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Spohn SKB, Schmidt-Hegemann NS, Ruf J, Mix M, Benndorf M, Bamberg F, Makowski MR, Kirste S, Rühle A, Nouvel J, Sprave T, Vogel MME, Galitsnaya P, Gschwend JE, Gratzke C, Stief C, Löck S, Zwanenburg A, Trapp C, Bernhardt D, Nekolla SG, Li M, Belka C, Combs SE, Eiber M, Unterrainer L, Unterrainer M, Bartenstein P, Grosu AL, Zamboglou C, Peeken JC. Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy. Eur J Nucl Med Mol Imaging 2023; 50:2537-2547. [PMID: 36929180 PMCID: PMC10250433 DOI: 10.1007/s00259-023-06195-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 03/07/2023] [Indexed: 03/18/2023]
Abstract
PURPOSE To develop a CT-based radiomic signature to predict biochemical recurrence (BCR) in prostate cancer patients after sRT guided by positron-emission tomography targeting prostate-specific membrane antigen (PSMA-PET). MATERIAL AND METHODS Consecutive patients, who underwent 68Ga-PSMA11-PET/CT-guided sRT from three high-volume centers in Germany, were included in this retrospective multicenter study. Patients had PET-positive local recurrences and were treated with intensity-modulated sRT. Radiomic features were extracted from volumes of interests on CT guided by focal PSMA-PET uptakes. After preprocessing, clinical, radiomics, and combined clinical-radiomic models were developed combining different feature reduction techniques and Cox proportional hazard models within a nested cross validation approach. RESULTS Among 99 patients, median interval until BCR was the radiomic models outperformed clinical models and combined clinical-radiomic models for prediction of BCR with a C-index of 0.71 compared to 0.53 and 0.63 in the test sets, respectively. In contrast to the other models, the radiomic model achieved significantly improved patient stratification in Kaplan-Meier analysis. The radiomic and clinical-radiomic model achieved a significantly better time-dependent net reclassification improvement index (0.392 and 0.762, respectively) compared to the clinical model. Decision curve analysis demonstrated a clinical net benefit for both models. Mean intensity was the most predictive radiomic feature. CONCLUSION This is the first study to develop a PSMA-PET-guided CT-based radiomic model to predict BCR after sRT. The radiomic models outperformed clinical models and might contribute to guide personalized treatment decisions.
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Affiliation(s)
- Simon K B Spohn
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Robert-Koch-Straße 3, 79106, Freiburg, Germany.
- German Cancer Consortium (DKTK) Partner Site Freiburg, Heidelberg, Germany.
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | | | - Juri Ruf
- Department of Nuclear Medicine, Faculty of Medicine, Medical Center, University of Freiburg, Freiburg, Germany
| | - Michael Mix
- German Cancer Consortium (DKTK) Partner Site Freiburg, Heidelberg, Germany
- Department of Nuclear Medicine, Faculty of Medicine, Medical Center, University of Freiburg, Freiburg, Germany
| | - Matthias Benndorf
- Department of Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Fabian Bamberg
- Department of Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Marcus R Makowski
- Department of Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Simon Kirste
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Robert-Koch-Straße 3, 79106, Freiburg, Germany
- German Cancer Consortium (DKTK) Partner Site Freiburg, Heidelberg, Germany
| | - Alexander Rühle
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Robert-Koch-Straße 3, 79106, Freiburg, Germany
- German Cancer Consortium (DKTK) Partner Site Freiburg, Heidelberg, Germany
| | - Jerome Nouvel
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Robert-Koch-Straße 3, 79106, Freiburg, Germany
- German Cancer Consortium (DKTK) Partner Site Freiburg, Heidelberg, Germany
| | - Tanja Sprave
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Robert-Koch-Straße 3, 79106, Freiburg, Germany
- German Cancer Consortium (DKTK) Partner Site Freiburg, Heidelberg, Germany
| | - Marco M E Vogel
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Polina Galitsnaya
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Jürgen E Gschwend
- Department of Urology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Christian Gratzke
- Department of Urology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Christian Stief
- Department of Urology, University Hospital, LMU Munich, Munich, Germany
| | - Steffen Löck
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
| | - Alex Zwanenburg
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany
- German Cancer Consortium (DKTK) Partner Site Dresden, Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Helmholtz Association/Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
| | - Christian Trapp
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Stephan G Nekolla
