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King AD, Ai QYH, Lam WKJ, Tse IOL, So TY, Wong LM, Tsang JYM, Leung HS, Zee BCY, Hui EP, Ma BBY, Vlantis AC, van Hasselt AC, Chan ATC, Woo JKS, Chan KCA. Early detection of nasopharyngeal carcinoma: performance of a short contrast-free screening magnetic resonance imaging. J Natl Cancer Inst 2024; 116:665-672. [PMID: 38171488 DOI: 10.1093/jnci/djad260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/31/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Although contrast-enhanced magnetic resonance imaging (MRI) detects early-stage nasopharyngeal carcinoma (NPC) not detected by endoscopic-guided biopsy (EGB), a short contrast-free screening MRI would be desirable for NPC screening programs. This study evaluated a screening MRI in a plasma Epstein-Barr virus (EBV)-DNA NPC screening program. METHODS EBV-DNA-screen-positive patients underwent endoscopy, and endoscopy-positive patients underwent EGB. EGB was negative if the biopsy was negative or was not performed. Patients also underwent a screening MRI. Diagnostic performance was based on histologic confirmation of NPC in the initial study or during a follow-up period of at least 2 years. RESULTS The study prospectively recruited 354 patients for MRI and endoscopy; 40/354 (11.3%) endoscopy-positive patients underwent EGB. Eighteen had NPC (5.1%), and 336 without NPC (94.9%) were followed up for a median of 44.8 months. MRI detected additional NPCs in 3/18 (16.7%) endoscopy-negative and 2/18 (11.1%) EGB-negative patients (stage I/II, n = 4; stage III, n = 1). None of the 24 EGB-negative patients who were MRI-negative had NPC. MRI missed NPC in 2/18 (11.1%), one of which was also endoscopy-negative. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of MRI, endoscopy, and EGB were 88.9%, 91.1%, 34.8%, 99.4%, and 91.0%; 77.8%, 92.3%, 35.0%, 98.7%, and 91.5%; and 66.7%, 92.3%, 31.6%, 98.1%, and 91.0%, respectively. CONCLUSION A quick contrast-free screening MRI complements endoscopy in NPC screening programs. In EBV-screen-positive patients, MRI enables early detection of NPC that is endoscopically occult or negative on EGB and increases confidence that NPC has not been missed.
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Affiliation(s)
- Ann D King
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Qi Yong H Ai
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - W K Jacky Lam
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
- Centre for Novostics, The Chinese University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Otorhinolaryngology, Head and Neck Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Irene O L Tse
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
- Centre for Novostics, The Chinese University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Tiffany Y So
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Lun M Wong
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jayden Yip Man Tsang
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Ho Sang Leung
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Benny C Y Zee
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Edwin P Hui
- State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Clinical Oncology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Brigette B Y Ma
- State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Clinical Oncology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Alexander C Vlantis
- Department of Otorhinolaryngology, Head and Neck Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Andrew C van Hasselt
- Department of Otorhinolaryngology, Head and Neck Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Anthony T C Chan
- State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Clinical Oncology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - John K S Woo
- Department of Otorhinolaryngology, Head and Neck Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - K C Allen Chan
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
- Centre for Novostics, The Chinese University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong SAR, China
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Zhang R, Wong LM, So TY, Cai Z, Deng Q, Tsang YM, Ai QYH, King AD. Deep learning for the automatic detection and segmentation of parotid gland tumors on MRI. Oral Oncol 2024; 152:106796. [PMID: 38615586 DOI: 10.1016/j.oraloncology.2024.106796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 04/03/2024] [Accepted: 04/06/2024] [Indexed: 04/16/2024]
Abstract
OBJECTIVES Parotid gland tumors (PGTs) often occur as incidental findings on magnetic resonance images (MRI) that may be overlooked. This study aimed to construct and validate a deep learning model to automatically identify parotid glands (PGs) with a PGT from normal PGs, and in those with a PGT to segment the tumor. MATERIALS AND METHODS The nnUNet combined with a PG-specific post-processing procedure was used to develop the deep learning model trained on T1-weighed images (T1WI) in 311 patients (180 PGs with tumors and 442 normal PGs) and fat-suppressed (FS)-T2WI in 257 patients (125 PGs with tumors and 389 normal PGs), for detecting and segmenting PGTs with five-fold cross-validation. Additional validation set separated by time, comprising T1WI in 34 and FS-T2WI in 41 patients, was used to validate the model performance. RESULTS AND CONCLUSION To identify PGs with tumors from normal PGs, using combined T1WI and FS-T2WI, the deep learning model achieved an accuracy, sensitivity and specificity of 98.2% (497/506), 100% (119/119) and 97.7% (378/387), respectively, in the cross-validation set and 98.5% (67/68), 100% (20/20) and 97.9% (47/48), respectively, in the validation set. For patients with PGTs, automatic segmentation of PGTs on T1WI and FS-T2WI achieved mean dice coefficients of 86.1% and 84.2%, respectively, in the cross-validation set, and of 85.9% and 81.0%, respectively, in the validation set. The proposed deep learning model may assist the detection and segmentation of PGTs and, by acting as a second pair of eyes, ensure that incidentally detected PGTs on MRI are not missed.
