1
|
Mirshahvalad SA, Eisazadeh R, Shahbazi-Akbari M, Pirich C, Beheshti M. Application of Artificial Intelligence in Oncologic Molecular PET-Imaging: A Narrative Review on Beyond [ 18F]F-FDG Tracers - Part I. PSMA, Choline, and DOTA Radiotracers. Semin Nucl Med 2024; 54:171-180. [PMID: 37752032 DOI: 10.1053/j.semnuclmed.2023.08.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 08/29/2023] [Indexed: 09/28/2023]
Abstract
Artificial intelligence (AI) has evolved significantly in the past few decades. This thriving trend has also been seen in medicine in recent years, particularly in the field of imaging. Machine learning (ML), deep learning (DL), and their methods (eg, SVM, CNN), as well as radiomics, are the terminologies that have been introduced to this field and, to some extent, become familiar to the expert clinicians. PET is one of the modalities that has been enhanced via these state-of-the-art algorithms. This robust imaging technique further merged with anatomical modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), to provide reliable hybrid modalities, PET/CT and PET/MRI. Applying AI-based algorithms on the different components (PET, CT, and MRI) has resulted in promising results, maximizing the value of PET imaging. However, [18F]F-FDG, the most commonly utilized tracer in molecular imaging, has been mainly in the spotlight. Thus, we aimed to look into the less discussed tracers in this review, moving beyond [18F]F-FDG. The novel non-[18F]F-FDG agents also showed to be valuable in various clinical tasks, including lesion detection and tumor characterization, accurate delineation, and prognostic impact. Regarding prostate patients, PSMA-based models were highly accurate in determining tumoral lesions' location and delineating them, particularly within the prostate gland. However, they also could assess whole-body images to detect extra-prostatic lesions in a patient automatically. Considering the prognostic value of prostate-specific membrane antigen (PSMA) PET using AI, it could predict response to treatment and patient survival, which are crucial in patient management. Choline imaging, another non-[18F]F-FDG tracer, similarly showed acceptable results that may be of benefit in the clinic, though the current evidence is significantly more limited than PSMA. Lastly, different subtypes of DOTA ligands were found to be valuable. They could diagnose tumoral lesions in challenging sites and even predict histopathology grade, being a highly advantageous noninvasive tool. In conclusion, the current limited investigations have shown promising results, leading us to a bright future for AI in molecular imaging beyond [18F]F-FDG.
Collapse
Affiliation(s)
- Seyed Ali Mirshahvalad
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Canada
| | - Roya Eisazadeh
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Malihe Shahbazi-Akbari
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Research Center for Nuclear Medicine, Department of Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Christian Pirich
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Mohsen Beheshti
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria.
| |
Collapse
|
2
|
Gu F, Wu Q. Quantitation of dynamic total-body PET imaging: recent developments and future perspectives. Eur J Nucl Med Mol Imaging 2023; 50:3538-3557. [PMID: 37460750 PMCID: PMC10547641 DOI: 10.1007/s00259-023-06299-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 06/05/2023] [Indexed: 10/04/2023]
Abstract
BACKGROUND Positron emission tomography (PET) scanning is an important diagnostic imaging technique used in disease diagnosis, therapy planning, treatment monitoring, and medical research. The standardized uptake value (SUV) obtained at a single time frame has been widely employed in clinical practice. Well beyond this simple static measure, more detailed metabolic information can be recovered from dynamic PET scans, followed by the recovery of arterial input function and application of appropriate tracer kinetic models. Many efforts have been devoted to the development of quantitative techniques over the last couple of decades. CHALLENGES The advent of new-generation total-body PET scanners characterized by ultra-high sensitivity and long axial field of view, i.e., uEXPLORER (United Imaging Healthcare), PennPET Explorer (University of Pennsylvania), and Biograph Vision Quadra (Siemens Healthineers), further stimulates valuable inspiration to derive kinetics for multiple organs simultaneously. But some emerging issues also need to be addressed, e.g., the large-scale data size and organ-specific physiology. The direct implementation of classical methods for total-body PET imaging without proper validation may lead to less accurate results. CONCLUSIONS In this contribution, the published dynamic total-body PET datasets are outlined, and several challenges/opportunities for quantitation of such types of studies are presented. An overview of the basic equation, calculation of input function (based on blood sampling, image, population or mathematical model), and kinetic analysis encompassing parametric (compartmental model, graphical plot and spectral analysis) and non-parametric (B-spline and piece-wise basis elements) approaches is provided. The discussion mainly focuses on the feasibilities, recent developments, and future perspectives of these methodologies for a diverse-tissue environment.
