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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] [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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Barzegar M, Schweitzer M, So TY, Chen Y, Ertürk ŞM. Editorial: Quantitative neuroradiology methods. Front Radiol 2024; 4:1366704. [PMID: 38410374 PMCID: PMC10895052 DOI: 10.3389/fradi.2024.1366704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 01/31/2024] [Indexed: 02/28/2024]
Affiliation(s)
- Mojtaba Barzegar
- Radiation-Oncology Department, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation (HMC), Doha, Qatar
- Society for Brain Mapping and Therapeutics, Los Angeles, CA, United States
- Intelligent Quantitative Bio-Medical Imaging (IQBMI), Tehran, Iran
| | - Mark Schweitzer
- Office of the Vice President for Health Affairs Office of the Vice President, Wayne State University, Detroit, MI, United States
| | - Tiffany Y So
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Yongsheng Chen
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI, United States
| | - Şükrü Mehmet Ertürk
- Department of Radiology, Faculty of Medicine, İstanbul University, Istanbul, Turkiye
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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 MRI. J Natl Cancer Inst 2024:djad260. [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] [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 While contrast-enhanced 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 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%; 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, Hung Hom, 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, 1/F Block 18E, Hong Kong Science and Technology Park, Shatin, New Territories, 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
- Centre for Novostics, The Chinese University of Hong Kong, 1/F Block 18E, Hong Kong Science and Technology Park, Shatin, New Territories, 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, 1/F Block 18E, Hong Kong Science and Technology Park, Shatin, New Territories, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong SAR, China
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Cai Z, Wong LM, Wong YH, Lee HL, Li KY, So TY. Dual-Level Augmentation Radiomics Analysis for Multisequence MRI Meningioma Grading. Cancers (Basel) 2023; 15:5459. [PMID: 38001719 PMCID: PMC10670283 DOI: 10.3390/cancers15225459] [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: 09/29/2023] [Revised: 11/07/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Preoperative, noninvasive prediction of meningioma grade is important for therapeutic planning and decision making. In this study, we propose a dual-level augmentation strategy incorporating image-level augmentation (IA) and feature-level augmentation (FA) to tackle class imbalance and improve the predictive performance of radiomics for meningioma grading on Magnetic Resonance Imaging (MRI). METHODS This study recruited 160 consecutive patients with pathologically proven meningioma (129 low-grade (WHO grade I) tumors; 31 high-grade (WHO grade II and III) tumors) with preoperative multisequence MRI imaging. A dual-level augmentation strategy combining IA and FA was applied and evaluated in 100 repetitions in 3-, 5-, and 10-fold cross-validation. RESULTS The best area under the receiver operating characteristics curve of our method in 100 repetitions was ≥0.78 in all cross-validations. The corresponding cross-validation sensitivities (cross-validation specificity) were 0.72 (0.69), 0.76 (0.71), and 0.63 (0.82) in 3-, 5-, and 10-fold cross-validation, respectively. The proposed method achieved significantly better performance and distribution of results, outperforming single-level augmentation (IA or FA) or no augmentation in each cross-validation. CONCLUSIONS The dual-level augmentation strategy using IA and FA significantly improves the performance of the radiomics model for meningioma grading on MRI, allowing better radiomics-based preoperative stratification and individualized treatment.
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Affiliation(s)
| | | | | | | | | | - Tiffany Y. So
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China; (Z.C.); (L.M.W.); (Y.H.W.); (H.-l.L.); (K.-y.L.)
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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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Wang L, Chen W, Qian Y, So TY. Repeatability of quantitative T1rho magnetic resonance imaging in normal brain tissues at 3.0T. Phys Med 2023; 112:102641. [PMID: 37480710 DOI: 10.1016/j.ejmp.2023.102641] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 05/21/2023] [Accepted: 07/05/2023] [Indexed: 07/24/2023] Open
Abstract
PURPOSE T1rho imaging is a promising MRI technique for imaging of brain disease. This study aimed to assess the repeatability of quantitative T1rho imaging in the normal brain grey and white matter. METHODS The study prospectively recruited 30 healthy volunteers without a history of neurological diseases or brain injury, and T1rho was performed and quantified from three imaging sessions. Repeat measures analysis of variance (ANOVA) and within-subject coefficients of variation (wCoV) was used to detect differences in T1rho values between the three scans. RESULTS The results showed low wCoVs of less than 4.3% (range 0.92-4.27%) across all the brain structures. No significant differences were observed in T1rho measurement between the three scans (p > 0.05). The amygdala and hippocampus showed the highest T1rho values of 91.79 ± 2.55 msec and 91.07 ± 2.11 msec respectively, and the palladium and putamen had the lowest values of 67.60 ± 1.84 msec and 71.83 ± 1.85 msec respectively. CONCLUSION T1rho shows high test-retest repeatability for whole brain imaging in serial imaging sessions, indicating it to be a reliable sequence for quantitative brain imaging.
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Affiliation(s)
- Lei Wang
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong Special Administrative Region
| | - Weitian Chen
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong Special Administrative Region
| | - Yurui Qian
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong Special Administrative Region
| | - Tiffany Y So
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong Special Administrative Region.
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So TY, Yu SCH, Wong WT, Wong JKT, Lee H, Wang YX. Chest computed tomography analysis of lung sparing morphology: differentiation of COVID-19 pneumonia from influenza pneumonia and bacterial pneumonia using the arched bridge and vacuole signs. Hong Kong Med J 2023; 29:39-48. [PMID: 36810239 DOI: 10.12809/hkmj219291] [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] [Indexed: 02/22/2023] Open
Abstract
INTRODUCTION This study evaluated the arched bridge and vacuole signs, which constitute morphological patterns of lung sparing in coronavirus disease 2019 (COVID-19), then examined whether these signs could be used to differentiate COVID-19 pneumonia from influenza pneumonia or bacterial pneumonia. METHODS In total, 187 patients were included: 66 patients with COVID-19 pneumonia, 50 patients with influenza pneumonia and positive computed tomography findings, and 71 patients with bacterial pneumonia and positive computed tomography findings. Images were independently reviewed by two radiologists. The incidences of the arched bridge sign and/or vacuole sign were compared among the COVID-19 pneumonia, influenza pneumonia, and bacterial pneumonia groups. RESULTS The arched bridge sign was much more common among patients with COVID-19 pneumonia (42/66, 63.6%) than among patients with influenza pneumonia (4/50, 8.0%; P<0.001) or bacterial pneumonia (4/71, 5.6%; P<0.001). The vacuole sign was also much more common among patients with COVID-19 pneumonia (14/66, 21.2%) than among patients with influenza pneumonia (1/50, 2.0%; P=0.005) or bacterial pneumonia (1/71, 1.4%; P<0.001). The signs occurred together in 11 (16.7%) patients with COVID-19 pneumonia, but they did not occur together in patients with influenza pneumonia or bacterial pneumonia. The arched bridge and vacuole signs predicted COVID-19 pneumonia with respective specificities of 93.4% and 98.4%. CONCLUSION The arched bridge and vacuole signs are much more common in patients with COVID-19 pneumonia and can help differentiate COVID-19 pneumonia from influenza and bacterial pneumonia.
