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Yang J, Bi Q, Jin Y, Yang Y, Du J, Zhang H, Wu K. Different MRI-based radiomics models for differentiating misdiagnosed or ambiguous pleomorphic adenoma and Warthin tumor of the parotid gland: a multicenter study. Front Oncol 2024; 14:1392343. [PMID: 38939335 PMCID: PMC11208325 DOI: 10.3389/fonc.2024.1392343] [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: 03/05/2024] [Accepted: 05/28/2024] [Indexed: 06/29/2024] Open
Abstract
Purpose To evaluate the effectiveness of MRI-based radiomics models in distinguishing between Warthin tumors (WT) and misdiagnosed or ambiguous pleomorphic adenoma (PA). Methods Data of patients with PA and WT from two centers were collected. MR images were used to extract radiomic features. The optimal radiomics model was found by running nine machine learning algorithms after feature reduction and selection. To create a clinical model, univariate logistic regression (LR) analysis and multivariate LR were used. The independent clinical predictors and radiomics were combined to create a nomogram. Two integrated models were constructed by the ensemble and stacking algorithms respectively based on the clinical model and the optimal radiomics model. The models' performance was evaluated using the area under the curve (AUC). Results There were 149 patients included in all. Gender, age, and smoking of patients were independent clinical predictors. With the greatest average AUC (0.896) and accuracy (0.839) in validation groups, the LR model was the optimal radiomics model. In the average validation group, the radiomics model based on LR did not have a higher AUC (0.795) than the clinical model (AUC = 0.909). The nomogram (AUC = 0.953) outperformed the radiomics model in terms of discrimination performance. The nomogram in the average validation group had a highest AUC than the stacking model (0.914) or ensemble model (0.798). Conclusion Misdiagnosed or ambiguous PA and WT can be non-invasively distinguished using MRI-based radiomics models. The nomogram exhibited excellent and stable diagnostic performance. In daily work, it is necessary to combine with clinical parameters for distinguishing between PA and WT.
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Affiliation(s)
- Jing Yang
- Department of MRI, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Qiu Bi
- Department of MRI, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Yiren Jin
- Department of Radiation, The Cancer Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yong Yang
- Department of MRI, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Ji Du
- Department of MRI, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Hongjiang Zhang
- Department of MRI, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Kunhua Wu
- Department of MRI, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
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Wang Y, Hu H, Ban X, Jiang Y, Su Y, Yang L, Shi G, Yang L, Han R, Duan X. Evaluation of Quantitative Dual-Energy Computed Tomography Parameters for Differentiation of Parotid Gland Tumors. Acad Radiol 2024; 31:2027-2038. [PMID: 37730491 DOI: 10.1016/j.acra.2023.08.024] [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: 06/27/2023] [Revised: 08/15/2023] [Accepted: 08/19/2023] [Indexed: 09/22/2023]
Abstract
RATIONALE AND OBJECTIVES To assess the diagnostic performance of quantitative parameters from dual-energy CT (DECT) in differentiating parotid gland tumors (PGTs). MATERIALS AND METHODS 101 patients with 108 pathologically proved PGTs were enrolled and classified into four groups: pleomorphic adenomas (PAs), warthin tumors (WTs), other benign tumors (OBTs), and malignant tumors (MTs). Conventional CT attenuation and DECT quantitative parameters, including iodine concentration (IC), normalized iodine concentration (NIC), effective atomic number (Zeff), electron density (Rho), double energy index (DEI), and the slope of the spectral Hounsfield unit curve (λHU), were obtained and compared between benign tumors (BTs) and MTs, and further compared among the four subgroups. Logistic regression analysis was used to assess the independent parameters and the receiver operating characteristic (ROC) curves were used to analyze the diagnostic performance. RESULTS Attenuation, Zeff, DEI, IC, NIC, and λHU in the arterial phase (AP) and venous phase (VP) were higher in MTs than in BTs (p < 0.001-0.047). λHU in VP and Zeff in AP were independent predictors with an area under the curve (AUC) of 0.84 after the combination. Furthermore, attenuation, Zeff, DEI, IC, NIC, and λHU in the AP and VP of MTs were higher than those of PAs (p < 0.001-0.047). Zeff and NIC in AP and λHU in VP were independent predictors with an AUC of 0.93 after the combination. Attenuation and Rho in the precontrast phase; attenuation, Rho, Zeff, DEI, IC, NIC, and λHU in AP; and the Rho in the VP of PAs were lower than those of WTs (p < 0.001-0.03). Rho in the precontrast phase and attenuation in AP were independent predictors with an AUC of 0.89 after the combination. MTs demonstrated higher Zeff, DEI, IC, NIC, and λHU in VP and lower Rho in the precontrast phase compared with WTs (p < 0.001-0.04); but no independent predictors were found. CONCLUSION DECT quantitative parameters can help to differentiate PGTs.
