fine needle aspiration cytology (FNAC) is the primary method to assess their malignancy. For the diagnosis of follicular thyroid nodules, however, FNAC has limitations. FNAC can classify them only as Bethesda IV nodules, leaving their exact malignant status and pathological type undetermined. This imprecise diagnosis creates difficulties in selecting the follow-up treatment. In this retrospective study, we collected ultrasound (US) image data of Bethesda IV thyroid nodules from 2006 to 2022 from five hospitals. Then, US image-based artificial intelligence (AI) models were trained to identify the specific category of Bethesda IV thyroid nodules. We tested the models using two independent datasets, and the best AI model achieved an area under the curve (AUC) between 0.90 and 0.95, demonstrating its potential value for clinical application. Our research findings indicate that AI could change the diagnosis and management process of Bethesda IV thyroid nodules.
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Affiliation(s)
- Jincao Yao
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou 310022, China
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou 310000, China
| | - Yanming Zhang
- Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou 310014, China
- Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou 310014, China
| | - Jiafei Shen
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
| | - Zhikai Lei
- Zhejiang University School of Medicine, Affiliated Hangzhou First People’s Hospital, Hangzhou 310003, China
| | - Jing Xiong
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, China
| | - Bojian Feng
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital(Taizhou Cancer Hospital), Taizhou 317502, China
| | - Xiaoxian Li
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Wei Li
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
| | - Di Ou
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
| | - Yidan Lu
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
| | - Na Feng
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
| | - Meiying Yan
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
| | - Jinjie Chen
- Department of Statistical Science, Baylor University, Waco, TX 76706, USA
| | - Liyu Chen
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
| | - Chen Yang
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
| | - Liping Wang
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
| | - Kai Wang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang 322100, China
| | - Jianhua Zhou
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Ping Liang
- Department of Ultrasound, Chinese PLA General Hospital, Chinese PLA Medical School, Beijing 100853, China
| | - Dong Xu
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou 310022, China
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou 310000, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital(Taizhou Cancer Hospital), Taizhou 317502, China
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Wang Y, Yu M, He M, Zhang G, Zhang L, Zhang B. Diagnostic value of a computer-assisted diagnosis system for the ultrasound features in thyroid nodules. ARCHIVES OF ENDOCRINOLOGY AND METABOLISM 2023; 68:e220501. [PMID: 37948567 PMCID: PMC10916796 DOI: 10.20945/2359-4292-2022-0501] [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: 01/04/2023] [Accepted: 03/15/2023] [Indexed: 11/12/2023]
Abstract
Objective To explore the diagnostic value of the TUIAS (SW_TH01/II) computer-aided diagnosis (CAD) software system for the ultrasound Thyroid Imaging Reporting and Data System (TI-RADS) features in thyroid nodules. Materials and methods This retrospective study enrolled patients with thyroid nodules in Shanghai East Hospital between January 2017 and October 2021. The novel CAD software (SW_TH01/II) and three sonographers performed a qualitative analysis of the ultrasound TI-RADS features in aspect ratio, margin irregularity, margin smoothness, calcification, and echogenicity of the thyroid nodules. Results A total of 225 patients were enrolled. The accuracy, sensitivity, and specificity of the CAD software in "aspect ratio" were 95.6%, 96.2%, and 95.4%, in "margin irregularity" were 90.7%, 90.5%, and 90.9%, in "margin smoothness" were 85.8%, 88.5%, and 83.0%, in "calcification" were 83.6%, 81.7%, and 82.0%, in "homogeneity" were 88.9%, 90.6%, and 82.2%, in "major echo" were 85.3%, 88.0%, and 85.4%, and in "contains very hypoechoic echo" were 92.0%, 90.0%, and 92.4%. The analysis time of the CAD software was significantly shorter than for the sonographers (2.7 ± 1.6 vs. 29.7 ± 12.7 s, P < 0.001). Conclusion The CAD system achieved high accuracy in describing thyroid nodule features. It might assist in clinical thyroid nodule analysis.
