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Song X, Skog S, Wei L, Qin J, Yang R, Li J, Zhou J, He E, Zhou J. Nomogram model of serum thymidine kinase 1 combined with ultrasonography for prediction of central lymph node metastasis risk in patients with papillary thyroid carcinoma pre-surgery. Front Endocrinol (Lausanne) 2024; 15:1366219. [PMID: 38887267 PMCID: PMC11180742 DOI: 10.3389/fendo.2024.1366219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 05/02/2024] [Indexed: 06/20/2024] Open
Abstract
Objective The aim of this study was to develop a nomogram, using serum thymidine kinase 1 protein (STK1p) combined with ultrasonography parameters, to early predict central lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) pre-surgery. Methods Patients with PTC pre-surgery in January 2021 to February 2023 were divided into three cohorts: the observation cohort (CLNM, n = 140), the control cohort (NCLNM, n = 128), and the external verification cohort (CLNM, n = 50; NCLNM, n = 50). STK1p was detected by an enzyme immunodot-blot chemiluminescence analyzer and clinical parameters were evaluated by ultrasonography. Results A suitable risk threshold value for STK1p of 1.7 pmol/L was selected for predicting CLNM risk by receiver operating characteristic (ROC) curve analysis. Multivariate analysis identified the following six independent risk factors for CLNM: maximum tumor size >1 cm [odds ratio (OR) = 2.406, 95% confidence interval (CI) (1.279-4.526), p = 0.006]; capsule invasion [OR = 2.664, 95% CI (1.324-5.360), p = 0.006]; irregular margin [OR = 2.922; 95% CI (1.397-6.111), p = 0.004]; CLN flow signal [OR = 3.618, 95% CI (1.631-8.027), p = 0.002]; tumor-foci number ≥2 [OR = 4.064, 95% CI (2.102-7.859), p < 0.001]; and STK1p ≥1.7 pmol/L [OR = 7.514, 95% CI (3.852-14.660), p < 0.001]. The constructed nomogram showed that the area under the ROC curve for the main dataset was 0.867 and that for the validation dataset was 0.830, exhibiting effectivity, and was recalculated to a total score of approximately 383. Through monitoring the response post-surgery, all patients were assessed as tumor-free at 12 months post-surgery, which was significantly associated with a reduction in STK1p to disease-free levels. Conclusion We demonstrate for the first time that a novel nomogram including STK1p combined with ultrasonography can assist in the clinical prevention of CLNM, by facilitating timely, individualized prophylactic CLNM dissection, thereby reducing the risk of secondary surgery and the probability of recurrence.
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Affiliation(s)
- Xiaolong Song
- Radioimmunoassay Center, Department of Clinical Laboratory, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Sven Skog
- Department of Medicine, Shenzhen Ellen-Sven Precision Medicine Institute, Shenzhen, China
| | - Long Wei
- Radioimmunoassay Center, Department of Clinical Laboratory, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Jinlv Qin
- Radioimmunoassay Center, Department of Clinical Laboratory, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Ru Yang
- Radioimmunoassay Center, Department of Clinical Laboratory, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Jin Li
- Department of Medicine, Shenzhen Ellen-Sven Precision Medicine Institute, Shenzhen, China
| | - Ji Zhou
- Department of Medicine, Shenzhen Ellen-Sven Precision Medicine Institute, Shenzhen, China
| | - Ellen He
- Department of Medicine, Shenzhen Ellen-Sven Precision Medicine Institute, Shenzhen, China
| | - Jianping Zhou
- Radioimmunoassay Center, Department of Clinical Laboratory, Shaanxi Provincial People’s Hospital, Xi’an, China
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Hu T, Zhou T, Zhang Y, Zhou L, Huang X, Cai Y, Qian S, Huang K, Luo D. The predictive value of the thyroid nodule benign and malignant based on the ultrasound nodule-to-muscle gray-scale ratio. JOURNAL OF CLINICAL ULTRASOUND : JCU 2024; 52:51-58. [PMID: 37915163 DOI: 10.1002/jcu.23601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/14/2023] [Accepted: 10/17/2023] [Indexed: 11/03/2023]
Abstract
OBJECTIVE To investigate the efficacy of the ultrasonic nodule to muscle gray scale ratio as a predictive tool for distinguishing between benign and malignant thyroid nodules. METHODS A retrospective study was undertaken at the First People's Hospital of Hangzhou, affiliated with the Zhejiang University School of Medicine, analyzing ultrasound and pathological data of patients with thyroid nodules between May 2020 and December 2022. The study extracted ultrasound features of nodules and employed univariate and multivariate logistic regression analyses to identify independent risk factors for malignant tumors in the nodules. Subsequently, a predictive model for distinguishing benign and malignant thyroid nodules was developed. RESULTS A total of 466 patients were included in this retrospective study, of which 275 cases were malignant tumors. Univariate and multivariate logistic regression analyses showed that the nodular-muscle gray-scale ratio, nodule diameter, margin status, aspect ratio, and calcification were closely related to thyroid malignant tumors. The area under the curve (AUC) of training group was 0.832, with a sensitivity, specificity, and accuracy of 85.5%, 67.4%, and 76.6%, respectively. The AUC of the external validation group was 0.819, with a sensitivity, specificity, and accuracy of 76.4%, 74.5%, and 75.7%, respectively. The calibration and decision curves showed that the model had good diagnostic value. CONCLUSION The research findings indicate that ratio is significantly associated with the malignant nature of thyroid nodules. The application of a line chart model based on these parameters exhibits a high level of predictive performance.