Collapse
Affiliation(s)
- Fengyun Gu
- School of Mathematics and Physics, North China Electric Power University, 102206, Beijing, China.
- School of Mathematical Sciences, University College Cork, T12XF62, Cork, Ireland.
| | - Qi Wu
- School of Mathematical Sciences, University College Cork, T12XF62, Cork, Ireland
| |
Collapse
|
3
|
Wu S, Hong G, Xu A, Zeng H, Chen X, Wang Y, Luo Y, Wu P, Liu C, Jiang N, Dang Q, Yang C, Liu B, Shen R, Chen Z, Liao C, Lin Z, Wang J, Lin T. Artificial intelligence-based model for lymph node metastases detection on whole slide images in bladder cancer: a retrospective, multicentre, diagnostic study. Lancet Oncol 2023; 24:360-370. [PMID: 36893772 DOI: 10.1016/s1470-2045(23)00061-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND Accurate lymph node staging is important for the diagnosis and treatment of patients with bladder cancer. We aimed to develop a lymph node metastases diagnostic model (LNMDM) on whole slide images and to assess the clinical effect of an artificial intelligence-assisted (AI) workflow. METHODS In this retrospective, multicentre, diagnostic study in China, we included consecutive patients with bladder cancer who had radical cystectomy and pelvic lymph node dissection, and from whom whole slide images of lymph node sections were available, for model development. We excluded patients with non-bladder cancer and concurrent surgery, or low-quality images. Patients from two hospitals (Sun Yat-sen Memorial Hospital of Sun Yat-sen University and Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China) were assigned before a cutoff date to a training set and after the date to internal validation sets for each hospital. Patients from three other hospitals (the Third Affiliated Hospital of Sun Yat-sen University, Nanfang Hospital of Southern Medical University, and the Third Affiliated Hospital of Southern Medical University, Guangzhou, Guangdong, China) were included as external validation sets. A validation subset of challenging cases from the five validation sets was used to compare performance between the LNMDM and pathologists, and two other datasets (breast cancer from the CAMELYON16 dataset and prostate cancer from the Sun Yat-sen Memorial Hospital of Sun Yat-sen University) were collected for a multi-cancer test. The primary endpoint was diagnostic sensitivity in the four prespecified groups (ie, the five validation sets, a single-lymph-node test set, the multi-cancer test set, and the subset for a performance comparison between the LNMDM and pathologists). FINDINGS Between Jan 1, 2013 and Dec 31, 2021, 1012 patients with bladder cancer had radical cystectomy and pelvic lymph node dissection and were included (8177 images and 20 954 lymph nodes). We excluded 14 patients (165 images) with concurrent non-bladder cancer and also excluded 21 low-quality images. We included 998 patients and 7991 images (881 [88%] men; 117 [12%] women; median age 64 years [IQR 56-72]; ethnicity data not available; 268 [27%] with lymph node metastases) to develop the LNMDM. The area under the curve (AUC) for accurate diagnosis of the LNMDM ranged from 0·978 (95% CI 0·960-0·996) to 0·998 (0·996-1·000) in the five validation sets. Performance comparisons between the LNMDM and pathologists showed that the diagnostic sensitivity of the model (0·983 [95% CI 0·941-0·998]) substantially exceeded that of both junior pathologists (0·906 [0·871-0·934]) and senior pathologists (0·947 [0·919-0·968]), and that AI assistance improved sensitivity for both junior (from 0·906 without AI to 0·953 with AI) and senior (from 0·947 to 0·986) pathologists. In the multi-cancer test, the LNMDM maintained an AUC of 0·943 (95% CI 0·918-0·969) in breast cancer images and 0·922 (0·884-0·960) in prostate cancer images. In 13 patients, the LNMDM detected tumour micrometastases that had been missed by pathologists who had previously classified these patients' results as negative. Receiver operating characteristic curves showed that the LNMDM would enable pathologists to exclude 80-92% of negative slides while maintaining 100% sensitivity in clinical application. INTERPRETATION We developed an AI-based diagnostic model that did well in detecting lymph node metastases, particularly micrometastases. The LNMDM showed substantial potential for clinical applications in improving the accuracy and efficiency of pathologists' work. FUNDING National Natural Science Foundation of China, the Science and Technology Planning Project of Guangdong Province, the National Key Research and Development Programme of China, and the Guangdong Provincial Clinical Research Centre for Urological Diseases.