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Affiliation(s)
- Yu Wang
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang Road West, Guangzhou 510120, Guangdong, China (Y.W., H.H., Y.J., Y.S., L.Y., G.S., L.Y., R.H., X.D.)
| | - Huijun Hu
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang Road West, Guangzhou 510120, Guangdong, China (Y.W., H.H., Y.J., Y.S., L.Y., G.S., L.Y., R.H., X.D.)
| | - Xiaohua Ban
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou 510060, Guangdong, China (X.B.)
| | - Yusong Jiang
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang Road West, Guangzhou 510120, Guangdong, China (Y.W., H.H., Y.J., Y.S., L.Y., G.S., L.Y., R.H., X.D.)
| | - Yun Su
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang Road West, Guangzhou 510120, Guangdong, China (Y.W., H.H., Y.J., Y.S., L.Y., G.S., L.Y., R.H., X.D.)
| | - Lingjie Yang
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang Road West, Guangzhou 510120, Guangdong, China (Y.W., H.H., Y.J., Y.S., L.Y., G.S., L.Y., R.H., X.D.)
| | - Guangzi Shi
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang Road West, Guangzhou 510120, Guangdong, China (Y.W., H.H., Y.J., Y.S., L.Y., G.S., L.Y., R.H., X.D.); Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, Guangdong, China (G.S., X.D.)
| | - Lu Yang
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang Road West, Guangzhou 510120, Guangdong, China (Y.W., H.H., Y.J., Y.S., L.Y., G.S., L.Y., R.H., X.D.)
| | - Riyu Han
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang Road West, Guangzhou 510120, Guangdong, China (Y.W., H.H., Y.J., Y.S., L.Y., G.S., L.Y., R.H., X.D.)
| | - Xiaohui Duan
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang Road West, Guangzhou 510120, Guangdong, China (Y.W., H.H., Y.J., Y.S., L.Y., G.S., L.Y., R.H., X.D.); Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, Guangdong, China (G.S., X.D.).
