1
|
Yadav N, Dass R, Virmani J. A systematic review of machine learning based thyroid tumor characterisation using ultrasonographic images. J Ultrasound 2024; 27:209-224. [PMID: 38536643 PMCID: PMC11178762 DOI: 10.1007/s40477-023-00850-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 11/22/2023] [Indexed: 06/15/2024] Open
Abstract
Ultrasonography is widely used to screen thyroid tumors because it is safe, easy to use, and low-cost. However, it is simultaneously affected by speckle noise and other artifacts, so early detection of thyroid abnormalities becomes difficult for the radiologist. Therefore, various researchers continuously address the limitations of sonography and improve the diagnosis potential of US images for thyroid tissue from the last three decays. Accordingly, the present study extensively reviewed various CAD systems used to classify thyroid tumor US (TTUS) images related to datasets, despeckling algorithms, segmentation algorithms, feature extraction and selection, assessment parameters, and classification algorithms. After the exhaustive review, the achievements and challenges have been reported, and build a road map for the new researchers.
Collapse
Affiliation(s)
- Niranjan Yadav
- Department of Electronics and Communication Engineering, Deenbandhu Chhotu Ram University of Science and Technology Murthal, Sonepat, 131039, India.
| | - Rajeshwar Dass
- Department of Electronics and Communication Engineering, Deenbandhu Chhotu Ram University of Science and Technology Murthal, Sonepat, 131039, India
| | - Jitendra Virmani
- Central Scientific Instruments Organization, Council of Scientific and Industrial Research, Chandigarh, 160030, India
| |
Collapse
|
2
|
Ryu YJ, Kim JW, Park SC, Hur YH, Kim HJ, Kim TH. Differential diagnosis of thyroid nodules using heterogeneity quantification software on ultrasound images: correlation with the Bethesda system and surgical pathology. Sci Rep 2024; 14:10288. [PMID: 38704392 PMCID: PMC11069538 DOI: 10.1038/s41598-024-60881-2] [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: 04/12/2023] [Accepted: 04/29/2024] [Indexed: 05/06/2024] Open
Abstract
Ultrasonography (US)-guided fine-needle aspiration cytology (FNAC) is the primary modality for evaluating thyroid nodules. However, in cases of atypia of undetermined significance (AUS) or follicular lesion of undetermined significance (FLUS), supplemental tests are necessary for a definitive diagnosis. Accordingly, we aimed to develop a non-invasive quantification software using the heterogeneity scores of thyroid nodules. This cross-sectional study retrospectively enrolled 188 patients who were categorized into four groups according to their diagnostic classification in the Bethesda system and surgical pathology [II-benign (B) (n = 24); III-B (n = 52); III-malignant (M) (n = 54); V/VI-M (n = 58)]. Heterogeneity scores were derived using an image pixel-based heterogeneity index, utilized as a coefficient of variation (CV) value, and analyzed across all US images. Differences in heterogeneity scores were compared using one-way analysis of variance with Tukey's test. Diagnostic accuracy was determined by calculating the area under the receiver operating characteristic (AUROC) curve. The results of this study indicated significant differences in mean heterogeneity scores between benign and malignant thyroid nodules, except in the comparison between III-M and V/VI-M nodules. Among malignant nodules, the Bethesda classification was not observed to be associated with mean heterogeneity scores. Moreover, there was a positive correlation between heterogeneity scores and the combined diagnostic category, which was based on the Bethesda system and surgical cytology grades (R = 0.639, p < 0.001). AUROC for heterogeneity scores showed the highest diagnostic performance (0.818; cut-off: 30.22% CV value) for differentiating the benign group (normal/II-B/III-B) from the malignant group (III-M/V&VI-M), with a diagnostic accuracy of 72.5% (161/122). Quantitative heterogeneity measurement of US images is a valuable non-invasive diagnostic tool for predicting the likelihood of malignancy in thyroid nodules, including AUS or FLUS.
Collapse
Affiliation(s)
- Young Jae Ryu
- Department of Surgery, Chonnam National University Medical School, Chonnam National University Hwasun Hospital, 322 Seoyang-ro, Hwasun-eup, Hwasun-gun, Jeonnam, 58128, Republic of Korea
| | - Jin Woong Kim
- Department of Radiology, Chosun University College of Medicine, Chosun University Hospital, Gwangju, 61452, Republic of Korea
| | - Sang Chun Park
- Department of Surgery, Chonnam National University Medical School, Chonnam National University Hwasun Hospital, 322 Seoyang-ro, Hwasun-eup, Hwasun-gun, Jeonnam, 58128, Republic of Korea
| | - Young Hoe Hur
- Department of Surgery, Chonnam National University Medical School, Chonnam National University Hwasun Hospital, 322 Seoyang-ro, Hwasun-eup, Hwasun-gun, Jeonnam, 58128, Republic of Korea
| | - Hyung Joong Kim
- Medical Science Research Institute, Kyung Hee University Hospital, 23 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Republic of Korea
| | - Tae-Hoon Kim
- Medical Science Research Institute, Kyung Hee University Hospital, 23 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Republic of Korea.
| |
Collapse
|
3
|
Li X, Fu C, Xu S, Sham CW. Thyroid Ultrasound Image Database and Marker Mask Inpainting Method for Research and Development. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:509-519. [PMID: 38267314 DOI: 10.1016/j.ultrasmedbio.2023.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 12/04/2023] [Accepted: 12/07/2023] [Indexed: 01/26/2024]
Abstract
OBJECTIVE The main objective of this study was to build a rich and high-quality thyroid ultrasound image database (TUD) for computer-aided diagnosis (CAD) systems to support accurate diagnosis and prognostic modeling of thyroid disorders. Because most of the raw thyroid ultrasound images contain artificial markers, which seriously affect the robustness of CAD systems because of their strong prior location information, we propose a marker mask inpainting (MMI) method to erase artificial markers and improve image quality. METHODS First, a set of thyroid ultrasound images were collected from the General Hospital of the Northern Theater Command. Then, two modules were designed in MMI, namely, the marker detection (MD) module and marker erasure (ME) module. The MD module detects all markers in the image and stores them in a binary mask. According to the binary mask, the ME module erases the markers and generates an unmarked image. Finally, a new TUD based on the marked images and unmarked images was built. The TUD is carefully annotated and statistically analyzed by professional physicians to ensure accuracy and consistency. Moreover, several normal thyroid gland images and some ancillary information on benign and malignant nodules are provided. RESULTS Several typical segmentation models were evaluated on the TUD. The experimental results revealed that our TUD can facilitate the development of more accurate CAD systems for the analysis of thyroid nodule-related lesions in ultrasound images. The effectiveness of our MMI method was determined in quantitative experiments. CONCLUSION The rich and high-quality resource TUD promotes the development of more effective diagnostic and treatment methods for thyroid diseases. Furthermore, MMI for erasing artificial markers and generating unmarked images is proposed to improve the quality of thyroid ultrasound images. Our TUD database is available at https://github.com/NEU-LX/TUD-Datebase.
Collapse
Affiliation(s)
- Xiang Li
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Chong Fu
- School of Computer Science and Engineering, Northeastern University, Shenyang, China.
| | - Sen Xu
- General Hospital of Northern Theatre Command, Shenyang, China
| | - Chiu-Wing Sham
- School of Computer Science, University of Auckland, Auckland, New Zealand
| |
Collapse
|
4
|
Kang YJ, Lee HW, Stybayeva G, Hwang SH. Comparison of Liquid-Based Preparations with Conventional Smears in Thyroid Fine-Needle Aspirates: A Systematic Review and Meta-Analysis. Cancers (Basel) 2024; 16:751. [PMID: 38398142 PMCID: PMC10886914 DOI: 10.3390/cancers16040751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 01/30/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND To compare conventional smears (CSs) and liquid-based preparations (LBPs) for diagnosing thyroid malignant or suspicious lesions. METHODS Studies in the PubMed, SCOPUS, Embase, Web of Science, and Cochrane database published up to December 2023. We reviewed 17 studies, including 15,861 samples. RESULTS The diagnostic odds ratio (DOR) for CS was 23.6674. The area under the summary receiver operating characteristic curve (AUC) was 0.879, with sensitivity, specificity, negative predictive value, and positive predictive value of 0.8266, 0.8668, 0.8969, and 0.7841, respectively. The rate of inadequate specimens was 0.1280. For LBP, the DOR was 25.3587, with an AUC of 0.865. The sensitivity, specificity, negative predictive value, and positive predictive value were 0.8190, 0.8833, 0.8515, and 0.8562. The rate of inadequate specimens was 0.1729. For CS plus LBP, the AUC was 0.813, with a lower DOR of 9.4557 compared to individual methods. Diagnostic accuracy did not significantly differ among CS, LBP, and CS plus LBP. Subgroup analysis was used to compare ThinPrep and SurePath. The DORs were 29.1494 and 19.7734. SurePath had a significantly higher AUC. CONCLUSIONS There was no significant difference in diagnostic accuracy or proportion of inadequate smears between CS and LBP. SurePath demonstrated higher diagnostic accuracy than ThinPrep. Recommendations for fine-needle aspiration cytology should consider cost, feasibility, and accuracy.
Collapse
Affiliation(s)
- Yun Jin Kang
- Department of Otorhinolaryngology-Head and Neck Surgery, Soonchunhyang University College of Medicine, Cheonan 14584, Republic of Korea; (Y.J.K.); (H.W.L.)
| | - Hyeon Woo Lee
- Department of Otorhinolaryngology-Head and Neck Surgery, Soonchunhyang University College of Medicine, Cheonan 14584, Republic of Korea; (Y.J.K.); (H.W.L.)
| | - Gulnaz Stybayeva
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA;
| | - Se Hwan Hwang
- Department of Otolaryngology-Head and Neck Surgery, Bucheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| |
Collapse
|
5
|
Zhong NN, Wang HQ, Huang XY, Li ZZ, Cao LM, Huo FY, Liu B, Bu LL. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Semin Cancer Biol 2023; 95:52-74. [PMID: 37473825 DOI: 10.1016/j.semcancer.2023.07.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 07/22/2023]
Abstract
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.
Collapse
Affiliation(s)
- Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Han-Qi Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Xin-Yue Huang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Fang-Yi Huo
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
| |
Collapse
|
6
|
Cao CL, Li QL, Tong J, Shi LN, Li WX, Xu Y, Cheng J, Du TT, Li J, Cui XW. Artificial intelligence in thyroid ultrasound. Front Oncol 2023; 13:1060702. [PMID: 37251934 PMCID: PMC10213248 DOI: 10.3389/fonc.2023.1060702] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 04/07/2023] [Indexed: 05/31/2023] Open
Abstract
Artificial intelligence (AI), particularly deep learning (DL) algorithms, has demonstrated remarkable progress in image-recognition tasks, enabling the automatic quantitative assessment of complex medical images with increased accuracy and efficiency. AI is widely used and is becoming increasingly popular in the field of ultrasound. The rising incidence of thyroid cancer and the workload of physicians have driven the need to utilize AI to efficiently process thyroid ultrasound images. Therefore, leveraging AI in thyroid cancer ultrasound screening and diagnosis cannot only help radiologists achieve more accurate and efficient imaging diagnosis but also reduce their workload. In this paper, we aim to present a comprehensive overview of the technical knowledge of AI with a focus on traditional machine learning (ML) algorithms and DL algorithms. We will also discuss their clinical applications in the ultrasound imaging of thyroid diseases, particularly in differentiating between benign and malignant nodules and predicting cervical lymph node metastasis in thyroid cancer. Finally, we will conclude that AI technology holds great promise for improving the accuracy of thyroid disease ultrasound diagnosis and discuss the potential prospects of AI in this field.
Collapse
Affiliation(s)
- Chun-Li Cao
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Qiao-Li Li
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Jin Tong
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
| | - Li-Nan Shi
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Wen-Xiao Li
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Ya Xu
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
| | - Jing Cheng
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
| | - Ting-Ting Du
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
| | - Jun Li
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| |
Collapse
|
7
|
Liu T, Wu C, Wang G, Jia Y, Zhu Y, Nie F. Clinical Value of Artificial Intelligence-Based Computer-Aided Diagnosis System Versus Contrast-Enhanced Ultrasound for Differentiation of Benign From Malignant Thyroid Nodules in Different Backgrounds. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023. [PMID: 36794594 DOI: 10.1002/jum.16195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 01/29/2023] [Accepted: 01/30/2023] [Indexed: 06/18/2023]
Abstract
OBJECTIVES The aim of this study was to compare the value of AI-SONIC ultrasound-assisted diagnosis system versus contrast-enhanced ultrasound (CEUS) for differential diagnosis of thyroid nodules in diffuse and non-diffuse backgrounds. METHODS A total of 555 thyroid nodules with pathologically confirmed diagnosis were included in this retrospective study. The diagnostic efficacies of AI-SONIC and CEUS for differentiating benign from malignant nodules in diffuse and non-diffuse backgrounds were evaluated, with pathological diagnosis as the gold standard. RESULTS The agreement between AI-SONIC diagnosis and pathological diagnosis was moderate in diffuse backgrounds (κ = 0.417) and almost perfect in non-diffuse backgrounds (κ = 0.81). The agreement between CEUS diagnosis and pathological diagnosis was substantial in diffuse backgrounds (κ = 0.684) and moderate in non-diffuse backgrounds (κ = 0.407). In diffuse backgrounds, AI-SONIC had slightly higher sensitivity (95.7 vs 89.4%, P = .375), but CEUS had significantly higher specificity (80.0 vs 40.0%, P = .008). In non-diffuse background, AI-SONIC had significantly higher sensitivity (96.2 vs 73.4%, P < .001), specificity (82.9 vs 71.2%, P = .007), and negative predictive value (90.3 vs 53.3%, P < .001). CONCLUSION In non-diffuse backgrounds, AI-SONIC is superior to CEUS for differentiating malignant from benign thyroid nodules. In diffuse backgrounds, AI-SONIC could be useful for screening of cases to detect suspicious nodules requiring further examination by CEUS.
