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Wu L, Zhou Y, Li L, Ma W, Deng H, Ye X. Application of ultrasound elastography and radiomic for predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma. Front Oncol 2024; 14:1354288. [PMID: 38800382 PMCID: PMC11116610 DOI: 10.3389/fonc.2024.1354288] [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: 12/12/2023] [Accepted: 04/11/2024] [Indexed: 05/29/2024] Open
Abstract
Objective This study aims to combine ultrasound (US) elastography (USE) and radiomic to predict central cervical lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC). Methods A total of 204 patients with 204 thyroid nodules who were confirmed with PTMC and treated in our hospital were enrolled and randomly assigned to the training set (n = 142) and the validation set (n = 62). US features, USE (gender, shape, echogenic foci, thyroid imaging reporting and data system (TIRADS) category, and elasticity score), and radiomic signature were employed to build three models. A nomogram was plotted for the combined model, and decision curve analysis was applied for clinical use. Results The combined model (USE and radiomic) showed optimal diagnostic performance in both training (AUC = 0.868) and validation sets (AUC = 0.857), outperforming other models. Conclusion The combined model based on USE and radiomic showed a superior performance in the prediction of CLNM of patients with PTMC, covering the shortage of low specificity of conventional US in detecting CLNM.
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Affiliation(s)
| | | | | | | | - Hongyan Deng
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xinhua Ye
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Pang L, Yang X, Zhang P, Ding L, Yuan J, Liu H, Liu J, Gong X, Yu M, Luo W. Development and Validation of a Nomogram Based on Multimodality Ultrasonography Images for Differentiating Malignant from Benign American College of Radiology Thyroid Imaging, Reporting and Data System (TI-RADS) 3-5 Thyroid Nodules. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:557-563. [PMID: 38262884 DOI: 10.1016/j.ultrasmedbio.2023.12.020] [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: 07/25/2023] [Revised: 12/06/2023] [Accepted: 12/21/2023] [Indexed: 01/25/2024]
Abstract
OBJECTIVE The aim of the work described here was to develop and validate a predictive nomogram based on combined image features of gray-scale ultrasonography (US), elastosonography (ES) and contrast-enhanced US (CEUS) to differentiate malignant from benign American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) 3-5 thyroid nodules. METHODS Among 2767 thyroid nodules scanned by CEUS in Xijing Hospital between April 2014 and November 2018, 669 nodules classified as ACR TI-RADS 3-5 were included, with confirmed diagnosis and ES examination. Four hundred fifty-five nodules were set as a training cohort and 214 as a validation cohort. Images were categorized as gray-scale US ACR TI-RADS 3, TI-RADS 4 and TI-RADS 5; ES patterns of ES-1 and ES-2; and CEUS patterns of either heterogeneous hypo-enhancement, concentric hypo-enhancement, homogeneous hyper-/iso-enhancement, no perfusion, hypo-enhancement with sharp margin, island-like enhancement or ring-like enhancement. On the basis of multivariate logistic regression analysis, a predictive nomogram model was developed and validated by receiver operating characteristic curve analysis. RESULTS In the training cohort, ACR TI-RADS 4 and 5, ES-2, heterogeneous hypo-enhancement, concentric hypo-enhancement and homogeneous hyper-/iso-enhancement were selected as predictors of malignancy by univariate logistic regression analysis. A predictive nomogram (combining indices of ACR TI-RADS, ES and CEUS) indicated excellent predictive ability for differentiating malignant from benign lesions in the training cohort: area under the receiver operating characteristic curve (AUC) = 0.93, 95% confidence interval (CI): 0.90-0.95. The prediction nomogram model was determined to have a sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 0.84, 0.88, 0.91 and 0.81. In the validation cohort, the AUC of the prediction nomogram model was significantly higher than those of the single modalities (p < 0.005) . The AUCs of the validation cohort were 0.93 (95% CI: 0.89-0.96) and 0.93 (95% CI: 0.89-0.97), respectively, for senior and junior radiologists. The prediction nomogram model has a sensitivity, specificity, PPV and NPV of 0.86, 0.87, 0.87 and 0.86. CONCLUSION A predictive nomogram model combining ACR TI-RADS, ES and CEUS exhibited potential clinical utility in differentiating malignant from benign ACR TI-RADS 3-5 thyroid nodules.
