Yr Athro Emiliano Spezi

(e/fe)

PhD FInstP FIPEM CPhys CSci

Articles

• Nabi, Z. et al. 2025. Regional lymph node delineation variability and its dosimetric impact in breast cancer radiotherapy. Clinical Oncology 42, article number: 103836. (10.1016/j.clon.2025.103836)
• Vincenzi, M. M. et al. 2025. Temporal validation of a PET-Radiomic model for distant-relapse-free-survival after radio-chemotherapy for pancreatic adenocarcinoma. Cancers 17(6), article number: 1036. (10.3390/cancers17061036)
• Duman, A., Sun, X., Thomas, S., Powell, J. R. and Spezi, E. 2024. Reproducible and interpretable machine learning-based radiomic analysis for overall survival prediction in glioblastoma multiforme. Cancers 16(19), article number: 3351. (10.3390/cancers16193351)
• Grassi, E. et al. 2024. Patient-specific dosimetry evaluations in Theranostics software for internal radiotherapy. Applied Sciences 14(16), article number: 7345. (10.3390/app14167345)
• Della Corte, A. et al. 2024. Preoperative MRI radiomic analysis for predicting local tumor progression in colorectal liver metastases before microwave ablation. International Journal of Hyperthermia 41(1), article number: 2349059. (10.1080/02656736.2024.2349059)
• Wheeler, P. A. et al. 2024. Multi-institutional evaluation of a Pareto navigation guided automated radiotherapy planning solution for prostate cancer. Radiation Oncology 19, article number: 45. (10.1186/s13014-024-02404-x)
• Whybra, P. et al. 2024. The image biomarker standardization initiative: Standardized convolutional filters for reproducible radiomics and enhanced clinical insights. Radiology 310(2) (10.1148/radiol.231319)
• Lou, J. et al. 2024. Predicting radiologists' gaze with computational saliency models in mammogram reading. IEEE Transactions on Multimedia 26, pp. 256-269. (10.1109/TMM.2023.3263553)
• Duman, A., Karakuş, O., Sun, X., Thomas, S., Powell, J. and Spezi, E. 2023. RFS+: A clinically adaptable and computationally efficient strategy for enhanced brain tumor segmentation. Cancers 15(23), article number: 5620. (10.3390/cancers15235620)
• Welgemoed, C., Spezi, E., Riddle, P., Gooding, M. J., Gujral, D., McLauchlan, R. and Aboagye, E. O. 2023. Clinical evaluation of atlas-based auto-segmentation in breast and nodal radiotherapy. British Journal of Radiology 96(1149), article number: 20230040. (10.1259/bjr.20230040)
• Whybra, P. and Spezi, E. 2023. Sensitivity of standardised radiomics algorithms to mask generation across different software platforms. Scientific Reports 13, article number: 14419. (10.1038/s41598-023-41475-w)
• Mukherjee, S. et al. 2023. Efficacy of early PET-CT directed switch to carboplatin and paclitaxel based definitive chemoradiotherapy in patients with oesophageal cancer who have a poor early response to induction cisplatin and capecitabine in the UK: a multi-centre randomised controlled phase II trial. EClinicalMedicine 61, article number: 102059. (10.1016/j.eclinm.2023.102059)
• Loi, S. et al. 2023. Limited impact of discretization/interpolation parameters on the predictive power of CT radiomic features in a surgical cohort of pancreatic cancer patients. La Radiologia Medica 128(7), pp. 799–807. (10.1007/s11547-023-01649-y)
• Mori, M. et al. 2023. External validation of an 18F-FDG-PET radiomic model predicting survival after radiotherapy for oropharyngeal cancer. European Journal of Nuclear Medicine and Molecular Imaging 50, pp. 1329-1336. (10.1007/s00259-022-06098-9)
• Lacan, F., Johnston, R., Carrington, R., Spezi, E. and Theobald, P. 2023. Towards using a multi-material, pellet-fed additive manufacturing platform to fabricate novel imaging phantoms. Journal of Medical Engineering & Technology 47, pp. 189-196. (10.1080/03091902.2023.2193267)
• Foster, I., Spezi, E. and Wheeler, P. 2023. Evaluating the use of machine learning to predict expert-driven pareto-navigated calibrations for personalised automated radiotherapy planning. Applied Sciences 13(7), article number: 4548. (10.3390/app13074548)
• Alsyed, E., Smith, R., Bartley, L., Marshall, C. and Spezi, E. 2023. A heterogeneous phantom study for investigating the stability of PET images radiomic features with varying reconstruction settings. Frontiers in Nuclear Medicine 3 (10.3389/fnume.2023.1078536)
• Cagni, E. et al. 2022. Evaluating the quality of patient-specific deformable image registration in adaptive radiotherapy using a digitally enhanced head and neck phantom. Applied Sciences 12(19), article number: 9493. (10.3390/app12199493)
• Theophanous, S. et al. 2022. Development and validation of prognostic models for anal cancer outcomes using distributed learning: protocol for the international multi-centre atomCAT2 study. Diagnostic and Prognostic Research 6(1), article number: 14. (10.1186/s41512-022-00128-8)
• Parkinson, C., Matthams, C., Foley, K. and Spezi, E. 2021. Artificial intelligence in radiation oncology: A review of its current status and potential application for the radiotherapy workforce. Radiography 27, pp. S63-S68. (10.1016/j.radi.2021.07.012)
• Palumbo, D. et al. 2021. Prediction of early distant recurrence in upfront resectable pancreatic adenocarcinoma: A multidisciplinary, machine learning-based approach. Cancers 13(19), article number: 4938. (10.3390/cancers13194938)
• Piazzese, C., Evans, E., Thomas, B., Staffurth, J., Gwynne, S. and Spezi, E. 2021. FIELDRT: an open-source platform for the assessment of target volume delineation in radiation therapy. British Journal of Radiology 94(1126), article number: 20210356. (10.1259/bjr.20210356)
• Cagni, E., Botti, A., Chendi, A., Iori, M. and Spezi, E. 2021. Use of knowledge based DVH predictions to enhance automated re-planning strategies in head and neck adaptive radiotherapy. Physics in Medicine and Biology 66(13), article number: 135004. (10.1088/1361-6560/ac08b0)
• Shi, Z. et al. 2021. Prediction of lymph node metastases using pre-treatment PET radiomics of the primary tumour in esophageal adenocarcinoma: an external validation study. British Journal of Radiology 94(1118), article number: 20201042. (10.1259/bjr.20201042)