Collapse
Affiliation(s)
- Ting Liu
- Ultrasound Medicine Center, Lanzhou University Second Hospital, Lanzhou, China
| | - Chuang Wu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
| | - Guojuan Wang
- Ultrasound Medicine Center, Lanzhou University Second Hospital, Lanzhou, China
| | - Yingying Jia
- Ultrasound Medicine Center, Lanzhou University Second Hospital, Lanzhou, China
| | - Yangyang Zhu
- Ultrasound Medicine Center, Lanzhou University Second Hospital, Lanzhou, China
| | - Fang Nie
- Ultrasound Medicine Center, Lanzhou University Second Hospital, Lanzhou, China
| |
Collapse
|
8
|
Ozturk AC, Haznedar H, Haznedar B, Ilgan S, Erogul O, Kalinli A. Differentiation of Benign and Malignant Thyroid Nodules with ANFIS by Using Genetic Algorithm and Proposing a Novel CAD-Based Risk Stratification System of Thyroid Nodules. Diagnostics (Basel) 2023; 13:diagnostics13040740. [PMID: 36832228 PMCID: PMC9954959 DOI: 10.3390/diagnostics13040740] [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: 12/12/2022] [Revised: 01/27/2023] [Accepted: 02/06/2023] [Indexed: 02/17/2023] Open
Abstract
The thyroid nodule risk stratification guidelines used in the literature are based on certain well-known sonographic features of nodules and are still subjective since the application of these characteristics strictly depends on the reading physician. These guidelines classify nodules according to the sub-features of limited sonographic signs. This study aims to overcome these limitations by examining the relationships of a wide range of ultrasound (US) signs in the differential diagnosis of nodules by using artificial intelligence methods. An innovative method based on training Adaptive-Network Based Fuzzy Inference Systems (ANFIS) by using Genetic Algorithm (GA) is used to differentiate malignant from benign thyroid nodules. The comparison of the results from the proposed method to the results from the commonly used derivative-based algorithms and Deep Neural Network (DNN) methods yielded that the proposed method is more successful in differentiating malignant from benign thyroid nodules. Furthermore, a novel computer aided diagnosis (CAD) based risk stratification system for the thyroid nodule's US classification that is not present in the literature is proposed.
Collapse
Affiliation(s)
- Ahmet Cankat Ozturk
- Institute of Natural Science, Department of Biomedical Engineering, TOBB University of Economics and Technology, 06560 Ankara, Türkiye
- Correspondence:
| | - Hilal Haznedar
- Institute of Natural Science, Department of Computer Engineering, Erciyes University, 38280 Kayseri, Türkiye
| | - Bulent Haznedar
- Department of Computer Engineering, Gaziantep University, 27310 Gaziantep, Türkiye
| | - Seyfettin Ilgan
- Department of Nuclear Medicine, Ankara Guven Hospital, 06540 Ankara, Türkiye
| | - Osman Erogul
- Institute of Natural Science, Department of Biomedical Engineering, TOBB University of Economics and Technology, 06560 Ankara, Türkiye
| | - Adem Kalinli
- Presidency Office, Rectorate, Middle East Technical University, 06800 Ankara, Türkiye
| |
Collapse
|
9
|
The Use of Artificial Intelligence in the Diagnosis and Classification of Thyroid Nodules: An Update. Cancers (Basel) 2023; 15:cancers15030708. [PMID: 36765671 PMCID: PMC9913834 DOI: 10.3390/cancers15030708] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 01/20/2023] [Accepted: 01/20/2023] [Indexed: 01/27/2023] Open
Abstract
The incidence of thyroid nodules diagnosed is increasing every year, leading to a greater risk of unnecessary procedures being performed or wrong diagnoses being made. In our paper, we present the latest knowledge on the use of artificial intelligence in diagnosing and classifying thyroid nodules. We particularly focus on the usefulness of artificial intelligence in ultrasonography for the diagnosis and characterization of pathology, as these are the two most developed fields. In our search of the latest innovations, we reviewed only the latest publications of specific types published from 2018 to 2022. We analyzed 930 papers in total, from which we selected 33 that were the most relevant to the topic of our work. In conclusion, there is great scope for the use of artificial intelligence in future thyroid nodule classification and diagnosis. In addition to the most typical uses of artificial intelligence in cancer differentiation, we identified several other novel applications of artificial intelligence during our review.
Collapse
|
10
|
Ha EJ, Lee JH, Lee DH, Na DG, Kim JH. Development of a machine learning-based fine-grained risk stratification system for thyroid nodules using predefined clinicoradiological features. Eur Radiol 2023; 33:3211-3221. [PMID: 36600122 DOI: 10.1007/s00330-022-09376-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 09/07/2022] [Accepted: 12/11/2022] [Indexed: 01/05/2023]
Abstract
OBJECTIVE We constructed and validated a machine learning-based malignancy risk estimation model using predefined clinicoradiological features, and evaluated its clinical utility for the management of thyroid nodules. METHODS In total, 5708 benign (n = 4597) and malignant (n = 1111) thyroid nodules were collected from 5081 consecutive patients treated in 26 institutions. Seventeen experienced radiologists evaluated nodule characteristics on ultrasonographic images. Eight predictive models were used to stratify the thyroid nodules according to malignancy risk; model performance was assessed via nested 10-fold cross-validation. The best-performing algorithm was externally validated using data for 454 thyroid nodules from a tertiary hospital, then compared to the Thyroid Imaging Reporting and Data System (TIRADS)-based interpretations of radiologists (American College of Radiology, European and Korean TIRADS, and AACE/ACE/AME guidelines). RESULTS The area under the receiver operating characteristic (AUROC) curves of the algorithms ranged from 0.773 to 0.862. The sensitivities, specificities, positive predictive values, and negative predictive values of the best-performing models were 74.1-76.6%, 80.9-83.4%, 49.2-51.9%, and 93.0-93.5%, respectively. For the external validation set, the ElasticNet values were 83.2%, 89.2%, 81.8%, and 90.1%, respectively. The corresponding TIRADS values were 66.5-85.0%, 61.3-80.8%, 45.9-72.1%, and 81.5-90.3%, respectively. The new model exhibited a significantly higher AUROC and specificity than did the TIRADS risk stratification, although its sensitivity was similar. CONCLUSION We developed a reliable machine learning-based predictive model that demonstrated enhanced specificity when stratifying thyroid nodules according to malignancy risk. This system will contribute to improved personalized management of thyroid nodules. KEY POINTS • The area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity of our model were 0.914, 83.2%, and 89.2%, respectively (derived using the validation dataset). • Compared to the TIRADS values, the AUROC and specificity are significantly higher, while the sensitivity is similar. • An interactive version of our AI algorithm is at http://tirads.cdss.co.kr .
Collapse
Affiliation(s)
- Eun Ju Ha
- Department of Radiology, Ajou University School of Medicine, Wonchon-Dong, Yeongtong-Gu, Suwon, 16499, South Korea
| | - Jeong Hoon Lee
- Department of Radiology, Ajou University School of Medicine, Wonchon-Dong, Yeongtong-Gu, Suwon, 16499, South Korea
| | - Da Hyun Lee
- Department of Radiology, Ajou University School of Medicine, Wonchon-Dong, Yeongtong-Gu, Suwon, 16499, South Korea
| | - Dong Gyu Na
- Department of Radiology, GangNeung Asan Hospital, University of Ulsan College of Medicine, Gangneung-si, Gangwon-do, 25440, South Korea
| | - Ji-Hoon Kim
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea.
| |
Collapse
|
11
|
Wang D, Zhao CK, Wang HX, Lu F, Li XL, Guo LH, Sun LP, Fu HJ, Zhang YF, Xu HX. Ultrasound-based computer-aided diagnosis for cytologically indeterminate thyroid nodules with different radiologists. Clin Hemorheol Microcirc 2022; 82:217-230. [PMID: 35848013 DOI: 10.3233/ch-221423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
PURPOSE To evaluate a computer-aided diagnosis (CAD) technique in predicting malignancy for cytologically indeterminate thyroid nodules (TNs) as compared with different experienced radiologists. METHOD 436 patients with 436 cytologically indeterminate TNs on fine-needle aspiration cytology (FNAC) were included and all were confirmed by surgical pathology. They were retrospectively analyzed with respect to ultrasound (US) characteristics using a commercially available CAD system (AmCAD-UT; AmCad BioMed, Taiwan, China) and reviewed by one junior and one senior radiologists.The CAD system and different experienced radiologists stratified the risk of malignancy using ACR TI-RADS category. The diagnostic performance by different experienced radiologists independently and after consulting the CAD (different experienced radiologists + CAD) and by the CAD alone were compared. RESULTS The different experienced radiologists showed significantly higher specificities than the CAD system alone. The combination of radiologist and CAD system showed improved diagnostic performance with an AUC (Area under the curve) of 0.740 in the senior radiologist and 0.677 in the junior radiologist, as compared with CAD (AUC: 0.585) alone (all P < 0.05). The combination of senior radiologist and CAD system had the highest diagnostic performance (AUC: 0.740) and specificity (68.9%) compared to the others (all P < 0.05). CONCLUSION The CAD system may play the potential role as a decision-making assistant alongside radiologists for differential diagnosis of TNs with indeterminate cytology.
Collapse
Affiliation(s)
- Dan Wang
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.,Ultrasound Research and Education Institute, Clinical Research Center for Interventional Medicine, School of Medicine, Tongji University, Shanghai, China.,Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China
| | - Chong-Ke Zhao
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Han-Xiang Wang
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.,Ultrasound Research and Education Institute, Clinical Research Center for Interventional Medicine, School of Medicine, Tongji University, Shanghai, China.,Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China
| | - Feng Lu
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.,Ultrasound Research and Education Institute, Clinical Research Center for Interventional Medicine, School of Medicine, Tongji University, Shanghai, China.,Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China
| | - Xiao-Long Li
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.,Ultrasound Research and Education Institute, Clinical Research Center for Interventional Medicine, School of Medicine, Tongji University, Shanghai, China.,Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China
| | - Le-Hang Guo
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.,Ultrasound Research and Education Institute, Clinical Research Center for Interventional Medicine, School of Medicine, Tongji University, Shanghai, China.,Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China
| | - Li-Ping Sun
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.,Ultrasound Research and Education Institute, Clinical Research Center for Interventional Medicine, School of Medicine, Tongji University, Shanghai, China.,Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China
| | - Hui-Jun Fu
- Department of Pathology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yi-Feng Zhang
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.,Ultrasound Research and Education Institute, Clinical Research Center for Interventional Medicine, School of Medicine, Tongji University, Shanghai, China.,Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| |
Collapse
|
12
|
Advancements in Thyroidectomy: A Mini Review. ENDOCRINES 2022. [DOI: 10.3390/endocrines3040065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Demand for minimally invasive surgery has driven the development of new gadgets and surgical techniques. Yet, questions about safety and skeptical views on new technology have prevented proliferation of new modes of surgery. This skepticism is perhaps due to unfamiliarity of new fields. Likewise, there are currently various remote-access techniques available for thyroid surgeons that only few regions in the world have adapted. This review will explore the history of minimally invasive techniques in thyroid surgery and introduce new technology to be implemented.
Collapse
|
13
|
Xiang Z, Zhuo Q, Zhao C, Deng X, Zhu T, Wang T, Jiang W, Lei B. Self-supervised multi-modal fusion network for multi-modal thyroid ultrasound image diagnosis. Comput Biol Med 2022; 150:106164. [PMID: 36240597 DOI: 10.1016/j.compbiomed.2022.106164] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 09/11/2022] [Accepted: 10/01/2022] [Indexed: 12/07/2022]
Abstract
Ultrasound is a typical non-invasive diagnostic method often used to detect thyroid cancer lesions. However, due to the limitations of the information provided by ultrasound images, shear wave elastography (SWE) and color doppler ultrasound (CDUS) are also used clinically to assist in diagnosis, which makes the diagnosis time-consuming, labor-intensive, and highly subjective process. Therefore, automatic diagnosis of benign and malignant thyroid nodules is beneficial for the clinical diagnosis of the thyroid. To this end, based on three modalities of gray-scale ultrasound images(US), SWE, and CDUS, we propose a deep learning-based multi-modal feature fusion network for the automatic diagnosis of thyroid disease based on the ultrasound images. First, three ResNet18s initialized by self-supervised learning are used as branches to extract the image information of each modality, respectively. Then, a multi-modal multi-head attention branch is used to remove the common information of three modalities, and the knowledge of each modal is combined for thyroid diagnosis. At the same time, to better integrate the features between modalities, a multi-modal feature guidance module is also proposed to guide the feature extraction of each branch and reduce the difference between each-modal feature. We verify the multi-modal thyroid ultrasound image diagnosis method on the self-collected dataset, and the results prove that this method could provide fast and accurate assistance for sonographers in diagnosing thyroid nodules.
