1
|
Wei W, Xu J, Xia F, Liu J, Zhang Z, Wu J, Wei T, Feng H, Ma Q, Jiang F, Zhu X, Zhang X. Deep learning-assisted diagnosis of benign and malignant parotid gland tumors based on automatic segmentation of ultrasound images: a multicenter retrospective study. Front Oncol 2024; 14:1417330. [PMID: 39184051 PMCID: PMC11341398 DOI: 10.3389/fonc.2024.1417330] [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: 04/14/2024] [Accepted: 07/18/2024] [Indexed: 08/27/2024] Open
Abstract
Objectives To construct deep learning-assisted diagnosis models based on automatic segmentation of ultrasound images to facilitate radiologists in differentiating benign and malignant parotid tumors. Methods A total of 582 patients histopathologically diagnosed with PGTs were retrospectively recruited from 4 centers, and their data were collected for analysis. The radiomics features of six deep learning models (ResNet18, Inception_v3 etc) were analyzed based on the ultrasound images that were obtained under the best automatic segmentation model (Deeplabv3, UNet++, and UNet). The performance of three physicians was compared when the optimal model was used and not. The Net Reclassification Index (NRI) and Integrated Discrimination Improvement (IDI) were utilized to evaluate the clinical benefit of the optimal model. Results The Deeplabv3 model performed optimally in terms of automatic segmentation. The ResNet18 deep learning model had the best prediction performance, with an area under the receiver-operating characteristic curve of 0.808 (0.694-0.923), 0.809 (0.712-0.906), and 0.812 (0.680-0.944) in the internal test set and external test sets 1 and 2, respectively. Meanwhile, the optimal model-assisted clinical and overall benefits were markedly enhanced for two out of three radiologists (in internal validation set, NRI: 0.259 and 0.213 [p = 0.002 and 0.017], IDI: 0.284 and 0.201 [p = 0.005 and 0.043], respectively; in external test set 1, NRI: 0.183 and 0.161 [p = 0.019 and 0.008], IDI: 0.205 and 0.184 [p = 0.031 and 0.045], respectively; in external test set 2, NRI: 0.297 and 0.297 [p = 0.038 and 0.047], IDI: 0.332 and 0.294 [p = 0.031 and 0.041], respectively). Conclusions The deep learning model constructed for automatic segmentation of ultrasound images can improve the diagnostic performance of radiologists for PGTs.
Collapse
Affiliation(s)
- Wei Wei
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital), Wuhu, China
| | - Jingya Xu
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital), Wuhu, China
| | - Fei Xia
- Department of Ultrasound, WuHu Hospital, East China Normal University (The Second People’s Hospital, WuHu), Wuhu, Anhui, China
| | - Jun Liu
- Department of Ultrasound, Linyi Central Hospital, Linyi, Shandong, China
| | - Zekai Zhang
- Department of Ultrasound, Zibo Central Hospital, Zibo, Shandong, China
| | - Jing Wu
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital), Wuhu, China
| | - Tianjun Wei
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital), Wuhu, China
| | - Huijun Feng
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital), Wuhu, China
| | - Qiang Ma
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital), Wuhu, China
| | - Feng Jiang
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital), Wuhu, China
| | - Xiangming Zhu
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital), Wuhu, China
| | - Xia Zhang
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital), Wuhu, China
| |
Collapse
|
2
|
Lopes-Santos G, Marques NGDO, Tjioe KC, Oliveira DT. Clinical behavior of recurrent pleomorphic adenoma in the palate: a systematic review. Acta Cir Bras 2024; 39:e390824. [PMID: 38422328 PMCID: PMC10911496 DOI: 10.1590/acb390824] [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: 10/19/2023] [Accepted: 11/28/2023] [Indexed: 03/02/2024] Open
Abstract
PURPOSE This systematic review analyzed the clinical behavior and odds of malignancy of the palatal recurrent pleomorphic adenomas. METHODS Systematic review of patients with recurrent pleomorphic adenoma arising in the palate. Database search: MEDLINE, Scopus, Web of Science, Cochrane, EMBASE, Virtual Health Library, Google Scholar, and OpenGrey. A binomial logistic regression was performed to assess the odds of detecting recurrence five, 10 and 20 years after the treatment of primary tumor. RESULTS Thirteen studies (n = 18 patients) out of 336 were included. The recurrent pleomorphic adenoma in palate was more common in females (61.6%), average age was 49 years old (range 9-73 years old). Four patients progressed to malignant transformation. The odds ratio (OR) of detecting a recurrence at 10 (OR = 5.57; 95% confidence interval - 95%CI 1.13-27.52), and 20 years (OR = 18.78; 95%CI 3.18-110.84) after treatment of primary pleomorphic adenoma was significantly higher than at one-year follow-up. CONCLUSIONS The recurrence of pleomorphic adenoma in palate remains a rare event of late occurrence. It mainly affects middle-aged female and carries a risk of malignant transformation. Although uncommon, patients with palatal pleomorphic adenoma should be warned about the possibility of recurrence or malignant transformation of tumor at advanced ages.