• Cagni, E. et al. 2021. Variations in head and neck treatment plan quality assessment among radiation oncologists and medical physicists in a single radiotherapy department. Frontiers in Oncology - Radiation Oncology 11, article number: 706034. (10.3389/fonc.2021.706034)
• Mori, M. et al. 2020. Training and validation of a robust PET radiomic-based index to predict distant-relapse-free-survival after radio-chemotherapy for locally advanced pancreatic cancer. Radiotherapy and Oncology 153, pp. 258-264. (10.1016/j.radonc.2020.07.003)
• Kazmierska, J. et al. 2020. From multisource data to clinical decision aids in radiation oncology: the need for a clinical data science community. Radiotherapy and Oncology 153, pp. 43-54. (10.1016/j.radonc.2020.09.054)
• Zwanenburg, A. et al. 2020. The Image Biomarker Standardization Initiative: standardized quantitative radiomics for high throughput image-based phenotyping. Radiology 295(2), pp. 328-338. (10.1148/radiol.2020191145)
• Deist, T. M. et al. 2020. Distributed learning on 20 000+ lung cancer patients - The Personal Health Train. Radiotherapy and Oncology 144, pp. 189-200. (10.1016/j.radonc.2019.11.019)
• Finocchiaro, D. et al. 2020. Comparison of different calculation techniques for absorbed dose assessment in patient specific peptide receptor radionuclide therapy. PLoS ONE 15(8), article number: e0236466. (10.1371/journal.pone.0236466)
• Wheeler, P. A. et al. 2019. Evaluating the application of Pareto navigation guided automated radiotherapy treatment planning to prostate cancer. Radiotherapy and Oncology 141, pp. 220-226. (10.1016/j.radonc.2019.08.001)
• Shi, Z. et al. 2019. External validation of radiation-induced dyspnea models on esophageal cancer radiotherapy patients. Frontiers in Oncology 9, article number: 1411. (10.3389/fonc.2019.01411)
• Piazzese, C., Foley, K., Whybra, P., Hurt, C., Crosby, T. and Spezi, E. 2019. Discovery of stable and prognostic CT-based radiomic features independent of contrast administration and dimensionality in oesophageal cancer. PLoS ONE 14(11), article number: e0225550. (10.1371/journal.pone.0225550)
• Hargreaves, S., Johnstone, E., Parkinson, C., Rackley, T., Spezi, E., Staffurth, J. and Evans, M. 2019. Interim 18F-FDG positron emission tomography/computed tomography during chemoradiotherapy in the management of cancer patients: a response [Letter]. Clinical Oncology 31(9), pp. 669-670. (10.1016/j.clon.2019.05.005)
• Whybra, P., Parkinson, C., Foley, K., Staffurth, J. and Spezi, E. 2019. Assessing radiomic feature robustness to interpolation in 18F-FDG PET imaging. Scientific Reports 9(1), article number: 9649. (10.1038/s41598-019-46030-0)
• Tsang, Y., Hoskin, P., Spezi, E., Landau, D., Lester, J., Miles, E. and Conibear, J. 2019. Assessment of contour variability in target volumes and organs at risk in lung cancer radiotherapy. Technical Innovations and Patient Support in Radiation Oncology 10, pp. 8-12. (10.1016/j.tipsro.2019.05.001)
• Foley, K., Christian, A., Peaker, J., Marshall, C., Spezi, E., Kynaston, H. and Roberts, S. A. 2019. Cyclo-oxygenase-2 expression is associated with mean standardised uptake value on 18F-fluorodeoxyglucose positron emission tomography in oesophageal adenocarcinoma. British Journal of Radiology 92(1099), article number: 20180668. (10.1259/bjr.20180668)
• Parkinson, C. et al. 2019. Machine-learned target volume delineation of 18F-FDG PET images after one cycle of induction chemotherapy. Physica Medica, European Journal of Medical Physics 61, pp. 85-93. (10.1016/j.ejmp.2019.04.020)
• Foley, K. G. et al. 2019. External validation of a prognostic model incorporating quantitative PET image features in esophageal cancer. Radiotherapy and Oncology 133, pp. 205-212. (10.1016/j.radonc.2018.10.033)
• Wheeler, P. A. et al. 2019. Utilisation of Pareto navigation techniques to calibrate a fully automated radiotherapy treatment planning solution. Physics and Imaging in Radiation Oncology 10, pp. 41-48. (10.1016/j.phro.2019.04.005)
• Evans, E., Piazzese, C., Spezi, E., Staffurth, J. and Gwynne, S. 2019. EP-1666 ARENA: Improving training in target volume delineation for radiotherapy. Radiotherapy and Oncology 133(S1), pp. S896-S897. (10.1016/S0167-8140(19)32086-9)
• Md Radzi, Y., Windle, R., Lewis, D. and Spezi, E. 2019. EP-1744 Enhancing the accuracy in VMAT dose verification by the use of EPID-based commercial software. Radiotherapy and Oncology 133(S1), pp. S940-S491. (10.1016/S0167-8140(19)32164-4)
• Cagni, E. et al. 2019. EP-2010 A QA method for evaluation of deformable image registration in head and neck adaptive radiotherapy. Radiotherapy and Oncology 133, pp. S1101. (10.1016/S0167-8140(19)32430-2)
• Md Radzi, Y., Windle, R., Lewis, D. and Spezi, E. 2019. EP-1748 Adaptive solution for an improved treatment verification using Dosimetry Check system. Radiotherapy and Oncology 133(S1), pp. S942-S943. (10.1016/S0167-8140(19)32168-1)
• Wheeler, P. et al. 2019. OC-0183 Multi-institutional evaluation of a Pareto navigation guided automated planning solution. Radiotherapy and Oncology 133(S1), pp. S92. (10.1016/S0167-8140(19)30603-6)
• Deist, T. et al. 2019. OC-0544 Distributed learning on 20 000+ lung cancer patients. Radiotherapy and Oncology 133(S1), pp. S287-S288. (10.1016/S0167-8140(19)30964-8)
• Piazzese, C., Whybra, P., Qasem, E., Harris, D., Gtaes, R., Foley, K. and Spezi, E. 2019. EP-1926 Radiomics in rectal cancer: prognostic significance of 3D features extracted from diagnostic MRI. Radiotherapy and Oncology 133(S1), pp. S1048. (10.1016/S0167-8140(19)32346-1)
• Piazzese, C., Whybra, P., Carrington, R., Crosby, T., Staffurth, J., Foley, K. and Spezi, E. 2019. PO-0964 Stability and prognostic significance of CT radiomic features from oesophageal cancer patients. Radiotherapy and Oncology 133(S1), pp. S524-S525. (10.1016/S0167-8140(19)31384-2)
• Whybra, P., Parkinson, C., Foley, K., Staffurth, J. and Spezi, E. 2019. PO-0963 A novel normalisation technique for voxel size dependent radiomic features in oesophageal cancer. Radiotherapy and Oncology 133(S1), pp. S523-S524. (10.1016/S0167-8140(19)31383-0)
