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Sun H, Sun Z, Wang W, Cha X, Jiang Q, Wang X, Li Q, Liu S, Liu H, Chen Q, Yuan W, Xiao Y. The value of T1- and FST2-Weighted-based radiomics nomogram in differentiating pleomorphic adenoma and Warthin tumor. Transl Oncol 2024; 49:102087. [PMID: 39159554 DOI: 10.1016/j.tranon.2024.102087] [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: 04/22/2024] [Revised: 07/23/2024] [Accepted: 08/11/2024] [Indexed: 08/21/2024] Open
Abstract
PURPOSE To establish a radiomics nomogram based on MRI radiomics features combined with clinical characteristics for distinguishing pleomorphic adenoma (PA) from warthin tumor (WT). METHODS 294 patients with PA (n = 159) and WT (n = 135) confirmed by histopathology were included in this study between July 2017 and June 2023. Clinical factors including clinical data and MRI features were analyzed to establish clinical model. 10 MRI radiomics features were extracted and selected from T1WI and FS-T2WI, used to establish radiomics model and calculate radiomics scores (Rad-scores). Clinical factors and Rad-scores were combined to serve as crucial parameters for combined model. Through Receiver operator characteristics (ROC) curve and decision curve analysis (DCA), the discriminative values of the three models were qualified and compared, the best-performing combined model was visualized in the form of a radiomics nomogram. RESULTS The combined model demonstrated excellent discriminative performance for PA and WT in the training set (AUC=0.998) and testing set (AUC=0.993) and performed better compared with the clinical model and radiomics model in the training set (AUC=0.996, 0.952) and testing model (AUC=0.954, 0.849). The DCA showed that the combined model provided more overall clinical usefulness in distinguishing parotid PA from WT than another two models. CONCLUSION An analytical radiomics nomogram based on MRI radiomics features, incorporating clinical factors, can effectively distinguish between PA and WT.
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Affiliation(s)
- Hongbiao Sun
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Zuoheng Sun
- Department of Otolaryngology, Changzheng Hospital, Navy Medical University, Shanghai, China; Department of Otolaryngology, Naval Specialty Medical Center, Naval Medical University, Shanghai, China
| | - Wenwen Wang
- Department of Neurology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Xudong Cha
- Department of Otolaryngology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Qinling Jiang
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Xiang Wang
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Qingchu Li
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Shiyuan Liu
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Huanhai Liu
- Department of Otolaryngology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Qi Chen
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China; Department of Radiology, Kunshan Third People's Hospital, Kunshan, Jiangsu, China
| | - Weimin Yuan
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China; Department of Radiology, Qingdao Special Servicemen Recuperation Center of PLA Navy, Qingdao, China
| | - Yi Xiao
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China.
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Zeng J, Li J, Saad M, Lineaweaver WC, Qi F, Pan Y. A Review of Surgical Incisions Used for the Excision of Benign Parotid Tumors. Ann Plast Surg 2024; 93:S69-S74. [PMID: 39101852 DOI: 10.1097/sap.0000000000003932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2024]
Abstract
OBJECTIVE To provide surgical references for selecting appropriate parotidectomy incisions, reviewing modified approaches, incision designs, and associated complications. METHODS We have systematically searched 5 medical literature databases examining parotidectomy incision designs and postoperative complications from 2008 to 2021. RESULTS There are a total of 9 novel incision designs: 1) posterior auricular hairline incision (PAHI); 2) combined preauricular and retroauricular incision (CPRI); 3) V-shaped incision (VI); 4) N-shaped incision (NI); 5) postaural incision (PI); 6) preauricular crutch incision (PCI); and 7) endaural incision (EI). Simultaneously, there are a total of 8 postoperative complications: 1) infection; 2) salivary fistula; 3) facial nerve palsy/paresis; 4) ear lobule numbness; 5) Frey syndrome; 6) facial deformity; 7) hematoma; and 8) tumor reoccurrence. CONCLUSIONS Over the last decade, a surge in modified parotidectomy incisions has been witnessed in clinical practice. This expansion is attributed to rapid technical advancements and a deeper understanding of anatomy and histopathology. These modified approaches contribute significantly to improving cosmetic outcomes, minimizing associated complications, and enhancing patient satisfaction.