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Affiliation(s)
- Rongli Zhang
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Lun M Wong
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Tiffany Y So
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Zongyou Cai
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Qiao Deng
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Yip Man Tsang
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Qi Yong H Ai
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
| | - Ann D King
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China.
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Li Z, Hung KF, Ai QYH, Gu M, Su YX, Shan Z. Radiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion. Diagnostics (Basel) 2024; 14:544. [PMID: 38473016 DOI: 10.3390/diagnostics14050544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 02/28/2024] [Accepted: 03/01/2024] [Indexed: 03/14/2024] Open
Abstract
Skeletal Class III malocclusion is one type of dentofacial deformity that significantly affects patients' facial aesthetics and oral health. The orthodontic treatment of skeletal Class III malocclusion presents challenges due to uncertainties surrounding mandibular growth patterns and treatment outcomes. In recent years, disease-specific radiographic features have garnered interest from researchers in various fields including orthodontics, for their exceptional performance in enhancing diagnostic precision and treatment effect predictability. The aim of this narrative review is to provide an overview of the valuable radiographic features in the diagnosis and management of skeletal Class III malocclusion. Based on the existing literature, a series of analyses on lateral cephalograms have been concluded to identify the significant variables related to facial type classification, growth prediction, and decision-making for tooth extractions and orthognathic surgery in patients with skeletal Class III malocclusion. Furthermore, we summarize the parameters regarding the inter-maxillary relationship, as well as different anatomical structures including the maxilla, mandible, craniofacial base, and soft tissues from conventional and machine learning statistical models. Several distinct radiographic features for Class III malocclusion have also been preliminarily observed using cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI).
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Affiliation(s)
- Zhuoying Li
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Kuo Feng Hung
- Applied Oral Sciences & Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Qi Yong H Ai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Min Gu
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Yu-Xiong Su
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Zhiyi Shan
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
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Ai QYH, King AD, Yuan H, Vardhanabhuti V, Mo FKF, Hung KF, Hui EP, Kwong DLW, Lee VHF, Ma BBY. Radiologic extranodal extension for nodal staging in nasopharyngeal carcinoma. Radiother Oncol 2024; 191:110050. [PMID: 38101457 DOI: 10.1016/j.radonc.2023.110050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 11/24/2023] [Accepted: 12/06/2023] [Indexed: 12/17/2023]
Abstract
PURPOSE Extranodal extension (ENE) has the potential to add value to the current nodal staging system (N8th) for predicting outcome in nasopharyngeal carcinoma (NPC). This study aimed to incorporate ENE, as well as cervical nodal necrosis (CNN) to the current stage N3 and evaluated their impact on outcome prediction. The findings were validated on an external cohort. METHODS & MATERIALS Pre-treatment MRI of 750 patients from the internal cohort were retrospectively reviewed. Predictive values of six modified nodal staging systems that incorporated four patterns of ENE and two patterns of CNN to the current stage N3 for disease-free survival (DFS) were compared with that of N8th using multivariate cox-regression and concordance statistics in the internal cohort. Performance of stage N3 for predicting disease recurrence was calculated. An external cohort of 179 patients was used to validate the findings. RESULTS Incorporation of advanced ENE, which infiltrates into adjacent muscle/skin/salivary glands outperformed the other five modifications for predicting outcomes (p < 0.01) and achieved a significantly higher c-index for 5-year DFS (0.69 vs 0.72) (p < 0.01) when compared with that of N8th staging system. By adding advanced ENE to the current N3 increased the sensitivity for predicting disease recurrence from 22.4 % to 47.1 %. The finding was validated in the external cohort (5-year DFS 0.65 vs. 0.72, p < 0.01; sensitivity of stage N3 increased from 14.0 % to 41.9 % for disease recurrence). CONCLUSION Results from two centre cohorts confirmed that the radiological advanced ENE should be considered as a criterion for stage N3 disease in NPC.