• Cagni, E., Botti, A., Orlandi, M., Sghedoni, R., Spezi, E. and Iori, M. 2019. PO-0996 A knowledge-based tool to estimate the gain of re-planning strategy for Head and Neck (HN) ART. Radiotherapy and Oncology 133(S1), pp. S548-S549. (10.1016/S0167-8140(19)31416-1)
• Piazzese, C., Whybra, P., Qasem, E., Harris, D., Gtaes, R., Foley, K. and Spezi, E. 2019. Radiomics in rectal cancer: prognostic significance of 3D features extracted from diagnostic MRI [Abstract]. Radiotherapy and Oncology 133, pp. S1048-S1048. (10.1016/S0167-8140(19)32346-1)
• Whybra, P., Parkinson, C., Foley, K., Staffurth, J. and Spezi, E. 2019. A novel normalisation technique for voxel size dependent radiomic features in oesophageal cancer [Abstract]. Radiotherapy and Oncology 133, pp. S523-S524. (10.1016/S0167-8140(19)31383-0)
• Finocchiaro, D. et al. 2019. Partial volume effect of SPECT images in PRRT with 177Lu labelled somatostatin analogues: A practical solution. Physica Medica 57, pp. 153-159. (10.1016/j.ejmp.2018.12.029)
• Apte, A. P. et al. 2018. Technical note: Extension of CERR for computational radiomics: a comprehensive MATLAB platform for reproducible radiomics research. Medical Physics 45(8), pp. 3713-3720. (10.1002/mp.13046)
• Portakal, Z., Shermer, S., Jenkins, C., Spezi, E., Perrett, T., Tuncel, N. and Phillips, J. 2018. Design and characterization of tissue-mimicking gel phantoms for diffusion kurtosis imaging. Medical Physics 45(6), pp. 2476-2485. (10.1002/mp.12907)
• Parkinson, C. et al. 2018. Evaluation of prognostic models developed using standardised image features from different PET automated segmentation methods. EJNMMI Research 8, article number: 29. (10.1186/s13550-018-0379-3)
• Whybra, P., Foley, K., Parkinson, C., Staffurth, J. and Spezi, E. 2018. Effect of interpolation on 3D texture analysis of PET imaging in oesophageal cancer. Radiotherapy and Oncology 127(S1), pp. S1164-S1165. (10.1016/S0167-8140(18)32426-5)
• Parkinson, C. et al. 2018. Target volume delineation of PET post one cycle of induction chemotherapy in oropharyngeal cancer. Radiotherapy and Oncology 127(S1), pp. S634-S635., article number: EP-1126. (10.1016/S0167-8140(18)31436-1)
• Parkinson, C. et al. 2018. Dependency of patient risk stratification on PET target volume definition in Oesophageal cancer. Radiotherapy and Oncology 127(S1), pp. S503-S504. (10.1016/S0167-8140(18)31241-6)
• Piazzese, C., Whybra, P., Carrington, R., Crosby, T., Staffurth, J., Foley, K. and Spezi, E. 2018. Evaluation of 2D and 3D radiomics features extracted from CT images of oesophageal cancer patients. Radiotherapy and Oncology 127, pp. S1180-S1181. (10.1016/S0167-8140(18)32450-2)
• Shi, Z. et al. 2018. External validation of radiation-induced dyspnea models on esophageal cancer radiotherapy patients. Radiotherapy and Oncology 127, pp. S168-S168. (10.1016/S0167-8140(18)30628-5)
• Foley, K. G. et al. 2018. Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer. European Radiology 28, pp. 428-436. (10.1007/s00330-017-4973-y)
• Grassi, E. et al. 2018. Effect of image registration on 3D absorbed dose calculations in 177 Lu-DOTATOC Peptide Receptor Radionuclide Therapy. Physica Medica: European Journal of Medical Physics 45, pp. 177-185. (10.1016/j.ejmp.2017.11.021)
• Gleisner, K. S. et al. 2017. Variations in the practice of molecular radiotherapy and implementation of dosimetry: results from a European survey. EJNMMI Physics 4, article number: 28. (10.1186/s40658-017-0193-4)
• D'Arienzo, M. et al. 2017. Phantom validation of quantitative Y-90 PET/CT based dosimetry in liver radioembolisation. EJNMMI Research 7, article number: 94. (10.1186/s13550-017-0341-9)
• Stokke, C. et al. 2017. Dosimetry-based treatment planning for molecular radiotherapy; a summary of the 2017 report from the Internal Dosimetry Task Force. EJNMMI Physics 4, article number: 27. (10.1186/s40658-017-0194-3)
• Berthon, B. et al. 2017. Toward a standard for the evaluation of PET-Auto-Segmentation methods following the recommendations of AAPM task group No. 211: Requirements and implementation. Medical Physics 44(8), pp. 4098-4111. (10.1002/mp.12312)
• Supanich, M., Schmidtlein, C., El Naqa, I., Kirov, A. and Spezi, E. 2017. How to select and evaluate a PET auto-segmentation tool-insights from AAPM TG211. Medical Physics 44(6), pp. 3285-3286. (10.1002/mp.12304)
• Hatt, M. et al. 2017. Classification and evaluation strategies of auto-segmentation approaches for PET: report of AAPM task group No. 211. Medical Physics 44(6), pp. e1-e42. (10.1002/mp.12124)
• Parkinson, C., Chan, J., Syndikus, I., Marshall, C., Staffurth, J. and Spezi, E. 2017. Impact of 18F-Choline PET scan acquisition time on delineation of GTV in prostate cancer [Poster Abstract]. Radiotherapy and Oncology 123(S1), pp. S714-S715. (10.1016/S0167-8140(17)31768-1)
• Berthon, B. et al. 2017. Head and neck target delineation using a novel PET automatic segmentation algorithm. Radiotherapy and Oncology 122(2), pp. 242-247. (10.1016/j.radonc.2016.12.008)
• Ward, G. et al. 2016. Superiority of deformable image co-registration in the integration of diagnostic positron emission tomography-computed tomography to the radiotherapy treatment planning pathway for oesophageal carcinoma. Clinical Oncology 28(10), pp. 655-662. (10.1016/j.clon.2016.05.009)
• D'Arienzo, M. et al. 2016. Calculation and measurement of absorbed doses for non uniform activity distributions in liver radioembolization using 90Y-PET imaging. European Journal of Nuclear Medicine and Molecular Imaging 43(S1), pp. S409-S410. (10.1007/s00259-016-3484-4)
• Fokas, E. et al. 2016. Comparison of investigator-delineated gross tumour volumes and quality assurance in pancreatic cancer: analysis of the on-trial cases for the SCALOP trial. Radiotherapy and Oncology 120(2), pp. 212-216. (10.1016/j.radonc.2016.07.002)
• Berthon, B., Marshall, C., Evans, M. and Spezi, E. 2016. ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography. Physics in Medicine and Biology 61(13), pp. 4855-4869. (10.1088/0031-9155/61/13/4855)