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Affiliation(s)
- Tao Hu
- Zhejiang Chinese Medical University, Fourth Clinical Medical College, Hangzhou, China
| | - Tianhan Zhou
- The Department of General Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Yu Zhang
- The Department of Oncological Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Li Zhou
- The Department of Oncological Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xuanwei Huang
- Zhejiang Chinese Medical University, Fourth Clinical Medical College, Hangzhou, China
| | - Yuan Cai
- Zhejiang Chinese Medical University, Fourth Clinical Medical College, Hangzhou, China
| | - Shuoying Qian
- Zhejiang Chinese Medical University, Fourth Clinical Medical College, Hangzhou, China
| | - Kaiyuan Huang
- Zhejiang Chinese Medical University, Fourth Clinical Medical College, Hangzhou, China
| | - Dingcun Luo
- The Department of Oncological Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Li W, Chen J, Ye F, Xu D, Fan X, Yang C. The diagnostic value of ultrasound on different-sized thyroid nodules based on ACR TI-RADS. Endocrine 2023; 82:569-579. [PMID: 37656349 DOI: 10.1007/s12020-023-03438-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 06/20/2023] [Indexed: 09/02/2023]
Abstract
OBJECTIVES The thyroid nodule is one of the most common endocrine system diseases. Risk classification models based on ultrasonic features have been created by multiple professional societies, including the American College of Radiology (ACR), which published the Thyroid Imaging Reporting and Data System (TI-RADS) in 2017. The effect of the size in the diagnostic value of ultrasound remains not well defined. The purposes of our study aims to explore diagnostic value of the ACR TI-RADS on different-sized thyroid nodules. METHODS A total of 1183 thyroid nodules were selected from 952 patients with thyroid nodules confirmed by surgical pathology from January 2021 to October 2022. Based on the maximum diameters of the nodules, they were stratified into groups A ( ≤ 10 mm), B ( > 10 mm, < 20 mm) and C ( ≥ 20 mm). The ultrasonic features of the thyroid nodules in each group were evaluated and scored based on ACR TI-RADS, and the receiver operating characteristic curve (ROC) was plotted to determine the optimal cut-off value for the ACR TI-RADS scores and categories in each group. Finally, the diagnostic efficacy of ACR TI-RADS on different-sized thyroid nodules was analyzed. RESULTS Among the 1183 thyroid nodules, 340 were benign, 10 were low-risk and 833 were malignant. For the convenience of statistical analysis, low-risk thyroid nodules were classified as malignant in this study. The ACR TI-RADS scores and categorical levels of malignant thyroid nodules in each group were higher than those of benign ones (p < 0.05). The areas under the ROCs (AUCs) plotted based on scores were 0.741, 0.907, and 0.904 respectively in the three groups, and the corresponding optimal cut-off values were > 6 points, > 5 points and > 4 points respectively. While the AUCs of the ACR TI-RADS categories were 0.668, 0.855, and 0.887 respectively in each group, with the optimal cut-off values were all > TR4. Besides, for thyroid nodules of larger sizes, ACR TI-RADS exhibited weaker sensitivity with lower positive prediction value (PPV), but the specificity and negative prediction value (NPV) were both higher, presenting with statistically significant differences (p < 0.05). CONCLUSION For thyroid nodules of different sizes, the diagnostic efficacy of ACR TI-RADS varies as well. The system shows better diagnostic efficacy on thyroid nodules of > 10 mm than on those ≤ 10 mm. Considering the favorable prognosis of thyroid microcarcinoma and the low diagnostic efficacy of ACR TI-RADS on it, the scoring and classification of thyroid micro-nodules can be left out in appropriate cases, so as to avoid the over-diagnosis and over-treatment of thyroid microcarcinoma to a certain extent.
