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Madani G, Arain Z, Awad Z. The radiological unknown primary of the head and neck: Recommendations for imaging strategies based on a systematic review. Clin Otolaryngol 2024; 49:16-28. [PMID: 37846889 DOI: 10.1111/coa.14111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/24/2023] [Accepted: 09/30/2023] [Indexed: 10/18/2023]
Abstract
OBJECTIVES To develop recommendations for the radiological investigation of clinically occult primary cancer in the head and neck. DESIGN AND SETTING In accordance with PRISMA guidelines, a search was performed on Medline, Embase and Cochrane library databases to investigate the efficacy of ultrasound guided Fine Needle Aspiration (US FNAC), contrast enhanced CT (CECT), magnetic resonance imaging (MRI) and 18F-fluorodeoxyglucose PET-CT (18F-FDG PET-CT) in the investigation of head and neck squamous cell carcinoma from an unknown primary (HNSCCUP) presenting with a metastatic cervical lymph node (s). The Quality Assessment of Diagnostic Accuracy Studies Version 2 tool and SIGN 50 guidelines were used to assess the risk of bias and quality of the included studies. PARTICIPANTS Adult patients presenting with metastatic cervical lymph nodes from a HNSCCUP. MAIN OUTCOME MEASURES Utility of different imaging modalities (PET-CT, MRI, CE CT and US FNAC in the management of HNSCCUP). RESULTS Twenty-eight studies met inclusion criteria; these were meta-analyses, systematic reviews, prospective and retrospective studies. CONCLUSIONS The optimal imaging strategy involves utilisation of various imaging modalities. US FNAC can provide the initial diagnosis and HPV status of the occult primary tumour. CECT and MRI detect up to 44% of occult tumours and guide management. FDG PET-CT is the most sensitive imaging modality for the detection of CUP and should be performed prior to panendoscopy.
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Affiliation(s)
- Gitta Madani
- Imperial College Healthcare NHS Trust, London, UK
| | - Zoya Arain
- Imperial College Healthcare NHS Trust, London, UK
| | - Zaid Awad
- Imperial College Healthcare NHS Trust, London, UK
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2
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Bao D, Zhao Y, Wu W, Zhong H, Yuan M, Li L, Lin M, Zhao X, Luo D. Added value of histogram analysis of ADC in predicting radiation-induced temporal lobe injury of patients with nasopharyngeal carcinoma treated by intensity-modulated radiotherapy. Insights Imaging 2022; 13:197. [PMID: 36528686 PMCID: PMC9759610 DOI: 10.1186/s13244-022-01338-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 11/20/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND This study evaluated the predictive potential of histogram analysis derived from apparent diffusion coefficient (ADC) maps in radiation-induced temporal lobe injury (RTLI) of nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT). RESULTS Pretreatment diffusion-weighted imaging (DWI) of the temporal lobes of 214 patients with NPC was retrospectively analyzed to obtain ADC histogram parameters. Of the 18 histogram parameters derived from ADC maps, 7 statistically significant variables in the univariate analysis were included in the multivariate logistic regression analysis. The final best prediction model selected by backward stepwise elimination with Akaike information criteria as the stopping rule included kurtosis, maximum energy, range, and total energy. A Rad-score was established by combining the four variables, and it provided areas under the curve (AUCs) of 0.95 (95% confidence interval [CI] 0.91-0.98) and 0.89 (95% CI 0.81-0.97) in the training and validation cohorts, respectively. The combined model, integrating the Rad-score with the T stage (p = 0.02), showed a favorable prediction performance in the training and validation cohorts (AUC = 0.96 and 0.87, respectively). The calibration curves showed a good agreement between the predicted and actual RTLI occurrences. CONCLUSIONS Pretreatment histogram analysis of ADC maps and their combination with the T stage showed a satisfactory ability to predict RTLI in NPC after IMRT.