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Affiliation(s)
- Yiwei Wang
- Graduate School of Dalian Medical University, Dalian, Liaoning, China,
| | - Ming Yu
- Tend. AI Medical Technology, China
| | | | | | - Libo Zhang
- Shanghai East Hospital, Department of Ultrasound in Medicine, Shanghai, China
| | - Bo Zhang
- Shanghai East Hospital, Tongji University School of Medicine, Department of Ultrasound in Medicine, Shanghai, China
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Lai M, Feng B, Yao J, Wang Y, Pan Q, Chen Y, Chen C, Feng N, Shi F, Tian Y, Gao L, Xu D. Value of Artificial Intelligence in Improving the Accuracy of Diagnosing TI-RADS Category 4 Nodules. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:2413-2421. [PMID: 37652837 DOI: 10.1016/j.ultrasmedbio.2023.08.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/18/2023] [Accepted: 08/08/2023] [Indexed: 09/02/2023]
Abstract
OBJECTIVE Considerable heterogeneity is observed in the malignancy rates of thyroid nodules classified as category 4 according to the Thyroid Imaging Reporting and Data System (TI-RADS). This study was aimed at comparing the diagnostic performance of artificial intelligence algorithms and radiologists with different experience levels in distinguishing benign and malignant TI-RADS 4 (TR4) nodules. METHODS Between January 2019 and September 2022, 1117 TR4 nodules with well-defined pathological findings were collected for this retrospective study. An independent external data set of 125 TR4 nodules was incorporated for testing purposes. Traditional feature-based machine learning (ML) models, deep convolutional neural networks (DCNN) models and a fusion model that integrated the prediction outcomes from all models were used to classify benign and malignant TR4 nodules. A fivefold cross-validation approach was employed, and the diagnostic performance of each model and radiologists was compared. RESULTS In the external test data set, the area under the receiver operating characteristic curve (AUROC) of the three DCNN-based secondary transfer learning models-InceptionV3, DenseNet121 and ResNet50-were 0.852, 0.837 and 0.856, respectively. These values were higher than those of the three traditional ML models-logistic regression, multilayer perceptron and random forest-at 0.782, 0.790, and 0.767, respectively, and higher than that of an experienced radiologist (0.815). The fusion diagnostic model we developed, with an AUROC of 0.880, was found to outperform the experienced radiologist in diagnosing TR4 nodules. CONCLUSION The integration of artificial intelligence algorithms into medical imaging studies could improve the accuracy of identifying high-risk TR4 nodules pre-operatively and have significant clinical application potential.
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Affiliation(s)
- Min Lai
- Second Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China; Taizhou Cancer Hospital, Taizhou, China; Key Laboratory of Minimally Invasive Interventional Therapy and Big Data Artificial Intelligence in Medicine of Taizhou, Taizhou, China
| | - Bojian Feng
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou, China; Key Laboratory of Minimally Invasive Interventional Therapy and Big Data Artificial Intelligence in Medicine of Taizhou, Taizhou, China; Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
| | - Jincao Yao
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou, China; Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
| | - Yifan Wang
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou, China; Key Laboratory of Minimally Invasive Interventional Therapy and Big Data Artificial Intelligence in Medicine of Taizhou, Taizhou, China
| | - Qianmeng Pan
- Taizhou Cancer Hospital, Taizhou, China; Key Laboratory of Minimally Invasive Interventional Therapy and Big Data Artificial Intelligence in Medicine of Taizhou, Taizhou, China
| | - Yuhang Chen
- Zhejiang Gongshang University, Hangzhou, China
| | - Chen Chen
- Taizhou Cancer Hospital, Taizhou, China; Key Laboratory of Minimally Invasive Interventional Therapy and Big Data Artificial Intelligence in Medicine of Taizhou, Taizhou, China; Graduate School, Wannan Medical College, Wuhu, China
| | - Na Feng
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou, China
| | - Fang Shi
- Capacity Building and Continuing Education Center of National Health Commission, Beijing, China
| | - Yuan Tian
- Capacity Building and Continuing Education Center of National Health Commission, Beijing, China
| | - Lu Gao
- Capacity Building and Continuing Education Center of National Health Commission, Beijing, China
| | - Dong Xu
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou, China; Taizhou Cancer Hospital, Taizhou, China; Key Laboratory of Minimally Invasive Interventional Therapy and Big Data Artificial Intelligence in Medicine of Taizhou, Taizhou, China; Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China.
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Bhattacharya S, Mahato RK, Singh S, Bhatti GK, Mastana SS, Bhatti JS. Advances and challenges in thyroid cancer: The interplay of genetic modulators, targeted therapies, and AI-driven approaches. Life Sci 2023; 332:122110. [PMID: 37734434 DOI: 10.1016/j.lfs.2023.122110] [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: 07/06/2023] [Revised: 09/08/2023] [Accepted: 09/18/2023] [Indexed: 09/23/2023]
Abstract
Thyroid cancer continues to exhibit a rising incidence globally, predominantly affecting women. Despite stable mortality rates, the unique characteristics of thyroid carcinoma warrant a distinct approach. Differentiated thyroid cancer, comprising most cases, is effectively managed through standard treatments such as thyroidectomy and radioiodine therapy. However, rarer variants, including anaplastic thyroid carcinoma, necessitate specialized interventions, often employing targeted therapies. Although these drugs focus on symptom management, they are not curative. This review delves into the fundamental modulators of thyroid cancers, encompassing genetic, epigenetic, and non-coding RNA factors while exploring their intricate interplay and influence. Epigenetic modifications directly affect the expression of causal genes, while long non-coding RNAs impact the function and expression of micro-RNAs, culminating in tumorigenesis. Additionally, this article provides a concise overview of the advantages and disadvantages associated with pharmacological and non-pharmacological therapeutic interventions in thyroid cancer. Furthermore, with technological advancements, integrating modern software and computing into healthcare and medical practices has become increasingly prevalent. Artificial intelligence and machine learning techniques hold the potential to predict treatment outcomes, analyze data, and develop personalized therapeutic approaches catering to patient specificity. In thyroid cancer, cutting-edge machine learning and deep learning technologies analyze factors such as ultrasonography results for tumor textures and biopsy samples from fine needle aspirations, paving the way for a more accurate and effective therapeutic landscape in the near future.
