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Jiang T, Chen C, Zhou Y, Cai S, Yan Y, Sui L, Lai M, Song M, Zhu X, Pan Q, Wang H, Chen X, Wang K, Xiong J, Chen L, Xu D. Deep learning-assisted diagnosis of benign and malignant parotid tumors based on ultrasound: a retrospective study. BMC Cancer 2024; 24:510. [PMID: 38654281 PMCID: PMC11036551 DOI: 10.1186/s12885-024-12277-8] [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: 01/31/2024] [Accepted: 04/16/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND To develop a deep learning(DL) model utilizing ultrasound images, and evaluate its efficacy in distinguishing between benign and malignant parotid tumors (PTs), as well as its practicality in assisting clinicians with accurate diagnosis. METHODS A total of 2211 ultrasound images of 980 pathologically confirmed PTs (Training set: n = 721; Validation set: n = 82; Internal-test set: n = 89; External-test set: n = 88) from 907 patients were retrospectively included in this study. The optimal model was selected and the diagnostic performance evaluation is conducted by utilizing the area under curve (AUC) of the receiver-operating characteristic(ROC) based on five different DL networks constructed at varying depths. Furthermore, a comparison of different seniority radiologists was made in the presence of the optimal auxiliary diagnosis model. Additionally, the diagnostic confusion matrix of the optimal model was calculated, and an analysis and summary of misjudged cases' characteristics were conducted. RESULTS The Resnet18 demonstrated superior diagnostic performance, with an AUC value of 0.947, accuracy of 88.5%, sensitivity of 78.2%, and specificity of 92.7% in internal-test set, and with an AUC value of 0.925, accuracy of 89.8%, sensitivity of 83.3%, and specificity of 90.6% in external-test set. The PTs were subjectively assessed twice by six radiologists, both with and without the assisted of the model. With the assisted of the model, both junior and senior radiologists demonstrated enhanced diagnostic performance. In the internal-test set, there was an increase in AUC values by 0.062 and 0.082 for junior radiologists respectively, while senior radiologists experienced an improvement of 0.066 and 0.106 in their respective AUC values. CONCLUSIONS The DL model based on ultrasound images demonstrates exceptional capability in distinguishing between benign and malignant PTs, thereby assisting radiologists of varying expertise levels to achieve heightened diagnostic performance, and serve as a noninvasive imaging adjunct diagnostic method for clinical purposes.
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Affiliation(s)
- Tian Jiang
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, 310022, Hangzhou, Zhejiang, China
- Postgraduate training base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), 310022, Hangzhou, Zhejiang, China
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, 310022, Hangzhou, Zhejiang, China
| | - Chen Chen
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, 310022, Hangzhou, Zhejiang, China
- Wenling Big Data and Artificial Intelligence Institute in Medicine, 317502, TaiZhou, Zhejiang, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), 317502, Taizhou, Zhejiang, China
| | - Yahan Zhou
- Wenling Big Data and Artificial Intelligence Institute in Medicine, 317502, TaiZhou, Zhejiang, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), 317502, Taizhou, Zhejiang, China
| | - Shenzhou Cai
- Wenling Big Data and Artificial Intelligence Institute in Medicine, 317502, TaiZhou, Zhejiang, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), 317502, Taizhou, Zhejiang, China
| | - Yuqi Yan
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, 310022, Hangzhou, Zhejiang, China
- Postgraduate training base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), 310022, Hangzhou, Zhejiang, China
- Wenling Big Data and Artificial Intelligence Institute in Medicine, 317502, TaiZhou, Zhejiang, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), 317502, Taizhou, Zhejiang, China
| | - Lin Sui
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, 310022, Hangzhou, Zhejiang, China
- Postgraduate training base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), 310022, Hangzhou, Zhejiang, China
- Wenling Big Data and Artificial Intelligence Institute in Medicine, 317502, TaiZhou, Zhejiang, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), 317502, Taizhou, Zhejiang, China
| | - Min Lai
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, 310022, Hangzhou, Zhejiang, China
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, 310022, Hangzhou, Zhejiang, China
- Second Clinical College, Zhejiang University of Traditional Chinese Medicine, 310022, Hangzhou, Zhejiang, China
| | - Mei Song
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, 310022, Hangzhou, Zhejiang, China
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, 310022, Hangzhou, Zhejiang, China
| | - Xi Zhu
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, 310022, Hangzhou, Zhejiang, China
- Wenling Big Data and Artificial Intelligence Institute in Medicine, 317502, TaiZhou, Zhejiang, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), 317502, Taizhou, Zhejiang, China
| | - Qianmeng Pan
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), 317502, Taizhou, Zhejiang, China
| | - Hui Wang
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), 317502, Taizhou, Zhejiang, China
| | - Xiayi Chen
- Wenling Big Data and Artificial Intelligence Institute in Medicine, 317502, TaiZhou, Zhejiang, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), 317502, Taizhou, Zhejiang, China
| | - Kai Wang
- Dongyang Hospital Affiliated to Wenzhou Medical University, 322100, Jinhua, Zhejiang, China
| | - Jing Xiong
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518000, Shenzhen, Guangdong, China
| | - Liyu Chen
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, 310022, Hangzhou, Zhejiang, China.
