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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 PMCID: PMC11380391 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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Sha X, Wang C, Qi S, Yuan X, Zhang H, Yang J. The efficacy of CBCT-based radiomics techniques in differentiating between conventional and unicystic ameloblastoma. Oral Surg Oral Med Oral Pathol Oral Radiol 2024:S2212-4403(24)00349-3. [PMID: 39227265 DOI: 10.1016/j.oooo.2024.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 06/02/2024] [Accepted: 06/16/2024] [Indexed: 09/05/2024]
Abstract
OBJECTIVE The aim of this study was to develop a cone beam computed tomography (CBCT) radiomics-based model that differentiates between conventional and unicystic ameloblastoma (AB). METHODS In this retrospective study, CBCT images were collected from 100 patients who had ABs that were diagnosed histopathologically as conventional or unicystic AB after surgical treatment. The patients were randomly divided into training (70) and validation (30) cohorts. Radiomics features were extracted from the images, and the optimal features were incorporated into 5 models: Logistic Regression, Support Vector Machine, Linear Discriminant Analysis, Random Forest, and XGBoost for prediction of tumor type. Model performance was evaluated using the area under the curve (AUC) from receiver operating characteristic analysis, sensitivity, specificity, accuracy, calibration curves, and decision curve analysis (DCA). RESULTS The 20 optimal radiomics features were incorporated into the Logistic Regression (LR) model, which exhibited the best overall performance with AUC = 0.936 (95% confidence interval [CI] = 0.877-0.996) for the training cohort and AUC = 0.929 (95% CI = 0.832-1.000) for the validation cohort. The nomogram combined the clinical features and the radiomics signature and resulted in the best predictive performance. CONCLUSIONS The LR model demonstrated the ability of radiomics and the nomogram to distinguish between the 2 types of AB and may have the potential to replace biopsies under noninvasive conditions.
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Affiliation(s)
- Xiaoyan Sha
- Department of Oral and Maxillofacial Radiology, School of Stomatology, Capital Medical University, Beijing, China
| | - Chao Wang
- Department of Clinical Research, SinoUnion Healthcare Inc., Beijing, China
| | - Senrong Qi
- Department of Oral and Maxillofacial Radiology, School of Stomatology, Capital Medical University, Beijing, China
| | - Xiaohong Yuan
- Department of Oral and Maxillofacial Pathology, School of Stomatology, Capital Medical University, Beijing, China
| | - Hui Zhang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Jigang Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
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Żurek M, Fus Ł, Niemczyk K, Rzepakowska A. Salivary gland pathologies: evolution in classification and association with unique genetic alterations. Eur Arch Otorhinolaryngol 2023; 280:4739-4750. [PMID: 37439929 PMCID: PMC10562281 DOI: 10.1007/s00405-023-08110-w] [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: 03/06/2023] [Accepted: 07/03/2023] [Indexed: 07/14/2023]
Abstract
PURPOSE The correct classification of salivary gland pathologies is crucial for choosing a treatment method and determining the prognosis. Better outcomes are now achievable thanks to the introduction of new therapy approaches, such as targeted therapies for malignant salivary gland tumors. To apply these in clinical routine, a clear classification of the lesions is required. METHODS The following review examines all changes from the first World Health Organization (WHO) Classification of salivary gland pathologies from 1972 to fifth edition from 2022. Possible developments in the diagnosis and classification of salivary gland pathology are also presented. RESULTS The current WHO classification is the fifth edition. With the development of new diagnostic methods, based on genetic alterations, it provides insight into the molecular basis of lesions. This has resulted in the evolution of classification, introduction of new entities and reclassification of existing ones. CONCLUSIONS Genetic alterations will become increasingly more significant in the identification of salivary gland pathologies in the future. These alterations will be helpful as prognostic and predictive biomarkers, and may also serve as targets for anti-cancer therapies.
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Affiliation(s)
- Michał Żurek
- Department of Otorhinolaryngology Head and Neck Surgery, Medical University of Warsaw, 1a Banacha Str, 02-097, Warsaw, Poland.