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Affiliation(s)
- T Y So
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
| | - S C H Yu
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
| | - W T Wong
- Department of Anaesthesia and Intensive Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
| | - J K T Wong
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
| | - H Lee
- Department of Diagnostic Radiology, Princess Margaret Hospital, Hong Kong
| | - Y X Wang
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
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Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, Bakas S. Author Correction: Federated learning enables big data for rare cancer boundary detection. Nat Commun 2023; 14:436. [PMID: 36702828 PMCID: PMC9879935 DOI: 10.1038/s41467-023-36188-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Satyam Ghodasara
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Felix Sahm
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Zenk
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Evan Calabrese
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey Rudie
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Soonmee Cha
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Manali Jadhav
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Umang Pandey
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - John Garrett
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Matthew Larson
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Robert Jeraj
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Stuart Currie
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Russell Frood
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Kavi Fatania
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Josep Puig
- Department of Radiology (IDI), Girona Biomedical Research Institute (IdIBGi), Josep Trueta University Hospital, Girona, Spain
| | - Johannes Trenkler
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Josef Pichler
- Department of Neurooncology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Georg Necker
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Andreas Haunschmidt
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Stephan Meckel
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
- Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Spencer Liem
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Gregory S Alexander
- Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA
| | - Joseph Lombardo
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joshua D Palmer
- Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Craig K Jones
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Meirui Jiang
- The Chinese University of Hong Kong, Hong Kong, China
| | - Tiffany Y So
- The Chinese University of Hong Kong, Hong Kong, China
| | - Cheng Chen
- The Chinese University of Hong Kong, Hong Kong, China
| | | | - Qi Dou
- The Chinese University of Hong Kong, Hong Kong, China
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Filip Lux
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Jan Michálek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Tereza Kopřivová
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
- Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Václav Vybíhal
- Department of Neurosurgery, Faculty of Medicine, Masaryk University, Brno, and University Hospital and Czech Republic, Brno, Czech Republic
| | - Michael A Vogelbaum
- Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - J Ross Mitchell
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Joaquim Farinhas
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Marco C Pinho
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Divya Reddy
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - James Holcomb
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Talia Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Minh-Son To
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
- Division of Surgery and Perioperative Medicine, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sargam Bhardwaj
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Chee Chong
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Marc Agzarian
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | | | - Bernardo C A Teixeira
- Instituto de Neurologia de Curitiba, Curitiba, Paraná, Brazil
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Flávia Sprenger
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - David Menotti
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Diego R Lucio
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Matthew Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Derrick Murcia
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Eric Fu
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Rourke Haas
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - John Thompson
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - David Ryan Ormond
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, OH, USA
| | - Andrew E Sloan
- Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Vachan Vadmal
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristin Waite
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
| | - Rivka R Colen
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linmin Pei
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat Ak
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashok Srinivasan
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - J Rajiv Bapuraj
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Ota Yoshiaki
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Toshio Moritani
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Sevcan Turk
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Snehal Prabhudesai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fanny Morón
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Mandel
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Konstantinos Kamnitsas
- Department of Computing, Imperial College London, London, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Luke V M Dixon
- Department of Radiology, Imperial College NHS Healthcare Trust, London, UK
| | - Matthew Williams
- Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, UK
| | - Peter Zampakis
- Department of NeuroRadiology, University of Patras, Patras, Greece
| | | | - Panagiotis Tsiganos
- Clinical Radiology Laboratory, Department of Medicine, University of Patras, Patras, Greece
| | - Sotiris Alexiou
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Ilias Haliassos
- Department of Neuro-Oncology, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | | | | | | | | | | | | | - Sung Soo Ahn
- Yonsei University College of Medicine, Seoul, Korea
| | - Bing Luo
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Laila Poisson
- Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | | | - Ruchika Verma
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
- Case Western Reserve University, Cleveland, OH, USA
| | - Rohan Bareja
- Case Western Reserve University, Cleveland, OH, USA
| | - Ipsa Yadav
- Case Western Reserve University, Cleveland, OH, USA
| | | | - Neeraj Kumar
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Sebastian R van der Voort
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Ahmed Alafandi
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Fatih Incekara
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Maarten M J Wijnenga
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Georgios Kapsas
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Joost W Schouten
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Hendrikus J Dubbink
- Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Arnaud J P E Vincent
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Martin J van den Bent
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Pim J French
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sonam Sharma
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tzu-Chi Tseng
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saba Adabi
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Olivier Keunen
- Translation Radiomics, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Ann-Christin Hau
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - David Fortin
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Martin Lepage
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Nuclear Medicine and Radiobiology, Sherbrooke Molecular Imaging Centre, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Bennett Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Silky Chotai
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Akshitkumar Mistry
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Reid C Thompson
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anousheh Sayah
- Division of Neuroradiology & Neurointerventional Radiology, Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Camelia Bencheqroun
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anas Belouali
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Haris Shuaib
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Carmen Dragos
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
| | | | | | | | | | - Shady Gamal
- University of Cairo School of Medicine, Giza, Egypt
| | | | | | | | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Jihye Yun
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, UK
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Sean Benson
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
| | - Jonas Teuwen
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | | | - William Escobar
- Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
- Universidad del Valle, Cali, Colombia
| | | | - Jose Bernal
- Universidad del Valle, Cali, Colombia
- The University of Edinburgh, Edinburgh, UK
| | | | - Joseph Choi
- Department of Industrial and Systems Engineering, University of Iowa, Iowa, USA
| | - Stephen Baek
- Department of Industrial and Systems Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Yusung Kim
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Heba Ismael
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Bryan Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | | | - Hongwei Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sampurna Shrestha
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Kartik M Mani
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Radiation Oncology, Stony Brook University, Stony Brook, NY, USA
| | - David Payne
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Enrique Pelaez
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | - Francis Loayza
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | | | | | | | - Franco Vera
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Elvis Ríos
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Eduardo López
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Sergio A Velastin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Godwin Ogbole
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mayowa Soneye
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | | | - Babatunde Osobu
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mustapha Shu'aibu
- Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
| | - Adeleye Dorcas
- Department of Radiology, Obafemi Awolowo University Ile-Ife, Ile-Ife, Osun, Nigeria
| | - Farouk Dako
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amber L Simpson
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Mohammad Hamghalam
- School of Computing, Queen's University, Kingston, ON, Canada
- Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Anh Tran
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Danielle Cutler
- The Faculty of Arts & Sciences, Queen's University, Kingston, ON, Canada
| | - Fabio Y Moraes
- Department of Oncology, Queen's University, Kingston, ON, Canada
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - James Gimpel
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Deepak Kattil Veettil
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Kendall Schmidt
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Brian Bialecki
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Sailaja Marella
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Cynthia Price
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Lisa Cimino
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Charles Apgar
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Jill S Barnholtz-Sloan
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), National Institute of Health, Bethesda, MD, USA
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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9
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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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10
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Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, Bakas S. Federated learning enables big data for rare cancer boundary detection. Nat Commun 2022; 13:7346. [PMID: 36470898 PMCID: PMC9722782 DOI: 10.1038/s41467-022-33407-5] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 09/16/2022] [Indexed: 12/12/2022] Open
Abstract
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.