Collapse
Affiliation(s)
- Zhuo Xiang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Qiuluan Zhuo
- Huazhong University of Science and Technology Union Shenzhen Hospital, Department of Ultrasound, China
| | - Cheng Zhao
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Xiaofei Deng
- Huazhong University of Science and Technology Union Shenzhen Hospital, Department of Ultrasound, China
| | - Ting Zhu
- Huazhong University of Science and Technology Union Shenzhen Hospital, Department of Ultrasound, China
| | - Tianfu Wang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Wei Jiang
- Huazhong University of Science and Technology Union Shenzhen Hospital, Department of Ultrasound, China.
| | - Baiying Lei
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China.
| |
Collapse
|
14
|
Zhang X, Lee VCS, Rong J, Lee JC, Song J, Liu F. A multi-channel deep convolutional neural network for multi-classifying thyroid diseases. Comput Biol Med 2022; 148:105961. [PMID: 35985185 DOI: 10.1016/j.compbiomed.2022.105961] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 07/28/2022] [Accepted: 08/06/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND AND OBJECTIVE Thyroid disease instances have been continuously increasing since the 1990s, and thyroid cancer has become the most rapidly rising disease among all the malignancies in recent years. Most existing studies focused on applying deep convolutional neural networks for detecting thyroid cancer. Despite their satisfactory performance on binary classification tasks, limited studies have explored multi-class classification of thyroid disease types; much less is known of the diagnosis of co-existence situation for different types of thyroid diseases. METHOD This study proposed a novel multi-channel convolutional neural network (CNN) architecture to address the multi-class classification task of thyroid disease. The multi-channel CNN merits from computed tomography characteristics to drive a comprehensive diagnostic decision for the overall thyroid gland, emphasizing the disease co-existence circumstance. Moreover, this study also examined alternative strategies to enhance the diagnostic accuracy of CNN models through concatenation of different scales of feature maps. RESULTS Benchmarking experiments demonstrate the improved performance of the proposed multi-channel CNN architecture compared with the standard single-channel CNN architecture. More specifically, the multi-channel CNN achieved an accuracy of 0.909±0.048, precision of 0.944±0.062, recall of 0.896±0.047, specificity of 0.994±0.001, and F1 of 0.917±0.057, in contrast to the single-channel CNN, which obtained 0.902±0.004, 0.892±0.005, 0.909±0.002, 0.993±0.001, 0.898±0.003, respectively. In addition, the proposed model was evaluated in different gender groups; it reached a diagnostic accuracy of 0.908 for the female group and 0.901 for the male group. CONCLUSION Collectively, the results highlight that the proposed multi-channel CNN has excellent generalization and has the potential to be deployed to provide computational decision support in clinical settings.
Collapse
Affiliation(s)
- Xinyu Zhang
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Melbourne, VIC 3800, Australia
| | - Vincent C S Lee
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Melbourne, VIC 3800, Australia.
| | - Jia Rong
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Melbourne, VIC 3800, Australia
| | - James C Lee
- Monash University Endocrine Surgery Unit, Alfred Hospital, Melbourne, VIC 3004, Australia; Department of Surgery, Monash University, Melbourne, VIC 3168, Australia
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, Melbourne, VIC 3800, Australia
| | - Feng Liu
- West China Hospital of Sichuan University, Chengdu City, Sichuan Province 332001, China
| |
Collapse
|
15
|
Chen L, Chen M, Li Q, Kumar V, Duan Y, Wu KA, Pierce TT, Samir AE. Machine Learning-Assisted Diagnostic System for Indeterminate Thyroid Nodules. ULTRASOUND IN MEDICINE & BIOLOGY 2022; 48:1547-1554. [PMID: 35660106 DOI: 10.1016/j.ultrasmedbio.2022.03.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 03/07/2022] [Accepted: 03/30/2022] [Indexed: 06/15/2023]
Abstract
To develop an ultrasound-based machine learning classifier to diagnose benignity within indeterminate thyroid nodules (ITNs) by fine-needle aspiration, 180 patients with 194 ITNs (Bethesda classes III, IV and V) undergoing surgery over a 5-y study period were analyzed. The data set was randomly divided into training and testing data sets with 155 and 39 ITNs, respectively. All nodules were evaluated by ultrasound using the American College of Radiology Thyroid Imaging Reporting and Data System by manually scoring composition, echogenicity, shape, margin and echogenic foci. Nodule size, participant age and patient sex were recorded. A support vector machine (SVM) model with a cost-sensitive approach was developed using the aforementioned eight parameters with surgical histopathology as the reference standard. Surgical pathology determined 90 (46.4%) ITNs were malignant and 104 (53.6%) were benign. The SVM model classified 14 nodules as benign in the testing data set, of which 13 were correct (sensitivity = 93.8%, specificity = 56.5%). Considering malignancy prevalence by Bethesda group, the negative predictive values of this model for Bethesda III and IV categories were 93.9% and 93. 8%, respectively. The high negative predictive value of the SVM ultrasound-based model suggests a pathway by which surgical excision of Bethesda III and IV ITNs classified as benign may be avoided.
Collapse
Affiliation(s)
- Lei Chen
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Department of Ultrasound, Peking University First Hospital, Beijing, China
| | - Minda Chen
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Northeastern University, Boston, Massachusetts, USA
| | - Qian Li
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Viksit Kumar
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Yu Duan
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Department of Ultrasound, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Kevin A Wu
- Duke University School of Medicine, Durham, North Carolina, USA
| | - Theodore T Pierce
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Anthony E Samir
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
| |
Collapse
|
16
|
Tai HC, Chen KY, Wu MH, Chang KJ, Chen CN, Chen A. Assessing Detection Accuracy of Computerized Sonographic Features and Computer-Assisted Reading Performance in Differentiating Thyroid Cancers. Biomedicines 2022; 10:biomedicines10071513. [PMID: 35884818 PMCID: PMC9313277 DOI: 10.3390/biomedicines10071513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 06/20/2022] [Accepted: 06/24/2022] [Indexed: 11/16/2022] Open
Abstract
For ultrasound imaging of thyroid nodules, medical guidelines are all based on findings of sonographic features to provide clinicians management recommendations. Due to the recent development of artificial intelligence and machine learning (AI/ML) technologies, there have been computer-assisted detection (CAD) software devices available for clinical use to detect and quantify the sonographic features of thyroid nodules. This study is to validate the accuracy of the computerized sonographic features (CSF) by a CAD software device, namely, AmCAD-UT, and then to assess how the reading performance of clinicians (readers) can be improved providing the computerized features. The feature detection accuracy is tested against the ground truth established by a panel of thyroid specialists and a multiple-reader multiple-case (MRMC) study is performed to assess the sequential reading performance with the assistance of the CSF. Five computerized features, including anechoic area, hyperechoic foci, hypoechoic pattern, heterogeneous texture, and indistinct margin, were tested, with AUCs ranging from 0.888~0.946, 0.825~0.913, 0.812~0.847, 0.627~0.77, and 0.676~0.766, respectively. With the five CSFs, the sequential reading performance of 18 clinicians is found significantly improved, with the AUC increasing from 0.720 without CSF to 0.776 with CSF. Our studies show that the computerized features are consistent with the clinicians’ findings and provide additional value in assisting sonographic diagnosis.
Collapse
Affiliation(s)
- Hao-Chih Tai
- Department of Surgery, National Taiwan University Hospital and College of Medicine, Taipei 100225, Taiwan; (H.-C.T.); (K.-Y.C.); (M.-H.W.); (K.-J.C.)
| | - Kuen-Yuan Chen
- Department of Surgery, National Taiwan University Hospital and College of Medicine, Taipei 100225, Taiwan; (H.-C.T.); (K.-Y.C.); (M.-H.W.); (K.-J.C.)
| | - Ming-Hsun Wu
- Department of Surgery, National Taiwan University Hospital and College of Medicine, Taipei 100225, Taiwan; (H.-C.T.); (K.-Y.C.); (M.-H.W.); (K.-J.C.)
| | - King-Jen Chang
- Department of Surgery, National Taiwan University Hospital and College of Medicine, Taipei 100225, Taiwan; (H.-C.T.); (K.-Y.C.); (M.-H.W.); (K.-J.C.)
| | - Chiung-Nien Chen
- Department of Surgery, National Taiwan University Hospital and College of Medicine, Taipei 100225, Taiwan; (H.-C.T.); (K.-Y.C.); (M.-H.W.); (K.-J.C.)
- Correspondence: (C.-N.C.); (A.C.)
| | - Argon Chen
- Graduate Institute of Industrial Engineering, National Taiwan University, Taipei 106216, Taiwan
- Correspondence: (C.-N.C.); (A.C.)
| |
Collapse
|
17
|
Zhang X, Lee VC, Rong J, Lee JC, Liu F. Deep convolutional neural networks in thyroid disease detection: A multi-classification comparison by ultrasonography and computed tomography. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 220:106823. [PMID: 35489145 DOI: 10.1016/j.cmpb.2022.106823] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 02/20/2022] [Accepted: 04/18/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE As one of the largest endocrine organs in the human body, the thyroid gland regulates daily metabolism. Early detection of thyroid disease leads to reduced mortality rates. The diagnosis of thyroid disease is usually made by radiologists and pathologists, which heavily relies on their experience and expertise. To mitigate human false-positive diagnostic rates, this paper proves that deep learning-driven techniques yield promising performance for automatic detection of thyroid diseases which offers clinicians assistance regarding diagnostic decision-making. METHOD This research study is the first of its kind, which adopts two pre-operative medical image modalities for multi-classifying thyroid disease types (i.e., normal, thyroiditis, cystic, multi-nodular goiter, adenoma, and cancer). Using the current state-of-the-art performing deep convolutional neural network (CNN) architecture, this study builds a thyroid disease diagnostic model for distinguishing among the disease types. RESULTS The model obtains unprecedented performance for both medical image sets, and it reaches an accuracy of 0.972 and 0.942 for ultrasound images and computed tomography (CT) scans correspondingly. CONCLUSION The experimental results illustrate that the selected CNN can be adapted to both image modalities, indicating the feasibility of the deep learning model and emphasizing its further applications in clinics.
Collapse
Affiliation(s)
- Xinyu Zhang
- Department of Data Science and AI, Faculty of IT, Monash University, Wellington Rd, Clayton, Melbourne, VIC 3800, Australia
| | - Vincent Cs Lee
- Department of Data Science and AI, Faculty of IT, Monash University, Wellington Rd, Clayton, Melbourne, VIC 3800, Australia.
| | - Jia Rong
- Department of Data Science and AI, Faculty of IT, Monash University, Wellington Rd, Clayton, Melbourne, VIC 3800, Australia
| | - James C Lee
- Monash University Endocrine Surgery Unit, Alfred Hospital, Melbourne, VIC 3004, Australia; Department of Surgery, Monash University, Melbourne, VIC 3168, Australia
| | - Feng Liu
- West China Hospital of Sichuan University, Chengdu City, Sichuan Province 332001, China
| |
Collapse
|
18
|
Wang L, Zhou X, Nie X, Lin X, Li J, Zheng H, Xue E, Chen S, Chen C, Du M, Tong T, Gao Q, Zheng M. A Multi-Scale Densely Connected Convolutional Neural Network for Automated Thyroid Nodule Classification. Front Neurosci 2022; 16:878718. [PMID: 35663553 PMCID: PMC9160335 DOI: 10.3389/fnins.2022.878718] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 04/13/2022] [Indexed: 11/13/2022] Open
Abstract
Automated thyroid nodule classification in ultrasound images is an important way to detect thyroid nodules and to make a more accurate diagnosis. In this paper, we propose a novel deep convolutional neural network (CNN) model, called n-ClsNet, for thyroid nodule classification. Our model consists of a multi-scale classification layer, multiple skip blocks, and a hybrid atrous convolution (HAC) block. The multi-scale classification layer first obtains multi-scale feature maps in order to make full use of image features. After that, each skip-block propagates information at different scales to learn multi-scale features for image classification. Finally, the HAC block is used to replace the downpooling layer so that the spatial information can be fully learned. We have evaluated our n-ClsNet model on the TNUI-2021 dataset. The proposed n-ClsNet achieves an average accuracy (ACC) score of 93.8% in the thyroid nodule classification task, which outperforms several representative state-of-the-art classification methods.