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Affiliation(s)
- Lina Pang
- Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China
| | - Xiao Yang
- Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China
| | - Peidi Zhang
- Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China
| | - Lei Ding
- Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China
| | - Jiani Yuan
- Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China
| | - Haijing Liu
- Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China
| | - Jin Liu
- Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China
| | - Xue Gong
- Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China
| | - Ming Yu
- Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China
| | - Wen Luo
- Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China.
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Xing G, Wang S, Gao J, Li X. Real-time reliable semantic segmentation of thyroid nodules in ultrasound images. Phys Med Biol 2024; 69:025016. [PMID: 38048630 DOI: 10.1088/1361-6560/ad1210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 12/04/2023] [Indexed: 12/06/2023]
Abstract
Objective.Low efficiency in medical image segmentation is a common issue that limits computer-aided diagnosis development. Due to the varying positions and sizes of nodules, it is not easy to accurately segment ultrasound images. This study aims to propose a segmentation model that maintains high efficiency while improving accuracy.Approach. We propose a novel layer that integrates the advantages of dense connectivity, dilated convolution, and factorized filters to maintain excellent efficiency while improving accuracy. Dense connectivity optimizes feature reuse, dilated convolution redesigns layers, and factorized convolution improves efficiency. Moreover, we propose a loss function optimization method from a pixel perspective to increase the network's accuracy further.Main results.Experiments on the Thyroid dataset show that our method achieves 81.70% intersection-over-union (IoU), 90.50% true positive rate (TPR), and 0.25% false positive rate (FPR). In terms of accuracy, our method outperforms the state-of-the-art methods, with twice faster inference and nearly 400 times fewer parameters. Meanwhile, in a test on an External Thyroid dataset, our method achieves 77.03% IoU, 82.10% TPR, and 0.16% FPR, demonstrating our proposed model's robustness.Significance.We propose a real-time semantic segmentation architecture for thyroid nodule segmentation in ultrasound images called fully convolution dense dilated network (FCDDN). Our method runs fast with a few parameters and is suitable for medical devices requiring real-time segmentation.
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Affiliation(s)
- Guangxin Xing
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, People's Republic of China
| | - Shuaijie Wang
- College of Intelligence and Computing, Tianjin University, Tianjin, People's Republic of China
- Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, People's Republic of China
- Tianjin Key Laboratory of Advanced Networking, Tianjin, People's Republic of China
| | - Jie Gao
- College of Intelligence and Computing, Tianjin University, Tianjin, People's Republic of China
- Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, People's Republic of China
- Tianjin Key Laboratory of Advanced Networking, Tianjin, People's Republic of China
| | - Xuewei Li
- College of Intelligence and Computing, Tianjin University, Tianjin, People's Republic of China
- Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, People's Republic of China
- Tianjin Key Laboratory of Advanced Networking, Tianjin, People's Republic of China
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Zheng LL, Ma SY, Zhou L, Yu C, Xu HS, Xu LL, Li SY. Diagnostic performance of artificial intelligence-based computer-aided diagnosis system in longitudinal and transverse ultrasonic views for differentiating thyroid nodules. Front Endocrinol (Lausanne) 2023; 14:1137700. [PMID: 36864838 PMCID: PMC9971597 DOI: 10.3389/fendo.2023.1137700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 02/03/2023] [Indexed: 02/16/2023] Open
Abstract
OBJECTIVE To evaluate the diagnostic performance of different ultrasound sections of thyroid nodule (TN) using computer-aided diagnosis system based on artificial intelligence (AI-CADS) in predicting thyroid malignancy. MATERIALS AND METHODS This is a retrospective study. From January 2019 to July 2019, patients with preoperative thyroid ultrasound data and postoperative pathological results were enrolled, which were divided into two groups: lower risk group (ACR TI-RADS 1, 2 and 3) and higher risk group (ACR TI-RADS 4 and 5). The malignant risk scores (MRS) of TNs were obtained from longitudinal and transverse sections using AI-CADS. The diagnostic performance of AI-CADS and the consistency of each US characteristic were evaluated between these sections. The receiver operating characteristic (ROC) curve and the Cohen κ-statistic were performed. RESULTS A total of 203 patients (45.61 ± 11.59 years, 163 female) with 221 TNs were enrolled. The area under the ROC curve (AUC) of criterion 3 [0.86 (95%CI: 0.80~0.91)] was lower than criterion 1 [0.94 (95%CI: 0.90~ 0.99)], 2 [0.93 (95%CI: 0.89~0.97)] and 4 [0.94 (95%CI: 0.90, 0.99)] significantly (P<0.001, P=0.01, P<0.001, respectively). In the higher risk group, the MRS of transverse section was higher than longitudinal section (P<0.001), and the agreement of extrathyroidal extension and shape was moderate and fair (κ =0.48, 0.31 respectively). The diagnostic agreement of other ultrasonic features was substantial or almost perfect (κ >0.60). CONCLUSION The diagnostic performance of computer-aided diagnosis system based on artificial intelligence (AI-CADS) in longitudinal and transverse ultrasonic views for differentiating thyroid nodules (TN) was different, which was higher in the transverse section. It was more dependent on the section for the AI-CADS diagnosis of suspected malignant TNs.