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Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Satyam Ghodasara
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Felix Sahm
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Zenk
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Evan Calabrese
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey Rudie
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Soonmee Cha
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Manali Jadhav
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Umang Pandey
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - John Garrett
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Matthew Larson
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Robert Jeraj
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Stuart Currie
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Russell Frood
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Kavi Fatania
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Josep Puig
- Department of Radiology (IDI), Girona Biomedical Research Institute (IdIBGi), Josep Trueta University Hospital, Girona, Spain
| | - Johannes Trenkler
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Josef Pichler
- Department of Neurooncology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Georg Necker
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Andreas Haunschmidt
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Stephan Meckel
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
- Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Spencer Liem
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Gregory S Alexander
- Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA
| | - Joseph Lombardo
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joshua D Palmer
- Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Craig K Jones
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Meirui Jiang
- The Chinese University of Hong Kong, Hong Kong, China
| | - Tiffany Y So
- The Chinese University of Hong Kong, Hong Kong, China
| | - Cheng Chen
- The Chinese University of Hong Kong, Hong Kong, China
| | | | - Qi Dou
- The Chinese University of Hong Kong, Hong Kong, China
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Filip Lux
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Jan Michálek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Tereza Kopřivová
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
- Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Václav Vybíhal
- Department of Neurosurgery, Faculty of Medicine, Masaryk University, Brno, and University Hospital and Czech Republic, Brno, Czech Republic
| | - Michael A Vogelbaum
- Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - J Ross Mitchell
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Joaquim Farinhas
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Marco C Pinho
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Divya Reddy
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - James Holcomb
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Talia Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Minh-Son To
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
- Division of Surgery and Perioperative Medicine, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sargam Bhardwaj
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Chee Chong
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Marc Agzarian
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | | | - Bernardo C A Teixeira
- Instituto de Neurologia de Curitiba, Curitiba, Paraná, Brazil
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Flávia Sprenger
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - David Menotti
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Diego R Lucio
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Matthew Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Derrick Murcia
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Eric Fu
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Rourke Haas
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - John Thompson
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - David Ryan Ormond
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, OH, USA
| | - Andrew E Sloan
- Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Vachan Vadmal
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristin Waite
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
| | - Rivka R Colen
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linmin Pei
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat Ak
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashok Srinivasan
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - J Rajiv Bapuraj
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Ota Yoshiaki
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Toshio Moritani
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Sevcan Turk
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Snehal Prabhudesai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fanny Morón
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Mandel
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Konstantinos Kamnitsas
- Department of Computing, Imperial College London, London, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Luke V M Dixon
- Department of Radiology, Imperial College NHS Healthcare Trust, London, UK
| | - Matthew Williams
- Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, UK
| | - Peter Zampakis
- Department of NeuroRadiology, University of Patras, Patras, Greece
| | | | - Panagiotis Tsiganos
- Clinical Radiology Laboratory, Department of Medicine, University of Patras, Patras, Greece
| | - Sotiris Alexiou
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Ilias Haliassos
- Department of Neuro-Oncology, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | | | | | | | | | | | | | - Sung Soo Ahn
- Yonsei University College of Medicine, Seoul, Korea
| | - Bing Luo
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Laila Poisson
- Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | | | - Ruchika Verma
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
- Case Western Reserve University, Cleveland, OH, USA
| | - Rohan Bareja
- Case Western Reserve University, Cleveland, OH, USA
| | - Ipsa Yadav
- Case Western Reserve University, Cleveland, OH, USA
| | | | - Neeraj Kumar
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Sebastian R van der Voort
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Ahmed Alafandi
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Fatih Incekara
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Maarten M J Wijnenga
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Georgios Kapsas
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Joost W Schouten
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Hendrikus J Dubbink
- Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Arnaud J P E Vincent
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Martin J van den Bent
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Pim J French
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sonam Sharma
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tzu-Chi Tseng
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saba Adabi
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Olivier Keunen
- Translation Radiomics, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Ann-Christin Hau
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - David Fortin
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Martin Lepage
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Nuclear Medicine and Radiobiology, Sherbrooke Molecular Imaging Centre, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Bennett Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Silky Chotai
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Akshitkumar Mistry
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Reid C Thompson
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anousheh Sayah
- Division of Neuroradiology & Neurointerventional Radiology, Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Camelia Bencheqroun
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anas Belouali
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Haris Shuaib
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Carmen Dragos
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
| | | | | | | | | | - Shady Gamal
- University of Cairo School of Medicine, Giza, Egypt
| | | | | | | | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Jihye Yun
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, UK
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Sean Benson
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
| | - Jonas Teuwen
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | | | - William Escobar
- Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
- Universidad del Valle, Cali, Colombia