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, 310022, Hangzhou, Zhejiang, China.
| | - Dong Xu
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, 310022, Hangzhou, Zhejiang, China.
- Postgraduate training base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), 310022, Hangzhou, Zhejiang, China.
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, 310022, Hangzhou, Zhejiang, China.
- Wenling Big Data and Artificial Intelligence Institute in Medicine, 317502, TaiZhou, Zhejiang, China.
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), 317502, Taizhou, Zhejiang, China.
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Wang Y, Wang L, Huang H, Ma J, Lin L, Liu L, Song Q, Liu A. Amide proton transfer-weighted magnetic resonance imaging for the differentiation of parotid gland tumors. Front Oncol 2023; 13:1223598. [PMID: 37664057 PMCID: PMC10471989 DOI: 10.3389/fonc.2023.1223598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/28/2023] [Indexed: 09/05/2023] Open
Abstract
Purpose To assess the usefulness of amide proton transfer-weighted (APTw) imaging in the differentiation of parotid gland tumors. Materials and methods Patients with parotid gland tumors who underwent APTw imaging were retrospectively enrolled and divided into groups according to pathology. Two radiologists evaluated the APTw image quality independently, and APTw images with quality score ≥3 were enrolled. The maximum and average values of APTw imaging for tumor lesions (APTmax and APTmean) were measured. The differences in APTmax and APTmean were compared between malignant tumors (MTs) and benign tumors (BTs), as well as between MTs and pleomorphic adenomas (PAs) and between MTs and Warthin tumors (WTs). Independent-samples t-test, Kruskal-Wallis H test, and receiver operating characteristic (ROC) curve analyses were used for statistical analysis. Results Seventy-three patients were included for image quality evaluation. In this study, 32/73 and 29/73 parotid tumors were scored as 4 and 3, respectively. After excluding lesions with quality score ≤2 (12/73), the APTmean and APTmax of MTs were 4.15% ± 1.33% and 7.43% ± 1.61%, higher than those of BTs 2.74% ± 1.04% and 5.25% ± 1.54%, respectively (p < 0.05). The areas under the ROC curve (AUCs) of the APTmean and APTmax for differentiation between MTs and BTs were 0.819 and 0.821, respectively. MTs indicated significantly higher APTmean and APTmax values than those of PAs (p < 0.05) and WTs (p < 0.05). The AUCs of the APTmean and APTmax for differentiation between MTs and PAs were 0.830 and 0.815 and between MTs and WTs were 0.847 and 0.920, respectively. Conclusion Most APTw images for parotid tumors had acceptable image quality for APTw value evaluation. Both APTmax and APTmean can be used to differentiate MTs from BTs and to differentiate MTs from subtype parotid gland tumors.
