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Moul JW, Shore ND, Pienta KJ, Czernin J, King MT, Freedland SJ. Application of next-generation imaging in biochemically recurrent prostate cancer. Prostate Cancer Prostatic Dis 2024; 27:202-211. [PMID: 37679601 PMCID: PMC11096127 DOI: 10.1038/s41391-023-00711-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 07/27/2023] [Accepted: 08/03/2023] [Indexed: 09/09/2023]
Abstract
BACKGROUND Biochemical recurrence (BCR) following primary interventional treatment occurs in approximately one-third of patients with prostate cancer (PCa). Next-generation imaging (NGI) can identify local and metastatic recurrence with greater sensitivity than conventional imaging, potentially allowing for more effective interventions. This narrative review examines the current clinical evidence on the utility of NGI for patients with BCR. METHODS A search of PubMed was conducted to identify relevant publications on NGI applied to BCR. Given other relevant recent reviews on the topic, this review focused on papers published between January 2018 to May 2023. RESULTS NGI technologies, including positron emission tomography (PET) radiotracers and multiparametric magnetic resonance imaging, have demonstrated increased sensitivity and selectivity for diagnosing BCR at prostate-specific antigen (PSA) concentrations <2.0 ng/ml. Detection rates range between 46% and 50%, with decreasing PSA levels for choline (1-3 ng/ml), fluciclovine (0.5-1 ng/ml), and prostate-specific membrane antigen (0.2-0.49 ng/ml) PET radiotracers. Expert working groups and European and US medical societies recommend NGI for patients with BCR. CONCLUSIONS Available data support the improved detection performance and selectivity of NGI modalities versus conventional imaging techniques; however, limited clinical evidence exists demonstrating the application of NGI to treatment decision-making and its impact on patient outcomes. The emergence of NGI and displacement of conventional imaging may require a reexamination of the current definitions of BCR, altering our understanding of early recurrence. Redefining the BCR disease state by formalizing the role of NGI in patient management decisions will facilitate greater alignment across research efforts and better reflect the published literature.
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Affiliation(s)
- Judd W Moul
- Duke Cancer Institute and Division of Urology, Duke University, Durham, NC, USA
| | - Neal D Shore
- Carolina Urologic Research Center, Myrtle Beach, SC, USA
| | | | - Johannes Czernin
- David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Martin T King
- Brigham and Women's Hospital and Dana-Farber Cancer Institute, Boston, MA, USA
| | - Stephen J Freedland
- Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Veterans Affairs Medical Center, Durham, NC, USA.
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2
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Gammel MCM, Solari EL, Eiber M, Rauscher I, Nekolla SG. A Clinical Role of PET-MRI in Prostate Cancer? Semin Nucl Med 2024; 54:132-140. [PMID: 37652782 DOI: 10.1053/j.semnuclmed.2023.08.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 08/11/2023] [Indexed: 09/02/2023]
Abstract
PET/MRI is a relevant application field for prostate cancer management, offering advantages in early diagnosis, staging, and therapy planning. Despite drawbacks such as higher costs, longer acquisition time, and the need for skilled personnel, the technical integration of PET and MRI provides valuable information for detecting primary tumors, identifying metastases, and characterizing the disease, leading to more accurate staging and personalized treatment strategies. However, PET/MRI adoption has been slow, but ongoing technological advancements and AI integration might overcome challenges and improve clinical utility. As precision medicine gains importance in oncology, PET/MRI's multiparametric data can tailor treatment plans to individual patients, providing a comprehensive assessment of tumor biology and aggressiveness for more effective therapeutic strategies.