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Affiliation(s)
- Junhao Zeng
- From the Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jianrui Li
- From the Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Mariam Saad
- Department of Plastic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | | | - Fazhi Qi
- From the Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yuyan Pan
- From the Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
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Rao Y, Ma Y, Wang J, Xiao W, Wu J, Shi L, Guo L, Fan L. Performance of radiomics in the differential diagnosis of parotid tumors: a systematic review. Front Oncol 2024; 14:1383323. [PMID: 39119093 PMCID: PMC11306159 DOI: 10.3389/fonc.2024.1383323] [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: 03/06/2024] [Accepted: 07/01/2024] [Indexed: 08/10/2024] Open
Abstract
Purpose A systematic review and meta-analysis were conducted to evaluate the diagnostic precision of radiomics in the differential diagnosis of parotid tumors, considering the increasing utilization of radiomics in tumor diagnosis. Although some researchers have attempted to apply radiomics in this context, there is ongoing debate regarding its accuracy. Methods Databases of PubMed, Cochrane, EMBASE, and Web of Science up to May 29, 2024 were systematically searched. The quality of included primary studies was assessed using the Radiomics Quality Score (RQS) checklist. The meta-analysis was performed utilizing a bivariate mixed-effects model. Results A total of 39 primary studies were incorporated. The machine learning model relying on MRI radiomics for diagnosis malignant tumors of the parotid gland, demonstrated a sensitivity of 0.80 [95% CI: 0.74, 0.86], SROC of 0.89 [95% CI: 0.27-0.99] in the validation set. The machine learning model based on MRI radiomics for diagnosis malignant tumors of the parotid gland, exhibited a sensitivity of 0.83[95% CI: 0.76, 0.88], SROC of 0.89 [95% CI: 0.17-1.00] in the validation set. The models also demonstrated high predictive accuracy for benign lesions. Conclusion There is great potential for radiomics-based models to improve the accuracy of diagnosing benign and malignant tumors of the parotid gland. To further enhance this potential, future studies should consider implementing standardized radiomics-based features, adopting more robust feature selection methods, and utilizing advanced model development tools. These measures can significantly improve the diagnostic accuracy of artificial intelligence algorithms in distinguishing between benign and malignant tumors of the parotid gland. Systematic review registration https://www.crd.york.ac.uk/prospero/, identifier CRD42023434931.
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Affiliation(s)
- Yilin Rao
- Department of Prosthodontics, The Affiliated Stomatology Hospital, Southwest Medical University, Luzhou, Sichuan, China
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, The Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, Sichuan, China
| | - Yuxi Ma
- Department of Prosthodontics, The Affiliated Stomatology Hospital, Southwest Medical University, Luzhou, Sichuan, China
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, The Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, Sichuan, China
| | - Jinghan Wang
- Department of Prosthodontics, The Affiliated Stomatology Hospital, Southwest Medical University, Luzhou, Sichuan, China
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, The Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, Sichuan, China
| | - Weiwei Xiao
- Department of Prosthodontics, The Affiliated Stomatology Hospital, Southwest Medical University, Luzhou, Sichuan, China
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, The Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, Sichuan, China
| | - Jiaqi Wu
- Department of Prosthodontics, The Affiliated Stomatology Hospital, Southwest Medical University, Luzhou, Sichuan, China
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, The Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, Sichuan, China
| | - Liang Shi
- Department of Prosthodontics, The Affiliated Stomatology Hospital, Southwest Medical University, Luzhou, Sichuan, China
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, The Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, Sichuan, China
| | - Ling Guo
- Department of Prosthodontics, The Affiliated Stomatology Hospital, Southwest Medical University, Luzhou, Sichuan, China
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, The Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, Sichuan, China
| | - Liyuan Fan
- Department of Prosthodontics, The Affiliated Stomatology Hospital, Southwest Medical University, Luzhou, Sichuan, China
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, The Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, Sichuan, China
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Mai W, Tan J, Zhang L, Wang L, Zhang D, Shi C, Liu X. Predicting High-risk Capsular Features in Pleomorphic Adenoma of the Parotid Gland Through a Nomogram Model Based on ADC Imaging. Acad Radiol 2024:S1076-6332(24)00362-3. [PMID: 38908917 DOI: 10.1016/j.acra.2024.06.003] [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: 04/30/2024] [Revised: 05/28/2024] [Accepted: 06/01/2024] [Indexed: 06/24/2024]