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Affiliation(s)
- Lin-lin Zheng
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Su-ya Ma
- YinZhou No.2 Hospital, Department of Ultrasound, Ningbo, China
| | - Ling Zhou
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Cong Yu
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Hai-shan Xu
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Li-long Xu
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Shi-yan Li
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- *Correspondence: Shi-yan Li,
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Cantisani V, De Silvestri A, Scotti V, Fresilli D, Tarsitano MG, Polti G, Guiban O, Polito E, Pacini P, Durante C, Grani G, Isidori AM, Giannetta E, Sorrenti S, Trimboli P, Catalano C, Cirocchi R, Lauro A, D'Andrea V. US-Elastography With Different Techniques for Thyroid Nodule Characterization: Systematic Review and Meta-analysis. Front Oncol 2022; 12:845549. [PMID: 35371974 PMCID: PMC8966910 DOI: 10.3389/fonc.2022.845549] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 02/16/2022] [Indexed: 12/25/2022] Open
Abstract
Background Thyroid nodules are frequent in adult population and thyroid cancer incidence has increased dramatically over the past three decades. The aim of this systematic review and meta-analysis was to evaluate the US-Elastosonography (USE) diagnostic performance in assessing the thyroid nodules malignancy risk. Methods PubMed and Embase databases were searched from January 2011 to July 2021. We extracted data from selected studies and calculated the overall diagnostic accuracy of qualitative USE, semi-quantitative USE and quantitative USE. Summary receiver operating characteristic (ROC) curve was elaborated to show the results. All statistical tests were performed using Metadisc and Medcal software package. Results Finally 72 studies with 13,505 patients and 14,015 thyroid nodules (33% malignant) undergoing elastography were included. The pooled sensitivity, specificity and AUC were 84%, 81%, and 0.89 respectively for qualitative USE; 83%, 80%, and 0.93 for semi-quantitative USE and 78%, 81% and 0.87, for quantitative USE. The qualitative and semiquantitative USE present very similar diagnostic accuracy values and both better than the quantitative USE. Conclusions USE is a useful imaging tool for thyroid nodule characterization. In accordance with recent guidelines and meta-analyses, the USE could be used daily in thyroid nodule malignancy risk stratification. Systematic Review Registration PROSPERO: CRD42021279257.