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Zhou J, Wen Y, Ding R, Liu J, Fang H, Li X, Zhao K, Wan Q. Radiomics signature based on robust features derived from diffusion data for differentiation between benign and malignant solitary pulmonary lesions. Cancer Imaging 2024; 24:14. [PMID: 38246984 PMCID: PMC10802010 DOI: 10.1186/s40644-024-00660-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/10/2024] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Classifying and characterizing pulmonary lesions are critical for clinical decision-making process to identify optimal therapeutic strategies. The purpose of this study was to develop and validate a radiomics nomogram for distinguishing between benign and malignant pulmonary lesions based on robust features derived from diffusion images. MATERIAL AND METHODS The study was conducted in two phases. In the first phase, we prospectively collected 30 patients with pulmonary nodule/mass who underwent twice EPI-DWI scans. The robustness of features between the two scans was evaluated using the concordance correlation coefficient (CCC) and dynamic range (DR). In the second phase, 139 patients who underwent pulmonary DWI were randomly divided into training and test sets in a 7:3 ratio. Maximum relevance minimum redundancy, least absolute shrinkage and selection operator, and logistic regression were used for feature selection and construction of radiomics signatures. Nomograms were established incorporating clinical features, radiomics signatures, and ADC(0, 800). The diagnostic efficiency of different models was evaluated using the area under the curve (AUC) and decision curve analysis. RESULTS Among the features extracted from DWI and ADC images, 42.7% and 37.4% were stable (both CCC and DR ≥ 0.85). The AUCs for distinguishing pulmonary lesions in the test set for clinical model, ADC, ADC radiomics signatures, and DWI radiomics signatures were 0.694, 0.802, 0.885, and 0.767, respectively. The nomogram exhibited the best differentiation performance (AUC = 0.923). The decision curve showed that the nomogram consistently outperformed ADC value and clinical model in lesion differentiation. CONCLUSION Our study demonstrates the robustness of radiomics features derived from lung DWI. The ADC radiomics nomogram shows superior clinical net benefits compared to conventional clinical models or ADC values alone in distinguishing solitary pulmonary lesions, offering a promising tool for noninvasive, precision diagnosis in lung cancer.
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Affiliation(s)
- Jiaxuan Zhou
- Department of Radiology, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Yu Wen
- Department of Radiology, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Ruolin Ding
- The Second Clinical Medicine School, Guangzhou Medical University, Guangzhou, China
| | - Jieqiong Liu
- Department of Radiology, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Hanzhen Fang
- Department of Radiology, Huilai County People's Hospital, Jieyang, China
| | - Xinchun Li
- Department of Radiology, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Kangyan Zhao
- Department of Radiology, The Affiliated Hospital of Hubei University of Arts and Science, Xiangyang Central Hospital, Xiangyang, 441021, Hubei, China.
| | - Qi Wan
- Department of Radiology, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China.
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HaLiMaiMaiTi N, Hong Y, Li M, Li H, Wang Y, Chen C, Lv X, Chen C. Classification of benign and malignant parotid tumors based on CT images combined with stack generalization model. Med Biol Eng Comput 2023; 61:3123-3135. [PMID: 37656333 DOI: 10.1007/s11517-023-02898-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 07/09/2023] [Indexed: 09/02/2023]
Abstract
Parotid tumors are among the most prevalent tumors in otolaryngology, and malignant parotid tumors are one of the main causes of facial paralysis in patients. Currently, the main diagnostic modality for parotid tumors is computed tomography, which relies mainly on the subjective judgment of clinicians and leads to practical problems such as high workloads. Therefore, to assist physicians in solving the preoperative classification problem, a stacked generalization model is proposed for the automated classification of parotid tumor images. A ResNet50 pretrained model is used for feature extraction. The first layer of the adopted stacked generalization model consists of multiple weak learners, and the results of the weak learners are integrated as input data in a meta-classifier in the second layer. The output results of the meta-classifier are the final classification results. The classification accuracy of the stacked generalization model reaches 91%. Comparing the classification results under different classifiers, the stacked generalization model used in this study can identify benign and malignant tumors in the parotid gland effectively, thus relieving physicians of tedious work pressure.
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Affiliation(s)
| | - Yue Hong
- People's Hospital of Xinjiang Uygur Autonomous Region, UrumqiXinjiang, 830001, China
| | - Min Li
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Hongtao Li
- The Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, 830011, China
| | - Yunling Wang
- The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830000, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi, 830046, China.
- Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, 830046, China.
- Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, 830046, China.
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi, 830046, China.