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Affiliation(s)
- Yihua Wang
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Lijun Wang
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Haitao Huang
- Department of Stomatology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Juntao Ma
- Department of Stomatology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Liangjie Lin
- Clinical and Technical Support, Philips Healthcare, Beijing, China
| | - Lin Liu
- Department of Stomatology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Qingwei Song
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Ailian Liu
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
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Markiet K, Glinska A, Nowicki T, Szurowska E, Mikaszewski B. Feasibility of Intravoxel Incoherent Motion (IVIM) and Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in Differentiation of Benign Parotid Gland Tumors. BIOLOGY 2022; 11:biology11030399. [PMID: 35336773 PMCID: PMC8945348 DOI: 10.3390/biology11030399] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 03/01/2022] [Accepted: 03/02/2022] [Indexed: 01/18/2023]
Abstract
Aim: The aim of this prospective study is to identify quantitative intravoxel incoherent motion and dynamic contrast-enhanced magnetic resonance imaging parameters of the most frequent benign parotid tumors, compare their utility and diagnostic accuracy. Methods: The study group consisted of 52 patients with 64 histopathologically confirmed parotid focal lesions. Parametric maps representing apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (FP) and transfer constant (Ktrans), reflux constant (Kep), extra-vascular extra-cellular volume fraction (Ve), and initial area under curve in 60 s (iAUC) have been obtained from multiparametric MRI. Results: Statistically significant (p < 0.001) inter-group differences were found between pleomorphic adenomas (PA) and Warthin tumors (WT) in all tested parameters but iAUC. Receiver operating characteristic curves were constructed to determine the optimal cut-off levels of the most significant parameters allowing differentiation between WT and PA. The Area Under the Curve (AUC) values and thresholds were for ADC: 0.931 and 1.05, D: 0.896 and 0.9, Kep: 0.964 and 1.1 and Ve: 0.939 and 0.299, respectively. Lesions presenting with a combination of ADC, D, and Ve values superior to the cut-off and Kep values inferior to the cut-off are classified as pleomorphic adenomas. Lesions presenting with combination of ADC, D, and Ve values inferior to the cut-off and Kep values superior to the cut-off are classified as Warthin tumors. Conclusions: DWI, IVIM and quantitative analysis of DCE-MRI derived parameters demonstrated distinctive features of PAs and WT and as such they seem feasible in differentiation of benign parotid gland tumors.
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Affiliation(s)
- Karolina Markiet
- 2nd Department of Radiology, Medical University of Gdansk, 80-214 Gdansk, Poland; (A.G.); (T.N.); (E.S.)
- Correspondence: ; Tel.: +48-58-349-36-80
| | - Anna Glinska
- 2nd Department of Radiology, Medical University of Gdansk, 80-214 Gdansk, Poland; (A.G.); (T.N.); (E.S.)
| | - Tomasz Nowicki
- 2nd Department of Radiology, Medical University of Gdansk, 80-214 Gdansk, Poland; (A.G.); (T.N.); (E.S.)
| | - Edyta Szurowska
- 2nd Department of Radiology, Medical University of Gdansk, 80-214 Gdansk, Poland; (A.G.); (T.N.); (E.S.)
| | - Boguslaw Mikaszewski
- Department of Otolaryngology, Medical University of Gdansk, 80-214 Gdansk, Poland;
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Xiang S, Ren J, Xia Z, Yuan Y, Tao X. Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging in the differential diagnosis of parotid tumors. BMC Med Imaging 2021; 21:194. [PMID: 34920706 PMCID: PMC8684181 DOI: 10.1186/s12880-021-00724-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 11/26/2021] [Indexed: 01/18/2023] Open
Abstract
Objective Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) histograms were used to investigate whether their parameters can distinguish between benign and malignant parotid gland tumors and further differentiate tumor subgroups. Materials and methods A total of 117 patients (32 malignant and 85 benign) who had undergone DCE-MRI for pretreatment evaluation were retrospectively included. Histogram parameters including mean, median, entropy, skewness, kurtosis and 10th, 90th percentiles were calculated from time to peak (TTP) (s), wash in rate (WIR) (l/s), wash out rate (WOR) (l/s), and maximum relative enhancement (MRE) (%) mono-exponential models. The Mann–Whitney U test was used to compare the differences between the benign and malignant groups. The diagnostic value of each significant parameter was determined on Receiver operating characteristic (ROC) analysis. Multivariate stepwise logistic regression analysis was used to identify the independent predictors of the different tumor groups. Results For both the benign and malignant groups and the comparisons among the subgroups, the parameters of TTP and MRE showed better performance among the various parameters. WOR can be used as an indicator to distinguish Warthin’s tumors from other tumors. Warthin’s tumors showed significantly lower values on 10th MRE and significantly higher values on skewness TTP and 10th WOR, and the combination of 10th MRE, skewness TTP and 10th WOR showed optimal diagnostic performance (AUC, 0.971) and provided 93.12% sensitivity and 96.70% specificity. After Warthin’s tumors were removed from among the benign tumors, malignant parotid tumors showed significantly lower values on the 10th TTP (AUC, 0.847; sensitivity 90.62%; specificity 69.09%; P < 0.05) and higher values on skewness MRE (AUC, 0.777; sensitivity 71.87%; specificity 76.36%; P < 0.05). Conclusion DCE-MRI histogram parameters, especially TTP and MRE parameters, show promise as effective indicators for identifying and classifying parotid tumors. Entropy TTP and kurtosis MRE were found to be independent differentiating variables for malignant parotid gland tumors. The 10th WOR can be used as an indicator to distinguish Warthin’s tumors from other tumors.