Collapse
Affiliation(s)
- Shaoxu Wu
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China; Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-sen University, Guangzhou, Guangdong, China; Guangdong Provincial Clinical Research Centre for Urological Diseases, Guangzhou, Guangdong, China
| | - Guibin Hong
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Abai Xu
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Hong Zeng
- Department of Pathology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xulin Chen
- Cells Vision Medical Technology, Guangzhou, Guangdong, China
| | - Yun Wang
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yun Luo
- Sun Yat-sen Memorial Hospital and Department of Urology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Peng Wu
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Cundong Liu
- Department of Urology, The Third Affiliated Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Ning Jiang
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Qiang Dang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Cheng Yang
- Department of Urology, The Third Affiliated Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Bohao Liu
- Sun Yat-sen Memorial Hospital and Department of Urology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Runnan Shen
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zeshi Chen
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Chengxiao Liao
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zhen Lin
- Cells Vision Medical Technology, Guangzhou, Guangdong, China
| | - Jin Wang
- Cells Vision Medical Technology, Guangzhou, Guangdong, China
| | - Tianxin Lin
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China; Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-sen University, Guangzhou, Guangdong, China; Guangdong Provincial Clinical Research Centre for Urological Diseases, Guangzhou, Guangdong, China.
| |
Collapse
|
4
|
Spadarella G, Stanzione A, Akinci D'Antonoli T, Andreychenko A, Fanni SC, Ugga L, Kotter E, Cuocolo R. Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative. Eur Radiol 2023; 33:1884-1894. [PMID: 36282312 PMCID: PMC9935718 DOI: 10.1007/s00330-022-09187-3] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/31/2022] [Accepted: 09/19/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVE The main aim of the present systematic review was a comprehensive overview of the Radiomics Quality Score (RQS)-based systematic reviews to highlight common issues and challenges of radiomics research application and evaluate the relationship between RQS and review features. METHODS The literature search was performed on multiple medical literature archives according to PRISMA guidelines for systematic reviews that reported radiomic quality assessment through the RQS. Reported scores were converted to a 0-100% scale. The Mann-Whitney and Kruskal-Wallis tests were used to compare RQS scores and review features. RESULTS The literature research yielded 345 articles, from which 44 systematic reviews were finally included in the analysis. Overall, the median of RQS was 21.00% (IQR = 11.50). No significant differences of RQS were observed in subgroup analyses according to targets (oncological/not oncological target, neuroradiology/body imaging focus and one imaging technique/more than one imaging technique, characterization/prognosis/detection/other). CONCLUSIONS Our review did not reveal a significant difference of quality of radiomic articles reported in systematic reviews, divided in different subgroups. Furthermore, low overall methodological quality of radiomics research was found independent of specific application domains. While the RQS can serve as a reference tool to improve future study designs, future research should also be aimed at improving its reliability and developing new tools to meet an ever-evolving research space. KEY POINTS • Radiomics is a promising high-throughput method that may generate novel imaging biomarkers to improve clinical decision-making process, but it is an inherently complex analysis and often lacks reproducibility and generalizability. • The Radiomics Quality Score serves a necessary role as the de facto reference tool for assessing radiomics studies. • External auditing of radiomics studies, in addition to the standard peer-review process, is valuable to highlight common limitations and provide insights to improve future study designs and practical applicability of the radiomics models.