Abstract
RATIONALE AND OBJECTIVES Based on Apparent Diffusion Coefficient (ADC) images, a nomogram model is established to accurately predict the high-risk capsular characteristics associated with pleomorphic adenoma of the parotid gland (PAP) recurrence. MATERIALS AND METHODS This retrospective study analyzed 190 patients with PAPs. Significant clinical radiological factors were identified through univariate difference analysis and multivariate regression analysis. The optimal threshold was determined by analyzing the average ADC value of the entire tumor, using the best Youden index and sensitivity analysis, and tumor subregions were delineated accordingly. Three radiomic models were constructed for the whole tumor and for high/low ADC areas, with the best model determined through statistical analysis. Ultimately, a nomogram model was constructed by combining the independent predictive factor of high-risk capsular features with the optimal radiomic predictive score. Model performance was comprehensively assessed by the area under the receiver operating characteristic curve (ROC AUC), accuracy, sensitivity, and specificity. RESULTS The best ADC division threshold as 1.25 × 10-3 mm2/s. Multivariate analysis identified High-ADC Zone Volume Percentage as an independent predictor for PAPs with high-risk capsular characteristics. The radiomic model based on the low ADC tumor subregion was optimal (AUC 0.899). The nomogram model, combining independent predictors and optimal imaging studies predictive score, demonstrated high performance (AUC 0.909). Decision curve analysis confirmed the nomogram's clinical applicability. CONCLUSION The nomogram model constructed from ADC quantitative imaging can predict PAPs patients with high-risk capsular features. These patients require intraoperative preventive measures to avoid tumor spillage and residuals, as well as extended postoperative follow-up.
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Affiliation(s)
- Wenfeng Mai
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou 510630, PR China
| | - Jingyi Tan
- Department of Radiology, YueBei People's hospital, Shaoguan 512026, PR China
| | - Lingtao Zhang
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou 510630, PR China
| | - Liaoyuan Wang
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou 510630, PR China
| | - Dong Zhang
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou 510630, PR China
| | - Changzheng Shi
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou 510630, PR China
| | - Xiangning Liu
- The First Affiliated Hospital of Jinan University, Clinical Research Platform for Interdiscipline of Stomatology, School of Stomatology, Jinan University, Guangzhou 510630, PR China.
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Mao Y, Jiang LP, Wang JL, Diao YH, Chen FQ, Zhang WP, Chen L, Liu ZX. Multi-feature Fusion Network on Gray Scale Ultrasonography: Effective Differentiation of Adenolymphoma and Pleomorphic Adenoma. Acad Radiol 2024:S1076-6332(24)00308-8. [PMID: 38871552 DOI: 10.1016/j.acra.2024.05.023] [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: 04/02/2024] [Revised: 05/12/2024] [Accepted: 05/13/2024] [Indexed: 06/15/2024]
Abstract
RATIONALE AND OBJECTIVES to develop a deep learning radiomics graph network (DLRN) that integrates deep learning features extracted from gray scale ultrasonography, radiomics features and clinical features, for distinguishing parotid pleomorphic adenoma (PA) from adenolymphoma (AL) MATERIALS AND METHODS: A total of 287 patients (162 in training cohort, 70 in internal validation cohort and 55 in external validation cohort) from two centers with histologically confirmed PA or AL were enrolled. Deep transfer learning features and radiomics features extracted from gray scale ultrasound images were input to machine learning classifiers including logistic regression (LR), support vector machines (SVM), KNN, RandomForest (RF), ExtraTrees, XGBoost, LightGBM, and MLP to construct deep transfer learning radiomics (DTL) models and Rad models respectively. Deep learning radiomics (DLR) models were constructed by integrating the two features and DLR signatures were generated. Clinical features were further combined with the signatures to develop a DLRN model. The performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis, calibration, decision curve analysis (DCA), and the Hosmer-Lemeshow test. RESULTS In the internal validation cohort and external validation cohort, comparing to Clinic (AUC=0.767 and 0.777), Rad (AUC=0.841 and 0.748), DTL (AUC=0.740 and 0.825) and DLR (AUC=0.863 and 0.859), the DLRN model showed greatest discriminatory ability (AUC=0.908 and 0.908) showed optimal discriminatory ability. CONCLUSION The DLRN model built based on gray scale ultrasonography significantly improved the diagnostic performance for benign salivary gland tumors. It can provide clinicians with a non-invasive and accurate diagnostic approach, which holds important clinical significance and value. Ensemble of multiple models helped alleviate overfitting on the small dataset compared to using Resnet50 alone.