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Minglun Li
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
- Institute of Radiation Medicine, Helmholtz Zentrum München, Munich, Germany
| | - Matthias Eiber
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Lena Unterrainer
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Marcus Unterrainer
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Anca-L Grosu
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Robert-Koch-Straße 3, 79106, Freiburg, Germany
- German Cancer Consortium (DKTK) Partner Site Freiburg, Heidelberg, Germany
| | - Constantinos Zamboglou
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Robert-Koch-Straße 3, 79106, Freiburg, Germany
- German Cancer Consortium (DKTK) Partner Site Freiburg, Heidelberg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- German Oncology Center, European University of Cyprus, Limassol, Cyprus
| | - Jan C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
- Institute of Radiation Medicine, Helmholtz Zentrum München, Munich, Germany
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Mori M, Deantoni C, Olivieri M, Spezi E, Chiara A, Baroni S, Picchio M, Del Vecchio A, Di Muzio NG, Fiorino C, Dell'Oca I. External validation of an 18F-FDG-PET radiomic model predicting survival after radiotherapy for oropharyngeal cancer. Eur J Nucl Med Mol Imaging 2023; 50:1329-1336. [PMID: 36604325 DOI: 10.1007/s00259-022-06098-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 12/24/2022] [Indexed: 01/06/2023]
Abstract
PURPOSE/OBJECTIVE The purpose of the study is to externally validate published 18F-FDG-PET radiomic models for outcome prediction in patients with oropharyngeal cancer treated with chemoradiotherapy. MATERIAL/METHODS Outcome data and pre-radiotherapy PET images of 100 oropharyngeal cancer patients (stage IV:78) treated with concomitant chemotherapy to 66-69 Gy/30 fr were available. Tumors were segmented using a previously validated semi-automatic method; 450 radiomic features (RF) were extracted according to IBSI (Image Biomarker Standardization Initiative) guidelines. Only one model for cancer-specific survival (CSS) prediction was suitable to be independently tested, according to our criteria. This model, in addition to HPV status, SUVmean and SUVmax, included two independent meta-factors (Fi), resulting from combining selected RF clusters. In a subgroup of 66 patients with complete HPV information, the global risk score R was computed considering the original coefficients and was tested by Cox regression as predictive of CSS. Independently, only the radiomic risk score RF derived from Fi was tested on the same subgroup to learn about the radiomics contribution to the model. The metabolic tumor volume (MTV) was also tested as a single predictor and its prediction performances were compared to the global and radiomic models. Finally, the validation of MTV and the radiomic score RF were also tested on the entire dataset. RESULTS Regarding the analysis of the subgroup with HPV information, with a median follow-up of 41.6 months, seven patients died due to cancer. R was confirmed to be associated to CSS (p value = 0.05) with a C-index equal 0.75 (95% CI=0.62-0.85). The best cut-off value (equal to 0.15) showed high ability in patient stratification (p=0.01, HR=7.4, 95% CI=1.6-11.4). The 5-year CSS for R were 97% (95% CI: 93-100%) vs 74% (56-92%) for low- and high-risk groups, respectively. RF and MTV alone were also significantly associated to CSS for the subgroup with an almost identical C-index. According to best cut-off value (RF>0.12 and MTV>15.5cc), the 5-year CSS were 96% (95% CI: 89-100%) vs 65% (36-94%) and 97% (95% CI: 88-100%) vs 77% (58-93%) for RF and MTV, respectively. Results regarding RF and MTV were confirmed in the overall group. CONCLUSION A previously published PET radiomic model for CSS prediction was independently validated. Performances of the model were similar to the ones of using only the MTV, without improvement of prediction accuracy.
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Affiliation(s)
- Martina Mori
- Department of Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - Chiara Deantoni
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | - Michela Olivieri
- Department of Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - Emiliano Spezi
- School of Engineering, Cardiff University, Cardiff, UK
- Department of Medical Physics, Velindre Cancer Centre, Cardiff, UK
| | - Anna Chiara
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | - Simone Baroni
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | - Maria Picchio
- Department of Nuclear Medicine, San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | | | - Nadia Gisella Di Muzio
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Claudio Fiorino
- Department of Medical Physics, San Raffaele Scientific Institute, Milano, Italy.