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Affiliation(s)
- Dan Bao
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Yanfeng Zhao
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Wenli Wu
- Medical Imaging Center, Liaocheng Tumor Hospital, Shandong, 252000 China
| | - Hongxia Zhong
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Meng Yuan
- grid.506261.60000 0001 0706 7839Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Lin Li
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Meng Lin
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Xinming Zhao
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Dehong Luo
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China ,grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116 China
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Schouten JPE, Noteboom S, Martens RM, Mes SW, Leemans CR, de Graaf P, Steenwijk MD. Automatic segmentation of head and neck primary tumors on MRI using a multi-view CNN. Cancer Imaging 2022; 22:8. [PMID: 35033188 PMCID: PMC8761340 DOI: 10.1186/s40644-022-00445-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 12/31/2021] [Indexed: 12/24/2022] Open
Abstract
Background Accurate segmentation of head and neck squamous cell cancer (HNSCC) is important for radiotherapy treatment planning. Manual segmentation of these tumors is time-consuming and vulnerable to inconsistencies between experts, especially in the complex head and neck region. The aim of this study is to introduce and evaluate an automatic segmentation pipeline for HNSCC using a multi-view CNN (MV-CNN). Methods The dataset included 220 patients with primary HNSCC and availability of T1-weighted, STIR and optionally contrast-enhanced T1-weighted MR images together with a manual reference segmentation of the primary tumor by an expert. A T1-weighted standard space of the head and neck region was created to register all MRI sequences to. An MV-CNN was trained with these three MRI sequences and evaluated in terms of volumetric and spatial performance in a cross-validation by measuring intra-class correlation (ICC) and dice similarity score (DSC), respectively. Results The average manual segmented primary tumor volume was 11.8±6.70 cm3 with a median [IQR] of 13.9 [3.22-15.9] cm3. The tumor volume measured by MV-CNN was 22.8±21.1 cm3 with a median [IQR] of 16.0 [8.24-31.1] cm3. Compared to the manual segmentations, the MV-CNN scored an average ICC of 0.64±0.06 and a DSC of 0.49±0.19. Improved segmentation performance was observed with increasing primary tumor volume: the smallest tumor volume group (<3 cm3) scored a DSC of 0.26±0.16 and the largest group (>15 cm3) a DSC of 0.63±0.11 (p<0.001). The automated segmentation tended to overestimate compared to the manual reference, both around the actual primary tumor and in false positively classified healthy structures and pathologically enlarged lymph nodes. Conclusion An automatic segmentation pipeline was evaluated for primary HNSCC on MRI. The MV-CNN produced reasonable segmentation results, especially on large tumors, but overestimation decreased overall performance. In further research, the focus should be on decreasing false positives and make it valuable in treatment planning.
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Affiliation(s)
- Jens P E Schouten
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Samantha Noteboom
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Roland M Martens
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Steven W Mes
- Department of Otolaryngology - Head and Neck Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - C René Leemans
- Department of Otolaryngology - Head and Neck Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Pim de Graaf
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Martijn D Steenwijk
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands. .,, De Boelelaan 1108, 1081 HZ, Amsterdam, The Netherlands.
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Albano D, Bruno F, Agostini A, Angileri SA, Benenati M, Bicchierai G, Cellina M, Chianca V, Cozzi D, Danti G, De Muzio F, Di Meglio L, Gentili F, Giacobbe G, Grazzini G, Grazzini I, Guerriero P, Messina C, Micci G, Palumbo P, Rocco MP, Grassi R, Miele V, Barile A. Dynamic contrast-enhanced (DCE) imaging: state of the art and applications in whole-body imaging. Jpn J Radiol 2021; 40:341-366. [PMID: 34951000 DOI: 10.1007/s11604-021-01223-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 11/17/2021] [Indexed: 12/18/2022]
Abstract
Dynamic contrast-enhanced (DCE) imaging is a non-invasive technique used for the evaluation of tissue vascularity features through imaging series acquisition after contrast medium administration. Over the years, the study technique and protocols have evolved, seeing a growing application of this method across different imaging modalities for the study of almost all body districts. The main and most consolidated current applications concern MRI imaging for the study of tumors, but an increasing number of studies are evaluating the use of this technique also for inflammatory pathologies and functional studies. Furthermore, the recent advent of artificial intelligence techniques is opening up a vast scenario for the analysis of quantitative information deriving from DCE. The purpose of this article is to provide a comprehensive update on the techniques, protocols, and clinical applications - both established and emerging - of DCE in whole-body imaging.
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Affiliation(s)
- Domenico Albano
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento Di Biomedicina, Neuroscienze E Diagnostica Avanzata, Sezione Di Scienze Radiologiche, Università Degli Studi Di Palermo, via Vetoio 1L'Aquila, 67100, Palermo, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy.
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy.