Collapse
Affiliation(s)
- Luoyan Wang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University, Fuzhou, China
| | - Xiaogen Zhou
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University, Fuzhou, China
| | - Xingqing Nie
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University, Fuzhou, China
| | - Xingtao Lin
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University, Fuzhou, China
| | - Jing Li
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University, Fuzhou, China
| | - Haonan Zheng
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University, Fuzhou, China
| | - Ensheng Xue
- Fujian Medical University Union Hospital, Fuzhou, China
- Fujian Medical Ultrasound Research Institute, Fuzhou, China
| | - Shun Chen
- Fujian Medical University Union Hospital, Fuzhou, China
| | - Cong Chen
- Fujian Medical University Union Hospital, Fuzhou, China
| | - Min Du
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University, Fuzhou, China
| | - Tong Tong
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University, Fuzhou, China
| | - Qinquan Gao
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University, Fuzhou, China
- *Correspondence: Qinquan Gao
| | - Meijuan Zheng
- Fujian Medical University Union Hospital, Fuzhou, China
- Meijuan Zheng
| |
Collapse
|
19
|
Pan L, Cai Y, Lin N, Yang L, Zheng S, Huang L. A two-stage network with prior knowledge guidance for medullary thyroid carcinoma recognition in ultrasound images. Med Phys 2022; 49:2413-2426. [PMID: 35103313 DOI: 10.1002/mp.15492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 01/09/2022] [Accepted: 01/12/2022] [Indexed: 11/05/2022] Open
Abstract
PURPOSE Accurate recognition of Medullary Thyroid Carcinoma (MTC) is of great importance in medical diagnosis, as MTC is rare but second-most malignant thyroid cancers with a high case-fatality ratio.[1] But there is a lower recognition rate on distinguishing MTC from other thyroid nodules in ultrasound images, even by experienced experts. This paper introduces the computer-aided method to tackle the challenge of recognizing MTC from ultrasound images, including limited MTC samples, and ambiguities among MTC, benign nodules, and Papillary Thyroid Carcinoma (PTC). METHODS The recognition of MTC based on large MTC samples of ultrasound images has never been explored, as only one existing work presented a relevant dataset with a limited 21 MTC samples. This study proposes a novel method for primarily differentiating MTC samples from benign nodules, and PTC which is the most common thyroid cancer. Our method is a two-stage schema with two important components including a cascaded coarse-to-fine segmentation network and a knowledge-based classification network. The cascaded coarse-to-fine segmentation network incorporates two U-Net++ networks for improving the segmentation results of thyroid nodules. Meanwhile, our knowledge-based classification network extracts and fuses semantic features of solid tissues and calcification for better recognizing the segmented nodules from the ultrasound images. In our experiments, Dice Similarity Coefficient (DSC), Intersection over Union (IoU), Precision, Recall, and Hausdorff Distance (HD) are adopted for evaluating the segmentation results of thyroid nodules, and Accuracy, Precision, Recall, and F1-score are used for classification evaluation. RESULTS We present a well-annotated dataset including samples of 248 MTC, 240 benign nodules, and 239 PTC. For thyroid nodule segmentation, our designed cascaded segmentation network attains values of 0.776 DSC, 0.689 IoU, 0.778 Precision, and 0.821 Recall, respectively. By incorporating prior knowledge, our method achieves a mean accuracy of 82.1% in classifying thyroid nodules of MTC, PTC, and benign ones. Especially, our method gains the higher performance in recognizing MTC with an accuracy of 86.8%, compared to nearly 70% diagnosis accuracy of experienced doctors. The experimental results on our Fujian Provincial Hospital (FPH) dataset further validate the efficiency of our proposed method. CONCLUSIONS Our proposed two-stage method incorporates pipelines of thyroid nodules segmentation and classification of MTC, individually. Quantitative and qualitative results indicate that our proposed model achieves accurate segmentation of thyroid nodules. The results also validate that our learning-based framework facilitates the recognition of MTC, which gains better classification accuracy than experienced doctors. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Lin Pan
- College of Physics and Informantion Engineering, Fuzhou University, Fujian, 350108, China
| | - Yanjing Cai
- College of Physics and Informantion Engineering, Fuzhou University, Fujian, 350108, China
| | - Ning Lin
- Department of Ultrasound, Fujian Provincial Hospital, Fujian, 350001, China.,Department of Ultrasound, Shengli Clinical Medical College of Fujian Medical University, Fujian, 350001, China
| | - Linxin Yang
- Department of Ultrasound, Fujian Provincial Hospital, Fujian, 350001, China.,Department of Ultrasound, Shengli Clinical Medical College of Fujian Medical University, Fujian, 350001, China
| | - Shaohua Zheng
- College of Physics and Informantion Engineering, Fuzhou University, Fujian, 350108, China
| | - Liqin Huang
- College of Physics and Informantion Engineering, Fuzhou University, Fujian, 350108, China
| |
Collapse
|
20
|
Application of Machine Learning Methods to Improve the Performance of Ultrasound in Head and Neck Oncology: A Literature Review. Cancers (Basel) 2022; 14:cancers14030665. [PMID: 35158932 PMCID: PMC8833587 DOI: 10.3390/cancers14030665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/19/2022] [Accepted: 01/26/2022] [Indexed: 01/06/2023] Open
Abstract
Simple Summary Ultrasound (US) is a non-invasive imaging method that is routinely utilized in head and neck cancer patients to assess the anatomic extent of tumors, nodal and non-nodal neck masses and for imaging the salivary glands. In this review, we summarize the present evidence on whether the application of machine learning (ML) methods can potentially improve the performance of US in head and neck cancer patients. We found that published clinical literature on ML methods applied to US datasets was limited but showed evidence of improved diagnostic and prognostic performance. However, a majority of these studies were based on retrospective evaluation and conducted at a single center with a limited number of datasets. The conduct of multi-center studies could help better validate the performance of ML-based US radiomics and facilitate the integration of these approaches into routine clinical practice. Abstract Radiomics is a rapidly growing area of research within radiology that involves the extraction and modeling of high-dimensional quantitative imaging features using machine learning/artificial intelligence (ML/AI) methods. In this review, we describe the published clinical evidence on the application of ML methods to improve the performance of ultrasound (US) in head and neck oncology. A systematic search of electronic databases (MEDLINE, PubMed, clinicaltrials.gov) was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Of 15,080 initial articles identified, 34 studies were selected for in-depth analysis. Twenty-five out of 34 studies (74%) focused on the diagnostic application of US radiomics while 6 (18%) studies focused on response assessment and 3 (8%) studies utilized US radiomics for modeling normal tissue toxicity. Support vector machine (SVM) was the most commonly employed ML method (47%) followed by multivariate logistic regression (24%) and k-nearest neighbor analysis (21%). Only 11/34 (~32%) of the studies included an independent validation set. A majority of studies were retrospective in nature (76%) and based on single-center evaluation (85%) with variable numbers of patients (12–1609) and imaging datasets (32–1624). Despite these limitations, the application of ML methods resulted in improved diagnostic and prognostic performance of US highlighting the potential clinical utility of this approach.
Collapse
|
21
|
Zhang X, Lee VCS, Rong J, Liu F, Kong H. Multi-channel convolutional neural network architectures for thyroid cancer detection. PLoS One 2022; 17:e0262128. [PMID: 35061759 PMCID: PMC8782508 DOI: 10.1371/journal.pone.0262128] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 12/17/2021] [Indexed: 02/05/2023] Open
Abstract
Early detection of malignant thyroid nodules leading to patient-specific treatments can reduce morbidity and mortality rates. Currently, thyroid specialists use medical images to diagnose then follow the treatment protocols, which have limitations due to unreliable human false-positive diagnostic rates. With the emergence of deep learning, advances in computer-aided diagnosis techniques have yielded promising earlier detection and prediction accuracy; however, clinicians' adoption is far lacking. The present study adopts Xception neural network as the base structure and designs a practical framework, which comprises three adaptable multi-channel architectures that were positively evaluated using real-world data sets. The proposed architectures outperform existing statistical and machine learning techniques and reached a diagnostic accuracy rate of 0.989 with ultrasound images and 0.975 with computed tomography scans through the single input dual-channel architecture. Moreover, the patient-specific design was implemented for thyroid cancer detection and has obtained an accuracy of 0.95 for double inputs dual-channel architecture and 0.94 for four-channel architecture. Our evaluation suggests that ultrasound images and computed tomography (CT) scans yield comparable diagnostic results through computer-aided diagnosis applications. With ultrasound images obtained slightly higher results, CT, on the other hand, can achieve the patient-specific diagnostic design. Besides, with the proposed framework, clinicians can select the best fitting architecture when making decisions regarding a thyroid cancer diagnosis. The proposed framework also incorporates interpretable results as evidence, which potentially improves clinicians' trust and hence their adoption of the computer-aided diagnosis techniques proposed with increased efficiency and accuracy.
Collapse
Affiliation(s)
- Xinyu Zhang
- Department of Data Science and AI/Faculty of IT, Monash University, Melbourne, Victoria, Australia
| | - Vincent C. S. Lee
- Department of Data Science and AI/Faculty of IT, Monash University, Melbourne, Victoria, Australia
| | - Jia Rong
- Department of Data Science and AI/Faculty of IT, Monash University, Melbourne, Victoria, Australia
| | - Feng Liu
- West China Hospital of Sichuan University, Chengdu City, Sichuan Province, China
| | - Haoyu Kong
- Department of Human-Centred Computing/Faculty of IT, Monash University, Melbourne, Victoria, Australia
| |
Collapse
|
22
|
Artificial Intelligence in Thyroid Field-A Comprehensive Review. Cancers (Basel) 2021; 13:cancers13194740. [PMID: 34638226 PMCID: PMC8507551 DOI: 10.3390/cancers13194740] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/19/2021] [Accepted: 09/20/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary The incidence of thyroid pathologies has been increasing worldwide. Historically, the detection of thyroid neoplasms relies on medical imaging analysis, depending mainly on the experience of clinicians. The advent of artificial intelligence (AI) techniques led to a remarkable progress in image-recognition tasks. AI represents a powerful tool that may facilitate understanding of thyroid pathologies, but actually, the diagnostic accuracy is uncertain. This article aims to provide an overview of the basic aspects, limitations and open issues of the AI methods applied to thyroid images. Medical experts should be familiar with the workflow of AI techniques in order to avoid misleading outcomes. Abstract Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient.
Collapse
|
23
|
Zhou J, Cao L, Chen Z. Differentiation of benign thyroid nodules from malignant thyroid nodules through miR-205-5p and thyroid-stimulating hormone receptor mRNA. Hormones (Athens) 2021; 20:571-580. [PMID: 33788150 DOI: 10.1007/s42000-021-00286-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 03/17/2021] [Indexed: 11/29/2022]
Abstract
PURPOSE This study was designed to determine the value of miR-205-5p and thyroid-stimulating hormone receptor (TSHR) mRNA in differentiating benign thyroid nodules from malignant thyroid nodules. METHODS A total of 67 patients with malignant thyroid nodules admitted to our hospital from October 2016 to March 2018 were enrolled in the malignant group, and 71 patients with benign thyroid nodules were enrolled in the benign group. Another 56 healthy individuals, as determined by physical examination, were enrolled in the control group. Quantitative real-time polymerase chain reaction (qRT-PCR) was adopted to determine the relative expression of miR-205-5p and TSHR mRNA in serum, and receiver operating characteristic (ROC) curves were used to analyze the diagnostic value of miR-205-5p and TSHR mRNA in thyroid nodules and their benignancy or malignancy. The correlation of miR-205-5p and TSHR mRNA with clinical data of the patients with thyroid carcinoma was analyzed, and multivariate logistic regression was used to analyze risk factors for lymph node metastasis of thyroid carcinoma. In addition, Pearson's test was used to analyze the relationship between miR-205-5p and TSHR mRNA in serum of patients with thyroid carcinoma. RESULTS The malignant group showed significantly higher expression of miR-205-5p and TSHR mRNA than the benign group and control group (both p <0.001), and the benign group showed significantly higher expression of these than the control group (p <0.01). miR-205-5p expression was related to tumor size, TNM staging, lymph node metastasis, capsular infiltration, and BRAF mutation (p <0.05), and TSHR mRNA expression was related to lymph node metastasis, capsular infiltration, BRAF mutation, and RAS mutation (p <0.05). Gender, number of lesions, capsular infiltration, miR-205-5p, and TSHR mRNA were independent risk factors for lymph node metastasis of patients with thyroid carcinoma. Moreover, there was a positive correlation between miR-205-5p and TSHR mRNA in patients with thyroid carcinoma (r=0.735, p<0.001). CONCLUSION MiR-205-5p and TSHR mRNA may be potential diagnosis indexes of thyroid nodules, their benignancy, or malignancy, while the independent risk factors for patients with thyroid carcinoma include a large number of lesions, occurrence of capsular infiltration, and relatively high expression of miR-205-5p and TSHR mRNA.