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Mao K, Wong LM, Zhang R, So TY, Shan Z, Hung KF, Ai QYH. Radiomics Analysis in Characterization of Salivary Gland Tumors on MRI: A Systematic Review. Cancers (Basel) 2023; 15:4918. [PMID: 37894285 PMCID: PMC10605883 DOI: 10.3390/cancers15204918] [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: 09/12/2023] [Revised: 10/06/2023] [Accepted: 10/08/2023] [Indexed: 10/29/2023] Open
Abstract
Radiomics analysis can potentially characterize salivary gland tumors (SGTs) on magnetic resonance imaging (MRI). The procedures for radiomics analysis were various, and no consistent performances were reported. This review evaluated the methodologies and performances of studies using radiomics analysis to characterize SGTs on MRI. We systematically reviewed studies published until July 2023, which employed radiomics analysis to characterize SGTs on MRI. In total, 14 of 98 studies were eligible. Each study examined 23-334 benign and 8-56 malignant SGTs. Least absolute shrinkage and selection operator (LASSO) was the most common feature selection method (in eight studies). Eleven studies confirmed the stability of selected features using cross-validation or bootstrap. Nine classifiers were used to build models that achieved area under the curves (AUCs) of 0.74 to 1.00 for characterizing benign and malignant SGTs and 0.80 to 0.96 for characterizing pleomorphic adenomas and Warthin's tumors. Performances were validated using cross-validation, internal, and external datasets in four, six, and two studies, respectively. No single feature consistently appeared in the final models across the studies. No standardized procedure was used for radiomics analysis in characterizing SGTs on MRIs, and various models were proposed. The need for a standard procedure for radiomics analysis is emphasized.
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Affiliation(s)
- Kaijing Mao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Lun M. Wong
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Rongli Zhang
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Tiffany Y. So
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Zhiyi Shan
- Paediatric Dentistry & Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Kuo Feng Hung
- Applied Oral Sciences & Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Qi Yong H. Ai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
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Chen F, Ge Y, Li S, Liu M, Wu J, Liu Y. Enhanced CT-based texture analysis and radiomics score for differentiation of pleomorphic adenoma, basal cell adenoma, and Warthin tumor of the parotid gland. Dentomaxillofac Radiol 2023; 52:20220009. [PMID: 36367128 PMCID: PMC9974237 DOI: 10.1259/dmfr.20220009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 10/20/2022] [Accepted: 10/21/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE To evaluate the diagnostic performance of computed tomography (CT) radiomics analysis for differentiating pleomorphic adenoma (PA), Warthin tumor (WT), and basal cell adenoma (BCA). METHODS A total of 189 patients with PA (n = 112), WT (n = 53) and BCA (n = 24) were divided into a training set (n = 133) and a test set (n = 56). The radiomics features were extracted from plain CT and contrast-enhanced CT images. After dimensionality reduction, plain CT, multiphase-enhanced CT, integrated radiomics signature models and radiomics score (Rad-score) were established and calculated. The receiver operating characteristic (ROC) curve analysis was taken for the assessment of the model performance, and then comparison was conducted among these models. Decision curve analysis (DCA) was adopted to assess the clinical benefits of the models. Diagnostic performances including sensitivity, specificity, and accuracy of the radiologists were evaluated. RESULTS Seven, nine, fourteen, and fourteen optimal features were used to constructed plain scan, arterial phase, venous phase, and integrated radiomics signature models, respectively. ROC analysis showed these four models were able to differentiate PA from BCA and WT, with the area under the ROC curve (AUC) values of 0.79, 0.90, 0.87, and 0.94 in the training set, and 0.79, 0.89, 0.86, and 0.94 in the test set, respectively. The integrated model had better diagnostic performance than single-phase radiomics model, but it had similar diagnostic performance to that of the radiomics model based on the arterial phase (p > 0.05). The sensitivity, specificity, and accuracy in the diagnosis of PA were 0.86, 0.46, and 0.70 for the non-subspecialized radiologist and 0.88, 0.77, and 0.84 for the subspecialized radiologist, respectively. Six venous phase parameters were finally selected in differentiating WT from BCA. The predictive effect of the model was favorable, with AUC value of 0.95, sensitivity of 0.96, specificity of 0.83, and accuracy of 0.92. The sensitivity, specificity, and accuracy in the diagnosis between WT and BCA were 0.26, 0.87, and 0.45 for the non-subspecialized radiologist and 0.85, 0.58, and 0.77 for the subspecialized radiologist, respectively. CONCLUSION The CT-based radiomics analysis showed favorable predictive performance for differentiating PA, WT, and BCA, thus may be helpful in the clinical decision-making.