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Affiliation(s)
- WeiMin Li
- Departments of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, 214000, Jiangsu, PR China
| | - JunMin Chen
- Department of Ultrasonography, Hangzhou Linping District Traditional Chinese Medicine Hospital, Hangzhou, 311199, Zhejiang, PR China
| | - Feng Ye
- School of nursing, Wuxi Medical College of Jiangnan University, Wuxi, 214000, Jiangsu, PR China
| | - Dong Xu
- Department of Ultrasonography, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, PR China
| | - XiaoFang Fan
- Departments of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, 214000, Jiangsu, PR China
| | - Chen Yang
- Department of Ultrasonography, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, PR China.
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Zhou Y, Li WM, Fan XF, Huang YL, Gao Q. Comparing Diagnostic Efficacy of C-TIRADS Positive Features on Different Sizes of Thyroid Nodules. Int J Gen Med 2023; 16:3483-3490. [PMID: 37601807 PMCID: PMC10438434 DOI: 10.2147/ijgm.s416403] [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: 04/28/2023] [Accepted: 07/25/2023] [Indexed: 08/22/2023] Open
Abstract
Purpose To explore the diagnostic value of positive features in the Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) for thyroid nodules of different sizes. Patients and Methods A total of 1864 patients with 2347 thyroid nodules were selected from January 2021 to December 2022 and assessed according to C-TIRADS. According to the maximum diameter, nodules were divided into the A1 group (≤10 mm), A2 group (>10 mm,<20 mm), and A3 group (≥20 mm). With surgical pathology as the golden standard, the receiver operating characteristic curves (ROC) were constructed, and each group's area under the curve (AUC) was calculated. The diagnostic value of positive features in C-TIRADS for different sizes of thyroid nodules was analyzed. Results In all groups, malignant thyroid nodules had a higher incidence of positive features than benign nodules (P < 0.05). In A1 group, the diagnostic efficiency of C-TIRADS positive features for thyroid nodules was vertical orientation> ill-defined/irregular margin or extrathyroidal extension> solid composition> markedly hypoechoic> microcalcifications. The AUCs were 0.718, 0.675, 0.609, 0.558, and 0.581, respectively. In A2 group, the diagnostic efficacy of each positive features for thyroid nodules was ill-defined/irregular margins or extra-thyroid invasion> solid composition> microcalcifications> markedly hypoechoic> vertical orientation. The AUCs were 0.854, 0.730, 0.719, 0.670, and 0.609, respectively. In A3 group, the diagnostic efficacy of each positive features for thyroid nodules was ill-defined/irregular margin or extrathyroidal extension> microcalcifications> solid composition> vertical orientation> markedly hypoechoic. The AUCs were 0.847, 0.778, 0.767, 0.584, and 0.560, respectively. Conclusion C-TIRADS positive features exhibited different diagnostic efficacy for thyroid nodules of various sizes, especially for thyroid nodules ≤10 mm, for which all positive features had low diagnostic efficacy.
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Affiliation(s)
- Yue Zhou
- Department of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, People’s Republic of China
| | - Wei-Min Li
- Department of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, People’s Republic of China
| | - Xiao-Fang Fan
- Department of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, People’s Republic of China
| | - Yan-Li Huang
- Department of Special Clinic, General Hospital of Eastern Theater Command, PLA, Nanjing, Jiangsu, People’s Republic of China
| | - Qi Gao
- Department of Ultrasonography, Zhongda Hospital Affiliated to Southeast University, Nanjing, Jiangsu, People’s Republic of China
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Wang L, Wen D, Yin Y, Zhang P, Wen W, Gao J, Jiang Z. Musculoskeletal Ultrasound Image-Based Radiomics for the Diagnosis of Achilles Tendinopathy in Skiers. JOURNAL OF ULTRASOUND IN MEDICINE 2023; 42:363-371. [PMID: 35841273 PMCID: PMC10084008 DOI: 10.1002/jum.16059] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 05/10/2022] [Accepted: 06/23/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVES Our study aimed to develop and validate an efficient ultrasound image-based radiomic model for determining the Achilles tendinopathy in skiers. METHODS A total of 88 feet of skiers clinically diagnosed with unilateral chronic Achilles tendinopathy and 51 healthy feet were included in our study. According to the time order of enrollment, the data were divided into a training set (n = 89) and a test set (n = 50). The regions of interest (ROIs) were segmented manually, and 833 radiomic features were extracted from red, green, blue color channels and grayscale of ROIs using Pyradiomics, respectively. Three feature selection and three machine learning modeling algorithms were implemented respectively, for determining the optimal radiomics pipeline. Finally, the area under the receiver operating characteristic curve (AUC), consistency analysis, and decision analysis were used to evaluate the diagnostic performance. RESULTS By comparing nine radiomics analysis strategies of three color channels and grayscale, the radiomic model under the green channel obtained the best diagnostic performance, using the Random Forest selection and Support Vector Machine modeling, which was selected as the final machine learning model. All the selected radiomic features were significantly associated with the Achilles tendinopathy (P < .05). The radiomic model had a training AUC of 0.98, a test AUC of 0.99, a sensitivity of 0.90, and a specificity of 1, which could bring sufficient clinical net benefits. CONCLUSIONS Ultrasound image-based radiomics achieved high diagnostic performance, which could be used as an intelligent auxiliary tool for the diagnosis of Achilles tendinopathy.