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Affiliation(s)
- Srinjan Bhattacharya
- Laboratory of Translational Medicine and Nanotherapeutics, Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda 151401, Punjab, India
| | - Rahul Kumar Mahato
- Laboratory of Translational Medicine and Nanotherapeutics, Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda 151401, Punjab, India
| | - Satwinder Singh
- Department of Computer Science and Technology, Central University of Punjab, Bathinda 151401, Punjab, India.
| | - Gurjit Kaur Bhatti
- Department of Medical Lab Technology, University Institute of Applied Health Sciences, Chandigarh University, Mohali, India
| | - Sarabjit Singh Mastana
- School of Sport, Exercise and Health Sciences, Loughborough University, Epinal Way, Leicestershire, Loughborough LE11 3TU, UK.
| | - Jasvinder Singh Bhatti
- Laboratory of Translational Medicine and Nanotherapeutics, Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda 151401, Punjab, India.
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Xu J, Xu HL, Cao YN, Huang Y, Gao S, Wu QJ, Gong TT. The performance of deep learning on thyroid nodule imaging predicts thyroid cancer: A systematic review and meta-analysis of epidemiological studies with independent external test sets. Diabetes Metab Syndr 2023; 17:102891. [PMID: 37907027 DOI: 10.1016/j.dsx.2023.102891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 10/06/2023] [Accepted: 10/15/2023] [Indexed: 11/02/2023]
Abstract
BACKGROUND AND AIMS It is still controversial whether deep learning (DL) systems add accuracy to thyroid nodule imaging classification based on the recent available evidence. We conducted this study to analyze the current evidence of DL in thyroid nodule imaging diagnosis in both internal and external test sets. METHODS Until the end of December 2022, PubMed, IEEE, Embase, Web of Science, and the Cochrane Library were searched. We included primary epidemiological studies using externally validated DL techniques in image-based thyroid nodule appraisal. This systematic review was registered on PROSPERO (CRD42022362892). RESULTS We evaluated evidence from 17 primary epidemiological studies using externally validated DL techniques in image-based thyroid nodule appraisal. Fourteen studies were deemed eligible for meta-analysis. The pooled sensitivity, specificity, and area under the curve (AUC) of these DL algorithms were 0.89 (95% confidence interval 0.87-0.90), 0.84 (0.82-0.86), and 0.93 (0.91-0.95), respectively. For the internal validation set, the pooled sensitivity, specificity, and AUC were 0.91 (0.89-0.93), 0.88 (0.85-0.91), and 0.96 (0.93-0.97), respectively. In the external validation set, the pooled sensitivity, specificity, and AUC were 0.87 (0.85-0.89), 0.81 (0.77-0.83), and 0.91 (0.88-0.93), respectively. Notably, in subgroup analyses, DL algorithms still demonstrated exceptional diagnostic validity. CONCLUSIONS Current evidence suggests DL-based imaging shows diagnostic performances comparable to clinicians for differentiating thyroid nodules in both the internal and external test sets.
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Affiliation(s)
- Jin Xu
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - He-Li Xu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yi-Ning Cao
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China; Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ying Huang
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China
| | - Song Gao
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qi-Jun Wu
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China; Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Key Laboratory of Reproductive and Genetic Medicine (China Medical University), National Health Commission, Shenyang, China.
| | - Ting-Ting Gong
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
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Winkelman J, Nguyen D, vanSonnenberg E, Kirk A, Lieberman S. Artificial Intelligence (AI) in pediatric endocrinology. J Pediatr Endocrinol Metab 2023; 36:903-908. [PMID: 37589444 DOI: 10.1515/jpem-2023-0287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 08/03/2023] [Indexed: 08/18/2023]
Abstract
Artificial Intelligence (AI) is integrating itself throughout the medical community. AI's ability to analyze complex patterns and interpret large amounts of data will have considerable impact on all areas of medicine, including pediatric endocrinology. In this paper, we review and update the current studies of AI in pediatric endocrinology. Specific topics that are addressed include: diabetes management, bone growth, metabolism, obesity, and puberty. Becoming knowledgeable and comfortable with AI will assist pediatric endocrinologists, the goal of the paper.
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Affiliation(s)
| | - Diep Nguyen
- University of Arizona College of Medicine Phoenix, Phoenix, USA
| | - Eric vanSonnenberg
- University of Arizona College of Medicine Phoenix, Phoenix, USA
- From the Departments of Radiology, University of Arizona College of Medicine Phoenix, Phoenix, USA
- Student Affairs, University of Arizona College of Medicine Phoenix, Phoenix, USA
| | - Alison Kirk
- University of Arizona College of Medicine Phoenix, Phoenix, USA
- Student Affairs, University of Arizona College of Medicine Phoenix, Phoenix, USA
- Pediatrics, University of Arizona College of Medicine Phoenix, Phoenix, USA
| | - Steven Lieberman
- University of Arizona College of Medicine Phoenix, Phoenix, USA
- Internal Medicine (Division of Endocrinology), University of Arizona College of Medicine Phoenix, Phoenix, USA
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