Collapse
Affiliation(s)
- Gaia Spadarella
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
| | - Tugba Akinci D'Antonoli
- Institute of Radiology and Nuclear Medicine, Cantonal Hospital Baselland, Liestal, Switzerland
| | - Anna Andreychenko
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | | | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Elmar Kotter
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, Baronissi, Italy
- Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
| |
Collapse
|
5
|
Horvat N, Miranda J, El Homsi M, Peoples JJ, Long NM, Simpson AL, Do RKG. A primer on texture analysis in abdominal radiology. Abdom Radiol (NY) 2022; 47:2972-2985. [PMID: 34825946 DOI: 10.1007/s00261-021-03359-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 11/16/2021] [Accepted: 11/17/2021] [Indexed: 01/18/2023]
Abstract
The number of publications on texture analysis (TA), radiomics, and radiogenomics has been growing exponentially, with abdominal radiologists aiming to build new prognostic or predictive biomarkers for a wide range of clinical applications including the use of oncological imaging to advance the field of precision medicine. TA is specifically concerned with the study of the variation of pixel intensity values in radiological images. Radiologists aim to capture pixel variation in radiological images to deliver new insights into tumor biology that cannot be derived from visual inspection alone. TA remains an active area of investigation and requires further standardization prior to its clinical acceptance and applicability. This review is for radiologists interested in this rapidly evolving field, who are thinking of performing research or want to better interpret results in this arena. We will review the main concepts in TA, workflow processes, and existing challenges and steps to overcome them, as well as look at publications in body imaging with external validation.
Collapse
Affiliation(s)
- Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Joao Miranda
- Department of Radiology, University of Sao Paulo, Sao Paulo, SP, Brazil
| | - Maria El Homsi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Niamh M Long
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Amber L Simpson
- School of Computing, Queen's University, Kingston, ON, Canada.,Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA.
| |
Collapse
|
6
|
Tamam MO, Tamam MC. Artificial intelligence technologies in nuclear medicine. World J Radiol 2022; 14:151-154. [PMID: 35978976 PMCID: PMC9258309 DOI: 10.4329/wjr.v14.i6.151] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 04/20/2022] [Accepted: 06/17/2022] [Indexed: 02/06/2023] Open
Abstract
The use of artificial intelligence plays a crucial role in developing precision medicine in nuclear medicine. Artificial intelligence refers to a field of computer science aimed at imitating the performance of tasks typically requiring human intelligence. From machine learning to generative adversarial networks, artificial intelligence automized the workflow of medical imaging. In this mini-review, we encapsulate artificial intelligence models and their use in nuclear medicine imaging workflow.
Collapse
Affiliation(s)
- Muge Oner Tamam
- Department of Nuclear Medicine, Prof. Dr. Cemil Tascioglu City Hospital, İstanbul 34381, Turkey
| | | |
Collapse
|
7
|
Yoo KH. Staging and response assessment of lymphoma: a brief review of the Lugano classification and the role of FDG-PET/CT. Blood Res 2022; 57:75-78. [PMID: 35483930 PMCID: PMC9057662 DOI: 10.5045/br.2022.2022055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 04/06/2022] [Accepted: 04/06/2022] [Indexed: 02/06/2023] Open
Abstract
The accurate assessment of initial disease status and therapeutic responses is critical to the optimal management of patients with lymphoma. Currently, staging and treatment response evaluation for lymphoma has been standardized into the Lugano classification. Lugano classification incorporates positron emission tomography (PET) into the existing response criteria, and response assessment using FDG-PET/CT has been proven to predict the prognosis in various lymphoma subtypes effectively. We will briefly review the current staging and response evaluation system and explore the role of functional imaging in the field of lymphoma.