Collapse
Affiliation(s)
- Jie Zhou
- Department of Breast and Thyroid Surgery, Yiyang Central Hospital, No.108 Kangfu North Road, Yiyang, 413000, People's Republic of China
| | - Li Cao
- Department of General Surgery, Yiyang Central Hospital, No. 108 Kangfu North Road, Yiyang, 413000, People's Republic of China
| | - Zhou Chen
- Department of Breast and Thyroid Surgery, Yiyang Central Hospital, No.108 Kangfu North Road, Yiyang, 413000, People's Republic of China.
| |
Collapse
|
24
|
Amniotic fluid segmentation based on pixel classification using local window information and distance angle pixel. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107196] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
|
25
|
Fresilli D, David E, Pacini P, Del Gaudio G, Dolcetti V, Lucarelli GT, Di Leo N, Bellini MI, D’Andrea V, Sorrenti S, Mascagni D, Biffoni M, Durante C, Grani G, De Vincentis G, Cantisani V. Thyroid Nodule Characterization: How to Assess the Malignancy Risk. Update of the Literature. Diagnostics (Basel) 2021; 11:1374. [PMID: 34441308 PMCID: PMC8391491 DOI: 10.3390/diagnostics11081374] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/19/2021] [Accepted: 07/27/2021] [Indexed: 12/17/2022] Open
Abstract
Ultrasound (US) is the first imaging modality for thyroid parenchyma evaluation. In the last decades, the role of ultrasound has been improved with the introduction of new US software, such as contrast-enhanced ultrasound (CEUS) and US-elastography (USE). USE is nowadays recognized as an essential part of the multiparametric ultrasound (MPUS) examination, in particular for the indeterminate thyroid nodule with possible fine-needle aspiration cytology (FNAC) number reduction; even if further and larger studies are needed to validate it. More controversial is the role of CEUS in thyroid evaluation, due to its high variability in sensitivity and specificity. Semi-automatic US systems based on the computer-aided diagnosis (CAD) system are producing interesting results, especially as an aid to less experienced operators. New knowledge on the molecular mechanisms involved in thyroid cancer is allowing practitioners to identify new genomic thyroid markers that could reduce the number of "diagnostic" thyroidectomies. We have therefore drawn up an updated representation of the current evidence in the literature for thyroid nodule multiparametric ultrasound (MPUS) evaluation with particular regard to USE, the US CAD system and CEUS.
Collapse
Affiliation(s)
- Daniele Fresilli
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy; (D.F.); (P.P.); (G.D.G.); (V.D.); (G.T.L.); (N.D.L.); (G.D.V.)
| | - Emanuele David
- Radiological Sciences, Radiology Unit, Papardo-Hospital, 98158 Messina, Italy;
| | - Patrizia Pacini
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy; (D.F.); (P.P.); (G.D.G.); (V.D.); (G.T.L.); (N.D.L.); (G.D.V.)
| | - Giovanni Del Gaudio
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy; (D.F.); (P.P.); (G.D.G.); (V.D.); (G.T.L.); (N.D.L.); (G.D.V.)
| | - Vincenzo Dolcetti
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy; (D.F.); (P.P.); (G.D.G.); (V.D.); (G.T.L.); (N.D.L.); (G.D.V.)
| | - Giuseppe Tiziano Lucarelli
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy; (D.F.); (P.P.); (G.D.G.); (V.D.); (G.T.L.); (N.D.L.); (G.D.V.)
| | - Nicola Di Leo
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy; (D.F.); (P.P.); (G.D.G.); (V.D.); (G.T.L.); (N.D.L.); (G.D.V.)
| | - Maria Irene Bellini
- Department of Surgical Sciences, Sapienza University, 00161 Rome, Italy; (M.I.B.); (V.D.); (S.S.); (D.M.); (M.B.)
| | - Vito D’Andrea
- Department of Surgical Sciences, Sapienza University, 00161 Rome, Italy; (M.I.B.); (V.D.); (S.S.); (D.M.); (M.B.)
| | - Salvatore Sorrenti
- Department of Surgical Sciences, Sapienza University, 00161 Rome, Italy; (M.I.B.); (V.D.); (S.S.); (D.M.); (M.B.)
| | - Domenico Mascagni
- Department of Surgical Sciences, Sapienza University, 00161 Rome, Italy; (M.I.B.); (V.D.); (S.S.); (D.M.); (M.B.)
| | - Marco Biffoni
- Department of Surgical Sciences, Sapienza University, 00161 Rome, Italy; (M.I.B.); (V.D.); (S.S.); (D.M.); (M.B.)
| | - Cosimo Durante
- Department of Translational and Precision Medicine, Sapienza University of Rome, 00185 Rome, Italy; (C.D.); (G.G.)
| | - Giorgio Grani
- Department of Translational and Precision Medicine, Sapienza University of Rome, 00185 Rome, Italy; (C.D.); (G.G.)
| | - Giuseppe De Vincentis
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy; (D.F.); (P.P.); (G.D.G.); (V.D.); (G.T.L.); (N.D.L.); (G.D.V.)
| | - Vito Cantisani
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy; (D.F.); (P.P.); (G.D.G.); (V.D.); (G.T.L.); (N.D.L.); (G.D.V.)
| |
Collapse
|
26
|
Kim GR, Lee E, Kim HR, Yoon JH, Park VY, Kwak JY. Convolutional Neural Network to Stratify the Malignancy Risk of Thyroid Nodules: Diagnostic Performance Compared with the American College of Radiology Thyroid Imaging Reporting and Data System Implemented by Experienced Radiologists. AJNR Am J Neuroradiol 2021; 42:1513-1519. [PMID: 33985947 DOI: 10.3174/ajnr.a7149] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 03/06/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND PURPOSE Comparison of the diagnostic performance for thyroid cancer on ultrasound between a convolutional neural network and visual assessment by radiologists has been inconsistent. Thus, we aimed to evaluate the diagnostic performance of the convolutional neural network compared with the American College of Radiology Thyroid Imaging Reporting and Data System (TI-RADS) for the diagnosis of thyroid cancer using ultrasound images. MATERIALS AND METHODS From March 2019 to September 2019, seven hundred sixty thyroid nodules (≥10 mm) in 757 patients were diagnosed as benign or malignant through fine-needle aspiration, core needle biopsy, or an operation. Experienced radiologists assessed the sonographic descriptors of the nodules, and 1 of 5 American College of Radiology TI-RADS categories was assigned. The convolutional neural network provided malignancy risk percentages for nodules based on sonographic images. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were calculated with cutoff values using the Youden index and compared between the convolutional neural network and the American College of Radiology TI-RADS. Areas under the receiver operating characteristic curve were also compared. RESULTS Of 760 nodules, 176 (23.2%) were malignant. At an optimal threshold derived from the Youden index, sensitivity and negative predictive values were higher with the convolutional neural network than with the American College of Radiology TI-RADS (81.8% versus 73.9%, P = .009; 94.0% versus 92.2%, P = .046). Specificity, accuracy, and positive predictive values were lower with the convolutional neural network than with the American College of Radiology TI-RADS (86.1% versus 93.7%, P < .001; 85.1% versus 89.1%, P = .003; and 64.0% versus 77.8%, P < .001). The area under the curve of the convolutional neural network was higher than that of the American College of Radiology TI-RADS (0.917 versus 0.891, P = .017). CONCLUSIONS The convolutional neural network provided diagnostic performance comparable with that of the American College of Radiology TI-RADS categories assigned by experienced radiologists.
Collapse
Affiliation(s)
- G R Kim
- From the Department of Radiology (G.R.K., J.H.Y., V.Y.P., J.Y.K.), Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - E Lee
- Department of Computational Science and Engineering (E.L.), Yonsei University, Seoul, Korea
| | - H R Kim
- Biostatistics Collaboration Unit (H.R.K.), Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Korea
| | - J H Yoon
- From the Department of Radiology (G.R.K., J.H.Y., V.Y.P., J.Y.K.), Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - V Y Park
- From the Department of Radiology (G.R.K., J.H.Y., V.Y.P., J.Y.K.), Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - J Y Kwak
- From the Department of Radiology (G.R.K., J.H.Y., V.Y.P., J.Y.K.), Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| |
Collapse
|
27
|
Dmitriev K, Marino J, Baker K, Kaufman AE. Visual Analytics of a Computer-Aided Diagnosis System for Pancreatic Lesions. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2021; 27:2174-2185. [PMID: 31613771 DOI: 10.1109/tvcg.2019.2947037] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Machine learning is a powerful and effective tool for medical image analysis to perform computer-aided diagnosis (CAD). Having great potential in improving the accuracy of a diagnosis, CAD systems are often analyzed in terms of the final accuracy, leading to a limited understanding of the internal decision process, impossibility to gain insights, and ultimately to skepticism from clinicians. We present a visual analytics approach to uncover the decision-making process of a CAD system for classifying pancreatic cystic lesions. This CAD algorithm consists of two distinct components: random forest (RF), which classifies a set of predefined features, including demographic features, and a convolutional neural network (CNN), which analyzes radiological (imaging) features of the lesions. We study the class probabilities generated by the RF and the semantical meaning of the features learned by the CNN. We also use an eye tracker to better understand which radiological features are particularly useful for a radiologist to make a diagnosis and to quantitatively compare with the features that lead the CNN to its final classification decision. Additionally, we evaluate the effects and benefits of supplying the CAD system with a case-based visual aid in a second-reader setting.
Collapse
|
28
|
Ha EJ, Baek JH. Applications of machine learning and deep learning to thyroid imaging: where do we stand? Ultrasonography 2021; 40:23-29. [PMID: 32660203 PMCID: PMC7758100 DOI: 10.14366/usg.20068] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 07/01/2020] [Accepted: 07/03/2020] [Indexed: 01/17/2023] Open
Abstract
Ultrasonography (US) is the primary diagnostic tool used to assess the risk of malignancy and to inform decision-making regarding the use of fine-needle aspiration (FNA) and postFNA management in patients with thyroid nodules. However, since US image interpretation is operator-dependent and interobserver variability is moderate to substantial, unnecessary FNA and/or diagnostic surgery are common in practice. Artificial intelligence (AI)-based computeraided diagnosis (CAD) systems have been introduced to help with the accurate and consistent interpretation of US features, ultimately leading to a decrease in unnecessary FNA. This review provides a developmental overview of the AI-based CAD systems currently used for thyroid nodules and describes the future developmental directions of these systems for the personalized and optimized management of thyroid nodules.
Collapse
Affiliation(s)
- Eun Ju Ha
- Department of Radiology, Ajou University School of Medicine, Suwon, Korea
| | - Jung Hwan Baek
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| |
Collapse
|
29
|
Ultrasound-elastic-image-assisted diagnosis of pulmonary nodules based on genetic algorithm. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04956-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
30
|
Qian X, Wodnicki R, Kang H, Zhang J, Tchelepi H, Zhou Q. Current Ultrasound Technologies and Instrumentation in the Assessment and Monitoring of COVID-19 Positive Patients. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2020; 67:2230-2240. [PMID: 32857693 PMCID: PMC7654715 DOI: 10.1109/tuffc.2020.3020055] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 08/23/2020] [Indexed: 05/04/2023]
Abstract
Since the emergence of the COVID-19 pandemic in December of 2019, clinicians and scientists all over the world have faced overwhelming new challenges that not only threaten their own communities and countries but also the world at large. These challenges have been enormous and debilitating, as the infrastructure of many countries, including developing ones, had little or no resources to deal with the crisis. Even in developed countries, such as Italy, health systems have been so inundated by cases that health care facilities became oversaturated and could not accommodate the unexpected influx of patients to be tested. Initially, resources were focused on testing to identify those who were infected. When it became clear that the virus mainly attacks the lungs by causing parenchymal changes in the form of multifocal pneumonia of different levels of severity, imaging became paramount in the assessment of disease severity, progression, and even response to treatment. As a result, there was a need to establish protocols for imaging of the lungs in these patients. In North America, the focus was on chest X-ray and computed tomography (CT) as these are widely available and accessible at most health facilities. However, in Europe and China, this was not the case, and a cost-effective and relatively fast imaging modality was needed to scan a large number of sick patients promptly. Hence, ultrasound (US) found its way into the hands of Chinese and European physicians and has since become an important imaging modality in those locations. US is a highly versatile, portable, and inexpensive imaging modality that has application across a broad spectrum of conditions and, in this way, is ideally suited to assess the lungs of COVID-19 patients in the intensive care unit (ICU). This bedside test can be done with little to no movement of the patients from the unit that keeps them in their isolated rooms, thereby limiting further exposure to other health personnel. This article presents a basic introduction to COVID-19 and the use of the US for lung imaging. It further provides a high-level overview of the existing US technologies that are driving development in current and potential future US imaging systems for lung, with a specific emphasis on portable and 3-D systems.