| | | | - Jose Bernal
- Universidad del Valle, Cali, Colombia
- The University of Edinburgh, Edinburgh, UK
| | | | - Joseph Choi
- Department of Industrial and Systems Engineering, University of Iowa, Iowa, USA
| | - Stephen Baek
- Department of Industrial and Systems Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Yusung Kim
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Heba Ismael
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Bryan Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | | | - Hongwei Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sampurna Shrestha
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Kartik M Mani
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Radiation Oncology, Stony Brook University, Stony Brook, NY, USA
| | - David Payne
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Enrique Pelaez
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | - Francis Loayza
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | | | | | | | - Franco Vera
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Elvis Ríos
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Eduardo López
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Sergio A Velastin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Godwin Ogbole
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mayowa Soneye
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | | | - Babatunde Osobu
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mustapha Shu'aibu
- Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
| | - Adeleye Dorcas
- Department of Radiology, Obafemi Awolowo University Ile-Ife, Ile-Ife, Osun, Nigeria
| | - Farouk Dako
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amber L Simpson
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Mohammad Hamghalam
- School of Computing, Queen's University, Kingston, ON, Canada
- Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Anh Tran
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Danielle Cutler
- The Faculty of Arts & Sciences, Queen's University, Kingston, ON, Canada
| | - Fabio Y Moraes
- Department of Oncology, Queen's University, Kingston, ON, Canada
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - James Gimpel
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Deepak Kattil Veettil
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Kendall Schmidt
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Brian Bialecki
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Sailaja Marella
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Cynthia Price
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Lisa Cimino
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Charles Apgar
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Jill S Barnholtz-Sloan
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), National Institute of Health, Bethesda, MD, USA
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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11
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Ai QYH, Hung KF, So TY, Mo FKF, Tsung Anthony Chin W, Hui EP, Ma BBY, Ying M, King AD. Prognostic value of cervical nodal necrosis on staging imaging of nasopharyngeal carcinoma in era of intensity-modulated radiotherapy: a systematic review and meta-analysis. Cancer Imaging 2022; 22:24. [PMID: 35596198 PMCID: PMC9123677 DOI: 10.1186/s40644-022-00462-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [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: 02/07/2022] [Accepted: 05/13/2022] [Indexed: 12/02/2022] Open
Abstract
Purposes To systematically review and perform meta-analysis to evaluate the prognostic value of cervical nodal necrosis (CNN) on the staging computed tomography/magnetic resonance imaging (MRI) of nasopharyngeal carcinoma (NPC) in era of intensity-modulated radiotherapy. Methods Literature search through PubMed, EMBASE, and Cochrane Library was conducted. The hazard ratios (HRs) with 95% confidence intervals (CIs) of CNN for distant metastasis-free survival (DMFS), disease free survival (DFS) and overall survival (OS) were extracted from the eligible studies and meta-analysis was performed to evaluate the pooled HRs with 95%CI. Results Nine studies, which investigated the prognostic values of 6 CNN patterns on MRI were included. Six/9 studies were eligible for meta-analysis, which investigated the CNN presence/absence in any nodal group among 4359 patients. The pooled unadjusted HRs showed that the CNN presence predicted poor DMFS (HR =1.89, 95%CI =1.72-2.08), DFS (HR =1.57, 95%CI =1.08-2.26), and OS (HR =1.87, 95%CI =1.69-2.06). The pooled adjusted HRs also showed the consistent results for DMFS (HR =1.34, 95%CI =1.17-1.54), DFS (HR =1.30, 95%CI =1.08-1.56), and OS (HR =1.61, 95%CI =1.27-2.04). Results shown in the other studies analysing different CNN patterns indicated the high grade of CNN predicted poor outcome, but meta-analysis was unable to perform because of the heterogeneity of the analysed CNN patterns. Conclusion The CNN observed on the staging MRI is a negative factor for NPC outcome, suggesting that the inclusion of CNN is important in the future survival analysis. However, whether and how should CNN be included in the staging system warrant further evaluation.
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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 S.A.R., P.R. China. .,Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, New Territories, Hong Kong S.A.R., P.R. China.
| | - Kuo Feng Hung
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, University of Hong Kong, Hong Kong, Hong Kong S.A.R., P.R. China
| | - Tiffany Y So
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, New Territories, Hong Kong S.A.R., P.R. China
| | - Frankie K F Mo
- Department of Clinical Oncology, State Key Laboratory in Oncology in South China, The Chinese University of Hong Kong, Prince of Wales Hospital, Sir Y.K. Pao Centre for Cancer, New Territories, Hong Kong S.A.R., P.R. China
| | - Wing Tsung Anthony Chin
- Department of Radiology and Organ Imaging, United Christian Hospital, Kowloon, Hong Kong S.A.R., P.R. China
| | - Edwin P Hui
- Department of Clinical Oncology, State Key Laboratory in Oncology in South China, The Chinese University of Hong Kong, Prince of Wales Hospital, Sir Y.K. Pao Centre for Cancer, New Territories, Hong Kong S.A.R., P.R. China
| | - Brigette B Y Ma
- Department of Clinical Oncology, State Key Laboratory in Oncology in South China, The Chinese University of Hong Kong, Prince of Wales Hospital, Sir Y.K. Pao Centre for Cancer, New Territories, Hong Kong S.A.R., P.R. China
| | - Michael Ying
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong S.A.R., P.R. China
| | - Ann D King
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, New Territories, Hong Kong S.A.R., P.R. China
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12
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Dou Q, So TY, Jiang M, Liu Q, Vardhanabhuti V, Kaissis G, Li Z, Si W, Lee HHC, Yu K, Feng Z, Dong L, Burian E, Jungmann F, Braren R, Makowski M, Kainz B, Rueckert D, Glocker B, Yu SCH, Heng PA. Author Correction: Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. NPJ Digit Med 2022; 5:56. [PMID: 35462562 PMCID: PMC9035308 DOI: 10.1038/s41746-022-00600-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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13
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Du R, Tsougenis ED, Ho JWK, Chan JKY, Chiu KWH, Fang BXH, Ng MY, Leung ST, Lo CSY, Wong HYF, Lam HYS, Chiu LFJ, So TY, Wong KT, Wong YCI, Yu K, Yeung YC, Chik T, Pang JWK, Wai AKC, Kuo MD, Lam TPW, Khong PL, Cheung NT, Vardhanabhuti V. Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph. Sci Rep 2021; 11:14250. [PMID: 34244563 PMCID: PMC8270945 DOI: 10.1038/s41598-021-93719-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 06/21/2021] [Indexed: 01/08/2023] Open
Abstract
Triaging and prioritising patients for RT-PCR test had been essential in the management of COVID-19 in resource-scarce countries. In this study, we applied machine learning (ML) to the task of detection of SARS-CoV-2 infection using basic laboratory markers. We performed the statistical analysis and trained an ML model on a retrospective cohort of 5148 patients from 24 hospitals in Hong Kong to classify COVID-19 and other aetiology of pneumonia. We validated the model on three temporal validation sets from different waves of infection in Hong Kong. For predicting SARS-CoV-2 infection, the ML model achieved high AUCs and specificity but low sensitivity in all three validation sets (AUC: 89.9-95.8%; Sensitivity: 55.5-77.8%; Specificity: 91.5-98.3%). When used in adjunction with radiologist interpretations of chest radiographs, the sensitivity was over 90% while keeping moderate specificity. Our study showed that machine learning model based on readily available laboratory markers could achieve high accuracy in predicting SARS-CoV-2 infection.