• Gwynne, S. et al. 2016. Prospective review of radiotherapy trials through implementation of standardised multi-centre workflow and IT infrastructure. British Journal of Radiology 89, article number: 20160020. (10.1259/bjr.20160020)
• Fokas, E. et al. 2016. P-222 Analysis of tumour contours and radiotherapy planning of 'on-trial' patients undergoing chemoradiotherapy (CRT) in SCALOP trial: does pre-trial Radiotherapy Quality Assurance (RTQA) improve the quality of 'on-trial' radiotherapy?. Annals of Oncology 27(sup. 2), pp. ii64.3-ii65. (10.1093/annonc/mdw199.214)
• Carrington, R., Spezi, E., Gwynne, S., Dutton, P., Hurt, C. N., Staffurth, J. N. and Crosby, T. 2016. The influence of dose distribution on treatment outcome in the SCOPE 1 oesophageal cancer trial. Radiation Oncology 11, article number: 19. (10.1186/s13014-016-0594-x)
• Carrington, R., Spezi, E., Gwynne, S., Dutton, P., Hurt, C. N., Staffurth, J. N. and Crosby, T. 2016. The influence of radiotherapy treatment method on dose distribution and its relation to patient outcome in the SCOPE 1 oesophageal cancer trial using Type B algorithms. Radiation Oncology (London, England) 11, article number: 19. (10.1186/s13014-016-0594-x)
• Berthon, B. et al. 2015. PETSTEP: Generation of synthetic PET lesions for fast evaluation of segmentation methods. Physica Medica 31(8), pp. 969-980. (10.1016/j.ejmp.2015.07.139)
• Fokas, E. et al. 2015. Comparison of investigator-delineated gross tumor volumes and quality assurance in pancreatic cancer: Analysis of the pretrial benchmark case for the SCALOP trial. Radiotherapy and Oncology 117(3), pp. 432-437. (10.1016/j.radonc.2015.08.026)
• Carrington, R. et al. 2015. The effect of dose escalation on gastric toxicity when treating lower oesophageal tumours: a radiobiological investigation. Radiation Oncology 10, article number: 236. (10.1186/s13014-015-0537-y)
• Alaei, P. and Spezi, E. 2015. Imaging dose from cone beam computed tomography in radiation therapy. Physica Medica 31(7), pp. 647-658. (10.1016/j.ejmp.2015.06.003)
• Fenwick, A. J., Johansson, L., Spezi, E., Evans, W. and Marshall, C. 2015. Metrology for Zr-89 in the clinic [Abstract]. European Journal of Nuclear Medicine and Molecular Imaging 42, pp. S342-S343.
• Fenwick, A., Marshall, C., Spezi, E., Evans, W. and Johansson, L. 2015. Quantitative Imaging of Zr-89 on a pre-clinical PET/CT system [Abstract]. European Journal of Nuclear Medicine and Molecular Imaging 42(S1), pp. S415-S415.
• Carrington, R. et al. 2015. A radiobiological investigation of dose escalation in lower oesophageal tumours with a focus on gastric toxicity. Medical Physics 42(6), pp. 3346-3347. (10.1118/1.4924430)
• Berthon, B., Marshall, C., Holmes, R. and Spezi, E. 2015. A novel phantom technique for evaluating the performance of PET auto-segmentation methods in delineating heterogeneous and irregular lesions. EJNMMI Physics 2(1), article number: 13. (10.1186/s40658-015-0116-1)
• Skripcak, T. et al. 2014. Creating a data exchange strategy for radiotherapy research: Towards federated databases and anonymised public datasets. Radiotherapy and Oncology 113(3), pp. 303-309. (10.1016/j.radonc.2014.10.001)
• Berthon, B. et al. 2014. Intensity modulated radiotherapy treatment of head and neck cancer patients using PET auto-segmentation to delineate primary tumour. European Journal of Nuclear Medicine and Molecular Imaging 41, pp. S196-S197.
• D'Arienzo, M. et al. 2014. 90Y-PET imaging after liver radioembolization: from PET calibration to absorbed dose determination using a Monte Carlo approach. European Journal of Nuclear Medicine and Molecular Imaging 41, pp. S331-S331.
• Berthon, B., Marshall, C. and Spezi, E. 2014. A predictive model for optimal segmentation of PET images. European Journal of Nuclear Medicine and Molecular Imaging 41, pp. S172-S172.
• Bagni, O., Spezi, E., Patterson, N., Filippi, L., D'Arienzo, M., Chiaramida, P. and Scopinaro, F. 2014. A workflow for treatment evaluation of 90Y microspheres SIRT therapy of hepatic lesions based on FDG-PET and Y90-PET. European Journal of Nuclear Medicine and Molecular Imaging 41, pp. S255-S255.
• Berthon, B. et al. 2014. A fast positron emission tomography simulator for synthetic lesion smulation.. European Journal of Nuclear Medicine and Molecular Imaging 41, pp. S367-S367.
• Gwynne, S. et al. 2014. Progress towards prospective real-time review of outlining in GI trials at a UK radiation therapy quality assurance center. International Journal of Radiation Oncology*Biology*Physics 90(S1), pp. S734-S735. (10.1016/j.ijrobp.2014.05.2138)
• Rackley, T. et al. 2014. Esophageal delineation - lessons learned from pre-accrual benchmark cases in the UK NeoSCOPE esophageal trial. International Journal of Radiation Oncology - Biology - Physics 90(1), pp. S10-S10. (10.1016/j.ijrobp.2014.05.088)
• Gwynne, S. et al. 2014. Prospective review of outlining in the UK NeoSCOPE esophageal trial. International Journal of Radiation Oncology - Biology - Physics 90(1), pp. S733-S733. (10.1016/j.ijrobp.2014.05.2135)
• Pettinato, C. et al. 2014. Pretherapeutic dosimetry in patients affected by metastatic thyroid cancer using I-124 PET/CT sequential scans for I-131 treatment planning. Clinical Nuclear Medicine 39(8), pp. E367-E374. (10.1097/RLU.0000000000000490)
• Alaei, P., Spezi, E. and Reynolds, M. 2014. Dose calculation and treatment plan optimization including imaging dose from kilovoltage cone beam computed tomography. Acta Oncologica 53(6), pp. 839-844. (10.3109/0284186X.2013.875626)
• Berthon, B., Marshall, C., Evans, M. and Spezi, E. 2014. Evaluation of advanced automatic PET segmentation methods using nonspherical thin-wall inserts. Medical Physics 41, article number: 22502. (10.1118/1.4863480)
• Berthon, B., Marshall, C., Evans, M. and Spezi, E. 2013. Implementation and optimization of automatic 18F-FDG PET segmentation methods. European Journal of Nuclear Medicine and Molecular Imaging 40, pp. S299-S299.