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Affiliation(s)
- Yi Mao
- Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
| | - Li-Ping Jiang
- Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
| | - Jing-Ling Wang
- Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
| | - Yu-Hong Diao
- Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
| | - Fang-Qun Chen
- Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
| | - Wei-Ping Zhang
- Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
| | - Li Chen
- Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
| | - Zhi-Xing Liu
- Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China; Department of Ultrasonography, GanJiang New District Peoples Hospital, Nanchang, 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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He Y, Zheng B, Peng W, Chen Y, Yu L, Huang W, Qin G. An ultrasound-based ensemble machine learning model for the preoperative classification of pleomorphic adenoma and Warthin tumor in the parotid gland. Eur Radiol 2024:10.1007/s00330-024-10719-2. [PMID: 38570381 DOI: 10.1007/s00330-024-10719-2] [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: 12/06/2023] [Revised: 02/24/2024] [Accepted: 03/13/2024] [Indexed: 04/05/2024]
Abstract
OBJECTIVES The preoperative classification of pleomorphic adenomas (PMA) and Warthin tumors (WT) in the parotid gland plays an essential role in determining therapeutic strategies. This study aims to develop and validate an ultrasound-based ensemble machine learning (USEML) model, employing nonradiative and noninvasive features to differentiate PMA from WT. METHODS A total of 203 patients with histologically confirmed PMA or WT who underwent parotidectomy from two centers were enrolled. Clinical factors, ultrasound (US) features, and radiomic features were extracted to develop three types of machine learning model: clinical models, US models, and USEML models. The diagnostic performance of the USEML model, as well as that of physicians based on experience, was evaluated and validated using receiver operating characteristic (ROC) curves in internal and external validation cohorts. DeLong's test was used for comparisons of AUCs. SHAP values were also utilized to explain the classification model. RESULTS The USEML model achieved the highest AUC of 0.891 (95% CI, 0.774-0.961), surpassing the AUCs of both the US (0.847; 95% CI, 0.720-0.932) and clinical (0.814; 95% CI, 0.682-0.908) models. The USEML model also outperformed physicians in both internal and external validation datasets (both p < 0.05). The sensitivity, specificity, negative predictive value, and positive predictive value of the USEML model and physician experience were 89.3%/75.0%, 87.5%/54.2%, 87.5%/65.6%, and 89.3%/65.0%, respectively. CONCLUSIONS The USEML model, incorporating clinical factors, ultrasound factors, and radiomic features, demonstrated efficient performance in distinguishing PMA from WT in the parotid gland. CLINICAL RELEVANCE STATEMENT This study developed a machine learning model for preoperative diagnosis of pleomorphic adenoma and Warthin tumor in the parotid gland based on clinical, ultrasound, and radiomic features. Furthermore, it outperformed physicians in an external validation dataset, indicating its potential for clinical application. KEY POINTS • Differentiating pleomorphic adenoma (PMA) and Warthin tumor (WT) affects management decisions and is currently done by invasive biopsy. • Integration of US-radiomic, clinical, and ultrasound findings in a machine learning model results in improved diagnostic accuracy. • The ultrasound-based ensemble machine learning (USEML) model consistently outperforms physicians, suggesting its potential applicability in clinical settings.
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Affiliation(s)
- Yanping He
- Department of Medical Ultrasonics, The First People's Hospital of Foshan, No. 81, Lingnan Avenue North, Foshan, 528000, China
| | - Bowen Zheng
- Department of Radiology, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Guangzhou, 510515, China
| | - Weiwei Peng
- Department of Medical Ultrasonics, The First People's Hospital of Foshan, No. 81, Lingnan Avenue North, Foshan, 528000, China
| | - Yongyu Chen
- Department of Medical Ultrasonics, The First People's Hospital of Foshan, No. 81, Lingnan Avenue North, Foshan, 528000, China
| | - Lihui Yu
- Department of Medical Ultrasonics, The First People's Hospital of Foshan, No. 81, Lingnan Avenue North, Foshan, 528000, China
| | - Weijun Huang
- Department of Medical Ultrasonics, The First People's Hospital of Foshan, No. 81, Lingnan Avenue North, Foshan, 528000, China.
| | - Genggeng Qin
- Department of Radiology, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Guangzhou, 510515, China.
- Medical Imaging Center, Ganzhou People's Hospital, 16th Meiguan Avenue, Ganzhou, 34100, China.