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Affiliation(s)
- Richard Du
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
- Artificial Intelligence Lab, Head Office Information Technology and Health Informatics Division, Hospital Authority, Hong Kong, SAR, China
| | - Efstratios D Tsougenis
- Artificial Intelligence Lab, Head Office Information Technology and Health Informatics Division, Hospital Authority, Hong Kong, SAR, China
| | - Joshua W K Ho
- The School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Joyce K Y Chan
- Clinical Systems, Information Technology and Health Informatics Division, Hospital Authority, Hong Kong, SAR, China
| | - Keith W H Chiu
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | | | - Ming Yen Ng
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
- Department of Medical Imaging, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Siu-Ting Leung
- Department of Radiology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, SAR, China
| | - Christine S Y Lo
- Department of Radiology, Hong Kong Sanatorium & Hospital, Hong Kong, SAR, China
| | - Ho-Yuen F Wong
- Department of Radiology, Queen Mary Hospital, Hong Kong, SAR, China
| | - Hiu-Yin S Lam
- Department of Radiology, Queen Mary Hospital, Hong Kong, SAR, China
| | - Long-Fung J Chiu
- Department of Radiology and Imaging, Queen Elizabeth Hospital, Hong Kong, SAR, China
| | - Tiffany Y So
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Ka Tak Wong
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, Hong Kong, SAR, China
| | - Yiu Chung I Wong
- Department of Radiology, Tuen Muen Hospital, Hong Kong, SAR, China
| | - Kevin Yu
- Department of Radiology, Tuen Muen Hospital, Hong Kong, SAR, China
| | - Yiu-Cheong Yeung
- Department of Medicine, Princess Margaret Hospital, Hong Kong, SAR, China
| | - Thomas Chik
- Department of Medicine, Princess Margaret Hospital, Hong Kong, SAR, China
| | - Joanna W K Pang
- Health Informatics, Information Technology and Health Informatics Division, Hospital Authority, Hong Kong, SAR, China
| | - Abraham Ka-Chung Wai
- Emergency Medicine Unit, Li Ka Shing, Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Michael D Kuo
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Tina P W Lam
- Department of Radiology, Queen Mary Hospital, Hong Kong, SAR, China
| | - Pek-Lan Khong
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Ngai-Tseung Cheung
- Information Technology and Health Informatics Division, Hospital Authority, Hong Kong, SAR, China
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China.
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14
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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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15
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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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16
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King AD, Woo JKS, Ai QY, Mo FKF, So TY, Lam WKJ, Tse IOL, Vlantis AC, Yip KWN, Hui EP, Ma BBY, Chiu RWK, Chan ATC, Lo YMD, Chan KCA. Early Detection of Cancer: Evaluation of MR Imaging Grading Systems in Patients with Suspected Nasopharyngeal Carcinoma. AJNR Am J Neuroradiol 2020; 41:515-521. [PMID: 32184223 DOI: 10.3174/ajnr.a6444] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 12/14/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND AND PURPOSE We evaluated modifications to our contrast-enhanced MR imaging grading system for symptomatic patients with suspected nasopharyngeal carcinoma, aimed at improving discrimination of early-stage cancer and benign hyperplasia. We evaluated a second non-contrast-enhanced MR imaging grading system for asymptomatic patients from nasopharyngeal carcinoma plasma screening programs. MATERIALS AND METHODS Dedicated nasopharyngeal MR imaging before (plain scan system) and after intravenous contrast administration (current and modified systems) was reviewed in patients from a nasopharyngeal carcinoma-endemic region, comprising 383 patients with suspected disease without nasopharyngeal carcinoma and 383 patients with nasopharyngeal carcinoma. The modified and plain scan systems refined primary tumor criteria, added a nodal assessment, and expanded the system from 4 to 5 grades. The overall combined sensitivity and specificity of the 3 systems were compared using the extended McNemar test (a χ2 value [Formula: see text]> 5.99 indicates significance). RESULTS The current, modified, and plain scan MR imaging systems yielded sensitivities of 99.74%, 97.91%, and 97.65%, respectively, and specificities of 63.45%, 89.56% and 86.42%, respectively. The modified system yielded significantly better performance than the current ([Formula: see text] = 122) and plain scan ([Formula: see text] = 6.1) systems. The percentages of patients with nasopharyngeal carcinoma in grades 1-2, grade 3, and grades 4-5 for the modified and plain scan MR imaging systems were 0.42% and 0.44%; 6.31% and 6.96%; and 90.36% and 87.79%, respectively. No additional cancers were detected after contrast administration in cases of a plain scan graded 1-2. CONCLUSIONS We propose a modified MR imaging grading system that improves diagnostic performance for nasopharyngeal carcinoma detection. Contrast was not valuable for low MR imaging grades, and the plain scan shows potential for use in screening programs.