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Affiliation(s)
- Michael C M Gammel
- Department of Nuclear Medicine, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Esteban L Solari
- Department of Nuclear Medicine, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Matthias Eiber
- Department of Nuclear Medicine, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Isabel Rauscher
- Department of Nuclear Medicine, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stephan G Nekolla
- Department of Nuclear Medicine, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
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3
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Lee HW, Kim E, Na I, Kim CK, Seo SI, Park H. Novel Multiparametric Magnetic Resonance Imaging-Based Deep Learning and Clinical Parameter Integration for the Prediction of Long-Term Biochemical Recurrence-Free Survival in Prostate Cancer after Radical Prostatectomy. Cancers (Basel) 2023; 15:3416. [PMID: 37444526 DOI: 10.3390/cancers15133416] [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: 05/12/2023] [Revised: 06/19/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
Radical prostatectomy (RP) is the main treatment of prostate cancer (PCa). Biochemical recurrence (BCR) following RP remains the first sign of aggressive disease; hence, better assessment of potential long-term post-RP BCR-free survival is crucial. Our study aimed to evaluate a combined clinical-deep learning (DL) model using multiparametric magnetic resonance imaging (mpMRI) for predicting long-term post-RP BCR-free survival in PCa. A total of 437 patients with PCa who underwent mpMRI followed by RP between 2008 and 2009 were enrolled; radiomics features were extracted from T2-weighted imaging, apparent diffusion coefficient maps, and contrast-enhanced sequences by manually delineating the index tumors. Deep features from the same set of imaging were extracted using a deep neural network based on pretrained EfficentNet-B0. Here, we present a clinical model (six clinical variables), radiomics model, DL model (DLM-Deep feature), combined clinical-radiomics model (CRM-Multi), and combined clinical-DL model (CDLM-Deep feature) that were built using Cox models regularized with the least absolute shrinkage and selection operator. We compared their prognostic performances using stratified fivefold cross-validation. In a median follow-up of 61 months, 110/437 patients experienced BCR. CDLM-Deep feature achieved the best performance (hazard ratio [HR] = 7.72), followed by DLM-Deep feature (HR = 4.37) or RM-Multi (HR = 2.67). CRM-Multi performed moderately. Our results confirm the superior performance of our mpMRI-derived DL algorithm over conventional radiomics.
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Affiliation(s)
- Hye Won Lee
- Samsung Medical Center, Department of Urology, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Eunjin Kim
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Inye Na
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Chan Kyo Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Seong Il Seo
- Samsung Medical Center, Department of Urology, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Hyunjin Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, Republic of Korea
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4
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Chan TH, Haworth A, Wang A, Osanlouy M, Williams S, Mitchell C, Hofman MS, Hicks RJ, Murphy DG, Reynolds HM. Detecting localised prostate cancer using radiomic features in PSMA PET and multiparametric MRI for biologically targeted radiation therapy. EJNMMI Res 2023; 13:34. [PMID: 37099047 PMCID: PMC10133419 DOI: 10.1186/s13550-023-00984-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 04/17/2023] [Indexed: 04/27/2023] Open
Abstract
BACKGROUND Prostate-Specific Membrane Antigen (PSMA) PET/CT and multiparametric MRI (mpMRI) are well-established modalities for identifying intra-prostatic lesions (IPLs) in localised prostate cancer. This study aimed to investigate the use of PSMA PET/CT and mpMRI for biologically targeted radiation therapy treatment planning by: (1) analysing the relationship between imaging parameters at a voxel-wise level and (2) assessing the performance of radiomic-based machine learning models to predict tumour location and grade. METHODS PSMA PET/CT and mpMRI data from 19 prostate cancer patients were co-registered with whole-mount histopathology using an established registration framework. Apparent Diffusion Coefficient (ADC) maps were computed from DWI and semi-quantitative and quantitative parameters from DCE MRI. Voxel-wise correlation analysis was conducted between mpMRI parameters and PET Standardised Uptake Value (SUV) for all tumour voxels. Classification models were built using radiomic and clinical features to predict IPLs at a voxel level and then classified further into high-grade or low-grade voxels. RESULTS Perfusion parameters from DCE MRI were more highly correlated with PET SUV than ADC or T2w. IPLs were best detected with a Random Forest Classifier using radiomic features from PET and mpMRI rather than either modality alone (sensitivity, specificity and area under the curve of 0.842, 0.804 and 0.890, respectively). The tumour grading model had an overall accuracy ranging from 0.671 to 0.992. CONCLUSIONS Machine learning classifiers using radiomic features from PSMA PET and mpMRI show promise for predicting IPLs and differentiating between high-grade and low-grade disease, which could be used to inform biologically targeted radiation therapy planning.