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Affiliation(s)
- Likun Wang
- Department of Ultrasound, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, China
| | - Dehui Wen
- Department of Ultrasound, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, China
| | - Yanlin Yin
- Department of Orthopedics, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, China
| | - Peinan Zhang
- Department of Orthopedics, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, China
| | - Wen Wen
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, 610000, China
| | - Jun Gao
- College of Computer Science, Sichuan University, Chengdu, 610000, China
| | - Zekun Jiang
- College of Computer Science, Sichuan University, Chengdu, 610000, China.,West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610000, China
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Han Z, Xie L, Wei P, Lei Z, Ding Z, Zhang M. Ultrasound gray scale ratio for differential diagnosis of papillary thyroid microcarcinoma from benign micronodule in patients with Hashimoto's thyroiditis. BMC Endocr Disord 2022; 22:187. [PMID: 35869461 PMCID: PMC9306152 DOI: 10.1186/s12902-022-01028-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 04/15/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND To investigate the diagnostic value of ultrasound gray scale ratio (UGSR) in differentiating papillary thyroid microcarcinomas (PTMCs) from benign micronodules (BMNs) in patients with Hashimoto's thyroiditis (HT). METHODS The ultrasound images of 285 PTMCs (from 247 patients) and 173 BMNs (from 140 patients) in the HT group, as well as 461 PTMCs (from 417 patients) and 234 BMNs (from 197 patients) in the non-HT group were retrospectively analyzed. The diagnosis of all cases was confirmed by histopathological examinations. The gray scale values of the nodules and surrounding thyroid tissues were measured and subsequently the UGSRs were calculated. Receiver operating characteristic curve analysis was used to determine the area under the curve (AUC), optimal UGSR threshold, sensitivity and specificity in differentiating PTMCs and BMNs in the two groups. RESULTS The UGSR of PTMC and BMN was 0.52 ± 0.12 and 0.85 ± 0.24 in the HT group (P < 0.001), and 0.57 ± 0.13 and 0.87 ± 0.20 in the non-HT group (P < 0.001), respectively. The difference in PTMC-UGSR was significant between the two groups (P < 0.001), whereas BMN-UGSR did not differ between the two groups (P = 0.416). The AUC, optimal UGSR threshold, sensitivity and specificity of UGSR for differentiating PTMC and BMN in the HT and non-HT group were 0.890 versus 0.901, 0.68 versus 0.72, 91.23% versus 90.67%, and 77.46% versus 82.05%, respectively. CONCLUSIONS The USGR of the HT group was lower than that of the non-HT group. Moreover, UGSR exhibited important diagnostic value in differentiating PTMC from BMN in both HT and non-HT groups.
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Affiliation(s)
- Zhijiang Han
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277, Yanta West Road, Xi'an, 710061, China
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lesi Xie
- Department of Pathology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Peiying Wei
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhikai Lei
- Department of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhongxiang Ding
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Hangzhou, 310006, China.
| | - Ming Zhang
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277, Yanta West Road, Xi'an, 710061, China.
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Sorrenti S, Dolcetti V, Radzina M, Bellini MI, Frezza F, Munir K, Grani G, Durante C, D’Andrea V, David E, Calò PG, Lori E, Cantisani V. Artificial Intelligence for Thyroid Nodule Characterization: Where Are We Standing? Cancers (Basel) 2022; 14:cancers14143357. [PMID: 35884418 PMCID: PMC9315681 DOI: 10.3390/cancers14143357] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/24/2022] [Accepted: 07/08/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary In the present review, an up-to-date summary of the state of the art of artificial intelligence (AI) implementation for thyroid nodule characterization and cancer is provided. The opinion on the real effectiveness of AI systems remains controversial. Taking into consideration the largest and most scientifically valid studies, it is possible to state that AI provides results that are comparable or inferior to expert ultrasound specialists and radiologists. Promising data approve AI as a support tool and simultaneously highlight the need for a radiologist supervisory framework for AI provided results. Therefore, current solutions might be more suitable for educational purposes. Abstract Machine learning (ML) is an interdisciplinary sector in the subset of artificial intelligence (AI) that creates systems to set up logical connections using algorithms, and thus offers predictions for complex data analysis. In the present review, an up-to-date summary of the current state of the art regarding ML and AI implementation for thyroid nodule ultrasound characterization and cancer is provided, highlighting controversies over AI application as well as possible benefits of ML, such as, for example, training purposes. There is evidence that AI increases diagnostic accuracy and significantly limits inter-observer variability by using standardized mathematical algorithms. It could also be of aid in practice settings with limited sub-specialty expertise, offering a second opinion by means of radiomics and computer-assisted diagnosis. The introduction of AI represents a revolutionary event in thyroid nodule evaluation, but key issues for further implementation include integration with radiologist expertise, impact on workflow and efficiency, and performance monitoring.