• Berthon, B., Marshall, C., Evans, M., Edwards, A. and Spezi, E. 2013. Performance of 18F-FDG PET automated segmentation methods for non-spherical objects. European Journal of Nuclear Medicine and Molecular Imaging 40, pp. S236-S237.
• Berthon, B., Holmes, R. B., Marshall, C., Jayaprakasam, V. S., Evans, M. and Spezi, E. 2013. Evaluation of several automatic PET-segmentation algorithms for radiotherapy treatment planning in H&N using a printed sub-resolution sandwich phantom. European Journal of Nuclear Medicine and Molecular Imaging 40, pp. S236-S236.
• Pettinato, C. et al. 2013. Negative predictive value of 124I-PET/CT imaging in patients affected by metastatic thyroid cancer and treated with 131I. European Journal of Nuclear Medicine and Molecular Imaging 40, pp. S276-S276.
• Pettinato, C. et al. 2013. Salivary gland dosimetry using 124I-PET/CT imaging and MIRD method. European Journal of Nuclear Medicine and Molecular Imaging 40, pp. S125-S126.
• Berthon, B., Marshall, C., Edwards, A., Evans, M. and Spezi, E. 2013. Influence of cold walls on PET image quantification and volume segmentation: A phantom study. Medical Physics 40(8), article number: 82505. (10.1118/1.4813302)
• Azimi, R. et al. 2013. Dosimetric and biological benchmarking of a murine total marrow irradiation platform. Medical Physics 40(6)
• Gwynne, S. et al. 2013. Oesophageal chemoradiotherapy in the UK-current practice and future directions. Clinical Oncology 25(6), pp. 368-377. (10.1016/j.clon.2013.01.006)
• Alaei, P., Spezi, E., Ehler, E. and Dusenbery, K. 2013. Assessing and minimizing the dose From KV cone beam CT to pediatric patients undergoing radiation therapy. Medical Physics 40(6), article number: 151. (10.1118/1.4814220)
• Marcatili, S., Pettinato, C., Daniels, S., Lewis, G., Edwards, P., Fanti, S. and Spezi, E. 2013. Development and validation of RAYDOSE: a Geant4-based application for molecular radiotherapy. Physics in Medicine and Biology 58(8), pp. 2491-2508. (10.1088/0031-9155/58/8/2491)
• Gwynne, S., Spezi, E., Sebag-Montefiore, D., Mukherjee, S., Miles, E., Conibear, J. and Staffurth, J. 2013. Improving radiotherapy quality assurance in clinical trials: assessment of target volume delineation of the pre-accrual benchmark case. British Journal of Radiology 86(1024), article number: 20120398. (10.1259/bjr.20120398)
• Spezi, E. and Leal, A. 2013. Special section: Selected papers from the third European workshop on Monte Carlo treatment planning (MCTP2012) PREFACE. Physics in Medicine and Biology 58(8) (10.1088/0031-9155/58/8/E01)
• Mukherjee, S. et al. 2013. Comparison of investigator-delineated GTV in chemoradiotherapy (CRT) for locally advanced nonmetastatic pancreatic cancer (LANPC): Analysis of the pretrial test case for the SCALOP trial. Journal of Clinical Oncology 31(4), article number: 230.
• Pettinato, C. et al. 2012. Usefulness of I-124 PET/CT imaging to predict absorbed doses in patients affected by metastatic thyroid cancer and treated with I-131. Quarterly journal of nuclear medicine and molecular imaging 56(6), pp. 509-514.
• Gwynne, S. H. et al. 2012. Toward semi-automated assessment of target volume delineation in radiotherapy trials: the SCOPE 1 pretrial test case. International Journal of Radiation Oncology*Biology*Physics 84(4), pp. 1037-1042. (10.1016/j.ijrobp.2012.01.094)
• Berthon, B., Spezi, E., Edwards, A. and Marshall, C. 2012. Quantitative effect of cold plastic walls on 18F-FDG PET images. European Journal of Nuclear Medicine and Molecular Imaging 39, pp. S385-S385.
• Berthon, B., Holmes, R. B., Marshall, C. and Spezi, E. 2012. Use of a printed subresolution sandwich phantom for simulation of FDG PET images. European Journal of Nuclear Medicine and Molecular Imaging 39, pp. S498-S498.
• Berthon, B., Spezi, E., Marshall, C. and Evans, M. 2012. Comparison of automatic segmentation methods of 18F-FDG PET images for radiation therapy planning in H&N: a phantom study. European Journal of Nuclear Medicine and Molecular Imaging 39, pp. S265-S265.
• Shepherd, T. et al. 2012. Design of a benchmark platform for evaluating PET-based contouring accuracy in oncology applications. European Journal of Nuclear Medicine and Molecular Imaging 39, pp. S264-S264.
• Marcatili, S. et al. 2012. Monte Carlo based 3D absorbed dose distributions for organs at risk in molecular radiotherapy. European Journal of Nuclear Medicine and Molecular Imaging 39, pp. S350-S350.
• Pettinato, C. et al. 2012. Usefulness of 124I PET/CT imaging to predict absorbed doses in patients affected by metastatic thyroid cancer and treated with 131I. European Journal of Nuclear Medicine and Molecular Imaging 39, pp. S202-S203.
• Alaei, P., Spezi, E. and Reynolds, M. 2012. Calculating the dose from KV cone beam CT within and outside the treatment volume using a treatment planning system. Medical Physics 39(6), pp. 3655-3655.
• Spezi, E., Downes, P., Jarvis, R., Radu, E. and Staffurth, J. N. 2012. Patient-specific three-dimensional concomitant dose from cone beam computed tomography exposure in image-guided radiotherapy. International Journal of Radiation Oncology*Biology*Physics 83(1), pp. 419-426. (10.1016/j.ijrobp.2011.06.1972)
• Gwynne, S., Mukherjee, S., Webster, R., Spezi, E., Staffurth, J., Coles, B. and Adams, R. 2012. Imaging for target volume delineation in rectal cancer radiotherapy - A systematic review. Clinical Oncology 24(1), pp. 52-63. (10.1016/j.clon.2011.10.001)
• Alaei, P. and Spezi, E. 2012. Commissioning kilovoltage cone-beam CT beams in a radiation therapy treatment planning system. Journal of Applied Clinical Medical Physics 13(6), pp. 19-33., article number: 3971.