Collapse
Affiliation(s)
- Gabriela Lopes-Santos
- Universidade de São Paulo – Faculdade de Odontologia de Bauru – Departamento de Cirurgia, Estomatologia, Patologia e Radiologia – Bauru (SP) – Brazil
| | - Najara Gomes de Oliveira Marques
- Universidade de São Paulo – Faculdade de Odontologia de Bauru – Departamento de Cirurgia, Estomatologia, Patologia e Radiologia – Bauru (SP) – Brazil
| | - Kellen Cristine Tjioe
- Augusta University – Georgia Cancer Center – Augusta (GA) – United States of America
| | - Denise Tostes Oliveira
- Universidade de São Paulo – Faculdade de Odontologia de Bauru – Departamento de Cirurgia, Estomatologia, Patologia e Radiologia – Bauru (SP) – Brazil
| |
Collapse
|
3
|
Yu Q, Ning Y, Wang A, Li S, Gu J, Li Q, Chen X, Lv F, Zhang X, Yue Q, Peng J. Deep learning-assisted diagnosis of benign and malignant parotid tumors based on contrast-enhanced CT: a multicenter study. Eur Radiol 2023; 33:6054-6065. [PMID: 37067576 DOI: 10.1007/s00330-023-09568-2] [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: 10/17/2022] [Revised: 01/31/2023] [Accepted: 02/26/2023] [Indexed: 04/18/2023]
Abstract
OBJECTIVES To develop deep learning-assisted diagnosis models based on CT images to facilitate radiologists in differentiating benign and malignant parotid tumors. METHODS Data from 573 patients with histopathologically confirmed parotid tumors from center 1 (training set: n = 269; internal-testing set: n = 116) and center 2 (external-testing set: n = 188) were retrospectively collected. Six deep learning models (MobileNet V3, ShuffleNet V2, Inception V3, DenseNet 121, ResNet 50, and VGG 19) based on arterial-phase CT images, and a baseline support vector machine (SVM) model integrating clinical-radiological features with handcrafted radiomics signatures were constructed. The performance of senior and junior radiologists with and without optimal model assistance was compared. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the clinical benefit of using the optimal model. RESULTS MobileNet V3 had the best predictive performance, with sensitivity increases of 0.111 and 0.207 (p < 0.05) in the internal- and external-testing sets, respectively, relative to the SVM model. Clinical benefit and overall efficiency of junior radiologist were significantly improved with model assistance; for the internal- and external-testing sets, respectively, the AUCs improved by 0.128 and 0.102 (p < 0.05), the sensitivity improved by 0.194 and 0.120 (p < 0.05), the NRIs were 0.257 and 0.205 (p < 0.001), and the IDIs were 0.316 and 0.252 (p < 0.001). CONCLUSIONS The developed deep learning models can assist radiologists in achieving higher diagnostic performance and hopefully provide more valuable information for clinical decision-making in patients with parotid tumors. KEY POINTS • The developed deep learning models outperformed the traditional SVM model in predicting benign and malignant parotid tumors. • Junior radiologist can obtain greater clinical benefits with assistance from the optimal deep learning model. • The clinical decision-making process can be accelerated in patients with parotid tumors using the established deep learning model.