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Affiliation(s)
- Fangfang Chen
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, China
| | | | - Shuang Li
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, China
| | - Mengqiu Liu
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, China
| | - Jiaoyan Wu
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, China
| | - Ying Liu
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, China
- GE Healthcare, Shanghai, China
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Value of T2-weighted-based radiomics model in distinguishing Warthin tumor from pleomorphic adenoma of the parotid. Eur Radiol 2022; 33:4453-4463. [PMID: 36502461 DOI: 10.1007/s00330-022-09295-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 11/01/2022] [Accepted: 11/09/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVES The differentiation of Warthin tumor and pleomorphic adenoma before treatment is crucial for clinical strategies. The aim of this study was to develop and test a T2-weighted-based radiomics model for differentiating pleomorphic adenoma from Warthin tumor of the parotid gland. METHODS A total of 117 patients, including 61 cases of Warthin tumor and 56 cases of pleomorphic adenoma, were retrospectively enrolled from two centers between January 2010 and June 2022. The training set included 82 cases, and the validation set included 35 cases. From T2-weighted images, 971 radiomics features were extracted. Seven radiomics features remained after a two-step selection process. We used the seven radiomics features and clinical factors through multivariable logistic regression to build radiomics and clinical models, respectively. A radiomics-clinical model was also built that combined the independent clinical predictors with the radiomics features. Through ROC curves, the three models were evaluated and compared. RESULTS In the radiomics model, AUCs were 0.826 and 0.796 in training and validation sets, respectively. In the clinical model, the AUCs were 0.923 and 0.926 in the training and validation sets, respectively. Decision curve analysis revealed that the radiomics-clinical model had the best diagnostic performance for distinguishing Warthin tumor from pleomorphic adenoma of the parotid gland (AUC = 0.962 and 0.934 for the training and validation sets, respectively). CONCLUSION The radiomics-clinical model performed well in differentiating pleomorphic adenoma from Warthin tumor of the parotid gland. KEY POINTS • The clinical model outperformed the radiomics model in distinguishing pleomorphic adenoma from Warthin tumor of the parotid gland. • The radiomics features extracted from T2-weighted images could help differentiate pleomorphic adenoma from Warthin tumor of the parotid gland. • The radiomics-clinical model was superior to the radiomics and the clinical models for differentiating pleomorphic adenoma from Warthin tumor of the parotid gland.
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The Role of Radiomics in Salivary Gland Imaging: A Systematic Review and Radiomics Quality Assessment. Diagnostics (Basel) 2022; 12:diagnostics12123002. [PMID: 36553009 PMCID: PMC9777175 DOI: 10.3390/diagnostics12123002] [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: 10/24/2022] [Revised: 11/16/2022] [Accepted: 11/29/2022] [Indexed: 12/04/2022] Open
Abstract
Background: Radiomics of salivary gland imaging can support clinical decisions in different clinical scenarios, such as tumors, radiation-induced xerostomia and sialadenitis. This review aims to evaluate the methodological quality of radiomics studies on salivary gland imaging. Material and Methods: A systematic search was performed, and the methodological quality was evaluated using the radiomics quality score (RQS). Subgroup analyses according to the first author's professional role (medical or not medical), journal type (radiological journal or other) and the year of publication (2021 or before) were performed. The correlation of RQS with the number of patients was calculated. Results: Twenty-three articles were included (mean RQS 11.34 ± 3.68). Most studies well-documented the imaging protocol (87%), while neither prospective validations nor cost-effectiveness analyses were performed. None of the included studies provided open-source data. A statistically significant difference in RQS according to the year of publication was found (p = 0.009), with papers published in 2021 having slightly higher RQSs than older ones. No differences according to journal type or the first author's professional role were demonstrated. A moderate relationship between the overall RQS and the number of patients was found. Conclusions: Radiomics application in salivary gland imaging is increasing. Although its current clinical applicability can be affected by the somewhat inadequate quality of the papers, a significant improvement in radiomics methodologies has been demonstrated in the last year.