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Affiliation(s)
- A D King
- From the Departments of Imaging and Interventional Radiology (A.D.K., Q.Y.A., T.Y.S., K.W.N.Y.)
| | - J K S Woo
- Otorhinolaryngology, Head and Neck Surgery (J.K.S.W., A.C.V.)
| | - Q-Y Ai
- Clinical Oncology (F.K.F.M., E.P.H., B.B.Y.M., A.T.C.C.)
| | - F K F Mo
- Chemical Pathology (W.K.J.L., I.O.L.T., R.W.K.C., Y.M.D.L., K.C.A.C.).,Li Ka Shing Institute of Health Sciences (F.K.F.M., W.K.J.L., I.O.L.T., E.P.H., B.B.Y.M., R.W.K.C., A.T.C.C., Y.M.D.L., K.C.A.C.), The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - T Y So
- From the Departments of Imaging and Interventional Radiology (A.D.K., Q.Y.A., T.Y.S., K.W.N.Y.)
| | - W K J Lam
- Chemical Pathology (W.K.J.L., I.O.L.T., R.W.K.C., Y.M.D.L., K.C.A.C.).,Li Ka Shing Institute of Health Sciences (F.K.F.M., W.K.J.L., I.O.L.T., E.P.H., B.B.Y.M., R.W.K.C., A.T.C.C., Y.M.D.L., K.C.A.C.), The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.,State Key Laboratory of Translational Oncology (F.K.F.M., W.K.J.L., I.O.L.T., E.P.H., B.B.Y.M., R.W.K.C., A.T.C.C., Y.M.D.L., K.C.A.C.), The Chinese University of Hong Kong, Hong Kong SAR, China
| | - I O L Tse
- Chemical Pathology (W.K.J.L., I.O.L.T., R.W.K.C., Y.M.D.L., K.C.A.C.).,Li Ka Shing Institute of Health Sciences (F.K.F.M., W.K.J.L., I.O.L.T., E.P.H., B.B.Y.M., R.W.K.C., A.T.C.C., Y.M.D.L., K.C.A.C.), The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.,State Key Laboratory of Translational Oncology (F.K.F.M., W.K.J.L., I.O.L.T., E.P.H., B.B.Y.M., R.W.K.C., A.T.C.C., Y.M.D.L., K.C.A.C.), The Chinese University of Hong Kong, Hong Kong SAR, China
| | - A C Vlantis
- Otorhinolaryngology, Head and Neck Surgery (J.K.S.W., A.C.V.)
| | - K W N Yip
- From the Departments of Imaging and Interventional Radiology (A.D.K., Q.Y.A., T.Y.S., K.W.N.Y.)
| | - E P Hui
- Clinical Oncology (F.K.F.M., E.P.H., B.B.Y.M., A.T.C.C.).,Chemical Pathology (W.K.J.L., I.O.L.T., R.W.K.C., Y.M.D.L., K.C.A.C.).,Li Ka Shing Institute of Health Sciences (F.K.F.M., W.K.J.L., I.O.L.T., E.P.H., B.B.Y.M., R.W.K.C., A.T.C.C., Y.M.D.L., K.C.A.C.), The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - B B Y Ma
- Clinical Oncology (F.K.F.M., E.P.H., B.B.Y.M., A.T.C.C.).,Chemical Pathology (W.K.J.L., I.O.L.T., R.W.K.C., Y.M.D.L., K.C.A.C.).,Li Ka Shing Institute of Health Sciences (F.K.F.M., W.K.J.L., I.O.L.T., E.P.H., B.B.Y.M., R.W.K.C., A.T.C.C., Y.M.D.L., K.C.A.C.), The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - R W K Chiu
- Chemical Pathology (W.K.J.L., I.O.L.T., R.W.K.C., Y.M.D.L., K.C.A.C.).,Li Ka Shing Institute of Health Sciences (F.K.F.M., W.K.J.L., I.O.L.T., E.P.H., B.B.Y.M., R.W.K.C., A.T.C.C., Y.M.D.L., K.C.A.C.), The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.,State Key Laboratory of Translational Oncology (F.K.F.M., W.K.J.L., I.O.L.T., E.P.H., B.B.Y.M., R.W.K.C., A.T.C.C., Y.M.D.L., K.C.A.C.), The Chinese University of Hong Kong, Hong Kong SAR, China
| | - A T C Chan
- Clinical Oncology (F.K.F.M., E.P.H., B.B.Y.M., A.T.C.C.).,Chemical Pathology (W.K.J.L., I.O.L.T., R.W.K.C., Y.M.D.L., K.C.A.C.).,Li Ka Shing Institute of Health Sciences (F.K.F.M., W.K.J.L., I.O.L.T., E.P.H., B.B.Y.M., R.W.K.C., A.T.C.C., Y.M.D.L., K.C.A.C.), The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Y M D Lo
- Chemical Pathology (W.K.J.L., I.O.L.T., R.W.K.C., Y.M.D.L., K.C.A.C.).,Li Ka Shing Institute of Health Sciences (F.K.F.M., W.K.J.L., I.O.L.T., E.P.H., B.B.Y.M., R.W.K.C., A.T.C.C., Y.M.D.L., K.C.A.C.), The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.,State Key Laboratory of Translational Oncology (F.K.F.M., W.K.J.L., I.O.L.T., E.P.H., B.B.Y.M., R.W.K.C., A.T.C.C., Y.M.D.L., K.C.A.C.), The Chinese University of Hong Kong, Hong Kong SAR, China
| | - K C A Chan
- Chemical Pathology (W.K.J.L., I.O.L.T., R.W.K.C., Y.M.D.L., K.C.A.C.).,Li Ka Shing Institute of Health Sciences (F.K.F.M., W.K.J.L., I.O.L.T., E.P.H., B.B.Y.M., R.W.K.C., A.T.C.C., Y.M.D.L., K.C.A.C.), The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.,State Key Laboratory of Translational Oncology (F.K.F.M., W.K.J.L., I.O.L.T., E.P.H., B.B.Y.M., R.W.K.C., A.T.C.C., Y.M.D.L., K.C.A.C.), The Chinese University of Hong Kong, Hong Kong SAR, China
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17
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Ai QY, King AD, So TY, Lam WKJ, Mo FKF, Tse IOL, Woo JKS, Chan KCA. MRI of benign hyperplasia in the nasopharynx: is there an association with Epstein-Barr virus? Clin Radiol 2020; 75:711.e13-711.e18. [PMID: 32571521 DOI: 10.1016/j.crad.2020.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 05/07/2020] [Indexed: 10/24/2022]
Abstract
AIM To evaluate whether there is an association between persistently positive plasma Epstein-Barr virus (EBV) DNA and the presence and the change in benign hyperplasia. MATERIALS AND METHODS One hundred and seventeen participants with positive-plasma EBV-DNA, but without NPC from previous nasopharyngeal carcinoma (NPC) screening, underwent follow-up magnetic resonance imaging (MRI) and plasma EBV-DNA after 2 years. Logistic regression was used to analyse associations between MRI (benign hyperplasia on the follow-up MRI and change from 2 years earlier), and plasma EBV-DNA, smoking, and age. RESULTS At follow-up, EBV-DNA positivity and smoking were independent parameters for the presence of benign hyperplasia (p=0.027 and 0.023 respectively). Compared with participants in whom EBV-DNA became negative (n=44/117 37.6%), those in whom EBV-DNA remained positive (n=73/117 62.4%) had a greater risk of benign hyperplasia developing (previous MRI normal), being stable or processing (52/73 71.2% versus 18/44 40.9%; p=0.001). CONCLUSION These results suggest a potential link between benign hyperplasia on MRI and the EBV. As EBV contributes to NPC oncogenesis, future MRI research is warranted to determine if persistent benign hyperplasia is a risk marker for development of NPC.