• Gwynne, S. et al. 2011. Inter-observer variation in outlining of pre-trial test case in the SCOPE1 trial: A United Kingdom definitive chemoradiotherapy trial for esophageal cancer. International Journal of Radiation Oncology - Biology - Physics 81(2), pp. S67-S68. (10.1016/j.ijrobp.2011.06.135)
• Marcatili, S. et al. 2011. RAYDOSE: a Geant4 application for 3D Targeted Radionuclide Therapy (TRT) dosimetry. European Journal of Nuclear Medicine and Molecular Imaging 38, pp. S201-S202.
• Spezi, E., Volken, W., Frei, D. and Fix, M. K. 2011. A virtual source model for Kilo-voltage cone beam CT: Source characteristics and model validation. Medical Physics 38(9), pp. 5254-5263. (10.1118/1.3626574)
• Gwynne, S., Webster, R., Adams, R., Mukherjee, S., Spezi, E. and Staffurth, J. 2011. Image guided radiotherapy for rectal cancer - a review. Clinical Oncology 23(3), pp. S37-S37.
• Gwynne, S., Webster, R., Mukherjee, S., Staffurth, J. N., Spezi, E. and Adams, R. 2011. Imaging for rectal cancer radiotherapy - a systematic review. Clinical Oncology 23(3), pp. S37-S37.
• Cufflin, R. S., Spezi, E., Millin, A. E. and Lewis, D. G. 2010. An investigation of the accuracy of Monte Carlo portal dosimetry for verification of IMRT with extended fields. Physics in Medicine and Biology 55(16), pp. 4589-4600. (10.1088/0031-9155/55/16/S12)
• Downes, P., Jarvis, R., Radu, E., Kawrakow, I. and Spezi, E. 2009. Monte Carlo simulation and patient dosimetry for a kilovoltage cone-beam CT unit. Medical Physics 36(9), pp. 4156-4167.
• Downes, P., Yaikhom, G., Giddy, J. P., Walker, D. W., Spezi, E. and Lewis, D. G. 2009. High-performance computing for Monte Carlo radiotherapy calculations. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367(1897), pp. 2607-2617. (10.1098/rsta.2009.0028)
• Downes, P. and Spezi, E. 2009. Simulating oblique incident irradiation using the BEAMnrc Monte Carlo code. Physics in Medicine and Biology 54(7), pp. N93-N100. (10.1088/0031-9155/54/7/N02)
• Cufflin, R. S., Spezi, E. and Lewis, D. G. 2009. IMRT verification using portal dosimetry and a 3D planning system. Clinical Oncology 21(3), pp. 264-265.
• Jarvis, R., Downes, P., Radu, E. and Spezi, E. 2009. Patient dose from the elekta XVI cone-beam CT: Phantom and Monte Carlo study. Clinical Oncology 21(3), pp. 270-270.
• Guido, A. et al. 2009. Combined F-18-FDG-PET/CT imaging in radiotherapy target delineation for head-and-neck cancer. International Journal of Radiation Oncology - Biology - Physics 73(3), pp. 759-763.
• Spezi, E., Downes, P., Radu, E. and Jarvis, R. 2009. Reply to "comment on 'Monte Carlo simulation of an x-ray volume imaging cone beam CT unit'". Medical Physics 36(3), pp. 1040-1040.
• Spezi, E., Downes, P., Radu, E. and Jarvis, R. 2009. Monte Carlo simulation of an x-ray volume imaging cone beam CT unit. Medical Physics 36(1), pp. 127-136. (10.1118/1.3031113)
• Palleri, F., Baruffald, F., Angelini, A. L., Ferri, A. and Spezi, E. 2008. Monte Carlo characterization of materials for prosthetic implants and dosimetric validation of Pinnacle(3) TPS. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 266(23), pp. 5001-5006. (10.1016/j.nimb.2008.08.013)
• Spezi, E., Angelini, A. L., Romani, F., Guido, A., Bunkheila, F., Ntreta, M. and Ferri, A. 2008. Evaluating the influence of the Siemens IGRT carbon fibre tabletop in head and neck IMRT. Radiotherapy and Oncology 89(1), pp. 114-122. (10.1016/j.radonc.2008.06.011)
• Spezi, E. and Lewis, G. 2008. An overview of Monte Carlo treatment planning for radiotherapy. Radiation Protection Dosimetry 131(1), pp. 123-129. (10.1093/rpd/ncn277)
• Spezi, E. 2007. Status of MCTP in Europe. Radiotherapy and Oncology 84, pp. S6-S7.
• Buettner, F., Spezi, E., Paganetti, H. and Seco, J. 2007. Dosimetric uncertainties in IMRT QA in plastic phantoms due to CT calibration. Medical Physics 34(6), pp. 2442-2442.
• Vanderstraeten, B. et al. 2007. Conversion of CT numbers into tissue parameters for Monte Carlo dose calculations: a multi-centre study. Physics in Medicine and Biology 52(3), pp. 539-562. (10.1088/0031-9155/52/3/001)
• Spezi, E. and Ferri, A. 2007. Dosimetric characteristics of the siemens IGRT carbon fiber tabletop. Medical Dosimetry 32(4), pp. 295-298.
• Spezi, E., Angelini, A. L. and Ferri, A. 2007. Monte Carlo simulation of the SIEMENS IGRT carbon fibre tabletop. Journal of Physics: Conference Series 74, pp. U145-U150. (10.1088/1742-6596/74/1/021017)
• Spezi, E., Palleri, F., Angelini, A. L., Ferri, A. and Baruffaldi, F. 2007. Characterization of materials for prosthetic implants using the BEAMnrc Monte Carlo code. Journal of Physics: Conference Series 74, pp. U141-U144. (10.1088/1742-6596/74/1/021016)
• Deasy, J. et al. 2006. The computational environment for radiotherapy research: New tools for multi-modality imaging, treatment plan comparisons, and plan evaluations. Medical Physics 33(6), pp. 2140-2140.
• Spezi, E. and Lewis, D. G. 2006. Gamma histograms for radiotherapy plan evaluation. Radiotherapy and Oncology 79(2), pp. 224-230. (10.1016/j.radonc.2006.03.020)
• Spezi, E., Angelini, A. L. and Ferri, A. 2006. A multiple acquisition sequence for IMRT verification with a 2D ion chamber array. Medical Dosimetry 31(4), pp. 269-272.
• Spezi, E., Angelini, A. L., Romani, F. and Ferri, A. 2005. IMRT plan verification using a 2D ion chamber array. Radiotherapy and Oncology 76, pp. S176-S177.