Collapse
Affiliation(s)
- Qiang Yu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Youquan Ning
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Anran Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Shuang Li
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China
| | - Jinming Gu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Quanjiang Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Xinwei Chen
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | | | - Qiang Yue
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China.
| | - Juan Peng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
| |
Collapse
|
4
|
Chen Z, Jiang J, Fan Y, Lu H. Pulmonary adenoid cystic carcinoma: molecular characteristics and literature review. Diagn Pathol 2023; 18:65. [PMID: 37198615 DOI: 10.1186/s13000-023-01354-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 05/07/2023] [Indexed: 05/19/2023] Open
Abstract
BACKGROUND Pulmonary adenoid cystic carcinoma (PACC) is an exceptionally rare salivary gland-type malignant neoplasm. Because of its clinical manifestations, imaging features are not different from other types of non-small cell lung cancer, which is a diagnostic challenge for most doctors. CONCLUSIONS A review of the literature shows that high amounts of immunohistochemical (IHC) markers, such as CK7, CD117, P63, SMA, CK5/6, and S-100 are helpful for PACC diagnosis. Surgical resection is the main treatment of PACC, but treatment options for advanced PACC patients are limited and the research of molecular targeted drugs is ongoing in advanced cases not eligible for surgery. Currently, research on PACC targeted therapy mainly focuses on the exploration of v-myb avian myeloblastosis virus oncogene homolog (MYB) and its downstream target genes. In addition, median tumor mutation burden and PD-1/PD-L1 were lower in PACC, which may indicate poor efficacy of immunotherapy in PACC patients. This review focuses on the pathologic features, molecular characteristics, diagnosis, treatment and prognosis of PACC to establish a comprehensive understanding of PACC.
Collapse
Affiliation(s)
- Zhixin Chen
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, P.R. China
- Zhejiang Key Laboratory of Diagnosis & Treatment Technology on Thoracic Oncology (lung and esophagus), Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, P.R. China
- Department of Thoracic Medical Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, P.R. China
| | - Jiapeng Jiang
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, P.R. China
- Zhejiang Key Laboratory of Diagnosis & Treatment Technology on Thoracic Oncology (lung and esophagus), Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, P.R. China
- Department of Thoracic Medical Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, P.R. China
| | - Ying Fan
- Department of Medical Oncology, Lishui Municipal Central Hospital, Lishui, Zhejiang, 323000, China
| | - Hongyang Lu
- Zhejiang Key Laboratory of Diagnosis & Treatment Technology on Thoracic Oncology (lung and esophagus), Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, P.R. China.
- Department of Thoracic Medical Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, P.R. China.
| |
Collapse
|
5
|
Meliante PG, Colizza A, Ralli M, Minni A, Angeletti D, Cambria F, de Vincentiis M. Treatment of relapses of benign latero-cervical pathology: a narrative literature review. ACTA OTORHINOLARYNGOLOGICA ITALICA : ORGANO UFFICIALE DELLA SOCIETA ITALIANA DI OTORINOLARINGOLOGIA E CHIRURGIA CERVICO-FACCIALE 2023; 43:S76-S83. [PMID: 37698104 PMCID: PMC10159646 DOI: 10.14639/0392-100x-suppl.1-43-2023-10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 01/08/2023] [Indexed: 09/13/2023]
Abstract
Benign laterocervical pathologies are not without pitfalls. Many may relapse after many years and, sometimes, they cannot be predicted. The purpose of this review is to describe the surgical measures necessary for the treatment of relapses of the most common benign laterocervical masses. We searched PubMed, Embase and Cochrane Central Register of Controlled Trials databases for articles describing the treatment of the most common benign cervical disease recurrences, and summarised available evidence in this narrative review. We overviewed observations about recurrent benign mixed tumour (pleomorphic adenoma), parapharyngeal space tumours and carotid body paragangliomas, thyroglossal duct anomalies and branchial cleft anomalies. Proper surgical technique is crucial for safely and effectively managing the relapses of benign latero-cervical diseases. Radiotherapy is indicated in several cases of recurrence such as pleomorphic adenoma and unresectable paraganglioma. Long-term follow-up is of utmost importance to promptly recognise and treat recurrencies.