- Doctoral School, Medical University of Warsaw, 61 Żwirki I Wigury Str, 02-091, Warsaw, Poland.
| | - Łukasz Fus
- Department of Pathology, Medical University of Warsaw, 7 Pawińskiego Str, 02-004, Warsaw, Poland
| | - Kazimierz Niemczyk
- Department of Otorhinolaryngology Head and Neck Surgery, Medical University of Warsaw, 1a Banacha Str, 02-097, Warsaw, Poland
| | - Anna Rzepakowska
- Department of Otorhinolaryngology Head and Neck Surgery, Medical University of Warsaw, 1a Banacha Str, 02-097, Warsaw, Poland
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Hung KF, Ai QYH, Wong LM, Yeung AWK, Li DTS, Leung YY. Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases. Diagnostics (Basel) 2022; 13:110. [PMID: 36611402 PMCID: PMC9818323 DOI: 10.3390/diagnostics13010110] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/23/2022] [Accepted: 12/24/2022] [Indexed: 12/31/2022] Open
Abstract
The increasing use of computed tomography (CT) and cone beam computed tomography (CBCT) in oral and maxillofacial imaging has driven the development of deep learning and radiomics applications to assist clinicians in early diagnosis, accurate prognosis prediction, and efficient treatment planning of maxillofacial diseases. This narrative review aimed to provide an up-to-date overview of the current applications of deep learning and radiomics on CT and CBCT for the diagnosis and management of maxillofacial diseases. Based on current evidence, a wide range of deep learning models on CT/CBCT images have been developed for automatic diagnosis, segmentation, and classification of jaw cysts and tumors, cervical lymph node metastasis, salivary gland diseases, temporomandibular (TMJ) disorders, maxillary sinus pathologies, mandibular fractures, and dentomaxillofacial deformities, while CT-/CBCT-derived radiomics applications mainly focused on occult lymph node metastasis in patients with oral cancer, malignant salivary gland tumors, and TMJ osteoarthritis. Most of these models showed high performance, and some of them even outperformed human experts. The models with performance on par with human experts have the potential to serve as clinically practicable tools to achieve the earliest possible diagnosis and treatment, leading to a more precise and personalized approach for the management of maxillofacial diseases. Challenges and issues, including the lack of the generalizability and explainability of deep learning models and the uncertainty in the reproducibility and stability of radiomic features, should be overcome to gain the trust of patients, providers, and healthcare organizers for daily clinical use of these models.
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Affiliation(s)
- Kuo Feng Hung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Qi Yong H. Ai
- Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lun M. Wong
- Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Dion Tik Shun Li
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Yiu Yan Leung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
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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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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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Radiomics and deep learning approach to the differential diagnosis of parotid gland tumors. Curr Opin Otolaryngol Head Neck Surg 2021; 30:107-113. [PMID: 34907957 DOI: 10.1097/moo.0000000000000782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Advances in computer technology and growing expectations from computer-aided systems have led to the evolution of artificial intelligence into subsets, such as deep learning and radiomics, and the use of these systems is revolutionizing modern radiological diagnosis. In this review, artificial intelligence applications developed with radiomics and deep learning methods in the differential diagnosis of parotid gland tumors (PGTs) will be overviewed. RECENT FINDINGS The development of artificial intelligence models has opened new scenarios owing to the possibility of assessing features of medical images that usually are not evaluated by physicians. Radiomics and deep learning models come to the forefront in computer-aided diagnosis of medical images, even though their applications in the differential diagnosis of PGTs have been limited because of the scarcity of data sets related to these rare neoplasms. Nevertheless, recent studies have shown that artificial intelligence tools can classify common PGTs with reasonable accuracy. SUMMARY All studies aimed at the differential diagnosis of benign vs. malignant PGTs or the identification of the commonest PGT subtypes were identified, and five studies were found that focused on deep learning-based differential diagnosis of PGTs. Data sets were created in three of these studies with MRI and in two with computed tomography (CT). Additional seven studies were related to radiomics. Of these, four were on MRI-based radiomics, two on CT-based radiomics, and one compared MRI and CT-based radiomics in the same patients.
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