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Affiliation(s)
- Qi Yong H Ai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China; Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, China
| | - Ann D King
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, China.
| | - Hui Yuan
- Department of Nuclear Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, PR China
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong, China
| | - Frankie K F Mo
- Department of Clinical Oncology, State Key Laboratory of Translational Oncology, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong, China
| | - Kuo Feng Hung
- Division of Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Edwin P Hui
- Department of Clinical Oncology, State Key Laboratory of Translational Oncology, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong, China
| | - Dora Lai-Wan Kwong
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Victor Ho-Fun Lee
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Brigette B Y Ma
- Department of Clinical Oncology, State Key Laboratory of Translational Oncology, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong, China
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Mao K, Wong LM, Zhang R, So TY, Shan Z, Hung KF, Ai QYH. Radiomics Analysis in Characterization of Salivary Gland Tumors on MRI: A Systematic Review. Cancers (Basel) 2023; 15:4918. [PMID: 37894285 PMCID: PMC10605883 DOI: 10.3390/cancers15204918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/06/2023] [Accepted: 10/08/2023] [Indexed: 10/29/2023] Open
Abstract
Radiomics analysis can potentially characterize salivary gland tumors (SGTs) on magnetic resonance imaging (MRI). The procedures for radiomics analysis were various, and no consistent performances were reported. This review evaluated the methodologies and performances of studies using radiomics analysis to characterize SGTs on MRI. We systematically reviewed studies published until July 2023, which employed radiomics analysis to characterize SGTs on MRI. In total, 14 of 98 studies were eligible. Each study examined 23-334 benign and 8-56 malignant SGTs. Least absolute shrinkage and selection operator (LASSO) was the most common feature selection method (in eight studies). Eleven studies confirmed the stability of selected features using cross-validation or bootstrap. Nine classifiers were used to build models that achieved area under the curves (AUCs) of 0.74 to 1.00 for characterizing benign and malignant SGTs and 0.80 to 0.96 for characterizing pleomorphic adenomas and Warthin's tumors. Performances were validated using cross-validation, internal, and external datasets in four, six, and two studies, respectively. No single feature consistently appeared in the final models across the studies. No standardized procedure was used for radiomics analysis in characterizing SGTs on MRIs, and various models were proposed. The need for a standard procedure for radiomics analysis is emphasized.
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Affiliation(s)
- Kaijing Mao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Lun M. Wong
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Rongli Zhang
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Tiffany Y. So
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Zhiyi Shan
- Paediatric Dentistry & Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Kuo Feng Hung
- Applied Oral Sciences & Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Qi Yong H. Ai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
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Chan KCA, Lam WKJ, King A, Lin VS, Lee PPH, Zee BCY, Chan SL, Tse IOL, Tsang AFC, Li MZJ, Jiang P, Ai QYH, Poon DMC, Au KH, Hui EP, Ma BBY, Van Hasselt AC, Chan ATC, Woo JKS, Lo YMD. Plasma Epstein-Barr Virus DNA and Risk of Future Nasopharyngeal Cancer. NEJM Evid 2023; 2:EVIDoa2200309. [PMID: 38320164 DOI: 10.1056/evidoa2200309] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
EBV DNA Rescreening StudyPatients who had participated in a previous plasma Epstein-Barr virus (EBV) DNA screening study were rescreened. Of the 17,838 rescreened patients, 423 had persistently detectable plasma EBV DNA; 24 of these patients developed nasopharyngeal carcinoma. Sixty-seven percent of them received a diagnosis of early-stage disease and had increased progression-free survival compared with historical controls.
