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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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Jiang S, Su Y, Liu Y, Zhou Z, Li M, Qiu S, Zhou J. Use of Computed Tomography-Based Texture Analysis to Differentiate Benign From Malignant Salivary Gland Lesions. J Comput Assist Tomogr 2024; 48:491-497. [PMID: 38157266 DOI: 10.1097/rct.0000000000001578] [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: 01/03/2024]
Abstract
OBJECTIVE Salivary gland lesions show overlapping morphological findings and types of time/intensity curves. This research aimed to evaluate the role of 2-phase multislice spiral computed tomography (MSCT) texture analysis in differentiating between benign and malignant salivary gland lesions. METHODS In this prospective study, MSCT was carried out on 90 patients. Each lesion was segmented on axial computed tomography (CT) images manually, and 33 texture features and morphological CT features were assessed. Logistic regression analysis was used to confirm predictors of malignancy ( P < 0.05 was considered to be statistically significant), followed by receiver operating characteristics analysis to assess the diagnostic performance. RESULTS Univariate logistic regression analysis revealed that morphological CT features (shape, size, and invasion of adjacent tissues) and 17 CT texture parameters had significant differences between benign and malignant lesions ( P < 0.05). Multivariate binary logistic regression demonstrated that shape, invasion of adjacent tissues, entropy, and inverse difference moment were independent factors for malignant tumors. The diagnostic accuracy values of multivariate binary logistic models based on morphological parameters, CT texture features, and a combination of both were 87.8%, 90%, and 93.3%, respectively. CONCLUSIONS Two-phase MSCT texture analysis was conducive to differentiating between malignant and benign neoplasms in the salivary gland, especially when combined with morphological CT features.
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Affiliation(s)
- Shuqi Jiang
- From the Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine
| | - Yangfan Su
- Department of Cardiovascular Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yanwen Liu
- From the Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine
| | - Zewang Zhou
- From the Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine
| | - Maotong Li
- From the Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine
| | - Shijun Qiu
- From the Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine
| | - Jie Zhou
- From the Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine
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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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