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Radiomics for Discriminating Benign and Malignant Salivary Gland Tumors; Which Radiomic Feature Categories and MRI Sequences Should Be Used? Cancers (Basel) 2022; 14:cancers14235804. [PMID: 36497285 PMCID: PMC9740105 DOI: 10.3390/cancers14235804] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 11/12/2022] [Accepted: 11/22/2022] [Indexed: 11/26/2022] Open
Abstract
The lack of a consistent MRI radiomic signature, partly due to the multitude of initial feature analyses, limits the widespread clinical application of radiomics for the discrimination of salivary gland tumors (SGTs). This study aimed to identify the optimal radiomics feature category and MRI sequence for characterizing SGTs, which could serve as a step towards obtaining a consensus on a radiomics signature. Preliminary radiomics models were built to discriminate malignant SGTs (n = 34) from benign SGTs (n = 57) on T1-weighted (T1WI), fat-suppressed (FS)-T2WI and contrast-enhanced (CE)-T1WI images using six feature categories. The discrimination performances of these preliminary models were evaluated using 5-fold-cross-validation with 100 repetitions and the area under the receiver operating characteristic curve (AUC). The differences between models’ performances were identified using one-way ANOVA. Results show that the best feature categories were logarithm for T1WI and CE-T1WI and exponential for FS-T2WI, with AUCs of 0.828, 0.754 and 0.819, respectively. These AUCs were higher than the AUCs obtained using all feature categories combined, which were 0.750, 0.707 and 0.774, respectively (p < 0.001). The highest AUC (0.846) was obtained using a combination of T1WI + logarithm and FS-T2WI + exponential features, which reduced the initial features by 94.0% (from 1015 × 3 to 91 × 2). CE-T1WI did not improve performance. Using one feature category rather than all feature categories combined reduced the number of initial features without compromising radiomic performance.
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Qi J, Gao A, Ma X, Song Y, zhao G, Bai J, Gao E, Zhao K, Wen B, Zhang Y, Cheng J. Differentiation of Benign From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics Nomogram. Front Oncol 2022; 12:937050. [PMID: 35898886 PMCID: PMC9309371 DOI: 10.3389/fonc.2022.937050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 06/20/2022] [Indexed: 12/12/2022] Open
Abstract
Objectives We aimed to develop and validate radiomic nomograms to allow preoperative differentiation between benign- and malignant parotid gland tumors (BPGT and MPGT, respectively), as well as between pleomorphic adenomas (PAs) and Warthin tumors (WTs). Materials and Methods This retrospective study enrolled 183 parotid gland tumors (68 PAs, 62 WTs, and 53 MPGTs) and divided them into training (n = 128) and testing (n = 55) cohorts. In total, 2553 radiomics features were extracted from fat-saturated T2-weighted images, apparent diffusion coefficient maps, and contrast-enhanced T1-weighted images to construct single-, double-, and multi-sequence combined radiomics models, respectively. The radiomics score (Rad-score) was calculated using the best radiomics model and clinical features to develop the radiomics nomogram. The receiver operating characteristic curve and area under the curve (AUC) were used to assess these models, and their performances were compared using DeLong’s test. Calibration curves and decision curve analysis were used to assess the clinical usefulness of these models. Results The multi-sequence combined radiomics model exhibited better differentiation performance (BPGT vs. MPGT, AUC=0.863; PA vs. MPGT, AUC=0.929; WT vs. MPGT, AUC=0.825; PA vs. WT, AUC=0.927) than the single- and double sequence radiomics models. The nomogram based on the multi-sequence combined radiomics model and clinical features attained an improved classification performance (BPGT vs. MPGT, AUC=0.907; PA vs. MPGT, AUC=0.961; WT vs. MPGT, AUC=0.879; PA vs. WT, AUC=0.967). Conclusions Radiomics nomogram yielded excellent diagnostic performance in differentiating BPGT from MPGT, PA from MPGT, and PA from WT.