| | - Italo Dell'Oca
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
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Bin X, Zhu C, Tang Y, Li R, Ding Q, Xia W, Tang Y, Tang X, Yao D, Tang A. Nomogram Based on Clinical and Radiomics Data for Predicting Radiation-induced Temporal Lobe Injury in Patients with Non-metastatic Stage T4 Nasopharyngeal Carcinoma. Clin Oncol (R Coll Radiol) 2022; 34:e482-e492. [PMID: 36008245 DOI: 10.1016/j.clon.2022.07.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/19/2022] [Accepted: 07/21/2022] [Indexed: 01/31/2023]
Abstract
AIMS To use pre-treatment magnetic resonance imaging-based radiomics data with clinical data to predict radiation-induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC) patients with stage T4/N0-3/M0 within 5 years after radiotherapy. MATERIALS AND METHODS This study retrospectively examined 98 patients (198 temporal lobes) with stage T4/N0-3/M0 NPC. Participants were enrolled into a training cohort or a validation cohort in a ratio of 7:3. Radiomics features were extracted from pre-treatment magnetic resonance imaging that were T1-and T2-weighted. Spearman rank correlation, the t-test and the least absolute shrinkage and selection operator (LASSO) algorithm were used to select significant radiomics features; machine-learning models were used to generate radiomics signatures (Rad-Scores). Rad-Scores and clinical factors were integrated into a nomogram for prediction of RTLI. Nomogram discrimination was evaluated using receiver operating characteristic analysis and clinical benefits were evaluated using decision curve analysis. RESULTS Participants were enrolled into a training cohort (n = 139) or a validation cohort (n = 59). In total, 3568 radiomics features were initially extracted from T1-and T2-weighted images. Age, Dmax, D1cc and 16 stable radiomics features (six from T1-weighted and 10 from T2-weighted images) were identified as independent predictive factors. A greater Rad-Score was associated with a greater risk of RTLI. The nomogram showed good discrimination, with a C-index of 0.85 (95% confidence interval 0.79-0.92) in the training cohort and 0.82 (95% confidence interval 0.71-0.92) in the validation cohort. CONCLUSION We developed models for the prediction of RTLI in patients with stage T4/N0-3/M0 NPC using pre-treatment radiomics data and clinical data. Nomograms from these pre-treatment data improved the prediction of RTLI. These results may allow the selection of patients for earlier clinical interventions.
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Affiliation(s)
- X Bin
- Department of Otolaryngology-Head and Neck Surgery, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China
| | - C Zhu
- Department of Radiation Oncology, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Y Tang
- Department of Neurology, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China
| | - R Li
- Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University Hangzhou, Zhejiang Province, China; Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Q Ding
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China
| | - W Xia
- Department of Otolaryngology-Head and Neck Surgery, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China
| | - Y Tang
- Department of Radiology, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China
| | - X Tang
- Department of Otolaryngology-Head and Neck Surgery, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China
| | - D Yao
- Department of Otolaryngology-Head and Neck Surgery, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China
| | - A Tang
- Department of Otolaryngology-Head and Neck Surgery, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China.
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Rajgor AD, Patel S, McCulloch D, Obara B, Bacardit J, McQueen A, Aboagye E, Ali T, O'Hara J, Hamilton DW. The application of radiomics in laryngeal cancer. Br J Radiol 2021; 94:20210499. [PMID: 34586899 PMCID: PMC8631034 DOI: 10.1259/bjr.20210499] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVES Radiomics is the conversion of medical images into quantitative high-dimensional data. Laryngeal cancer, one of the most common head and neck cancers, has risen globally by 58.7%. CT, MRI and PET are acquired during the diagnostic process providing potential data for radiomic analysis and correlation with outcomes.This review aims to examine the applications of this technique to laryngeal cancer and the future considerations for translation into clinical practice. METHODS A comprehensive systematic review-informed search of the MEDLINE and EMBASE databases was undertaken. Keywords "laryngeal cancer" OR "larynx" OR "larynx cancer" OR "head and neck cancer" were combined with "radiomic" OR "signature" OR "machine learning" OR "artificial intelligence". Additional articles were obtained from bibliographies using the "snowball method". RESULTS The included studies (n = 15) demonstrated that radiomic features are significantly associated with various clinical outcomes (including stage, overall survival, treatment response, progression-free survival) and that predictive models incorporating radiomic features are superior to those that do not. Two studies demonstrated radiomics could improve laryngeal cancer staging whilst 12 studies affirmed its predictive capability for clinical outcomes. CONCLUSIONS Radiomics has potential for improving multiple aspects of laryngeal cancer care; however, the heterogeneous cohorts and lack of data on laryngeal cancer exclusively inhibits firm conclusions. Large prospective well-designed studies in laryngeal cancer are required to progress this field. Furthermore, to implement radiomics into clinical practice, a unified research effort is required to standardise radiomics practice. ADVANCES IN KNOWLEDGE This review has highlighted the value of radiomics in enhancing laryngeal cancer care (including staging, prognosis and predicting treatment response).