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Affiliation(s)
- K C Allen Chan
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- Centre for Novostics, The Chinese University of Hong Kong, Hong Kong Science and Technology Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - W K Jacky Lam
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- Centre for Novostics, The Chinese University of Hong Kong, Hong Kong Science and Technology Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
- Department of Otorhinolaryngology, Head and Neck Surgery, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - Ann King
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - Vivien S Lin
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- Centre for Novostics, The Chinese University of Hong Kong, Hong Kong Science and Technology Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Patrick P H Lee
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- Centre for Novostics, The Chinese University of Hong Kong, Hong Kong Science and Technology Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Benny C Y Zee
- The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - Stephen L Chan
- State Key Laboratory of Translational Oncology, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- Department of Clinical Oncology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - Irene O L Tse
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- Centre for Novostics, The Chinese University of Hong Kong, Hong Kong Science and Technology Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Amy F C Tsang
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- Centre for Novostics, The Chinese University of Hong Kong, Hong Kong Science and Technology Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Maggie Z J Li
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- Centre for Novostics, The Chinese University of Hong Kong, Hong Kong Science and Technology Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Peiyong Jiang
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- Centre for Novostics, The Chinese University of Hong Kong, Hong Kong Science and Technology Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Qi Yong H Ai
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Darren M C Poon
- Department of Clinical Oncology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - K H Au
- Department of Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China
| | - Edwin P Hui
- State Key Laboratory of Translational Oncology, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- Department of Clinical Oncology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - Brigette B Y Ma
- State Key Laboratory of Translational Oncology, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- Department of Clinical Oncology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - Andrew C Van Hasselt
- Department of Otorhinolaryngology, Head and Neck Surgery, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - Anthony T C Chan
- State Key Laboratory of Translational Oncology, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- Department of Clinical Oncology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - John K S Woo
- Department of Otorhinolaryngology, Head and Neck Surgery, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - Y M Dennis Lo
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- Centre for Novostics, The Chinese University of Hong Kong, Hong Kong Science and Technology Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
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7
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Zhang R, King AD, Wong LM, Bhatia KS, Qamar S, Mo FKF, Vlantis AC, Ai QYH. Discriminating between benign and malignant salivary gland tumors using diffusion-weighted imaging and intravoxel incoherent motion at 3 Tesla. Diagn Interv Imaging 2023; 104:67-75. [PMID: 36096875 DOI: 10.1016/j.diii.2022.08.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 08/01/2022] [Accepted: 08/09/2022] [Indexed: 02/07/2023]
Abstract
PURPOSE The purpose of this study was to retrospectively evaluate the diagnostic performances of diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM) for discriminating between benign and malignant salivary gland tumors (SGTs). MATERIALS AND METHODS Sixty-seven patients with 71 SGTs who underwent MRI examination at 3 Tesla were included. There were 34 men and 37 women with a mean age of 57 ± 17 (SD) years (age range: 20-90 years). SGTs included 21 malignant tumors (MTs) and 50 benign SGTs (33 pleomorphic adenomas [PAs] and 17 Warthin's tumors [WTs]). For each SGT, DWI and IVIM parameters, mean, skewness, and kurtosis of apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*) and perfusion volume fraction (f) were calculated and further compared between SGTs using univariable analysis. Areas under the curves (AUC) of receiver operating characteristic of significant parameters were compared using the Delong test. RESULTS Significant differences in ADCmean, Dmean and D*mean were found between SGTs (P < 0.001). The highest AUC values were obtained for ADCmean (0.949) for identifying PAs and D*mean (0.985) for identifying WTs and skewness and kurtosis did not outperform mean. To discriminate benign from malignant SGTs with thresholds set to maximize Youden index, IVIM and DWI produced accuracies of 85.9% (61/71; 95% CI: 75.6-93.0) and 77.5% (55/71; 95% CI: 66.0-86.5) but misdiagnosed MTs as benign in 28.6% (6/21) and 61.9% (13/21) of SGTs, respectively. After maximizing specificity to 100% for benign SGTs, the accuracies of IVIM and DWI decreased to 76.1% (54/71; 95% CI: 64.5-85.4) and 64.8% (46/71; 95% CI: 52.5-75.8) but no MTs were misdiagnosed as benign. IVIM and DWI correctly diagnosed 66.0% (33/50) and 50.0% (25/50) of benign SGTs and 46.5% (33/71) and 35.2% (25/71) of all SGTs, respectively. CONCLUSION IVIM is more accurate than DWI for discriminating between benign and malignant SGTs because of its advantage in detecting WTs. Thresholds set by maximizing specificity for benign SGTs may be advantageous in a clinical setting.