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Affiliation(s)
- Jinbo Qi
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ankang Gao
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoyue Ma
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yang Song
- Magnetic Resonance Scientific Marketing, Siemens Healthineers Ltd., Shanghai, China
| | - Guohua zhao
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jie Bai
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Eryuan Gao
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kai Zhao
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Baohong Wen
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Baohong Wen, ; Yong Zhang, ; Jingliang Cheng,
| | - Yong Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Baohong Wen, ; Yong Zhang, ; Jingliang Cheng,
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Baohong Wen, ; Yong Zhang, ; Jingliang Cheng,
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Faggioni L, Gabelloni M, De Vietro F, Frey J, Mendola V, Cavallero D, Borgheresi R, Tumminello L, Shortrede J, Morganti R, Seccia V, Coppola F, Cioni D, Neri E. Usefulness of MRI-based radiomic features for distinguishing Warthin tumor from pleomorphic adenoma: performance assessment using T2-weighted and post-contrast T1-weighted MR images. Eur J Radiol Open 2022; 9:100429. [PMID: 35757232 PMCID: PMC9214819 DOI: 10.1016/j.ejro.2022.100429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 06/13/2022] [Indexed: 11/27/2022] Open
Abstract
Purpose Differentiating Warthin tumor (WT) from pleomorphic adenoma (PA) is of primary importance due to differences in patient management, treatment and outcome. We sought to evaluate the performance of MRI-based radiomic features in discriminating PA from WT in the preoperative setting. Methods We retrospectively evaluated 81 parotid gland lesions (48 PA and 33 WT) on T2-weighted (T2w) images and 52 of them on post-contrast fat-suppressed T1-weighted (pcfsT1w) images. All MRI examinations were carried out on a 1.5-Tesla MRI scanner, and images were segmented manually using the software ITK-SNAP (www.itk-snap.org). Results The most discriminative feature on pcfsT1w images was GLCM_InverseVariance, yielding area under the curve (AUC), sensitivity and specificity of 0.9, 86 % and 87 %, respectively. Skewness was the feature extracted from T2w images with the highest specificity (88 %) in discriminating WT from PA. Conclusion Radiomic analysis could be an important tool to improve diagnostic accuracy in differentiating PA from WT.