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Affiliation(s)
- Shiyu Xiang
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai, 200011, China
| | - Jiliang Ren
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai, 200011, China
| | - Zhipeng Xia
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai, 200011, China
| | - Ying Yuan
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai, 200011, China.
| | - Xiaofeng Tao
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai, 200011, China.
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Li Q, Jiang T, Zhang C, Zhang Y, Huang Z, Zhou H, Huang P. A nomogram based on clinical information, conventional ultrasound and radiomics improves prediction of malignant parotid gland lesions. Cancer Lett 2021; 527:107-114. [PMID: 34929334 DOI: 10.1016/j.canlet.2021.12.015] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 12/01/2021] [Accepted: 12/10/2021] [Indexed: 12/15/2022]
Abstract
Although conventional ultrasound (CUS) allows for clear detection of parotid gland lesions (PGLs), it fails to accurately provide benign-malignant differentiation due to overlapping morphological features. Radiomics is capable of processing large-quantity volume of data hidden in CUS image undiscovered by naked eyes. The aim was to explore the potential of CUS-based radiomics score (Rad-score) in distinguishing benign (BPGLs) and malignant PGLs (MPGLs). A consecutive of 281 PGLs (197 in training set and 84 in test set) with definite pathological confirmation was retrospectively enrolled. 1465 radiomics features were extracted from CUS images and Rad-score was constructed by using Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Different nomogram models, including clinic-radiomics (Clin + Rad-score), CUS-clinic (CUS + Clin) and combined CUS-clinic-radiomics (CUS + Clin + Rad-score), were built using logistic regression. The diagnostic performance of different models were calculated and compared by area under receiver operating curve (AUC) and corresponding sensitivity and specificity. Finally, 26 radiomics features were independent signatures for predicting MPGLs, with MPGLs having higher Rad-scores in both cohorts (both P < 0.05). In the test population, CUS + Clin + Rad-score obtained an excellent diagnostic result, with significantly higher AUC value (AUC = 0.91) when compared to that of CUS + Clin (AUC = 0.84) and Clin + Rad-score (AUC = 0.74), respectively (both P < 0.05). In addition, the sensitivity of this combined model was higher than that of single Rad-score model (100.00% vs. 71.43%, P = 0.031) without compromising the specificity value (82.86% vs. 88.57%, P = 0.334). The calibration curve and decision curve analysis also indicated the clinical effectiveness of the proposed combined nomogram. The combined CUS-clinic-radiomics model may help improve the discrimination of BPGLs from MPGLs.
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Affiliation(s)
- Qunying Li
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Tao Jiang
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Chao Zhang
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Ying Zhang
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Zixuan Huang
- Dalian University of Technology, Dalian, 116024, China
| | - Hang Zhou
- Department of In-patient Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150081, China.
| | - Pintong Huang
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China.