| | - Andrea Agostini
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Clinical, Special and Dental Sciences, Department of Radiology, University Politecnica delle Marche, University Hospital "Ospedali Riuniti Umberto I - G.M. Lancisi - G. Salesi", Ancona, Italy
| | - Salvatore Alessio Angileri
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Radiology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Massimo Benenati
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Dipartimento di Diagnostica per Immagini, Fondazione Policlinico Universitario A. Gemelli IRCCS, Oncologia ed Ematologia, RadioterapiaRome, Italy
| | - Giulia Bicchierai
- Diagnostic Senology Unit, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Michaela Cellina
- Department of Radiology, ASST Fatebenefratelli Sacco, Ospedale Fatebenefratelli, Milan, Italy
| | - Vito Chianca
- Ospedale Evangelico Betania, Naples, Italy
- Clinica Di Radiologia, Istituto Imaging Della Svizzera Italiana - Ente Ospedaliero Cantonale, Lugano, Switzerland
| | - Diletta Cozzi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Emergency Radiology, Careggi University Hospital, Florence, Italy
| | - Ginevra Danti
- Department of Emergency Radiology, Careggi University Hospital, Florence, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
| | - Letizia Di Meglio
- Postgraduation School in Radiodiagnostics, University of Milan, Milan, Italy
| | - Francesco Gentili
- Unit of Diagnostic Imaging, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Giuliana Giacobbe
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Giulia Grazzini
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Irene Grazzini
- Department of Radiology, Section of Neuroradiology, San Donato Hospital, Arezzo, Italy
| | - Pasquale Guerriero
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
| | | | - Giuseppe Micci
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Dipartimento Di Biomedicina, Neuroscienze E Diagnostica Avanzata, Sezione Di Scienze Radiologiche, Università Degli Studi Di Palermo, via Vetoio 1L'Aquila, 67100, Palermo, Italy
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Abruzzo Health Unit 1, Department of diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, L'Aquila, Italy
| | - Maria Paola Rocco
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Roberto Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Antonio Barile
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
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López F, Mäkitie A, de Bree R, Franchi A, de Graaf P, Hernández-Prera JC, Strojan P, Zidar N, Strojan Fležar M, Rodrigo JP, Rinaldo A, Centeno BA, Ferlito A. Qualitative and Quantitative Diagnosis in Head and Neck Cancer. Diagnostics (Basel) 2021; 11:diagnostics11091526. [PMID: 34573868 PMCID: PMC8466857 DOI: 10.3390/diagnostics11091526] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 08/14/2021] [Accepted: 08/20/2021] [Indexed: 12/11/2022] Open
Abstract
The diagnosis is the art of determining the nature of a disease, and an accurate diagnosis is the true cornerstone on which rational treatment should be built. Within the workflow in the management of head and neck tumours, there are different types of diagnosis. The purpose of this work is to point out the differences and the aims of the different types of diagnoses and to highlight their importance in the management of patients with head and neck tumours. Qualitative diagnosis is performed by a pathologist and is essential in determining the management and can provide guidance on prognosis. The evolution of immunohistochemistry and molecular biology techniques has made it possible to obtain more precise diagnoses and to identify prognostic markers and precision factors. Quantitative diagnosis is made by the radiologist and consists of identifying a mass lesion and the estimation of the tumour volume and extent using imaging techniques, such as CT, MRI, and PET. The distinction between the two types of diagnosis is clear, as the methodology is different. The accurate establishment of both diagnoses plays an essential role in treatment planning. Getting the right diagnosis is a key aspect of health care, and it provides an explanation of a patient’s health problem and informs subsequent decision. Deep learning and radiomics approaches hold promise for improving diagnosis.
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Affiliation(s)
- Fernando López
- Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitario Central de Asturias, 33011 Oviedo, Spain;
- Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto Universitario de Oncología del Principado de Asturias (IUOPA), University of Oviedo CIBERONC-ISCIII, 33011 Oviedo, Spain
- Correspondence:
| | - Antti Mäkitie
- Department of Otorhinolaryngology–Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, 00029 Helsinki, Finland;
| | - Remco de Bree
- Department of Head and Neck Surgical Oncology, University Medical Center Utrecht, 3584CX Utrecht, The Netherlands;
| | - Alessandro Franchi
- Department of Translational Research, School of Medicine, University of Pisa, 56124 Pisa, Italy;
| | - Pim de Graaf
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 Amsterdam, The Netherlands;
| | | | - Primoz Strojan
- Department of Radiation Oncology, Institute of Oncology, 1000 Ljubljana, Slovenia;
| | - Nina Zidar
- Department of Head and Neck Pathology, Faculty of Medicine, Institute of Pathology, University of Ljubljana, 1000 Ljubljana, Slovenia;
| | - Margareta Strojan Fležar
- Department of Cytopathology, Faculty of Medicine, Institute of Pathology, University of Ljubljana, 1000 Ljubljana, Slovenia;
| | - Juan P. Rodrigo
- Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitario Central de Asturias, 33011 Oviedo, Spain;
- Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto Universitario de Oncología del Principado de Asturias (IUOPA), University of Oviedo CIBERONC-ISCIII, 33011 Oviedo, Spain
| | | | - Barbara A. Centeno
- Department of Pathology, Moffitt Cancer Center, Tampa, FL 33612, USA; (J.C.H.-P.); (B.A.C.)
| | - Alfio Ferlito
- Coordinator of the International Head and Neck Scientific Group, 35100 Padua, Italy;
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Huasong H, Shurui S, Shi G, Bin J. Performance of 18F-FDG-PET/CT as a next step in the search of occult primary tumors for patients with head and neck squamous cell carcinoma of unknown primary: a systematic review and meta-analysis. Clin Transl Imaging 2021. [DOI: 10.1007/s40336-021-00429-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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