Collapse
Affiliation(s)
| | - Andrea Colizza
- Department of Sense Organs, Sapienza University of Rome, Rome, Italy
| | | | | | | | | | | |
Collapse
|
6
|
Pei Y, Li W. Clinical parameters predictors of malignant transformation of recurrent parotid pleomorphic adenoma. Sci Rep 2023; 13:4543. [PMID: 36941273 PMCID: PMC10027859 DOI: 10.1038/s41598-023-29714-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 02/09/2023] [Indexed: 03/23/2023] Open
Abstract
Malignant transformation (MT) in recurrent parotid pleomorphic adenomas (PAs) is rare; therefore its occurrence lacks reliable predictive factors. Our goal was to clarify the predictors for MT of recurrent parotid PAs based on preoperative clinical parameters. Patients with a clinical diagnosis of recurrent parotid PA were retrospectively enrolled. The association between clinicopathologic variables and MT of PA was assessed using univariate and multivariate analyses. MT occurred in 11.8% of the 467 patients. In univariate analysis, three or more previous recurrences, newly developed facial nerve paralysis, difficulty in mouth opening, tumors with the largest tumor diameter ≥ 2.4 cm, and abnormal neck lymph node enlargement were associated with MT occurrence. Further, multivariate analysis showed that three or more previous recurrences, newly developed facial nerve paralysis, difficulty in mouth opening, and abnormal neck lymph node enlargement were independently related to MT. MT of recurrent PA was not uncommon. Clinical signs of malignancy included newly developed facial nerve paralysis, difficulty in mouth opening, three or more previous recurrences, and abnormal neck lymph node enlargement.
Collapse
Affiliation(s)
- Yu Pei
- Department of Stomatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wenlu Li
- Department of Stomatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| |
Collapse
|
7
|
Thompson LDR, Xu B. Top Ten Differentials to Mull Over for Head and Neck Myoepithelial Neoplasms. Head Neck Pathol 2023; 17:1-15. [PMID: 36928733 PMCID: PMC10063767 DOI: 10.1007/s12105-022-01502-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 09/27/2022] [Indexed: 03/18/2023]
Abstract
BACKGROUND Myoepithelial neoplasms of the salivary gland are benign or malignant neoplasms composed exclusively of neoplastic myoepithelial cells. These tumors, including the benign myoepithelioma and the malignant counterpart myoepithelial carcinoma, exhibit a wide range of cytomorphologic features and architectural patterns. METHODS Review. RESULTS Myoepithelial cells can be epithelial, plasmacytoid, clear cell, spindle cell, and/or oncocytic cell, arranging as trabeculae, solid sheets, nests, cords, and/or single cells. A stromal component is commonly but not universally present, Therefore, their differential diagnoses are quite broad, including salivary gland neoplasms especially those with a myoepithelial component, plasmacytoma, melanoma, and various mesenchymal tumors. CONCLUSION In this review, we summarize the characteristic histologic features, useful immunohistochemical panel, and common molecular alterations of myoepithelial tumors and their top differential diagnoses. A logical stepwise algorithmic approach and an immunohistochemical panel to include multiple myoepithelial markers are essential to establish the correct diagnosis.