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Wei P, Shao C, Tian M, Wu M, Wang H, Han Z, Hu H. Quantitative Analysis and Pathological Basis of Signal Intensity on T2-Weighted MR Images in Benign and Malignant Parotid Tumors. Cancer Manag Res 2021; 13:5423-5431. [PMID: 34262350 PMCID: PMC8275037 DOI: 10.2147/cmar.s319466] [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: 05/14/2021] [Accepted: 06/25/2021] [Indexed: 11/29/2022] Open
Abstract
Objective To investigate the value of the signal intensity on T2-weighted magnetic resonance (MR) imaging using quantitative analysis in the differentiation of parotid tumors. Materials and Methods MR data of 80 pleomorphic adenomas (PAs), 68 Warthin tumors (WTs), and 34 malignant tumors (MTs) confirmed by surgery and histology were retrospectively analyzed. The signal intensities of tumor, normal parotid gland, spinal cord, and buccal subcutaneous fat were measured, and the signal intensity ratios (SIRs) between the tumor and the three references were calculated. Receiver operating characteristic curve was used to determine the optimal threshold and diagnostic efficiency of SIR for differentiating PAs, WTs, and MTs. Results The area under the curve (AUC) of tumor to parotid gland SIR (SIRP), tumor to spinal cord SIR (SIRC), and tumor to buccal subcutaneous fat SIR (SIRF) for differentiating PAs and WTs was 0.922, 0.918, and 0.934, respectively. The sensitivity and specificity at an optimal SIR threshold were 86.3% and 91.2%, 80.0% and 97.1%, and 85.0% and 94.1%, respectively. The AUC of SIRP, SIRC, and SIRF for distinguishing PAs from MTs was 0.793, 0.802, and 0.774, respectively. The sensitivity and specificity at an optimal SIR threshold was 86.3% and 61.8%, 80.0% and 73.5%, and 82.5% and 73.5%, respectively. The AUC of SIRP, SIRC, and SIRF for distinguishing WTs from MTs was 0.716, 0.709, and 0.759, respectively. The sensitivity and specificity at an optimal SIR threshold were 61.8% and 82.4%, 55.9% and 82.4%, and 64.7% and 86.8%, respectively. Conclusion SIRP, SIRC, and SIRF on T2-weighted MR images had high diagnostic efficiency for differentiating between PAs and WTs, while SIRP and SIRC for differentiating between PAs and MTs, and SIRF for differentiating between WTs and MTs had relatively high diagnostic efficiency.
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Affiliation(s)
- Peiying Wei
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.,Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Chang Shao
- Department of Pathology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Min Tian
- The Fourth Clinical Medical College, Zhejiang Traditional Chinese Medicine University, Hangzhou, People's Republic of China
| | - Mengwei Wu
- Department of Radiology, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, People's Republic of China
| | - Haibin Wang
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Zhijiang Han
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
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Psychogios G, Bohr C, Constantinidis J, Canis M, Vander Poorten V, Plzak J, Knopf A, Betz C, Guntinas-Lichius O, Zenk J. Author's response to the letter of the editor regarding the "Review of surgical techniques and guide for decision making in the treatment of benign parotid tumors". Eur Arch Otorhinolaryngol 2020; 277:3539-3540. [PMID: 32980892 DOI: 10.1007/s00405-020-06359-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 09/08/2020] [Indexed: 11/25/2022]
Affiliation(s)
- Georgios Psychogios
- Department of Otorhinolaryngology and Head and Neck Surgery, University Hospital Ioannina, Leof. Stavrou Niarchou, 455 00, Ioannina, Greece. .,Multidisciplinary Salivary Gland Society, Geneva, Switzerland.
| | - Christopher Bohr
- Department of Otorhinolaryngology and Head and Neck Surgery, University Hospital Regensburg, Franz-Josef-Strauss-Allee 11, D-93053, Regensburg, Germany
| | - Jannis Constantinidis
- 1st Department of ORL, Head and Neck Surgery, Aristotle University, AHEPA Hospital, Thessaloniki, Greece
| | - Martin Canis
- Department of Otorhinolaryngology and Head and Neck Surgery, LMU Klinikum, Marchioninistr. 15, 81377, Munich, Germany
| | - Vincent Vander Poorten
- Otorhinolaryngology-Head and Neck Surgery and Department of Oncology, Section Head and Neck Oncology, University Hospitals Leuven, KU Leuven, Leuven, Belgium.,Multidisciplinary Salivary Gland Society, Geneva, Switzerland
| | - Jan Plzak
- Department of Otorhinolaryngology and Head and Neck Surgery, 1st Faculty of Medicine, Charles University, Motol University Hospital, V Uvalu 84, 150 06, Prague 5, Czech Republic
| | - Andreas Knopf
- Department of Otorhinolarygology/Head and Neck Surgery, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Christian Betz
- Department of Otorhinolaryngology, Universitätsklinikum Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany
| | - Orlando Guntinas-Lichius
- Department of Otorhinolaryngology-Head and Neck, Surgery, Jena University Hospital, Jena, Germany.,Multidisciplinary Salivary Gland Society, Geneva, Switzerland
| | - Johannes Zenk
- Department of Otorhinolaryngology and Head and Neck Surgery, University Hospital Augsburg, Sauerbruchstraße 6, 86179, Augsburg, Germany.,Multidisciplinary Salivary Gland Society, Geneva, Switzerland
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