Collapse
Affiliation(s)
- Xuejun Qian
- Department of Biomedical EngineeringUniversity of Southern CaliforniaLos AngelesCA90089USA
- NIH Resource Center forMedical Ultrasonic Transducer TechnologyUniversity of Southern CaliforniaLos AngelesCA90089USA
- Keck School of MedicineRoski Eye Institute, University of Southern CaliforniaLos AngelesCA90033USA
| | - Robert Wodnicki
- Department of Biomedical EngineeringUniversity of Southern CaliforniaLos AngelesCA90089USA
- NIH Resource Center forMedical Ultrasonic Transducer TechnologyUniversity of Southern CaliforniaLos AngelesCA90089USA
| | - Haochen Kang
- Department of Biomedical EngineeringUniversity of Southern CaliforniaLos AngelesCA90089USA
- NIH Resource Center forMedical Ultrasonic Transducer TechnologyUniversity of Southern CaliforniaLos AngelesCA90089USA
| | - Junhang Zhang
- Department of Biomedical EngineeringUniversity of Southern CaliforniaLos AngelesCA90089USA
- NIH Resource Center forMedical Ultrasonic Transducer TechnologyUniversity of Southern CaliforniaLos AngelesCA90089USA
| | - Hisham Tchelepi
- Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCA90033USA
| | - Qifa Zhou
- Department of Biomedical EngineeringUniversity of Southern CaliforniaLos AngelesCA90089USA
- NIH Resource Center forMedical Ultrasonic Transducer TechnologyUniversity of Southern CaliforniaLos AngelesCA90089USA
- Keck School of MedicineRoski Eye Institute, University of Southern CaliforniaLos AngelesCA90033USA
| |
Collapse
|
31
|
Chung SR, Baek JH, Lee MK, Ahn Y, Choi YJ, Sung TY, Song DE, Kim TY, Lee JH. Computer-Aided Diagnosis System for the Evaluation of Thyroid Nodules on Ultrasonography: Prospective Non-Inferiority Study according to the Experience Level of Radiologists. Korean J Radiol 2020; 21:369-376. [PMID: 32090529 PMCID: PMC7039724 DOI: 10.3348/kjr.2019.0581] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 11/04/2019] [Indexed: 12/27/2022] Open
Abstract
Objective To determine whether a computer-aided diagnosis (CAD) system for the evaluation of thyroid nodules is non-inferior to radiologists with different levels of experience. Materials and Methods Patients with thyroid nodules with a decisive diagnosis of benign or malignant nodule were consecutively enrolled from November 2017 to September 2018. Three radiologists with different levels of experience (1 month, 4 years, and 7 years) in thyroid ultrasound (US) reviewed the thyroid US with and without using the CAD system. Statistical analyses included non-inferiority testing of the diagnostic accuracy for malignant thyroid nodules between the CAD system and the three radiologists with a non-inferiority margin of 10%, comparison of the diagnostic performance, and the added value of the CAD system to the radiologists. Results Altogether, 197 patients were included in the study cohort. The diagnostic accuracy of the CAD system (88.48%, 95% confidence interval [CI] = 82.65–92.53) was non-inferior to that of the radiologists with less experience (1 month and 4 year) of thyroid US (83.03%, 95% CI = 76.52–88.02; p < 0.001), whereas it was inferior to that of the experienced radiologist (7 years) (95.76%, 95% CI = 91.37–97.96; p = 0.138). The sensitivity and negative predictive value of the CAD system were significantly higher than those of the less-experienced radiologists were, whereas no significant difference was found with those of the experienced radiologist. A combination of US and the CAD system significantly improved sensitivity and negative predictive value, although the specificity and positive predictive value deteriorated for the less-experienced radiologists. Conclusion The CAD system may offer support for decision-making in the diagnosis of malignant thyroid nodules for operators who have less experience with thyroid US.
Collapse
Affiliation(s)
- Sae Rom Chung
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jung Hwan Baek
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
| | - Min Kyoung Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Yura Ahn
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Young Jun Choi
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Tae Yon Sung
- Department of Surgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Dong Eun Song
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Tae Yong Kim
- Department of Endocrinology and Metabolism, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jeong Hyun Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| |
Collapse
|
32
|
Diagnosis of thyroid nodules on ultrasonography by a deep convolutional neural network. Sci Rep 2020; 10:15245. [PMID: 32943696 PMCID: PMC7498581 DOI: 10.1038/s41598-020-72270-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 08/28/2020] [Indexed: 01/09/2023] Open
Abstract
The purpose of this study was to evaluate and compare the diagnostic performances of the deep convolutional neural network (CNN) and expert radiologists for differentiating thyroid nodules on ultrasonography (US), and to validate the results in multicenter data sets. This multicenter retrospective study collected 15,375 US images of thyroid nodules for algorithm development (n = 13,560, Severance Hospital, SH training set), the internal test (n = 634, SH test set), and the external test (n = 781, Samsung Medical Center, SMC set; n = 200, CHA Bundang Medical Center, CBMC set; n = 200, Kyung Hee University Hospital, KUH set). Two individual CNNs and two classification ensembles (CNNE1 and CNNE2) were tested to differentiate malignant and benign thyroid nodules. CNNs demonstrated high area under the curves (AUCs) to diagnose malignant thyroid nodules (0.898-0.937 for the internal test set and 0.821-0.885 for the external test sets). AUC was significantly higher for CNNE2 than radiologists in the SH test set (0.932 vs. 0.840, P < 0.001). AUC was not significantly different between CNNE2 and radiologists in the external test sets (P = 0.113, 0.126, and 0.690). CNN showed diagnostic performances comparable to expert radiologists for differentiating thyroid nodules on US in both the internal and external test sets.
Collapse
|
33
|
Werth K, Ledbetter L. Artificial Intelligence in Head and Neck Imaging: A Glimpse into the Future. Neuroimaging Clin N Am 2020; 30:359-368. [PMID: 32600636 DOI: 10.1016/j.nic.2020.04.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Artificial intelligence, specifically machine learning and deep learning, is a rapidly developing field in imaging sciences with the potential to improve the efficiency and effectiveness of radiologists. This review covers common technical terms and basic concepts in imaging artificial intelligence and briefly reviews the application of these techniques to general imaging as well as head and neck imaging. Artificial intelligence has the potential to contribute improvements to all areas of patient care, including image acquisition, processing, segmentation, automated detection of findings, integration of clinical information, quality improvement, and research. Numerous challenges remain, however, before widespread imaging clinical adoption and integration occur.
Collapse
Affiliation(s)
- Kyle Werth
- Department of Radiology, University of Kansas Medical Center, 3901 Rainbow Boulevard, Mailstop 4032, Kansas City, KS 66160, USA
| | - Luke Ledbetter
- Department of Radiology, David Geffen School of Medicine at UCLA, 757 Westwood Plaza, Suite 1621D, Los Angeles, CA 90095, USA.
| |
Collapse
|
34
|
Sun C, Zhang Y, Chang Q, Liu T, Zhang S, Wang X, Guo Q, Yao J, Sun W, Niu L. Evaluation of a deep learning-based computer-aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images. Med Phys 2020; 47:3952-3960. [PMID: 32473030 DOI: 10.1002/mp.14301] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 05/18/2020] [Accepted: 05/20/2020] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Computer-aided diagnosis (CAD) systems assist in solving subjective diagnosis problems that typically rely on personal experience. A CAD system has been developed to differentiate malignant thyroid nodules from benign thyroid nodules in ultrasound images based on deep learning methods. The diagnostic performance was compared between the CAD system and the experienced attending radiologists. METHODS The ultrasound image dataset for training the CAD system included 651 malignant nodules and 386 benign nodules while the database for testing included 422 malignant nodules and 128 benign nodules. All the nodules were confirmed by pathology results. In the proposed CAD system, a support vector machine (SVM) is used for classification and fused features which combined the deep features extracted by a convolutional neural network (CNN) with the hand-crafted features such as the histogram of oriented gradient (HOG), local binary patterns (LBP), and scale invariant feature transform (SIFT) were obtained. The optimal feature subset was formed by selecting these fused features based on the maximum class separation distance and used as the training sample for the SVM. RESULTS The accuracy, sensitivity, and specificity of the CAD system were 92.5%, 96.4%, and 83.1%, respectively, which were higher than those of the experienced attending radiologists. The areas under the ROC curves of the CAD system and the attending radiologists were 0.881 and 0.819, respectively. CONCLUSIONS The CAD system for thyroid nodules exhibited a better diagnostic performance than experienced attending radiologists. The CAD system could be a reliable supplementary tool to diagnose thyroid nodules using ultrasonography. Macroscopic features in ultrasound images, such as the margins and shape of thyroid nodules, could influence the diagnostic efficiency of the CAD system.
Collapse
Affiliation(s)
- Chao Sun
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yukang Zhang
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China
| | - Qing Chang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Tianjiao Liu
- Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China
| | - Shaohang Zhang
- Department of Ultrasound, Beijing Haidian Hospital, Haidian Section of Peking University Third Hospital, Beijing, 100080, China
| | - Xi Wang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Qianqian Guo
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jinpeng Yao
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Weidong Sun
- Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China
| | - Lijuan Niu
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| |
Collapse
|
35
|
Li T, Jiang Z, Lu M, Zou S, Wu M, Wei T, Wang L, Li J, Hu Z, Cheng X, Liao J. Computer-aided diagnosis system of thyroid nodules ultrasonography: Diagnostic performance difference between computer-aided diagnosis and 111 radiologists. Medicine (Baltimore) 2020; 99:e20634. [PMID: 32502044 PMCID: PMC7306365 DOI: 10.1097/md.0000000000020634] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
To evaluate the diagnostic efficiency of computer-aided diagnosis (CAD) system and 111 radiologists with different experience in identifying benign and malignant thyroid nodules, and to summarize the ultrasound features that may affect the diagnostic of CAD and radiologists.Fifty thyroid nodules and 111 radiologists were enrolled in this study. All the 50 nodules were diagnosed by the 111 radiologists and the CAD system simultaneously. The diagnostic performance of the CAD system, senior and junior radiologists with the maximum accuracy were calculated and compared. Interobserver agreement for different ultrasound characteristics between the CAD and senior radiologist were analyzed.CAD system showed a higher specificity than junior radiologist (87.5% vs 70.4%, P = .03), and a lower sensitivity than the senior radiologist and junior radiologist but the statistics were not significant (76.9% vs 86.9%, P > .5; 76.9% vs 82.6%, P > .5). The CAD system and senior radiologist got larger AUC than junior radiologist but the differences were not statistically significant (0.82 vs 0.76, respectively; P = .5). The interobserver agreement for the US characteristics between the CAD system and senior radiologist were: substantial agreement for hypoechoic and taller than wide (kappa value = 0.66, 0.78), and moderate agreement for irregular margin and micro-calcifications (kappa value = 0.52, 0.42).The CAD system achieved equal diagnostic accuracy to the senior radiologists and higher accuracy than the junior radiologists. The interobserver agreements in the US features between the CAD system and senior radiologist were substantial agreement for hypoechoic and taller than wide; moderate agreement for irregular margin and micro-calcifications. The location of a thyroid nodule and the feature of macrocalcification with wide acoustic shadow may influence the analysis of the CAD system.
Collapse
Affiliation(s)
- Tingting Li
- Ultrasound Medical Center, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Zirui Jiang
- Electrical and computer Engineering, University of Wisconsin Madison, Madison, Wisconsin
| | - Man Lu
- Ultrasound Medical Center, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Shibin Zou
- Ultrasound Medical Center, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Minggang Wu
- Ultrasound Medical Center, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Ting Wei
- Ultrasound Medical Center, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Lu Wang
- Ultrasound Medical Center, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Juan Li
- Ultrasound Medical Center, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Ziyue Hu
- Ultrasound Medical Center, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xueqing Cheng
- Ultrasound Medical Center, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jifen Liao
- Ultrasound Medical Center, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
36
|
Computer-aided diagnostic system for thyroid nodule sonographic evaluation outperforms the specificity of less experienced examiners. J Ultrasound 2020; 23:169-174. [PMID: 32246401 DOI: 10.1007/s40477-020-00453-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Accepted: 03/13/2020] [Indexed: 12/15/2022] Open
Abstract
PURPOSE Computer-aided diagnosis (CAD) may improve interobserver agreement in the risk stratification of thyroid nodules. This study aims to evaluate the performance of the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) classification as estimated by an expert radiologist, a senior resident, a medical student, and a CAD system, as well as the interobserver agreement among them. METHODS Between July 2016 and 2018, 107 nodules (size 5-40 mm, 27 malignant) were classified according to the K-TIRADS by an expert radiologist and CAD software. A third-year resident and a medical student with basic imaging training, both blinded to previous findings, retrospectively estimated the K-TIRADS classification. The diagnostic performance was calculated, including sensitivity, specificity, positive and negative predictive values, and the area under the receiver operating characteristic curve. RESULTS The CAD system and the expert achieved a sensitivity of 70.37% (95% CI 49.82-86.25%) and 81.48% (61.92-93.7%) and a specificity of 87.50% (78.21-93.84%) and 88.75% (79.72-94.72%), respectively. The specificity of the student was significantly lower (76.25% [65.42-85.05%], p = 0.02). CONCLUSION In our opinion, the CAD evaluation of thyroid nodules stratification risk has a potential role in a didactic field and does not play a real and effective role in the clinical field, where not only images but also specialistic medical practice is fundamental to achieve a diagnosis based on family history, genetics, lab tests, and so on. The CAD system may be useful for less experienced operators as its specificity was significantly higher.