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Affiliation(s)
- Amarkumar Dhirajlal Rajgor
- Otolaryngology Department, Newcastle-Upon-Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK.,Applied Cancer Therapeutics and Outcomes, Newcastle University, Newcastle Upon Tyne, UK.,National Institute for Health Research, Academic Clinical Fellow, Newcastle University, Newcastle Upon Tyne, UK
| | - Shreena Patel
- East of England NHS Foundation Trainee, Bedfordshire, UK
| | - David McCulloch
- Radiology Department, Newcastle-Upon-Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Boguslaw Obara
- School of Computing, Newcastle University, Urban Sciences Building, Newcastle upon Tyne, UK
| | - Jaume Bacardit
- School of Computing, Newcastle University, Urban Sciences Building, Newcastle upon Tyne, UK
| | - Andrew McQueen
- Radiology Department, Newcastle-Upon-Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Eric Aboagye
- Imperial College London Cancer Imaging Centre, Department of Surgery & Cancer, Hammersmith Hospital, London, UK
| | - Tamir Ali
- Radiology Department, Newcastle-Upon-Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - James O'Hara
- Otolaryngology Department, Newcastle-Upon-Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK.,Applied Cancer Therapeutics and Outcomes, Newcastle University, Newcastle Upon Tyne, UK
| | - David Winston Hamilton
- Otolaryngology Department, Newcastle-Upon-Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK.,Applied Cancer Therapeutics and Outcomes, Newcastle University, Newcastle Upon Tyne, UK
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Davey A, van Herk M, Faivre-Finn C, Brown S, McWilliam A. Optimising use of 4D-CT phase information for radiomics analysis in lung cancer patients treated with stereotactic body radiotherapy. Phys Med Biol 2021; 66. [PMID: 33882470 PMCID: PMC8144744 DOI: 10.1088/1361-6560/abfa34] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 04/21/2021] [Indexed: 11/11/2022]
Abstract
Purpose. 4D-CT is routine imaging for lung cancer patients treated with stereotactic body radiotherapy. No studies have investigated optimal 4D phase selection for radiomics. We aim to determine how phase data should be used to identify prognostic biomarkers for distant failure, and test whether stability assessment is required. A phase selection approach will be developed to aid studies with different 4D protocols and account for patient differences. Methods. 186 features were extracted from the tumour and peritumour on all phases for 258 patients. Feature values were selected from phase features using four methods: (A) mean across phases, (B) median across phases, (C) 50% phase, and (D) the most stable phase (closest in value to two neighbours), coined personalised selection. Four levels of stability assessment were also analysed, with inclusion of: (1) all features, (2) stable features across all phases, (3) stable features across phase and neighbour phases, and (4) features averaged over neighbour phases. Clinical-radiomics models were built for twelve combinations of feature type and assessment method. Model performance was assessed by concordance index (c-index) and fraction of new information from radiomic features. Results. The most stable phase spanned the whole range but was most often near exhale. All radiomic signatures provided new information for distant failure prediction. The personalised model had the highest c-index (0.77), and 58% of new information was provided by radiomic features when no stability assessment was performed. Conclusion. The most stable phase varies per-patient and selecting this improves model performance compared to standard methods. We advise the single most stable phase should be determined by minimising feature differences to neighbour phases. Stability assessment over all phases decreases performance by excessively removing features. Instead, averaging of neighbour phases should be used when stability is of concern. The models suggest that higher peritumoural intensity predicts distant failure.
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Affiliation(s)
- Angela Davey
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
| | - Marcel van Herk
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.,Department of Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Corinne Faivre-Finn
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.,Department of Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, United Kingdom.,Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Sean Brown
- Department of Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, United Kingdom.,Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Alan McWilliam
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.,Department of Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, United Kingdom
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