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Affiliation(s)
- Q-Y Ai
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - A D King
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China.
| | - T Y So
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - W K J Lam
- Department of Chemical Pathology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - F K F Mo
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China; Department of Clinical Oncology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - I O L Tse
- Department of Chemical Pathology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - J 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
| | - K C A Chan
- Department of Chemical Pathology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
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18
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Qamar S, King AD, Ai QYH, So TY, Mo FKF, Chen W, Poon DMC, Tong M, Ma BB, Hui EP, Yeung DKW, Wang YX, Yuan J. Pre-treatment intravoxel incoherent motion diffusion-weighted imaging predicts treatment outcome in nasopharyngeal carcinoma. Eur J Radiol 2020; 129:109127. [PMID: 32563165 DOI: 10.1016/j.ejrad.2020.109127] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [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: 01/30/2020] [Revised: 05/28/2020] [Accepted: 06/07/2020] [Indexed: 01/01/2023]
Abstract
PURPOSE To evaluate whether pre-treatment intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) can predict treatment outcome after 2 years in patients with nasopharyngeal carcinoma (NPC). METHOD One hundred and sixty-one patients with newly diagnosed NPC underwent pre-treatment IVIM-DWI. Univariate Cox regression analysis was performed to evaluate the correlation of the mean values of the pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction and apparent diffusion coefficient with local relapse-free survival (LRFS), regional relapse-free survival (RRFS), distant metastases-free survival (DMFS) and disease-free survival (DFS). Significant diffusion parameters, together with staging, age, gender and treatment as confounding factors, were added into a multivariate model. The area under the curves (AUCs) of significant parameters for disease relapse were compared using the Delong test. RESULTS Disease relapse occurred in 30 % of the patients at a median follow-up time of 52.1 months. The multivariate analysis showed that high D and T-staging were correlated with poor LRFS (p = 0.042 and 0.020, respectively) and poor DFS (p = 0.023 and 0.001, respectively); low D* and high T-staging with poor RRFS (p = 0.020 and 0.033, respectively); and high N-staging with poor DMFS (p = 0.006). D with the optimal threshold of ≥0.68 × 10-3 mm2/s and T-staging showed similar AUCs (AUC = 0.614 and 0.651, respectively; p = 0.493) for predicting disease relapse. CONCLUSION High D and low D* were predictors of poor locoregional outcome but none of the diffusion parameters predicted DMFS in NPC.
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Affiliation(s)
- Sahrish Qamar
- 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.
| | - 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, China
| | - Tiffany Y So
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, China
| | - Frankie Kwok Fai Mo
- Department of Clinical Oncology, State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, China
| | - Weitian Chen
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, 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, Shatin, New Territories, 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, Shatin, New Territories, Hong Kong, China
| | - Brigette B Ma
- Department of Clinical Oncology, State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, 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, Shatin, New Territories, Hong Kong, China
| | - David Ka-Wai Yeung
- Department of Clinical Oncology, State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, China
| | - Yi-Xiang Wang
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, China
| | - Jing Yuan
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
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19
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Wijetunga C, O'Donnell C, So TY, Varma D, Cameron P, Burke M, Bassed R, Smith K, Beck B. Injury Detection in Traumatic Death: Postmortem Computed Tomography vs. Open Autopsy. Forensic Imaging 2020. [DOI: 10.1016/j.jofri.2019.100349] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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So TY, Dixon A, Kavnoudias H, Paul E, Maclaurin W. Traumatic dural venous sinus gas predicts a higher likelihood of dural venous sinus thrombosis following blunt head trauma. J Med Imaging Radiat Oncol 2019; 63:311-317. [DOI: 10.1111/1754-9485.12865] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2018] [Accepted: 02/05/2019] [Indexed: 11/30/2022]
Affiliation(s)
- Tiffany Y So
- Department of Radiology Alfred Hospital Melbourne Victoria Australia
| | - Andrew Dixon
- Department of Radiology Alfred Hospital Melbourne Victoria Australia
| | - Helen Kavnoudias
- Department of Radiology Alfred Hospital Melbourne Victoria Australia
| | - Eldho Paul
- Department of Epidemiology and Preventive Medicine Monash University Melbourne Victoria Australia
| | - William Maclaurin
- Department of Radiology Alfred Hospital Melbourne Victoria Australia
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21
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Burlak K, So TY, Maclaurin WA, Dixon AF. Foramen tympanicum with symptomatic temporomandibular joint herniation. Radiol Case Rep 2018; 13:822-824. [PMID: 29988923 PMCID: PMC6034154 DOI: 10.1016/j.radcr.2018.05.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [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: 04/15/2018] [Revised: 05/09/2018] [Accepted: 05/13/2018] [Indexed: 11/22/2022] Open
Abstract
Foramen tympanicum (FT), or foramen of Huschke, describes an uncommon anatomicvariant of a persistent bony defect connecting the external acoustic meatus to the temporomandibular joint (TMJ). Although rare, it can be associated with significant complications, such as TMJ herniation, salivary gland fistula, infectious or tumoral spread between the external acoustic meatus and the TMJ, or result in inadvertent ear injury during TMJ arthroscopy. To the best of our knowledge, this is the first case report of a symptomatic FT with a full description of computed tomography and magnetic resonance imaging findings. Surgical exploration confirmed the presence of FT with TMJ herniation with subsequent successful closure of the defect obtained.