Collapse
Affiliation(s)
- Lester D. R. Thompson
- Head and Neck Pathology Consultations, 22543 Ventura Blvd, Ste 220 PMB1034, 91364 Woodland Hills, CA USA
| | - Bin Xu
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY USA
| |
Collapse
|
8
|
Xu W, Zhang X, Wu Y, Zhu Y, Liu S, Lu H, Yang W. Recurrent pleomorphic adenoma of the parotid gland: A population-based study with emphasis on re-recurrence and malignant transformation. Head Neck 2023; 45:697-705. [PMID: 36563305 DOI: 10.1002/hed.27286] [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: 09/08/2022] [Revised: 11/24/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND This study aimed to analyze the prognostic determinants for re-recurrence and malignant transformation of parotid recurrent pleomorphic adenoma (RPA). METHODS Between January 2011 and December 2020, all the cases of RPA of the parotid gland at our single institution were reviewed. RESULTS A total of 168 patients were included in this study, with a median age of 41.5 years, and 20 (11.9%) patients developed re-recurrence during follow-up. It has been evidenced that recurrence times (single or multiple) was an independent prognostic factor for recurrence-free survival (p = 0.0264). A total of 26 (15.48%) patients experienced malignant transformation of parotid RPA, and older age (>45 year old), male sex, and higher clinical tumor staging (T3-T4) were significantly associated with malignant transformation of parotid RPA. CONCLUSIONS Recurrence times was an independent prognostic factor for re-recurrence of parotid RPA, and older male with high T-stage may more easily develop malignant transformation.
Collapse
Affiliation(s)
- Wanlin Xu
- Department of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China.,National Center for Stomatology, Shanghai, China.,National Clinical Research Center for Oral Diseases, Shanghai, China.,Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Xu Zhang
- Department of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China.,National Center for Stomatology, Shanghai, China.,National Clinical Research Center for Oral Diseases, Shanghai, China.,Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Yifan Wu
- Department of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China.,National Center for Stomatology, Shanghai, China.,National Clinical Research Center for Oral Diseases, Shanghai, China.,Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Yun Zhu
- Department of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China.,National Center for Stomatology, Shanghai, China.,National Clinical Research Center for Oral Diseases, Shanghai, China.,Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Shengwen Liu
- Department of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China.,National Center for Stomatology, Shanghai, China.,National Clinical Research Center for Oral Diseases, Shanghai, China.,Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Hao Lu
- Department of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China.,National Center for Stomatology, Shanghai, China.,National Clinical Research Center for Oral Diseases, Shanghai, China.,Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Wenjun Yang
- Department of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China.,National Center for Stomatology, Shanghai, China.,National Clinical Research Center for Oral Diseases, Shanghai, China.,Shanghai Key Laboratory of Stomatology, Shanghai, China
| |
Collapse
|
9
|
Chromosome Translocations, Gene Fusions, and Their Molecular Consequences in Pleomorphic Salivary Gland Adenomas. Biomedicines 2022; 10:biomedicines10081970. [PMID: 36009517 PMCID: PMC9405559 DOI: 10.3390/biomedicines10081970] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 08/09/2022] [Accepted: 08/11/2022] [Indexed: 11/23/2022] Open
Abstract
Salivary gland tumors are a heterogeneous group of tumors originating from the major and minor salivary glands. The pleomorphic adenoma (PA), which is the most common subtype, is a benign lesion showing a remarkable morphologic diversity and that, upon recurrence or malignant transformation, can cause significant clinical problems. Cytogenetic studies of >500 PAs have revealed a complex and recurrent pattern of chromosome rearrangements. In this review, we discuss the specificity and frequency of these rearrangements and their molecular/clinical consequences. The genomic hallmark of PA is translocations with breakpoints in 8q12 and 12q13-15 resulting in gene fusions involving the transcription factor genes PLAG1 and HMGA2. Until recently, the association between these two oncogenic drivers was obscure. Studies of the Silver−Russel syndrome, a growth retardation condition infrequently caused by mutations in IGF2/HMGA2/PLAG1, have provided new clues to the understanding of the molecular pathogenesis of PA. These studies have demonstrated that HMGA2 is an upstream regulator of PLAG1 and that HMGA2 regulates the expression of IGF2 via PLAG1. This provides a novel explanation for the 8q12/12q13-15 aberrations in PA and identifies IGF2 as a major oncogenic driver and therapeutic target in PA. These studies have important diagnostic and therapeutic implications for patients with PA.
Collapse
|