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Affiliation(s)
- Salvatore Sorrenti
- Department of Surgical Sciences, “Sapienza” University of Rome, 00161 Rome, Italy; (S.S.); (V.D.); (E.L.)
| | - Vincenzo Dolcetti
- Department of Radiological, Anatomo-Pathological Sciences, “Sapienza” University of Rome, 00161 Rome, Italy; (V.D.); (V.C.)
| | - Maija Radzina
- Radiology Research Laboratory, Riga Stradins University, LV-1007 Riga, Latvia;
- Medical Faculty, University of Latvia, Diagnostic Radiology Institute, Paula Stradina Clinical University Hospital, LV-1007 Riga, Latvia
| | - Maria Irene Bellini
- Department of Surgical Sciences, “Sapienza” University of Rome, 00161 Rome, Italy; (S.S.); (V.D.); (E.L.)
- Correspondence:
| | - Fabrizio Frezza
- Department of Information Engineering, Electronics and Telecommunications, “Sapienza” University of Rome, 00184 Rome, Italy; (F.F.); (K.M.)
- Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT), Viale G.P. Usberti 181/A Sede Scientifica di Ingegneria-Palazzina 3, 43124 Parma, Italy
| | - Khushboo Munir
- Department of Information Engineering, Electronics and Telecommunications, “Sapienza” University of Rome, 00184 Rome, Italy; (F.F.); (K.M.)
| | - Giorgio Grani
- Department of Translational and Precision Medicine, “Sapienza” University of Rome, 00161 Rome, Italy; (G.G.); (C.D.); (E.D.)
| | - Cosimo Durante
- Department of Translational and Precision Medicine, “Sapienza” University of Rome, 00161 Rome, Italy; (G.G.); (C.D.); (E.D.)
| | - Vito D’Andrea
- Department of Surgical Sciences, “Sapienza” University of Rome, 00161 Rome, Italy; (S.S.); (V.D.); (E.L.)
| | - Emanuele David
- Department of Translational and Precision Medicine, “Sapienza” University of Rome, 00161 Rome, Italy; (G.G.); (C.D.); (E.D.)
| | - Pietro Giorgio Calò
- Department of Surgical Sciences, “Policlinico Universitario Duilio Casula”, University of Cagliari, 09042 Monserrato, Italy;
| | - Eleonora Lori
- Department of Surgical Sciences, “Sapienza” University of Rome, 00161 Rome, Italy; (S.S.); (V.D.); (E.L.)
| | - Vito Cantisani
- Department of Radiological, Anatomo-Pathological Sciences, “Sapienza” University of Rome, 00161 Rome, Italy; (V.D.); (V.C.)
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xia F, qin W, feng J, zhou X, sun E, xu J, li C. Differential diagnostic value of tumor morphology, long/short diameter ratio and ultrasound gray scale ratio for three parotid neoplasms. Oral Surg Oral Med Oral Pathol Oral Radiol 2022; 134:484-491. [DOI: 10.1016/j.oooo.2022.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 05/19/2022] [Accepted: 05/25/2022] [Indexed: 11/28/2022]
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Gong Y, Yao X, Yu L, Wei P, Han Z, Fang J, Ao W, Xu C. Ultrasound grayscale ratio: a reliable parameter for differentiating between papillary thyroid microcarcinoma and micronodular goiter. BMC Endocr Disord 2022; 22:75. [PMID: 35331216 PMCID: PMC8952271 DOI: 10.1186/s12902-022-00994-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 03/18/2022] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND The present study aimed to quantify and differentiate the echo levels of papillary thyroid microcarcinomas (PTMCs) and micronodular goiters (MNGs) using the ultrasound grayscale ratio (UGSR) and to investigate the repeatability of UGSR. METHODS The ultrasound (US) data of 241 patients with 265 PTMCs and 141 patients with 168 MNGs confirmed by surgery and pathology were retrospectively analyzed. All patients had received outpatient ultrasonic examination and preoperative ultrasonic positioning. The RADinfo radiograph reading system was used to measure the grayscales of PTMC, MNG, and thyroid tissues at the same gain level, and the UGSR values of the PTMC, MNG, and thyroid tissue were calculated. The patients were divided into outpatient examination, preoperative positioning, and mean value groups, and the receiver operating characteristic (ROC) curves were calculated to obtain the optimal UGSR threshold to distinguish PTMC from MNG. The interclass correlation coefficient (ICC) was used to assess the consistency of UGSR measured in three groups. RESULTS The UGSR values of the PTMC and MNG were 0.56 ± 0.14 and 0.80 ± 0.19 (t = 5.84, P < 0.001) in the outpatient examination group, 0.55 ± 0.14 and 0.80 ± 0.19 (t = 18.74, P < 0.001) in the preoperative positioning group, and 0.56 ± 0.12 and 0.80 ± 0.18 (t = 16.49, P < 0.001) in the mean value group. The areas under the ROC curves in the three groups were 0.860, 0.856, and 0.875, respectively. When the UGSR values for the outpatient examination, preoperative positioning, and mean value groups were 0.649, 0.646, and 0.657, respectively, each group obtained its largest Youden index. A reliable UGSR value was obtained between the outpatient examination and preoperative positioning groups (ICC = 0.79, P = 0.68). CONCLUSION UGSR is a simple and repeatable method to distinguish PTMC from MNG, and hence, can be widely applicable.