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Affiliation(s)
- Rongli Zhang
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Ann D King
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.
| | - Lun M Wong
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Kunwar S Bhatia
- Department of Imaging, St Mary's Hospital, Imperial College Healthcare, National Health Service Trust, London, UK
| | - Sahrish Qamar
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Frankie K F Mo
- Department of Clinical Oncology, State Key Laboratory of Translational Oncology, Sir YK Pao Centre for Cancer, Hong Kong Cancer Institute, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Alexander C Vlantis
- Department of Otorhinolaryngology, Head and Neck Surgery, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - Qi Yong H Ai
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China; Department of Health Technology and Informatics, The Polytechnic University of Hong Kong, Hung Hom, Hong Kong SAR, China
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King AD, Ai QYH. Letter to the editor regarding "MRI detection of suspected nasopharyngeal carcinoma: a systematic review and meta-analysis". Neuroradiology 2023; 65:1-2. [PMID: 36350360 DOI: 10.1007/s00234-022-03071-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 10/12/2022] [Indexed: 11/10/2022]
Affiliation(s)
- Ann D King
- Department of Imaging and Interventional Radiology, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, S.A.R., People's Republic of China.
| | - Qi Yong H Ai
- Department of Imaging and Interventional Radiology, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, S.A.R., People's Republic of China
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, S.A.R., People's Republic of China
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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:diagnostics13010110. [PMID: 36611402 PMCID: PMC9818323 DOI: 10.3390/diagnostics13010110] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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
- Correspondence:
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Ai QYH, So TY, Hung KF, King AD. Normal size of benign upper neck nodes on MRI: parotid, submandibular, occipital, facial, retroauricular and level IIb nodal groups. Cancer Imaging 2022; 22:66. [PMID: 36482491 PMCID: PMC9730594 DOI: 10.1186/s40644-022-00504-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 11/10/2022] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Nodal size is an important imaging criterion for differentiating benign from malignant nodes in the head and neck cancer staging. This study evaluated the size of normal nodes in less well-documented nodal groups in the upper head and neck on magnetic resonance imaging (MRI). METHODS Analysis was performed on 289 upper head and neck MRIs of patients without head and neck cancer. The short axial diameters (SAD) of the largest node in the parotid, submandibular, occipital, facial, retroauricular and Level IIb of the upper internal jugular nodal groups were documented and compared to the commonly used threshold of ≥ 10 mm for diagnosis of a malignant node. RESULTS Normal nodes in the parotid, occipital, retroauricular and Level IIb groups were small with a mean SAD ranging from 3.8 to 4.4 mm, nodes in the submandibular group were larger with a mean SAD of 5.5 mm and facial nodes were not identified. A size ≥ 10 mm was found in 0.8% of submandibular nodes. Less than 10% of the other nodal group had a SAD of ≥ 6 mm and none of them had a SAD ≥ 8 mm. CONCLUSION To identify malignant neck nodes in these groups there is scope to reduce the size threshold of ≥ 10 mm to improve sensitivity without substantial loss of specificity.
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Affiliation(s)
- Qi Yong H. Ai
- grid.16890.360000 0004 1764 6123Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong S.A.R, P.R. China ,grid.415197.f0000 0004 1764 7206Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong S.A.R, P.R. China
| | - Tiffany Y. So
- grid.415197.f0000 0004 1764 7206Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong S.A.R, P.R. China
| | - Kuo Feng Hung
- grid.194645.b0000000121742757Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, University of Hong Kong, Hong Kong S.A.R, P.R. China
| | - Ann D. King
- grid.415197.f0000 0004 1764 7206Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong S.A.R, P.R. China
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Wong LM, Ai QYH, Zhang R, Mo F, King AD. Radiomics for Discrimination between Early-Stage Nasopharyngeal Carcinoma and Benign Hyperplasia with Stable Feature Selection on MRI. Cancers (Basel) 2022; 14:cancers14143433. [PMID: 35884494 PMCID: PMC9324280 DOI: 10.3390/cancers14143433] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/11/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Discriminating early-stage nasopharyngeal carcinoma (NPC) from benign hyperplasia (BH) on MRI is a challenging but important task for the early detection of NPC in screening programs. Radiomics models have the potential to meet this challenge, but instability in the feature selection step may reduce their reliability. Therefore, in this study, we aim to discriminate between early-stage T1 NPC and BH on MRI using radiomics and propose a method to improve the stability of the feature selection step in the radiomics pipeline. A radiomics model was trained using data from 442 patients (221 early-stage T1 NPC and 221 with BH) scanned at 3T and tested on 213 patients (99 early-stage T1 NPC and 114 BH) scanned at 1.5T. To verify the improvement in feature selection stability, we compared our proposed ensemble technique, which uses a combination of bagging and boosting (BB-RENT), with the well-established elastic net. The proposed radiomics model achieved an area under the curve of 0.85 (95% confidence interval (CI): 0.82−0.89) and 0.80 (95% CI: 0.74−0.86) in discriminating NPC and BH in the 3T training and 1.5T testing cohort, respectively, using 17 features selected from a pool of 422 features by the proposed feature selection technique. BB-RENT showed a better feature selection stability compared to the elastic net (Jaccard index = 0.39 ± 0.14 and 0.24 ± 0.06, respectively; p < 0.001).