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Affiliation(s)
- Kateryna Burlak
- Department of Radiology, Alfred Hospital, PO Box 315, Prahran, Melbourne, VIC 3181, Australia
| | - Tiffany Y So
- Department of Radiology, Alfred Hospital, PO Box 315, Prahran, Melbourne, VIC 3181, Australia
| | - William A Maclaurin
- Department of Radiology, Alfred Hospital, PO Box 315, Prahran, Melbourne, VIC 3181, Australia
| | - Andrew F Dixon
- Department of Radiology, Alfred Hospital, PO Box 315, Prahran, Melbourne, VIC 3181, Australia
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22
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Miguel E, Chevalier V, Ayelet G, Ben Bencheikh MN, Boussini H, Chu DK, El Berbri I, Fassi-Fihri O, Faye B, Fekadu G, Grosbois V, Ng BC, Perera RA, So TY, Traore A, Roger F, Peiris M. Risk factors for MERS coronavirus infection in dromedary camels in Burkina Faso, Ethiopia, and Morocco, 2015. Euro Surveill 2017; 22:30498. [PMID: 28382915 PMCID: PMC5388105 DOI: 10.2807/1560-7917.es.2017.22.13.30498] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 02/03/2017] [Indexed: 11/20/2022] Open
Abstract
Understanding Middle East respiratory syndrome coronavirus (MERS-CoV) transmission in dromedary camels is important, as they consitute a source of zoonotic infection to humans. To identify risk factors for MERS-CoV infection in camels bred in diverse conditions in Burkina Faso, Ethiopia and Morocco, blood samples and nasal swabs were sampled in February-March 2015. A relatively high MERS-CoV RNA rate was detected in Ethiopia (up to 15.7%; 95% confidence interval (CI): 8.2-28.0), followed by Burkina Faso (up to 12.2%; 95% CI: 7-20.4) and Morocco (up to 7.6%; 95% CI: 1.9-26.1). The RNA detection rate was higher in camels bred for milk or meat than in camels for transport (p = 0.01) as well as in younger camels (p = 0.06). High seropositivity rates (up to 100%; 95% CI: 100-100 and 99.4%; 95% CI: 95.4-99.9) were found in Morocco and Ethiopia, followed by Burkina Faso (up to 84.6%; 95% CI: 77.2-89.9). Seropositivity rates were higher in large/medium herds (≥51 camels) than small herds (p = 0.061), in camels raised for meat or milk than for transport (p = 0.01), and in nomadic or sedentary herds than in herds with a mix of these lifestyles (p < 0.005).
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Affiliation(s)
- Eve Miguel
- Cirad UPR AGIRs, Montpellier, France
- UMR CNRS, IRD, UM, 5290 MIVEGEC, Montpellier, France
| | | | | | | | | | - Daniel Kw Chu
- School of Public Health, The University of Hong Kong, Hong Kong Special Adminstrative Region, China
| | | | | | | | | | | | - Bryan Cy Ng
- School of Public Health, The University of Hong Kong, Hong Kong Special Adminstrative Region, China
| | - Ranawaka Apm Perera
- School of Public Health, The University of Hong Kong, Hong Kong Special Adminstrative Region, China
| | - T Y So
- School of Public Health, The University of Hong Kong, Hong Kong Special Adminstrative Region, China
| | | | | | - Malik Peiris
- School of Public Health, The University of Hong Kong, Hong Kong Special Adminstrative Region, China
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So TY, Mitchell PJ, Dowling RJ, Yan B. Efficacy, complications and clinical outcome of endovascular treatment for intracranial intradural arterial dissections. Clin Neurol Neurosurg 2014; 117:6-11. [DOI: 10.1016/j.clineuro.2013.11.015] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2013] [Revised: 11/08/2013] [Accepted: 11/14/2013] [Indexed: 11/26/2022]
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So TY, Huang JG, Dowling R, Costello AJ, Whishaw M. Bilateral deep venous thrombosis associated with bladder compression of the iliac veins. Intern Med J 2013; 43:836-8. [PMID: 23841766 DOI: 10.1111/imj.12183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2012] [Accepted: 12/09/2012] [Indexed: 11/27/2022]
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So TY, Dowling R, Mitchell PJ, Laidlaw J, Yan B. Risk of growth in unruptured intracranial aneurysms: A retrospective analysis. J Clin Neurosci 2010; 17:29-33. [DOI: 10.1016/j.jocn.2009.04.010] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2009] [Accepted: 04/23/2009] [Indexed: 10/20/2022]
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Chu W, Choy WK, So TY. The effect of solution pH and peroxide in the TiO2-induced photocatalysis of chlorinated aniline. J Hazard Mater 2007; 141:86-91. [PMID: 16916576 DOI: 10.1016/j.jhazmat.2006.06.093] [Citation(s) in RCA: 63] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2006] [Revised: 06/12/2006] [Accepted: 06/26/2006] [Indexed: 05/11/2023]
Abstract
Chlorinated anilines are frequently used in the industry as starting materials for chemical synthesis. This type of compounds can end up as pollutants in wastewater. 2-Chloroaniline (2-ClA) was selected irradiating under monochromatic UV light at 300nm. The reaction rate could be enhanced by introducing low level of H(2)O(2) into the UV/TiO(2) system. Excess H(2)O(2) could not increase the HO* generation but retarded the reaction rate. The pH effect was also investigated in UV/TiO(2) and UV/TiO(2)/H(2)O(2) systems. All the experimental results show that pH is a sensitive parameter to the rate of degradation. Low reaction rate at acidic pH could be accounted by the dark adsorption test which has also proven the photocatalysis of TiO(2) may contribute to a two-step process: (1) 2-ClA pre-adsorbed onto TiO(2) and (2) photoexcitation of TiO(2). At high pH, rate enhancement could be observed at UV/TiO(2) system because of the increase generation of HO*. However, the introduction of H(2)O(2) slowdown the decay rate at such alkaline medium.
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Affiliation(s)
- W Chu
- Department of Civil and Structural Engineering, Research Centre for Environmental Technology and Management, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
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Affiliation(s)
- T Y So
- Department of Orthopaedics and Traumatology, Queen Elizabeth Hospital, Kowloon, Hong Kong
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