• Cora, S. et al. 2005. Evaluation of the treatment planning system of the Cyberknife by means of a comparison to Monte Carlo calculation. Radiotherapy and Oncology 76, pp. S96-S96. (10.1016/S0167-8140(05)81172-7)
• Spezi, E., Angelini, A. L., Romani, F. and Ferri, A. 2005. Characterization of a 2D ion chamber array for the verification of radiotherapy treatments. Physics in Medicine and Biology 50(14), pp. 3361-3373. (10.1088/0031-9155/50/14/012)
• Alaly, J., Zakarian, K., Lindsay, P., El Naqa, I., Hope, A., Spezi, E. and Deasy, I. 2005. Software tools for 4-D and adaptive treatment planning data visualization and manipulation (CERR version 3). Medical Physics 32(6), pp. 2033-2033.
• Alaly, J., Deasy, J., Zakarian, C., Hope, A., Spezi, E., Bosch, W. and Purdy, J. 2004. Modeling radiotherapy treatment outcomes: Open-source data collection, database, and plan review tools. Medical Physics 31(6), pp. 1901-1902.
• Chin, P. W., Lewis, D. G. and Spezi, E. 2004. Correction for dose-response variations in a scanning liquid ion chamber EPID as a function of linac gantry angle. Physics in Medicine and Biology 49(8), pp. N93-N103. (10.1088/0031-9155/49/8/N01)
• Spezi, E. and Lewis, D. G. 2004. Full forward Monte Carlo calculation of portal dose from MLC collimated treatment beams (vol 47, pg 377, 2002). Physics in Medicine and Biology 49(2), pp. 355-355.
• Spezi, E. and Deasy, J. 2003. An open source DICOM-RT/MATLAB based computational platform for radiotherapy research. Radiotherapy and Oncology 68(s1), pp. S106.
• Spezi, E., Lewis, D. G., Millin, A. and Cuffin, R. 2003. MC based QA of IMRT. Radiotherapy and Oncolocy 68(S1), pp. S46-S46. (10.1016/S0167-8140(03)80135-4)
• Chin, P. W., Spezi, E. and Lewis, D. G. 2003. Monte Carlo simulation of portal dosimetry on a rectilinear voxel geometry: a variable gantry angle solution. Physics in Medicine and Biology 48(16), pp. N231-N238. (10.1088/0031-9155/48/16/401)

Book sections

• Sykes, J., Alaei, P. and Spezi, E. 2017. Imaging dose in radiation therapy. In: Mijnheer, B. ed. Clinical 3D Dosimetry in Modern Radiation Therapy. CRC Press, pp. 561-588.

Books

• Spezi, E. and Bray, M. eds. 2024. Proceedings of the Cardiff University Engineering Research Conference 2023. Cardiff: Cardiff University Press. (10.18573/conf1)

Conferences

• Warren, F., Paisey, S., Spezi, E., Lai, Y. and Smith, R. 2025. A feature-driven acquisition strategy using scale-invariant descriptors for deep active learning in preclinical CT segmentation. Presented at: 29th Medical Image Understanding and Analysis Conference, Leeds, UK, 15-17 July 2025 Presented at Ali, S., Hogg, D. C. and Peckham, M. eds.Proceedings of MIUA, Vol. 15918. Annual Conference on Medical Image Understanding and Analysis Springer pp. 129-145., (10.1007/978-3-031-98694-9_10)
• Oyibo, P. et al. 2025. 3D DeepLab-based automated GTV segmentation in head and neck cancer using PET/CT imaging. Presented at: ESTRO 2025, Vienna, Austria, 2 - 6 May 2025Radiotherapy and Oncology, Vol. 206. Elsevier pp. S2536-S2538., (10.1016/S0167-8140(25)01892-4)
• Oyibo, P., Brynolfsson, P. and Spezi, E. 2024. Integrating radiomic image analysis in the Hero Imaging platform. Presented at: Cardiff University School of Engineering Research Conference 2024, Cardiff, UK, 12th - 14th June 2024 Presented at Spezi, E. and Bray, M. eds.Proceedings of the Cardiff University School of Engineering Research Conference 2024. Cardiff University Press pp. 23-27., (10.18573/conf3.g)
• Smith, R. L. et al. 2024. From radiomics to deep learning: Leveraging gramian matrix features in CNNs for NSCLC survival analysis. Presented at: 2024 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference, Tampa, FL, USA, 26 October - 2 November 20242024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE pp. 1-2., (10.1109/nss/mic/rtsd57108.2024.10657697)
• Spezi, E. and Bray, M. 2024. Foreword. Presented at: Cardiff University Engineering Research Conference 2023, Cardiff, UK, 12-14 July 2023 Presented at Spezi, E. and Bray, M. eds.Proceedings of the Cardiff University Engineering Research Conference 2023. Cardiff: Cardiff University Press, (10.18573/conf1.a)
• Duman, A., Powell, J., Thomas, S., Sun, X. and Spezi, E. 2024. Generalizability of deep learning models on brain tumour segmentation. Presented at: Cardiff University Engineering Research Conference 2023, Cardiff, UK, 12-14 July 2023 Presented at Spezi, E. and Bray, M. eds.Proceedings of the Cardiff University Engineering Research Conference 2023. Cardiff: Cardiff University Press pp. 3-5., (10.18573/conf1.b)
• Cagni, E., Trojani, V., Botti, A., Lewis, A. and Spezi, E. 2024. A tool for radiotherapy plan evaluation analysis: generalise Uniform Ideal Dose (gUIDE). Presented at: Cardiff University Engineering Research Conference 2023, Cardiff, UK, 12-14 July 2023 Presented at Spezi, E. and Bray, M. eds.Proceedings of the Cardiff University Engineering Research Conference 2023. Cardiff: Cardiff University Press pp. 6-10., (10.18573/conf1.c)
• Doherty, C., Duman, A., Chuter, R., Hutton, M. and Spezi, E. 2024. Investigating the feasibility of MRI auto-segmentation for Image Guided Brachytherapy. Presented at: Cardiff University Engineering Research Conference 2023, Cardiff, UK, 12-14 July 2023 Presented at Spezi, E. and Bray, M. eds.Proceedings of the Cardiff University Engineering Research Conference 2023. Cardiff: Cardiff University Press pp. 11-14., (10.18573/conf1.d)
• Duman, A., Powell, J., Thomas, S. and Spezi, E. 2024. Evaluation of radiomic analysis over the comparison of machine learning approach and radiomic risk score on glioblastoma. Presented at: Cardiff University Engineering Research Conference 2023, Cardiff, UK, 12-14 July 2023 Presented at Spezi, E. and Bray, M. eds.Proceedings of the Cardiff University Engineering Research Conference 2023. Cardiff: Cardiff University Press pp. 19-22., (10.18573/conf1.f)
• Foster, I., Spezi, E. and Wheeler, P. 2024. Inter-planner variability in expert driven Pareto-guided automated planning solutions. Presented at: Cardiff University Engineering Research Conference 2023, Cardiff, UK, 12-14 July 2023 Presented at Spezi, E. and Bray, M. eds.Proceedings of the Cardiff University Engineering Research Conference 2023. Cardiff: Cardiff University Press pp. 23-26., (10.18573/conf1.g)
• Alsyed, E., Smith, R., Paisey, S., Marshall, C. and Spezi, E. 2021. A self organizing map for exploratory analysis of PET radiomic features. Presented at: 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Boston, MA, USA, 31 October -7 November 20202020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE, (10.1109/NSS/MIC42677.2020.9507846)
• Spezi, E. et al. 2020. Metabolic tumour volume segmentation for oesophageal cancer on hybrid PET/CT using convolutional network architecture. Presented at: 33rd Annual European Association of Nuclear Medicine Congress (EANM 2020), Virtual, 22-30 October 2020European Journal of Nuclear Medicine and Molecular Imaging, Vol. 47. Vol. S1. Springer Verlag (Germany) pp. 5481-5482., (10.1007/s00259-020-04988-4)
• Parkinson, C. et al. 2020. Qualitative assessment of oesophageal cancer metabolic tumour volumes delineated by an artificial intelligence algorithm. Presented at: NCRI Virtual Showcase 2020, Virtual, 2-3 November 2020.