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Lin W, Ye W, Ma J, Wang S, Chen P, Yang Y, Yin B. Differentiation of parotid pleomorphic adenoma from Warthin tumor using signal intensity ratios on fat-suppressed T2-weighted magnetic resonance imaging. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 137:310-319. [PMID: 38195353 DOI: 10.1016/j.oooo.2023.12.786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 11/28/2023] [Accepted: 12/09/2023] [Indexed: 01/11/2024]
Abstract
OBJECTIVE To investigate the value of magnetic resonance imaging (MRI) signal intensity ratios (SIRs) based on fat-suppressed T2-weighted imaging (FS-T2WI), together with demographic features, MRI anatomical characteristics, and SIRs of histopathological patterns of the tumors, in the differentiation of parotid pleomorphic adenoma (PA) from Warthin tumor (WT). STUDY DESIGN In total, 90 patients with PA and 56 patients with WT were enrolled in the study. SIRs of tumor to normal parotid gland (SIR-T/P), spinal cord (SIR-T/S), and muscle (SIR-T/M) were calculated. Demographic and radiological features of the 2-patient groups were compared with univariate analysis and multivariate logistic regression analysis. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were analyzed to evaluate the utility of SIRs in distinguishing between PA and WT. RESULTS SIR-T/P exhibited outstanding discriminating ability (AUC = 0.934), SIR-T/S had excellent discrimination (AUC = 0.839), and SIR-T/M showed acceptable discrimination (AUC = 0.728). When SIR-T/P of 1.96 was selected as the cutoff value, sensitivity and specificity were 0.756 and 0.982, respectively. SIR-T/P, age, sex, and number of lesions were identified as independent predictors by multivariate logistic regression analysis. Differences in SIRs between histopathological patterns were significant. CONCLUSION SIR-T/P based on FS-T2WI is an effective discriminator in the differential diagnosis between PA and WT. Age, sex, and number of lesions provided additional value in differentiation.
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Affiliation(s)
- Wenqing Lin
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China; School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Weihu Ye
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China; School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Jingzhi Ma
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China; School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Shiwen Wang
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China; School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Pan Chen
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China; School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Yan Yang
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China; School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China.
| | - Bing Yin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China.
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Sunnetci KM, Kaba E, Celiker FB, Alkan A. Deep Network-Based Comprehensive Parotid Gland Tumor Detection. Acad Radiol 2024; 31:157-167. [PMID: 37271636 DOI: 10.1016/j.acra.2023.04.028] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 04/19/2023] [Accepted: 04/21/2023] [Indexed: 06/06/2023]
Abstract
RATIONALE AND OBJECTIVES Salivary gland tumors constitute 2%-6% of all head and neck tumors and are most common in the parotid gland. Magnetic resonance (MR) imaging is the most sensitive imaging modality for diagnosis. Tumor type, localization, and relationship with surrounding structures are important factors for treatment. Therefore, parotid gland tumor segmentation is important. Specialists widely use manual segmentation in diagnosis and treatment. However, considering the development of artificial intelligence-based models today, it is seen that artificial intelligence-based automatic segmentation models can be used instead of manual segmentation, which is a time-consuming technique. Therefore, we segmented parotid gland tumor (PGT) using deep learning-based architectures in the paper. MATERIALS AND METHODS The dataset used in the study includes 102 T1-w, 102 contrast-enhanced T1-w (T1C-w), and 102 T2-w MR images. After cropping the raw and manually segmented images by experts, we obtained the masks of these images. After standardizing the image sizes, we split these images into approximately 80% training set and 20% test set. Hereabouts, we trained six models for these images using ResNet18 and Xception-based DeepLab v3+. We prepared a user-friendly Graphical User Interface application that includes each of these models. RESULTS From the results, the accuracy and weighted Intersection over Union values of the ResNet18-based DeepLab v3+ architecture trained for T1C-w, which is the most successful model in the study, are equal to 0.96153 and 0.92601, respectively. Regarding the results and the literature, it can be seen that the proposed system is competitive in terms of both using MR images and training the models independently for T1-w, T1C-w, and T2-w. Expressing that PGT is usually segmented manually in the literature, we predict that our study can contribute significantly to the literature. CONCLUSION In this study, we prepared and presented a software application that can be easily used by users for automatic PGT segmentation. In addition to predicting the reduction of costs and workload through the study, we developed models with meaningful performance metrics according to the literature.