Collapse
|
37
|
Barczyński M, Stopa-Barczyńska M, Wojtczak B, Czarniecka A, Konturek A. Clinical validation of S-Detect TM mode in semi-automated ultrasound classification of thyroid lesions in surgical office. Gland Surg 2020; 9:S77-S85. [PMID: 32175248 DOI: 10.21037/gs.2019.12.23] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background In recent years well-recognized scientific societies introduced guidelines for ultrasound (US) malignancy risk stratification of thyroid nodules. These guidelines categorize the risk of malignancy in relation to a combination of several US features. Based on these US image lexicons an US-based computer-aided diagnosis (CAD) systems were developed. Nevertheless, their clinical utility has not been evaluated in any study of surgeon-performed office US of the thyroid. Hence, the aim of this pilot study was to validate s-DetectTM mode in semi-automated US classification of thyroid lesions during surgeon-performed office US. Methods This is a prospective study of 50 patients who underwent surgeon-performed thyroid US (basic US skills without CAD vs. with CAD vs. expert US skills without CAD) in the out-patient office as part of the preoperative workup. The real-time CAD system software using artificial intelligence (S-DetectTM for Thyroid; Samsung Medison Co.) was integrated into the RS85 US system. Primary outcome was CAD system added-value to the surgeon-performed office US evaluation. Secondary outcomes were: diagnostic accuracy of CAD system, intra and interobserver variability in the US assessment of thyroid nodules. Surgical pathology report was used to validate the pre-surgical diagnosis. Results CAD system added-value to thyroid assessment by a surgeon with basic US skills was equal to 6% (overall accuracy of 82% for evaluation with CAD vs. 76% for evaluation without CAD system; P<0.001), and final diagnosis was different than predicted by US assessment in 3 patients (1 more true-positive and 2 more true-negative results). However, CAD system was inferior to thyroid assessment by a surgeon with expert US skills in 6 patients who had false-positive results (P<0.001). Conclusions The sensitivity and negative predictive value of CAD system for US classification of thyroid lesions were similar as surgeon with expert US skills whereas specificity and positive predictive value were significantly inferior but markedly better than judgement of a surgeon with basic US skills alone.
Collapse
Affiliation(s)
- Marcin Barczyński
- Department of Endocrine Surgery, Third Chair of General Surgery, Jagiellonian University Medical College, Kraków, Poland
| | - Małgorzata Stopa-Barczyńska
- Clinical Ward of General Surgery and Oncology, Gabriel Narutowicz Memorial Municipal Hospital, Kraków, Poland
| | - Beata Wojtczak
- Department of General, Minimally Invasive and Endocrine Surgery, Wroclaw Medical University, Wroclaw, Poland
| | - Agnieszka Czarniecka
- Department of Oncological and Reconstructive Surgery, M. Sklodowska-Curie Institute - Oncology Centre, Gliwice, Poland
| | - Aleksander Konturek
- Department of Endocrine Surgery, Third Chair of General Surgery, Jagiellonian University Medical College, Kraków, Poland
| |
Collapse
|
38
|
Quantitative Framework for Risk Stratification of Thyroid Nodules With Ultrasound: A Step Toward Automated Triage of Thyroid Cancer. AJR Am J Roentgenol 2020; 214:885-892. [PMID: 31967504 DOI: 10.2214/ajr.19.21350] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
OBJECTIVE. The purpose of this study was to explore whether a quantitative framework can be used to sonographically differentiate benign and malignant thyroid nodules at a level comparable to that of experts. MATERIALS AND METHODS. A dataset of ultrasound images of 92 biopsy-confirmed nodules was collected retrospectively. The nodules were delineated and annotated by two expert radiologists using the standardized Thyroid Imaging Reporting and Data System lexicon of the American College of Radiology. In the framework studied, quantitative features of echogenicity, texture, edge sharpness, and margin curvature properties of thyroid nodules were analyzed in a regularized logistic regression model to predict malignancy of a nodule. The framework was validated by leave-one-out cross-validation technique, and ROC AUC, sensitivity, and specificity were used to compare with those obtained with six expert annotation-based classifiers. RESULTS. The AUC of the proposed method was 0.828 (95% CI, 0.715-0.942), which was greater than or comparable to that of the expert classifiers, for which the AUC values ranged from 0.299 to 0.829 (p = 0.99). Use of the proposed framework could have avoided biopsy of 20 of 46 benign nodules in a curative strategy (at sensitivity of 1, statistically significantly higher than three expert classifiers) or helped identify 10 of 46 malignancies in a conservative strategy (at specificity of 1, statistically significantly higher than five expert classifiers). CONCLUSION. When the proposed quantitative framework was used, thyroid nodule malignancy was predicted at the level of expert classifiers. Such a framework may ultimately prove useful as the basis for a fully automated system of thyroid nodule triage.
Collapse
|
39
|
Update on thyroid ultrasound: a narrative review from diagnostic criteria to artificial intelligence techniques. Chin Med J (Engl) 2020; 132:1974-1982. [PMID: 31348028 PMCID: PMC6708700 DOI: 10.1097/cm9.0000000000000346] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Objective Ultrasound imaging is well known to play an important role in the detection of thyroid disease, but the management of thyroid ultrasound remains inconsistent. Both standardized diagnostic criteria and new ultrasound technologies are essential for improving the accuracy of thyroid ultrasound. This study reviewed the global guidelines of thyroid ultrasound and analyzed their common characteristics for basic clinical screening. Advances in the application of a combination of thyroid ultrasound and artificial intelligence (AI) were also presented. Data sources An extensive search of the PubMed database was undertaken, focusing on research published after 2001 with keywords including thyroid ultrasound, guideline, AI, segmentation, image classification, and deep learning. Study selection Several types of articles, including original studies and literature reviews, were identified and reviewed to summarize the importance of standardization and new technology in thyroid ultrasound diagnosis. Results Ultrasound has become an important diagnostic technique in thyroid nodules. Both standardized diagnostic criteria and new ultrasound technologies are essential for improving the accuracy of thyroid ultrasound. In the standardization, since there are no global consensus exists, common characteristics such as a multi-feature diagnosis, the performance of lymph nodes, explicit indications of fine needle aspiration, and the diagnosis of special populations should be focused on. Besides, evidence suggests that AI technique has a good effect on the unavoidable limitations of traditional ultrasound, and the combination of diagnostic criteria and AI may lead to a great promotion in thyroid diagnosis. Conclusion Standardization and development of novel techniques are key factors to improving thyroid ultrasound, and both should be considered in normal clinical use.
Collapse
|
40
|
Convolutional Neural Network for Breast and Thyroid Nodules Diagnosis in Ultrasound Imaging. BIOMED RESEARCH INTERNATIONAL 2020; 2020:1763803. [PMID: 32420322 PMCID: PMC7199615 DOI: 10.1155/2020/1763803] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 11/11/2019] [Accepted: 12/14/2019] [Indexed: 12/12/2022]
Abstract
Objective The incidence of superficial organ diseases has increased rapidly in recent years. New methods such as computer-aided diagnosis (CAD) are widely used to improve diagnostic efficiency. Convolutional neural networks (CNNs) are one of the most popular methods, and further improvements of CNNs should be considered. This paper aims to develop a multiorgan CAD system based on CNNs for classifying both thyroid and breast nodules and investigate the impact of this system on the diagnostic efficiency of different preprocessing approaches. Methods The training and validation sets comprised randomly selected thyroid and breast nodule images. The data were subgrouped into 4 models according to the different preprocessing methods (depending on segmentation and the classification method). A prospective data set was selected to verify the clinical value of the CNN model by comparison with ultrasound guidelines. Diagnostic efficiency was assessed based on receiver operating characteristic (ROC) curves. Results Among the 4 models, the CNN model using segmented images for classification achieved the best result. For the validation set, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC) of our CNN model were 84.9%, 69.0%, 62.5%, 88.2%, 75.0%, and 0.769, respectively. There was no statistically significant difference between the CNN model and the ultrasound guidelines. The combination of the two methods achieved superior diagnostic efficiency compared with their use individually. Conclusions The study demonstrates the probability, feasibility, and clinical value of CAD in the ultrasound diagnosis of multiple organs. The use of segmented images and classification by the nature of the disease are the main factors responsible for the improvement of the CNN model. Moreover, the combination of the CNN model and ultrasound guidelines results in better diagnostic performance, which will contribute to the improved diagnostic efficiency of CAD systems.
Collapse
|
41
|
Jin Z, Zhu Y, Zhang S, Xie F, Zhang M, Zhang Y, Tian X, Zhang J, Luo Y, Cao J. Ultrasound Computer-Aided Diagnosis (CAD) Based on the Thyroid Imaging Reporting and Data System (TI-RADS) to Distinguish Benign from Malignant Thyroid Nodules and the Diagnostic Performance of Radiologists with Different Diagnostic Experience. Med Sci Monit 2020; 26:e918452. [PMID: 31929498 PMCID: PMC6977643 DOI: 10.12659/msm.918452] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
<strong>BACKGROUND</strong> The diagnosis of thyroid cancer and distinguishing benign from malignant thyroid nodules by junior radiologists can be challenging. This study aimed to develop a computer-aided diagnosis (CAD) system based on the Thyroid Imaging Reporting and Data System (TI-RADS) to distinguish benign from malignant thyroid nodules by analyzing ultrasound images to improve the diagnostic performance of junior radiologists. <strong>MATERIAL AND METHODS</strong> A modified TI-RADS based on a convolutional neural network (CNN) was used to develop the CAD system. This retrospective study reviewed 789 thyroid nodules from 695 patients and included radiologists with different diagnostic experience. Five study groups included the CAD group, the junior radiologist group, the intermediate-level radiologist group, the senior radiologist group, and the group in which the junior radiologist used the CAD system. The ultrasound findings were reviewed and compared with the histopathology diagnosis. <strong>RESULTS</strong> The CAD system for the diagnosis of thyroid cancer showed an accuracy of 80.35%, a sensitivity of 80.64%, a specificity of 80.13%, a positive predictive value (PPV) of 76.02%, a negative predictive value (NPV) of 84.12%, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.87. The accuracy of the junior radiologists in diagnosing thyroid cancer using CAD was similar to that of intermediate-level radiologists (79.21% <i>vs</i>. 77.57%; P=0.427). <strong>CONCLUSIONS</strong> The use of ultrasound CAD based on the TI-RADS showed potential for distinguishing between benign and malignant thyroid nodules and improved the diagnostic performance of junior radiologists.
Collapse
Affiliation(s)
- Zhuang Jin
- Department of Ultrasound, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China (mainland).,Department of Ultrasound, General Hospital of Northern Theater Command, Shenyang, Liaoning, China (mainland).,Medical School of Chinese People's Liberation Army (PLA), Beijing, China (mainland)
| | - Yaqiong Zhu
- Department of Ultrasound, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China (mainland).,Nankai University, Tianjin, China (mainland)
| | | | - Fang Xie
- Department of Ultrasound, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China (mainland)
| | - Mingbo Zhang
- Department of Ultrasound, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China (mainland)
| | - Ying Zhang
- Nankai University, Tianjin, China (mainland)
| | - Xiaoqi Tian
- Nankai University, Tianjin, China (mainland)
| | - Jue Zhang
- Peking University, Beijing, China (mainland)
| | - Yukun Luo
- Department of Ultrasound, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China (mainland).,Medical School of Chinese People's Liberation Army (PLA), Beijing, China (mainland)
| | - Junying Cao
- Department of Ultrasound, General Hospital of Northern Theater Command, Shenyang, Liaoning, China (mainland)
| |
Collapse
|
42
|
Bi Q, Goodman KE, Kaminsky J, Lessler J. What is Machine Learning? A Primer for the Epidemiologist. Am J Epidemiol 2019; 188:2222-2239. [PMID: 31509183 DOI: 10.1093/aje/kwz189] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 07/29/2019] [Accepted: 08/14/2019] [Indexed: 12/22/2022] Open
Abstract
Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on "Big Data," it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.
Collapse
Affiliation(s)
- Qifang Bi
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Katherine E Goodman
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Joshua Kaminsky
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Justin Lessler
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| |
Collapse
|
43
|
Differentiation of thyroid nodules on US using features learned and extracted from various convolutional neural networks. Sci Rep 2019; 9:19854. [PMID: 31882683 PMCID: PMC6934479 DOI: 10.1038/s41598-019-56395-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 12/11/2019] [Indexed: 11/08/2022] Open
Abstract
Thyroid nodules are a common clinical problem. Ultrasonography (US) is the main tool used to sensitively diagnose thyroid cancer. Although US is non-invasive and can accurately differentiate benign and malignant thyroid nodules, it is subjective and its results inevitably lack reproducibility. Therefore, to provide objective and reliable information for US assessment, we developed a CADx system that utilizes convolutional neural networks and the machine learning technique. The diagnostic performances of 6 radiologists and 3 representative results obtained from the proposed CADx system were compared and analyzed.