• Alsyed, E., Smith, R., Marshall, C., Paisey, S. and Spezi, E. 2019. The statistical influence of imaging time and segmentation volume on PET radiomic features: A preclinical study. Presented at: 2019 IEEE NSS-MIC, 26 October - 2 November 2019IEEE Nuclear Science Symposium and Medical Imaging Conference. IEEE, (10.1109/NSS/MIC42101.2019.9059863)
• Alsyed, E., Smith, R., Marshall, C., Paisey, S. and Spezi, E. 2019. Stability of PET radiomic features: A preclinical study [Abstract]. Presented at: Annual Congress of the European Association of Nuclear Medicine, Barcelona, Spain, 12-16 Oct 2019.
• Ackerley, I. et al. 2019. Using deep learning to detect esophageal lesions in PET-CT scans. Presented at: SPIE Medical Imaging 2019, San Diego, California, USA, 16-21 February 2019 Presented at Gimi, B. and Krol, A. eds.Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging. SPIE, (10.1117/12.2511738)
• Parkinson, C. et al. 2018. Dependency of patient risk stratification on PET target volume definition in oesophageal cancer. Presented at: ESTRO, Barcelona, Spain, 20-24 April 2018.
• Parkinson, C., Whybra, P., Staffurth, J., Marshall, C. and Spezi, E. 2018. ATLAAS - Investigation into the incorporation of morphological data on automated segmentation. Presented at: EANM Congress 2018: European Association of Nuclear Medicine., Dusseldorf, Germany, 12-18 October 2018.
• Parkinson, C. et al. 2018. Target volume delineation of FDG PET images post one cycle of induction chemotherapy in oropharyngeal cancer using advanced automated segmentation methods. Presented at: ESTRO 37, Barcelona, 20-24 April 2018.
• Dabkowski, A., Paisey, S. J., Spezi, E., Chester, J. and Marshall, C. 2017. Optimization of Zirconium-89 production in IBA cyclone 18/9 cyclotron with COSTIS solid target system. Presented at: WTTC16: Proceedings of the 16th International Workshop on Targetry and Target Chemistry, Santa Fe, NM, USA, 29 August–1 September 2016 Presented at Engle, J. W. et al. eds.AIP Conference Proceedings, Vol. 1845. Melville, NY: AIP pp. 20005., (10.1063/1.4983536)
• Yaikhom, G., Giddy, J. P., Walker, D. W., Downes, P., Spezi, E. and Lewis, D. G. 2008. A distributed simulation framework for conformal radiotherapy. Presented at: 22nd IEEE International Symposium on Parallel & Distributed Processing, Miami, FL, USA, 14-18 April 2008Proceedings of the IEEE International Symposium on Parallel and Distributed Processing, 2008 (IPDPS 2008), Miami, FL, 14-18 April 2008. Piscataway, NJ: IEEE, (10.1109/IPDPS.2008.4536471)

Monographs

• Gleisner, K. S. et al. 2017. Treatment planning for molecular radiotherapy: potential and prospects. European Association of Nuclear Medicine. Available at: http://www.eanm.org/publications/idtf-report

• Zwanenburg, A. et al. 2020. The Image Biomarker Standardization Initiative: standardized quantitative radiomics for high throughput image-based phenotyping. Radiology 295(2), pp. 328-338. (10.1148/radiol.2020191145)
• Deist, T. M. et al. 2020. Distributed learning on 20 000+ lung cancer patients - The Personal Health Train. Radiotherapy and Oncology 144, pp. 189-200. (10.1016/j.radonc.2019.11.019)
• Piazzese, C., Foley, K., Whybra, P., Hurt, C., Crosby, T. and Spezi, E. 2019. Discovery of stable and prognostic CT-based radiomic features independent of contrast administration and dimensionality in oesophageal cancer. PLoS ONE 14(11), article number: e0225550. (10.1371/journal.pone.0225550)
• Whybra, P., Parkinson, C., Foley, K., Staffurth, J. and Spezi, E. 2019. Assessing radiomic feature robustness to interpolation in 18F-FDG PET imaging. Scientific Reports 9(1), article number: 9649. (10.1038/s41598-019-46030-0)
• Foley, K. G. et al. 2018. Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer. European Radiology 28, pp. 428-436. (10.1007/s00330-017-4973-y)
• Berthon, B. et al. 2017. Toward a standard for the evaluation of PET-Auto-Segmentation methods following the recommendations of AAPM task group No. 211: Requirements and implementation. Medical Physics 44(8), pp. 4098-4111. (10.1002/mp.12312)
• Hatt, M. et al. 2017. Classification and evaluation strategies of auto-segmentation approaches for PET: report of AAPM task group No. 211. Medical Physics 44(6), pp. e1-e42. (10.1002/mp.12124)
• Berthon, B., Marshall, C., Evans, M. and Spezi, E. 2016. ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography. Physics in Medicine and Biology 61(13), pp. 4855-4869. (10.1088/0031-9155/61/13/4855)
• Skripcak, T. et al. 2014. Creating a data exchange strategy for radiotherapy research: Towards federated databases and anonymised public datasets. Radiotherapy and Oncology 113(3), pp. 303-309. (10.1016/j.radonc.2014.10.001)
• Marcatili, S., Pettinato, C., Daniels, S., Lewis, G., Edwards, P., Fanti, S. and Spezi, E. 2013. Development and validation of RAYDOSE: a Geant4-based application for molecular radiotherapy. Physics in Medicine and Biology 58(8), pp. 2491-2508. (10.1088/0031-9155/58/8/2491)