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Affiliation(s)
- Yun Gong
- Department of Pediatrics, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China
| | - Xiuzhen Yao
- Department of Ultrasound, Shanghai Putuo District People's Hospital, Shanghai, China
| | - Lifang Yu
- Department of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Shangcheng District, Zhejiang, 310006, Hangzhou, China
| | - Peiying Wei
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zhijiang Han
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jianhua Fang
- Department of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Shangcheng District, Zhejiang, 310006, Hangzhou, China
| | - Weiqun Ao
- Department of Radiology, Tongde Hospital of Zhejiang Province, No.234, Gucui Road, Zhejiang, 310012, Hangzhou, China.
| | - Chenke Xu
- Department of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Shangcheng District, Zhejiang, 310006, Hangzhou, China.
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Feng N, Wei P, Kong X, Xu J, Yao J, Cheng F, Ou D, Wang L, Xu D, Han Z. The value of ultrasound grayscale ratio in the diagnosis of papillary thyroid microcarcinomas and benign micronodules in patients with Hashimoto's thyroiditis: A two-center controlled study. Front Endocrinol (Lausanne) 2022; 13:949847. [PMID: 36034442 PMCID: PMC9412962 DOI: 10.3389/fendo.2022.949847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE The value of ultrasound grayscale ratio (UGSR) in the diagnosis of papillary thyroid microcarcinomas (PTMCs) and benign micronodules (BMNs) has been recognized by some authors, but studies have not examined these aspects in patients with Hashimoto's thyroiditis (HT). This retrospective study investigated the value of UGSR in the diagnosis of PTMCs and BMNs in patients with HT using data from two medical centers. METHODS Ultrasound images of 428 PTMCs in 368 patients with HT and 225 BMNs in 181 patients with HT in center A were retrospectively analyzed and compared to the ultrasound images of 412 PTMCs in 324 patients with HT and 315 BMNs in 229 patients with HT in medical center B. All of the cases were surgically confirmed. The UGSR was calculated as the ratio of the grayscale value of lesions to the surrounding normal thyroid tissues. The optimal UGSR thresholds for the PTMCs and BMNs in patients with HT from the two medical centers were determined using a receiver operating characteristic (ROC) curve. Furthermore, other statistics, including the area under the curve (AUC), the optimal UGSR threshold, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of the two medical centers, were pair analyzed in this study. RESULTS The UGSR of PTMCs and BMNs in patients with HT from medical center A were 0.513 (0.442, 0.592) and 0.857 (0.677, 0.977) (Z = -15.564, p = 0), and those from medical center B were 0.514 (0.431, 0.625) and 0.917 (0.705, 1.131) (Z = -15.564, p = 0). For both medical centers A and B, the AUC, optimal UGSR threshold, sensitivity, specificity, PPV, NPV, and diagnostic accuracy of the UGSR in differentiating between PTMCs and BMNs in patients with HT were 0.870 and 0.889, 0.68 and 0.70, 0.921 and 0.898, 0.747 and 0.759, 0.874 and 0.829, 0.832 and 0.848, and 0.861 and 0.836, respectively. There were no significant differences in the UGSR for the PTMCs between patients from the two medical centers (Z = -0.815, p = 0.415), while there was a significant difference in the UGSR of the BMNs between patients from the two medical centers (Z = -3.637, p = 0). CONCLUSION In the context of HT, UGSR still has high sensitivity, accuracy, and stability in differentiating between PTMCs and BMNs, making it a complementary differentiator of thyroid imaging reporting and data systems. However, due to its low specificity, a comprehensive analysis of other ultrasound signs is required.