Collapse
|
44
|
Park VY, Han K, Seong YK, Park MH, Kim EK, Moon HJ, Yoon JH, Kwak JY. Diagnosis of Thyroid Nodules: Performance of a Deep Learning Convolutional Neural Network Model vs. Radiologists. Sci Rep 2019; 9:17843. [PMID: 31780753 PMCID: PMC6882804 DOI: 10.1038/s41598-019-54434-1] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Accepted: 11/08/2019] [Indexed: 01/05/2023] Open
Abstract
Computer-aided diagnosis (CAD) systems hold potential to improve the diagnostic accuracy of thyroid ultrasound (US). We aimed to develop a deep learning-based US CAD system (dCAD) for the diagnosis of thyroid nodules and compare its performance with those of a support vector machine (SVM)-based US CAD system (sCAD) and radiologists. dCAD was developed by using US images of 4919 thyroid nodules from three institutions. Its diagnostic performance was prospectively evaluated between June 2016 and February 2017 in 286 nodules, and was compared with those of sCAD and radiologists, using logistic regression with the generalized estimating equation. Subgroup analyses were performed according to experience level and separately for small thyroid nodules 1-2 cm. There was no difference in overall sensitivity, specificity, positive predictive value (PPV), negative predictive value and accuracy (all p > 0.05) between radiologists and dCAD. Radiologists and dCAD showed higher specificity, PPV, and accuracy than sCAD (all p < 0.001). In small nodules, experienced radiologists showed higher specificity, PPV and accuracy than sCAD (all p < 0.05). In conclusion, dCAD showed overall comparable diagnostic performance with radiologists and assessed thyroid nodules more effectively than sCAD, without loss of sensitivity.
Collapse
Affiliation(s)
- Vivian Y Park
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Kyunghwa Han
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Yeong Kyeong Seong
- Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seoul, Korea
| | - Moon Ho Park
- Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seoul, Korea
| | - Eun-Kyung Kim
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Hee Jung Moon
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Jung Hyun Yoon
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Jin Young Kwak
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea.
| |
Collapse
|
45
|
Chambara N, Ying M. The Diagnostic Efficiency of Ultrasound Computer-Aided Diagnosis in Differentiating Thyroid Nodules: A Systematic Review and Narrative Synthesis. Cancers (Basel) 2019; 11:E1759. [PMID: 31717365 PMCID: PMC6896127 DOI: 10.3390/cancers11111759] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 11/03/2019] [Accepted: 11/06/2019] [Indexed: 12/20/2022] Open
Abstract
Computer-aided diagnosis (CAD) techniques have emerged to complement qualitative assessment in the diagnosis of benign and malignant thyroid nodules. The aim of this review was to summarize the current evidence on the diagnostic performance of various ultrasound CAD in characterizing thyroid nodules. PUBMED, EMBASE and Cochrane databases were searched for studies published until August 2019. The Quality Assessment of Studies of Diagnostic Accuracy included in Systematic Review 2 (QUADAS-2) tool was used to assess the methodological quality of the studies. Reported diagnostic performance data were analyzed and discussed. Fourteen studies with 2232 patients and 2675 thyroid nodules met the inclusion criteria. The study quality based on QUADAS-2 assessment was moderate. At best performance, grey scale CAD had a sensitivity of 96.7% while Doppler CAD was 90%. Combined techniques of qualitative grey scale features and Doppler CAD assessment resulted in overall increased sensitivity (92%) and optimal specificity (85.1%). The experience of the CAD user, nodule size and the thyroid malignancy risk stratification system used for interpretation were the main potential factors affecting diagnostic performance outcomes. The diagnostic performance of CAD of thyroid ultrasound is comparable to that of qualitative visual assessment; however, combined techniques have the potential for better optimized diagnostic accuracy.
Collapse
Affiliation(s)
| | - Michael Ying
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, SAR, China;
| |
Collapse
|
46
|
Diagnostic Value of Machine Learning-Based Quantitative Texture Analysis in Differentiating Benign and Malignant Thyroid Nodules. JOURNAL OF ONCOLOGY 2019; 2019:6328329. [PMID: 31781216 PMCID: PMC6874925 DOI: 10.1155/2019/6328329] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 10/03/2019] [Accepted: 10/04/2019] [Indexed: 12/31/2022]
Abstract
Aim The aim of this study is to evaluate the diagnostic value of machine learning- (ML-) based quantitative texture analysis in the differentiation of benign and malignant thyroid nodules. Materials and methods A sum of 306 quantitative textural features of 235 thyroid nodules (102 malignant, 43.4%; 133 benign, 56.4%) of a total of 198 patients were investigated using the random forest ML classifier. Feature selection and dimension reduction were conducted using reproducibility testing and a wrapper method. The diagnostic accuracy, sensitivity, specificity, and area under curve (AUC) of the proposed method were compared with the histopathological or cytopathological findings as reference methods. Results Of the 306 initial texture features, 284 (92.2%) showed good reproducibility (intraclass correlation ≥0.80). The random forest classifier accurately identified 87 out of 102 malignant thyroid nodules and 117 out of 133 benign thyroid nodules, which is a diagnostic sensitivity of 85.2%, specificity of 87.9%, and accuracy of 86.8%. The AUC of the model was 0.92. Conclusions Quantitative textural analysis of thyroid nodules using ML classification can accurately discriminate benign and malignant thyroid nodules. Our findings should be validated by multicenter prospective studies using completely independent external data.
Collapse
|
47
|
Kim HL, Ha EJ, Han M. Real-World Performance of Computer-Aided Diagnosis System for Thyroid Nodules Using Ultrasonography. ULTRASOUND IN MEDICINE & BIOLOGY 2019; 45:2672-2678. [PMID: 31262524 DOI: 10.1016/j.ultrasmedbio.2019.05.032] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 05/29/2019] [Accepted: 05/30/2019] [Indexed: 05/22/2023]
Abstract
This study evaluated the diagnostic performance of a commercially available computer-aided diagnosis (CAD) system (S-Detect 1 and S-Detect 2 for thyroid) for detecting thyroid cancers. Among 218 thyroid nodules in 106 patients, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the CAD systems were 80.2%, 82.6%, 75.0%, 86.3% and 81.7%, respectively, for the S-Detect 1 and 81.4%, 68.2%, 62.5%, 84.9% and 73.4%, respectively, for the S-Detect 2. The inter-observer agreement between the CAD system and radiologist for the description of calcifications was fair (kappa = 0.336), while the final diagnosis and each ultrasonographic descriptor showed moderate to substantial agreement for the S-Detect 2. To conclude, the current CAD systems had limited specificity in the diagnosis of thyroid cancer. One of the main limitations of the S-Detect 2 was its inaccuracy in recognizing calcifications, which meant that differentiation had to be undertaken by the radiologist.
Collapse
Affiliation(s)
- Hye Lin Kim
- Department of Radiology, Ajou University School of Medicine, Suwon, South Korea
| | - Eun Ju Ha
- Department of Radiology, Ajou University School of Medicine, Suwon, South Korea.
| | - Miran Han
- Department of Radiology, Ajou University School of Medicine, Suwon, South Korea
| |
Collapse
|
48
|
Prediction of Immunohistochemistry of Suspected Thyroid Nodules by Use of Machine Learning-Based Radiomics. AJR Am J Roentgenol 2019; 213:1348-1357. [PMID: 31461321 DOI: 10.2214/ajr.19.21626] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
OBJECTIVE. The purpose of this study was to develop and validate a radiomics model for evaluating immunohistochemical characteristics in patients with suspected thyroid nodules. MATERIALS AND METHODS. A total of 103 patients (training cohort-to-validation cohort ratio, ≈ 3:1) with suspected thyroid nodules who had undergone thyroidectomy and immunohistochemical analysis were enrolled. The immunohistochemical markers were cytokeratin 19, galectin 3, thyroperoxidase, and high-molecular-weight cytokeratin. All patients underwent CT before surgery, and a 3D slicer was used to analyze images of the surgical specimen. Test-retest and Spearman correlation coefficient (ρ) were used to select reproducible and nonredundant features. The Kruskal-Wallis test (p < 0.05) was used for feature selection, and a feature-based model was built by support vector machine methods. The performance of the radiomic models was assessed with respect to accuracy, sensitivity, specificity, corresponding AUC, and independent validation. RESULTS. Eighty-six reproducible and nonredundant features selected from the 828 features were used to build the model. The best performance of the cytokeratin 19 model yielded accuracy of 84.4% in the training cohort and 80.0% in the validation cohort. The thyroperoxidase and galectin 3 predictive models yielded accuracies of 81.4% and 82.5% in the training cohort and 84.2% and 85.0% in the validation cohort. The performance of the high-molecular-weight cytokeratin predictive model was not good (accuracy, 65.7%) and could not be validated. CONCLUSION. A radiomics model with excellent performance was developed for individualized noninvasive prediction of the presence of cytokeratin 19, galectin 3, and thyroperoxidase based on CT images. This model may be used to identify benign and malignant thyroid nodules.
Collapse
|
49
|
Cuocolo R, Perillo T, De Rosa E, Ugga L, Petretta M. Current applications of big data and machine learning in cardiology. J Geriatr Cardiol 2019; 16:601-607. [PMID: 31555327 PMCID: PMC6748901 DOI: 10.11909/j.issn.1671-5411.2019.08.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 07/24/2019] [Accepted: 07/26/2019] [Indexed: 12/14/2022] Open
Abstract
Machine learning (ML) is a software solution with the ability of making predictions without prior explicit programming, aiding in the analysis of large amounts of data. These algorithms can be trained through supervised or unsupervised learning. Cardiology is one of the fields of medicine with the highest interest in its applications. They can facilitate every step of patient care, reducing the margin of error and contributing to precision medicine. In particular, ML has been proposed for cardiac imaging applications such as automated computation of scores, differentiation of prognostic phenotypes, quantification of heart function and segmentation of the heart. These tools have also demonstrated the capability of performing early and accurate detection of anomalies in electrocardiographic exams. ML algorithms can also contribute to cardiovascular risk assessment in different settings and perform predictions of cardiovascular events. Another interesting research avenue in this field is represented by genomic assessment of cardiovascular diseases. Therefore, ML could aid in making earlier diagnosis of disease, develop patient-tailored therapies and identify predictive characteristics in different pathologic conditions, leading to precision cardiology.
Collapse
Affiliation(s)
- Renato Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy
| | - Teresa Perillo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy
| | - Eliana De Rosa
- Department of Translational Medical Sciences, University of Naples “Federico II”, Naples, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy
| | - Mario Petretta
- Department of Translational Medical Sciences, University of Naples “Federico II”, Naples, Italy
| |
Collapse
|
50
|
Zhao WJ, Fu LR, Huang ZM, Zhu JQ, Ma BY. Effectiveness evaluation of computer-aided diagnosis system for the diagnosis of thyroid nodules on ultrasound: A systematic review and meta-analysis. Medicine (Baltimore) 2019; 98:e16379. [PMID: 31393347 PMCID: PMC6709241 DOI: 10.1097/md.0000000000016379] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND More and more automated efficient ultrasound image analysis techniques, such as ultrasound-based computer-aided diagnosis system (CAD), were developed to obtain accurate, reproducible, and more objective diagnosis results for thyroid nodules. So far, whether the diagnostic performance of existing CAD systems can reach the diagnostic level of experienced radiologists is still controversial. The aim of the meta-analysis was to evaluate the accuracy of CAD for thyroid nodules' diagnosis by reviewing current literatures and summarizing the research status. METHODS A detailed literature search on PubMed, Embase, and Cochrane Libraries for articles published until December 2018 was carried out. The diagnostic performances of CAD systems vs radiologist were evaluated by meta-analysis. We determined the sensitivity and the specificity across studies, calculated positive and negative likelihood ratios and constructed summary receiver-operating characteristic (SROC) curves. Meta-analysis of studies was performed using a mixed-effect, hierarchical logistic regression model. RESULTS Five studies with 536 patients and 723 thyroid nodules were included in this meta-analysis. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio (DOR) for CAD system were 0.87 (95% confidence interval [CI], 0.73-0.94), 0.79 (95% CI 0.63-0.89), 4.1 (95% CI 2.5-6.9), 0.17 (95% CI 0.09-0.32), and 25 (95% CI 15-42), respectively. The SROC curve indicated that the area under the curve was 0.90 (95% CI 0.87-0.92). The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and DOR for experienced radiologists were 0.82 (95% CI 0.69-0.91), 0.83 (95% CI 0.76-0.89), 4.9 (95% CI 3.4-7.0), 0.22 (95% CI 0.12-0.38), and 23 (95% CI 11-46), respectively. The SROC curve indicated that the area under the curve was 0.96 (95% CI 0.94-0.97). CONCLUSION The sensitivity of the CAD system in the diagnosis of thyroid nodules was similar to that of experienced radiologists. However, the CAD system had lower specificity and DOR than experienced radiologists. The CAD system may play the potential role as a decision-making assistant alongside radiologists in the thyroid nodules' diagnosis. Future technical improvements would be helpful to increase the accuracy as well as diagnostic efficiency.
Collapse
Affiliation(s)
- Wan-Jun Zhao
- Department of Thyroid & Parathyroid Surgery, West China Hospital
| | - Lin-Ru Fu
- West China School of Medicine, Sichuan University, Sichuan
| | - Zhi-Mian Huang
- Business College, New York University in Shanghai, Shanghai
| | - Jing-Qiang Zhu
- Department of Thyroid & Parathyroid Surgery, West China Hospital
| | - Bu-Yun Ma
- Department of Ultrasonography, West China Hospital, Sichuan University, Sichuan, China
| |
Collapse
|