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Key Words
- ADC, apparent diffusion coefficient
- AUC, area under the curve
- FNAC, fine needle aspiration cytology
- GLCM, gray level co-occurrence matrix
- GLDM, gray level dependence matrix
- GLRLM, gray level run length matrix
- GLSZM, gray level size zone matrix
- Head and neck cancer
- IBSI Image, Biomarker Standardization Initiative
- Magnetic resonance imaging
- NGTDM, neighboring gray tone difference matrix
- PA, pleomorphic adenoma
- Parotid neoplasm
- PcfsT1W, post-contrast fat-suppressed T1-weighted
- Pleomorphic adenoma
- ROC, receiver operating characteristics
- Radiomics
- WT, Warthin tumor
- Warthin tumor
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Affiliation(s)
- Lorenzo Faggioni
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Michela Gabelloni
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Fabrizio De Vietro
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Jessica Frey
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Vincenzo Mendola
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Diletta Cavallero
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Rita Borgheresi
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Lorenzo Tumminello
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Jorge Shortrede
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Riccardo Morganti
- Department of Clinical and Experimental Medicine, Section of Statistics, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Veronica Seccia
- Otolaryngology, Audiology, and Phoniatric Operative Unit, Department of Surgical, Medical, Molecular Pathology, and Critical Care Medicine, Azienda Ospedaliero Universitaria Pisana, University of Pisa, 56124 Pisa, Italy
| | - Francesca Coppola
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, 40138, Bologna, Italy.,Italian Society of Medical and Interventional Radiology, SIRM Foundation, Via della Signora 2, 20122, Milano, Italy
| | - Dania Cioni
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy.,Italian Society of Medical and Interventional Radiology, SIRM Foundation, Via della Signora 2, 20122, Milano, Italy
| | - Emanuele Neri
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy.,Italian Society of Medical and Interventional Radiology, SIRM Foundation, Via della Signora 2, 20122, Milano, Italy
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Han Z, Ren H, Sun J, Jin L, Wang Q, Guo C, Tian Z. Integrated weighted gene coexpression network analysis identifies Frizzled 2 (FZD2) as a key gene in invasive malignant pleomorphic adenoma. J Transl Med 2022; 20:15. [PMID: 34986855 PMCID: PMC8734245 DOI: 10.1186/s12967-021-03204-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 12/17/2021] [Indexed: 12/02/2022] Open
Abstract
Background Invasive malignant pleomorphic adenoma (IMPA) is a highly malignant neoplasm of the oral salivary glands with a poor prognosis and a considerable risk of recurrence. Many disease-causing genes of IMPA have been identified in recent decades (e.g., P53, PCNA and HMGA2), but many of these genes remain to be explored. Weighted gene coexpression network analysis (WGCNA) is a newly emerged algorithm that can cluster genes and form modules based on similar gene expression patterns. This study constructed a gene coexpression network of IMPA via WGCNA and then carried out multifaceted analysis to identify novel disease-causing genes. Methods RNA sequencing (RNA-seq) was performed for 10 pairs of IMPA and normal tissues to acquire the gene expression profiles. Differentially expressed genes (DEGs) were screened out with the cutoff criteria of |log2 Fold change (FC)|> 1 and adjusted p value < 0.05. Then, WGCNA was applied to systematically identify the hidden diagnostic hub genes of IMPA. Results In this research, a total of 1970 DEGs were screened out in IMPA tissues, including 1056 upregulated DEGs and 914 downregulated DEGs. Functional enrichment analysis was performed for identified DEGs and revealed an enrichment of tumor-associated GO terms and KEGG pathways. We used WGCNA to identify gene module most relevant with the histological grade of IMPA. The gene FZD2 was then recognized as the hub gene of the selected module with the highest module membership (MM) value and intramodule connectivity in protein–protein interaction (PPI) network. According to immunohistochemistry (IHC) staining, the expression level of FZD2 was higher in low-grade IMPA than in high-grade IMPA. Conclusion FZD2 shows an expression dynamic that is negatively correlated with the clinical malignancy of IMPA and it plays a central role in the transcription network of IMPA. Thus, FZD2 serves as a promising histological indicator for the precise prediction of IMPA histological stages. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-03204-7.
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Affiliation(s)
- Zhenyuan Han
- Department of Oral Pathology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,National Clinical Research Center for Oral Diseases, Shanghai, China.,Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
| | - Huiping Ren
- Department of Prosthodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University & Shandong Provincial Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Jinan, Shandong, China
| | - Jingjing Sun
- Department of Oral Pathology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,National Clinical Research Center for Oral Diseases, Shanghai, China
| | - Lihui Jin
- Pediatric Heart Center, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qin Wang
- Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Chuanbin Guo
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China.
| | - Zhen Tian
- Department of Oral Pathology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. .,National Clinical Research Center for Oral Diseases, Shanghai, China.
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