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Affiliation(s)
- Na Feng
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Peiying Wei
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiangkai Kong
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Jingjing Xu
- Department of Pathology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Jincao Yao
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Research Center for Cancer Intelligent Diagnosis and Molecular Technology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Fang Cheng
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Di Ou
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Liping Wang
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Dong Xu
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Research Center for Cancer Intelligent Diagnosis and Molecular Technology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- *Correspondence: Dong Xu, ; Zhijiang Han,
| | - Zhijiang Han
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Dong Xu, ; Zhijiang Han,
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Sharafeldeen A, Elsharkawy M, Khaled R, Shaffie A, Khalifa F, Soliman A, Abdel Razek AAK, Hussein MM, Taman S, Naglah A, Alrahmawy M, Elmougy S, Yousaf J, Ghazal M, El-Baz A. Texture and shape analysis of diffusion-weighted imaging for thyroid nodules classification using machine learning. Med Phys 2021; 49:988-999. [PMID: 34890061 DOI: 10.1002/mp.15399] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 10/28/2021] [Accepted: 11/12/2021] [Indexed: 12/21/2022] Open
Abstract
PURPOSE To assess whether the integration between (a) functional imaging features that will be extracted from diffusion-weighted imaging (DWI); and (b) shape and texture imaging features as well as volumetric features that will be extracted from T2-weighted magnetic resonance imaging (MRI) can noninvasively improve the diagnostic accuracy of thyroid nodules classification. PATIENTS AND METHODS In a retrospective study of 55 patients with pathologically proven thyroid nodules, T2-weighted and diffusion-weighted MRI scans of the thyroid gland were acquired. Spatial maps of the apparent diffusion coefficient (ADC) were reconstructed in all cases. To quantify the nodules' morphology, we used spherical harmonics as a new parametric shape descriptor to describe the complexity of the thyroid nodules in addition to traditional volumetric descriptors (e.g., tumor volume and cuboidal volume). To capture the inhomogeneity of the texture of the thyroid nodules, we used the histogram-based statistics (e.g., kurtosis, entropy, skewness, etc.) of the T2-weighted signal. To achieve the main goal of this paper, a fusion system using an artificial neural network (NN) is proposed to integrate both the functional imaging features (ADC) with the structural morphology and texture features. This framework has been tested on 55 patients (20 patients with malignant nodules and 35 patients with benign nodules), using leave-one-subject-out (LOSO) for training/testing validation tests. RESULTS The functionality, morphology, and texture imaging features were estimated for 55 patients. The accuracy of the computer-aided diagnosis (CAD) system steadily improved as we integrate the proposed imaging features. The fusion system combining all biomarkers achieved a sensitivity, specificity, positive predictive value, negative predictive value, F1-score, and accuracy of 92.9 % (confidence interval [CI]: 78.9 % -- 99.5 % ), 95.8 % (CI: 87.4 % -- 99.7 % ), 93 % (CI: 80.7 % -- 99.5 % ), 96 % (CI: 88.8 % -- 99.7 % ), 92.8 % (CI: 83.5 % -- 98.5 % ), and 95.5 % (CI: 88.8 % -- 99.2 % ), respectively, using the LOSO cross-validation approach. CONCLUSION The results demonstrated in this paper show the promise that integrating the functional features with morphology as well as texture features by using the current state-of-the-art machine learning approaches will be extremely useful for identifying thyroid nodules as well as diagnosing their malignancy.