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Affiliation(s)
- Kubilay Muhammed Sunnetci
- Osmaniye Korkut Ata University, Department of Electrical and Electronics Engineering, Osmaniye 80000, Turkey (K.M.S.); Kahramanmaraş Sütçü İmam University, Department of Electrical and Electronics Engineering, Kahramanmaraş 46050, Turkey (K.M.S., A.A.).
| | - Esat Kaba
- Recep Tayyip Erdogan University, Department of Radiology, Rize, Turkey (E.K., F.B.C.)
| | - Fatma Beyazal Celiker
- Recep Tayyip Erdogan University, Department of Radiology, Rize, Turkey (E.K., F.B.C.)
| | - Ahmet Alkan
- Kahramanmaraş Sütçü İmam University, Department of Electrical and Electronics Engineering, Kahramanmaraş 46050, Turkey (K.M.S., A.A.)
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Abdullah AM, Qaradakhy AJ, Saeed YA, Salih AM, Karim S, Ali OA, Hassan SH, Nasraldeen SA, Mohammed SH, Kakamad FH. Papillary thyroid carcinoma associated with non‑functioning parathyroid carcinoma with Warthin's tumor of the parotid gland: A case report and brief literature review. MEDICINE INTERNATIONAL 2023; 3:26. [PMID: 37304572 PMCID: PMC10251473 DOI: 10.3892/mi.2023.86] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 04/28/2023] [Indexed: 06/13/2023]
Abstract
Multiple neck pathologies occurring simultaneously are a rare condition. The present study describes an extremely rare case of papillary thyroid carcinoma, non-functioning parathyroid carcinoma (PC) and Warthin's tumor of the parotid gland. A 59-year-old male presented with a 3-month history of anterior neck swelling. The neck ultrasound revealed a left-sided thyroid nodule associated with pathological lymph nodes. There was a parotid gland mass. A fine-needle aspiration of the left parotid mass was not diagnostic, although the left thyroid nodule revealed a malignancy with metastasis to the left cervical group lymph nodes. The patient underwent total thyroidectomy, left central and left lateral cervical lymph node dissection. A superficial parotidectomy was also performed. A histopathological examination revealed three different pathologies: Papillary thyroid microcarcinoma, PC and Warthin's tumor. The simultaneous occurrence of a Warthin's tumor, papillary thyroid microcarcinoma and PC is an unusual condition. The concurrent findings of these three pathologies have not yet been reported in the literature, at least to the best of our knowledge. The synchronous findings of PTC, non-functioning PC and Warthin's tumor are extremely rare, yet possible. Surgical intervention remains the most appropriate treatment strategy.
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Affiliation(s)
- Ari M. Abdullah
- Department of Scientific Affairs, Smart Health Tower, Sulaimani, Kurdistan 46000, Iraq
- Department of Pathology, Sulaimani Teaching Hospital, Sulaimani, Kurdistan 46000, Iraq
| | - Aras J. Qaradakhy
- Department of Scientific Affairs, Smart Health Tower, Sulaimani, Kurdistan 46000, Iraq
- Department of Radiology, Shorsh Teaching Hospital, Sulaimani, Kurdistan 46000, Iraq
| | - Yadgar A. Saeed
- Department of Pathology, Sulaimani Teaching Hospital, Sulaimani, Kurdistan 46000, Iraq
| | - Abdulwahid M. Salih
- Department of Scientific Affairs, Smart Health Tower, Sulaimani, Kurdistan 46000, Iraq
- College of Medicine, University of Sulaimani, Sulaimani, Kurdistan 46000, Iraq
| | - Seema Karim
- Department of Scientific Affairs, Smart Health Tower, Sulaimani, Kurdistan 46000, Iraq
| | - Osama A. Ali
- Department of Scientific Affairs, Smart Health Tower, Sulaimani, Kurdistan 46000, Iraq
| | - Shko H. Hassan
- Department of Scientific Affairs, Smart Health Tower, Sulaimani, Kurdistan 46000, Iraq
| | - Shalaw A. Nasraldeen
- Department of Scientific Affairs, Smart Health Tower, Sulaimani, Kurdistan 46000, Iraq
| | - Shvan H. Mohammed
- Kscien Organization for Scientific Research, Sulaimani, Kurdistan 46000, Iraq
| | - Fahmi H. Kakamad
- Department of Scientific Affairs, Smart Health Tower, Sulaimani, Kurdistan 46000, Iraq
- College of Medicine, University of Sulaimani, Sulaimani, Kurdistan 46000, Iraq
- Kscien Organization for Scientific Research, Sulaimani, Kurdistan 46000, Iraq
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