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Affiliation(s)
- Lun M. Wong
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; (L.M.W.); (R.Z.)
| | - Qi Yong H. Ai
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; (L.M.W.); (R.Z.)
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
- Correspondence: (Q.Y.H.A.); (A.D.K.)
| | - Rongli Zhang
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; (L.M.W.); (R.Z.)
| | - Frankie Mo
- Department of Clinical Oncology, State Key Laboratory of Translational Oncology, Sir YK Pao Centre for Cancer, Hong Kong Cancer Institute and Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China;
| | - Ann D. King
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; (L.M.W.); (R.Z.)
- Correspondence: (Q.Y.H.A.); (A.D.K.)
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12
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Wong LM, Ai QYH, Poon DMC, Tong M, Ma BBY, Hui EP, Shi L, King AD. A convolutional neural network combined with positional and textural attention for the fully automatic delineation of primary nasopharyngeal carcinoma on non-contrast-enhanced MRI. Quant Imaging Med Surg 2021; 11:3932-3944. [PMID: 34476179 PMCID: PMC8339644 DOI: 10.21037/qims-21-196] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 05/13/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND Convolutional neural networks (CNNs) have the potential to automatically delineate primary nasopharyngeal carcinoma (NPC) on magnetic resonance imaging (MRI), but currently, the literature lacks a module to introduce valuable pre-computed features into a CNN. In addition, most CNNs for primary NPC delineation have focused on contrast-enhanced MRI. To enable the use of CNNs in clinical applications where it would be desirable to avoid contrast agents, such as cancer screening or intra-treatment monitoring, we aim to develop a CNN algorithm with a positional-textural fully-connected attention (FCA) module that can automatically delineate primary NPCs on contrast-free MRI. METHODS This retrospective study was performed in 404 patients with NPC who had undergone staging MRI. A proposed CNN algorithm incorporated with our positional-textural FCA module (Aproposed ) was trained on manually delineated tumours (M1st ) to automatically delineate primary NPCs on non-contrast-enhanced T2-weighted fat-suppressed (NE-T2W-FS) images. The performance of Aproposed , three well-established CNNs, Unet (Aunet ), Attention-Unet (Aatt ) and Dense-Unet (Adense ), and a second manual delineation repeated to evaluate human variability (M 2 nd ) were measured by comparing to the reference standard M 1 st to obtain the Dice similarity coefficient (DSC) and average surface distance (ASD). The Wilcoxon rank test was used to compare the performance of Aproposed against Aunet , Aatt , Adense and M 2 nd . RESULTS Aproposed showed a median DSC of 0.79 (0.10) and ASD of 0.66 (0.84) mm. It performed better than the well-established networks Aunet [DSC =0.75 (0.12) and ASD =1.22 (1.73) mm], Aatt [DSC =0.75 (0.10) and ASD =0.96 (1.16) mm] and Adense [DSC =0.71 (0.14) and ASD =1.67 (1.92) mm] (all P<0.01), but slightly worse when compared to M 2 nd [DSC =0.81 (0.07) and ASD =0.56 (0.80) mm] (P<0.001). CONCLUSIONS The proposed CNN algorithm has potential to accurately delineate primary NPCs on non-contrast-enhanced MRI.
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Affiliation(s)
- Lun M. Wong
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Qi Yong H. Ai
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Darren M. C. Poon
- Department of Clinical Oncology, State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Macy Tong
- Department of Clinical Oncology, State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Brigette B. Y. Ma
- Department of Clinical Oncology, State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Edwin P. Hui
- Department of Clinical Oncology, State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Lin Shi
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Ann D. King
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
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Ko KWS, Bhatia KS, Ai QYH, King AD. Imaging of head and neck mucosa-associated lymphoid tissue lymphoma (MALToma). Cancer Imaging 2021; 21:10. [PMID: 33436095 PMCID: PMC7805088 DOI: 10.1186/s40644-020-00380-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 12/29/2020] [Indexed: 12/24/2022] Open
Abstract
Marginal zone B-cell lymphoma of mucosa-associated lymphoid tissue (MALToma) arises in extranodal sites in the head and neck. Chronic inflammatory, infectious or autoimmune conditions are implicated in its pathogenesis. Within the head and neck, MALToma is often multifocal and indolent and the imaging appearances may be mistaken for non-malignant disease in the head and neck. The aim of this article is to illustrate the varied radiological and clinical features of MALToma in the head and neck, an awareness of which is needed for timely and correct diagnosis to guide subsequent disease management.