Collapse
Affiliation(s)
- Kwai Han Yoo
- Division of Hematology, Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, Korea
| |
Collapse
|
8
|
Cegla P, Currie G, Wróblewska JP, Cholewiński W, Kaźmierska J, Marszałek A, Kubiak A, Golusinski P, Golusiński W, Majchrzak E. Influence of Semiquantitative [18F]FDG PET and Hematological Parameters on Survival in HNSCC Patients Using Neural Network Analysis. Pharmaceuticals (Basel) 2022; 15:ph15020224. [PMID: 35215335 PMCID: PMC8875232 DOI: 10.3390/ph15020224] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/02/2022] [Accepted: 02/09/2022] [Indexed: 02/08/2023] Open
Abstract
The aim of this study is to assess the influence of semiquantitative PET-derived parameters as well as hematological parameters in overall survival in HNSCC patients using neural network analysis. Retrospective analysis was performed on 106 previously untreated HNSCC patients. Several PET-derived parameters (SUVmax, SUVmean, TotalSUV, MTV, TLG, TLRmax, TLRmean, TLRTLG, and HI) for primary tumor and lymph node with highest activity were assessed. Additionally, hematological parameters (LEU, LEU%, NEU, NEU%, MON, MON%, PLT, PLT%, NRL, and LMR) were also assessed. Patients were divided according to the diagnosis into the good and bad group. The data were evaluated using an artificial neural network (Neural Analyzer version 2.9.5) and conventional statistic. Statistically significant differences in PET-derived parameters in 5-year survival rate between group of patients with worse prognosis and good prognosis were shown in primary tumor SUVmax (10.0 vs. 7.7; p = 0.040), SUVmean (5.4 vs. 4.4; p = 0.047), MTV (23.2 vs. 14.5; p = 0.010), and TLG (155.0 vs. 87.5; p = 0.05), and mean liver TLG (27.8 vs. 30.4; p = 0.031), TLRmax (3.8 vs. 2.6; p = 0.019), TLRmean (2.8 vs. 1.9; p = 0.018), and in TLRTLG (5.6 vs. 2.3; p = 0.042). From hematological parameters, only LMR showed significant differences (2.5 vs. 3.2; p = 0.009). Final neural network showed that for ages above 60, primary tumors SUVmax, TotalSUV, MTV, TLG, TLRmax, and TLRmean over (9.7, 2255, 20.6, 145, 3.6, 2.6, respectively) are associated with worse survival. Our study shows that the neural network could serve as a supplement to PET-derived parameters and is helpful in finding prognostic parameters for overall survival in HNSCC.
Collapse
Affiliation(s)
- Paulina Cegla
- Department of Nuclear Medicine, Greater Poland Cancer Center, 61-866 Poznan, Poland;
- Correspondence:
| | - Geoffrey Currie
- School of Dentistry and Health Science, Charles Sturt University, Wagga Wagga 2678, Australia;
| | - Joanna P. Wróblewska
- Department of Oncologic Pathology and Prophylaxis, Poznan University of Medical Sciences, 61-701 Poznan, Poland; (J.P.W.); (A.M.)
- Department of Tumor Pathology, Greater Poland Cancer Centre, 61-866 Poznan, Poland
| | - Witold Cholewiński
- Department of Nuclear Medicine, Greater Poland Cancer Center, 61-866 Poznan, Poland;
- Department of Electroradiology, Poznan University of Medical Science, 61-701 Poznan, Poland;
| | - Joanna Kaźmierska
- Department of Electroradiology, Poznan University of Medical Science, 61-701 Poznan, Poland;
- 2nd Radiotherapy Department, Greater Poland Cancer Center, 61-866 Poznan, Poland
| | - Andrzej Marszałek
- Department of Oncologic Pathology and Prophylaxis, Poznan University of Medical Sciences, 61-701 Poznan, Poland; (J.P.W.); (A.M.)
- Department of Tumor Pathology, Greater Poland Cancer Centre, 61-866 Poznan, Poland
| | - Anna Kubiak
- Greater Poland Cancer Registry, Greater Poland Cancer Centre, 61-866 Poznan, Poland;
| | - Pawel Golusinski
- Department of Otolaryngology and Maxillofacial Surgery, University of Zielona Gora, 65-046 Zielona Góra, Poland;
| | - Wojciech Golusiński
- Department of Head and Neck Surgery, Poznan University of Medical Sciences, Greater Poland Cancer Center, 61-866 Poznan, Poland; (W.G.); (E.M.)
| | - Ewa Majchrzak
- Department of Head and Neck Surgery, Poznan University of Medical Sciences, Greater Poland Cancer Center, 61-866 Poznan, Poland; (W.G.); (E.M.)
| |
Collapse
|