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Affiliation(s)
- Vito Cantisani
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Annalisa De Silvestri
- Servizio di Epidemiologia Clinica e Biometria Direzione Scientifica-Fondazione Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS) Policlinico san Matteo, Pavia, Italy
| | - Valeria Scotti
- Servizio di Epidemiologia Clinica e Biometria Direzione Scientifica-Fondazione Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS) Policlinico san Matteo, Pavia, Italy
| | - Daniele Fresilli
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Maria Grazia Tarsitano
- Department of Clinical and Surgical Science, "Magna Graecia" University, Catanzaro, Italy
| | - Giorgia Polti
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Olga Guiban
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Eleonora Polito
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Patrizia Pacini
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Cosimo Durante
- Department of Translational and Precision Medicine, "Sapienza" University of Rome, Rome, Italy
| | - Giorgio Grani
- Department of Translational and Precision Medicine, "Sapienza" University of Rome, Rome, Italy
| | - Andrea M Isidori
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Elisa Giannetta
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Salvatore Sorrenti
- Department of Surgical Sciences, Hospital "Policlinico Umberto I", "Sapienza" University of Rome, Rome, Italy
| | - Pierpaolo Trimboli
- Clinic for Endocrinology and Diabetology, Lugano Regional Hospital, Ente Ospedaliero Cantonale, Lugano, Switzerland
| | - Carlo Catalano
- Department of Radiological Sciences, Oncology and Pathology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Roberto Cirocchi
- Department of Surgery and Biomedical Sciences, University of Perugia, Perugia, Italy
| | - Augusto Lauro
- Department of Surgical Sciences, Hospital "Policlinico Umberto I", "Sapienza" University of Rome, Rome, Italy
| | - Vito D'Andrea
- Department of Surgical Sciences, Hospital "Policlinico Umberto I", "Sapienza" University of Rome, Rome, Italy
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Author Correction: Impact of imaging cross-section on visualization of thyroid microvessels using ultrasound: Pilot study. Sci Rep 2020; 10:11965. [PMID: 32665701 PMCID: PMC7360610 DOI: 10.1038/s41598-020-69042-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
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Nayak R, Nawar N, Webb J, Fatemi M, Alizad A. Impact of imaging cross-section on visualization of thyroid microvessels using ultrasound: Pilot study. Sci Rep 2020; 10:415. [PMID: 31942039 PMCID: PMC6962275 DOI: 10.1038/s41598-019-57330-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 12/13/2019] [Indexed: 11/10/2022] Open
Abstract
Non-invasive, contrast-free microvascular imaging of human thyroids can be potentially beneficial in reducing the large number of benign biopsies of suspicious nodules. However, motion incurred by thyroid due to its proximity to the pulsating carotid artery significantly impacts the visualization of blood flow in small vessels. Singular value based spatiotemporal clutter filtering (SVD-STF) improves the performance of tissue rejection in the presence of motion. However, despite effective clutter filtering, motion in thyroid imaging can impact coherent integration of the Doppler ensemble and degrade the visualization of the underlying vasculature. Recently studies have demonstrated that motion correction using 2D normalized cross-correlation based speckle tracking can address this issue, however, only in-plane motion can be tracked and corrected. Given the natural anatomical orientation of the rigid trachea, thyroid and the pulsating carotid artery, we hypothesize that imaging of thyroid microvessels may be more reliable in the longitudinal view than in the transverse. Specifically, distal presence of rigid trachea can limit out-of-plane motion in the longitudinal view. We tested this hypothesis on 48 acquisitions obtained from 24 thyroid patients having at least one suspicious nodule. In each patient, ultrasound images of the thyroid were acquired in both longitudinal and transverse views. Compounded plane-wave imaging was used to acquire the ultrasound images at high frame-rate, which is important for contrast-free small vessel blood flow imaging. Thyroid motion was tracked using 2D normalized cross-correlation based speckle tracking. Tissue clutter was rejected using singular value decomposition based spatiotemporal clutter filtering. The clutter-filtered Doppler ensemble was motion corrected prior to slow-time power Doppler integration. Signal-to-noise and contrast-to-noise ratios were computed to assess the improvement in quality of the power Doppler images. Out-of-plane motion was detected by estimating normalized ensemble cross-correlation coefficient. The results demonstrated that motion associated with the thyroid due to the carotid artery was primarily in the lateral direction, which could be estimated and corrected using 2D speckle tracking. However, the motion in the transverse view displayed increased speckle decorrelation. The average ensemble cross-correlation coefficient of the thyroid ultrasound images were significantly higher (p < 0.05) in the longitudinal view than in the transverse view. The largest improvement in SNR and CNR of the estimated PD images upon motion correction was observed in the longitudinal view (12.95 ± 3.76 dB and 16.48 ± 4.6 dB) than in the transverse view (3.72 ± 0.894 dB and 6.217 ± 1.689 dB). These preliminary results show that motion encountered by the thyroid due to carotid pulsations can be effectively tracked and corrected in the longitudinal view relative to transverse, which is important for reliably visualizing the underlying blood flow.
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Affiliation(s)
- Rohit Nayak
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, 55905, United States
| | - Noshin Nawar
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, 55905, United States
| | - Jeremy Webb
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, 55905, United States
| | - Mostafa Fatemi
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, 55905, United States
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, 55905, United States.
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