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Affiliation(s)
- K W S Ko
- Department of Radiology and Imaging, Queen Elizabeth Hospital, 30 Gascoigne Road, Kowloon, Hong Kong, SAR, China
| | - Kunwar S Bhatia
- Department of Imaging, St Mary's Hospital, Imperial College Healthcare, National Health Service Trust, London, UK
| | - Qi Yong H Ai
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, 30-32 Ngan Shing Street, Shatin, New Territories, Hong Kong, SAR, China
| | - Ann D King
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, 30-32 Ngan Shing Street, Shatin, New Territories, Hong Kong, SAR, China.
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Ai QYH, Zhang H, Jiang B, So TY, Mo FKF, Qamar S, Chen W, King AD. Test-retest repeatability of T1rho (T1ρ) MR imaging in the head and neck. Eur J Radiol 2020; 135:109489. [PMID: 33395595 DOI: 10.1016/j.ejrad.2020.109489] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 12/15/2020] [Accepted: 12/18/2020] [Indexed: 12/27/2022]
Abstract
PURPOSE T1rho imaging is a new quantitative MRI sequence for head and neck cancer and the repeatability for this region is unknown. This study aimed to evaluate the repeatability of quantitative T1rho imaging in the head and neck. MATERIALS AND METHODS T1rho imaging of the head and neck was prospectively performed in 15 healthy participants on three occasions. Scan 1 and 2 were performed with a time interval of 30 minutes (intra-session) and scan 3 was performed 14 days later (inter-session). T1rho values for normal tissues (parotid glands, palatine tonsils, pterygoid muscles, and tongue) were obtained on each scan. Intra-class coefficients (ICCs), within-subject coefficient of variances (wCoVs), and repeatability coefficient (RCs) of the intra-session scan (scan 1 vs 2) and inter-session scan (scan 1 vs 3) for the normal tissues were calculated. RESULTS The ICCs of T1rho values for normal tissues were almost perfect (0.83-0.97) for intra-session scans and were substantial (0.71-0.80) for inter-session scans. The wCoVs showed a small range (2.46%-3.30%) for intra-session scans, and slightly greater range (3.27%-6.51%) for inter-session scan. The greatest and lowest wCoVs of T1rho were found in the parotid gland and muscles, respectively. The T1rho RCs varied for all tissues between intra- and inter- sessions, and the greatest RC of 10.07 msec was observed for parotid gland on inter-session scan. CONCLUSION T1rho imaging is a repeatable quantitative MRI sequence in the head and neck but variances of T1rho values among tissues should be take into account during analysis.
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Affiliation(s)
- Qi Yong H Ai
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong.
| | - Huimin Zhang
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong
| | - Baiyan Jiang
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong
| | - Tiffany Y So
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong
| | - Frankie K F Mo
- Department of Clinical Oncology, State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong
| | - Sahrish Qamar
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong
| | - Weitian Chen
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong
| | - Ann D King
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong
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Ai QYH, Chen W, So TY, Lam WKJ, Jiang B, Poon DMC, Qamar S, Mo FKF, Blu T, Chan Q, Ma BBY, Hui EP, Chan KCA, King AD. Quantitative T1ρ MRI of the Head and Neck Discriminates Carcinoma and Benign Hyperplasia in the Nasopharynx. AJNR Am J Neuroradiol 2020; 41:2339-2344. [PMID: 33122214 DOI: 10.3174/ajnr.a6828] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 08/07/2020] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND PURPOSE T1ρ imaging is a new quantitative MR imaging pulse sequence with the potential to discriminate between malignant and benign tissue. In this study, we evaluated the capability of T1ρ imaging to characterize tissue by applying T1ρ imaging to malignant and benign tissue in the nasopharynx and to normal tissue in the head and neck. MATERIALS AND METHODS Participants with undifferentiated nasopharyngeal carcinoma and benign hyperplasia of the nasopharynx prospectively underwent T1ρ imaging. T1ρ measurements obtained from the histogram analysis for nasopharyngeal carcinoma in 43 participants were compared with those for benign hyperplasia and for normal tissue (brain, muscle, and parotid glands) in 41 participants using the Mann-Whitney U test. The area under the curve of significant T1ρ measurements was calculated and compared using receiver operating characteristic analysis and the Delong test, respectively. A P < . 05 indicated statistical significance. RESULTS There were significant differences in T1ρ measurements between nasopharyngeal carcinoma and benign hyperplasia and between nasopharyngeal carcinoma and normal tissue (all, P < . 05). Compared with benign hyperplasia, nasopharyngeal carcinoma showed a lower T1ρ mean (62.14 versus 65.45 × ms), SD (12.60 versus 17.73 × ms), and skewness (0.61 versus 0.76) (all P < .05), but no difference in kurtosis (P = . 18). The T1ρ SD showed the highest area under the curve of 0.95 compared with the T1ρ mean (area under the curve = 0.72) and T1ρ skewness (area under the curve = 0.72) for discriminating nasopharyngeal carcinoma and benign hyperplasia (all, P < .05). CONCLUSIONS Quantitative T1ρ imaging has the potential to discriminate malignant from benign and normal tissue in the head and neck.