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Affiliation(s)
- Ahmed Sharafeldeen
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA
| | - Mohamed Elsharkawy
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA
| | - Reem Khaled
- Radiology Department, Mansoura University, Mansoura, Egypt
| | - Ahmed Shaffie
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA
| | - Fahmi Khalifa
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA
| | - Ahmed Soliman
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA
| | | | | | - Saher Taman
- Radiology Department, Mansoura University, Mansoura, Egypt
| | - Ahmed Naglah
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA
| | - Mohammed Alrahmawy
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Samir Elmougy
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Jawad Yousaf
- Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, UAE
| | - Mohammed Ghazal
- Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, UAE
| | - Ayman El-Baz
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA
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Han Z, Feng N, Lu Y, Li M, Wei P, Yao J, Zhu Q, Lei Z, Xu D. A Control Study on the Value of the Ultrasound Grayscale Ratio for the Differential Diagnosis of Thyroid Micropapillary Carcinoma and Micronodular Goiter in Two Medical Centers. Front Oncol 2021; 10:625238. [PMID: 33569350 PMCID: PMC7868544 DOI: 10.3389/fonc.2020.625238] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 12/07/2020] [Indexed: 11/14/2022] Open
Abstract
Objective To investigate the value of ultrasound gray-scale ratio (UGSR) for the differential diagnosis of papillary thyroid microcarcinoma (PTMC) and micronodular goiter (MNG) in two medical centers. Methods Ultrasound images of 881 PTMCs from 785 patients and 744 MNGs from 687 patients in center A were retrospectively analyzed and compared with 243 PTMCs from 203 patients and 251 MNGs from 198 patients in center B. All cases were confirmed by surgery and histology. The grayscale values of thyroid lesions and surrounding normal tissues were measured, and the UGSR was calculated. The optimal UGSR threshold for identifying PTMCs and MNGs in two medical centers was determined by receiver operating characteristic (ROC) curve, and the area under the curve (AUC), optimal UGSR threshold, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were compared between the two medical centers. Results The UGSR values of PTMCs and MNGs in medical center A were 0.5537 (0.4699, 0.6515) and 0.8708 (0.7616, 1.0123) (Z = -27.691, P = 0), respectively, whereas those in medical center B were 0.5517 (0.4698, 0.6377) and 0.8539 (0.7366, 0.9929) (Z = -16.057, P = 0), respectively. The UGSR of PTMCs and MNGs did not differ significantly between the two medical centers (Z = -0.609, P = 0.543 and Z = -1.394, P = 0.163, respectively). The AUC, optimal UGSR threshold, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the two medical centers were 0.898 vs. 0.918, 0.7214 vs. 0.6911, 0.881 vs. 0.868, 0.817 vs. 0.833, 0.851 vs. 0.834, 0.853 vs. 0.867, and 0.852 vs. 0.850, respectively. Conclusions UGSR can quantify the echo intensity of PTMCs and MNGs and is therefore valuable for the differential diagnosis of the two diseases. The diagnostic efficacy was consistent between the two medical centers. This method should be widely promoted and applied.
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Affiliation(s)
- Zhijiang Han
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Na Feng
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, China
| | - Yidan Lu
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, China
| | - Mingkui Li
- Department of Ultrasonography, Zhejiang Xiaoshan Hospital, Hangzhou, China
| | - Peiying Wei
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jincao Yao
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, China
| | - Qiaodan Zhu
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, China
| | - Zhikai Lei
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Dong Xu
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, China
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Chen X, Gao M, Hu L, Zhu J, Zhang S, Wei X. The diagnostic value of the ultrasound gray scale ratio for different sizes of thyroid nodules. Cancer Med 2019; 8:7644-7649. [PMID: 31691509 PMCID: PMC6912051 DOI: 10.1002/cam4.2653] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 09/18/2019] [Accepted: 10/14/2019] [Indexed: 12/19/2022] Open
Abstract
At present, hypoechogenicity, as one of the clinically relevant features associated with suspicion of malignant thyroid disease, is affected by the variability of modules and the experience of sonographers, thus leading to unsatisfying results. We propose the ultrasound gray scale ratio (UGSR) to obtain an objective, numerical estimate of the echogenicity degree in different‐sized thyroid nodules, and we then evaluate its diagnostic efficacy in differentiating benign and malignant thyroid lesions. In total, 553 ultrasound images of thyroid nodules from one kind of ultrasonographic scanner were analyzed, among which 281 were papillary thyroid carcinomas (PTCs) and 272 were nodular goiters (NGs). The UGSR of the PTCs, NGs, and surrounding normal thyroid tissue was measured by image analysis software. The best cut‐off value for distinguishing various sizes of PTCs and NGs was determined by receiver operating characteristic (ROC) curve analysis. As the UGSR increased, the sensitivity of the diagnosing PTCs decreased, and the specificity increased. When the maximum Jordan index was 0.611, the best cut‐off value was 0.692, and the corresponding sensitivity and specificity of diagnosing PTCs were 87.9% and 73.2%, respectively. For the analysis of subgroups of different tumor sizes, as the size of thyroid nodules increased from 0.3 to 2 cm, the sensitivity of the diagnosis of PTCs decreased from 97.5% to 58.8%, and the specificity increased from 72.4% to 90.9%. These results strongly suggest that the UGSR is an appropriate objective, numerical method for estimating the echogenicity degree and has various diagnostic efficacies in different‐sized thyroid nodules. Thus, the UGSR can be used as an additional ultrasound parameter in the diagnosis of different‐sized PTCs and NGs.
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Affiliation(s)
- Xiaoyu Chen
- Department of Diagnostic and Therapeutic Ultrasonography, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Ming Gao
- Department of Thyroid and Cervical Tumor, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Tianjin, China
| | - Linfei Hu
- Department of Thyroid and Cervical Tumor, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Tianjin, China
| | - Jialin Zhu
- Department of Diagnostic and Therapeutic Ultrasonography, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Sheng Zhang
- Department of Diagnostic and Therapeutic Ultrasonography, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Xi Wei
- Department of Diagnostic and Therapeutic Ultrasonography, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China
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