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Affiliation(s)
- Q Y H Ai
- From the Department of Imaging and Interventional Radiology (Q.Y.H.A., W.C., T.Y.S., B.J., S.Q., A.D.K.)
| | - W Chen
- From the Department of Imaging and Interventional Radiology (Q.Y.H.A., W.C., T.Y.S., B.J., S.Q., A.D.K.)
| | - T Y So
- From the Department of Imaging and Interventional Radiology (Q.Y.H.A., W.C., T.Y.S., B.J., S.Q., A.D.K.)
| | - W K J Lam
- Li Ka Shing Institute of Health Sciences (W.K.J.L., D.M.C.P., B.B.Y.M., E.P.H., K.C.A.C.).,State Key Laboratory of Translational Oncology (W.K.J.L., D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H., K.C.A.C.).,Department of Chemical Pathology (W.K.J.L., K.C.A.C.), State Key Laboratory in Oncology in South China, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, SAR
| | - B Jiang
- From the Department of Imaging and Interventional Radiology (Q.Y.H.A., W.C., T.Y.S., B.J., S.Q., A.D.K.)
| | - D M C Poon
- Li Ka Shing Institute of Health Sciences (W.K.J.L., D.M.C.P., B.B.Y.M., E.P.H., K.C.A.C.).,Department of Clinical Oncology (D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H.), State Key Laboratory in Oncology in South China, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, SAR.,State Key Laboratory of Translational Oncology (W.K.J.L., D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H., K.C.A.C.)
| | - S Qamar
- From the Department of Imaging and Interventional Radiology (Q.Y.H.A., W.C., T.Y.S., B.J., S.Q., A.D.K.)
| | - F K F Mo
- Department of Clinical Oncology (D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H.), State Key Laboratory in Oncology in South China, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, SAR.,State Key Laboratory of Translational Oncology (W.K.J.L., D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H., K.C.A.C.)
| | - T Blu
- Department of Electrical Engineering (T.B.), The Chinese University of Hong Kong, Hong Kong, SAR
| | - Q Chan
- Philips Healthcare (Q.C.), Hong Kong, SAR
| | - B B Y Ma
- Li Ka Shing Institute of Health Sciences (W.K.J.L., D.M.C.P., B.B.Y.M., E.P.H., K.C.A.C.).,Department of Clinical Oncology (D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H.), State Key Laboratory in Oncology in South China, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, SAR.,State Key Laboratory of Translational Oncology (W.K.J.L., D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H., K.C.A.C.)
| | - E P Hui
- Li Ka Shing Institute of Health Sciences (W.K.J.L., D.M.C.P., B.B.Y.M., E.P.H., K.C.A.C.).,Department of Clinical Oncology (D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H.), State Key Laboratory in Oncology in South China, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, SAR.,State Key Laboratory of Translational Oncology (W.K.J.L., D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H., K.C.A.C.)
| | - K C A Chan
- Li Ka Shing Institute of Health Sciences (W.K.J.L., D.M.C.P., B.B.Y.M., E.P.H., K.C.A.C.).,State Key Laboratory of Translational Oncology (W.K.J.L., D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H., K.C.A.C.).,Department of Chemical Pathology (W.K.J.L., K.C.A.C.), State Key Laboratory in Oncology in South China, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, SAR
| | - A D King
- From the Department of Imaging and Interventional Radiology (Q.Y.H.A., W.C., T.Y.S., B.J., S.Q., A.D.K.)
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