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Affiliation(s)
- Tsz Him Chan
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Annette Haworth
- Institute of Medical Physics, School of Physics, The University of Sydney, Sydney, NSW, Australia
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- Centre for Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Mahyar Osanlouy
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Scott Williams
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
- Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Catherine Mitchell
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Michael S Hofman
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
- Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Rodney J Hicks
- Department of Medicine, St Vincent's Hospital Medical School, The University of Melbourne, Melbourne, VIC, Australia
| | - Declan G Murphy
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Hayley M Reynolds
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
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Predicting Local Failure after Partial Prostate Re-Irradiation Using a Dosiomic-Based Machine Learning Model. J Pers Med 2022; 12:jpm12091491. [PMID: 36143276 PMCID: PMC9505150 DOI: 10.3390/jpm12091491] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/02/2022] [Accepted: 09/05/2022] [Indexed: 12/22/2022] Open
Abstract
The aim of this study is to predict local failure after partial prostate re-irradiation for the treatment of isolated locally recurrent prostate cancer by using a machine learning classifier based on radiomic features from pre-treatment computed tomography (CT), positron-emission tomography (PET) and biological effective dose distribution (BED) of the radiotherapy plan. The analysis was conducted on a monocentric dataset of 43 patients with evidence of isolated intraprostatic recurrence of prostate cancer after primary external beam radiotherapy. All patients received partial prostate re-irradiation delivered by volumetric modulated arc therapy. The gross tumor volume (GTV) of each patient was manually contoured from planning CT, choline-PET and dose maps. An ensemble machine learning pipeline including unbalanced data correction and feature selection was trained using the radiomic and dosiomic features as input for predicting occurrence of local failure. The model performance was assessed using sensitivity, specificity, accuracy and area under receiver operating characteristic curves of the score function in 10-fold cross validation repeated 100 times. Local failure was observed in 13 patients (30%), with a median time to recurrence of 36.7 months (range = 6.1–102.4 months). A four variables ensemble machine learning model resulted in accuracy of 0.62 and AUC 0.65. According to our results, a dosiomic machine learning classifier can predict local failure after partial prostate re-irradiation.
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Fernandes MC, Yildirim O, Woo S, Vargas HA, Hricak H. The role of MRI in prostate cancer: current and future directions. MAGMA (NEW YORK, N.Y.) 2022; 35:503-521. [PMID: 35294642 PMCID: PMC9378354 DOI: 10.1007/s10334-022-01006-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 01/16/2022] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
There has been an increasing role of magnetic resonance imaging (MRI) in the management of prostate cancer. MRI already plays an essential role in the detection and staging, with the introduction of functional MRI sequences. Recent advancements in radiomics and artificial intelligence are being tested to potentially improve detection, assessment of aggressiveness, and provide usefulness as a prognostic marker. MRI can improve pretreatment risk stratification and therefore selection of and follow-up of patients for active surveillance. MRI can also assist in guiding targeted biopsy, treatment planning and follow-up after treatment to assess local recurrence. MRI has gained importance in the evaluation of metastatic disease with emerging technology including whole-body MRI and integrated positron emission tomography/MRI, allowing for not only better detection but also quantification. The main goal of this article is to review the most recent advances on MRI in prostate cancer and provide insights into its potential clinical roles from the radiologist's perspective. In each of the sections, specific roles of MRI tailored to each clinical setting are discussed along with its strengths and weakness including already established material related to MRI and the introduction of recent advancements on MRI.
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Affiliation(s)
- Maria Clara Fernandes
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Onur Yildirim
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Sungmin Woo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA.
| | - Hebert Alberto Vargas
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Hedvig Hricak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
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7
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Esfahani SA, Torrado-Carvajal A, Amorim BJ, Groshar D, Domachevsky L, Bernstine H, Stein D, Gervais D, Catalano OA. PET/MRI and PET/CT Radiomics in Primary Cervical Cancer: A Pilot Study on the Correlation of Pelvic PET, MRI, and CT Derived Image Features. Mol Imaging Biol 2021; 24:60-69. [PMID: 34622425 DOI: 10.1007/s11307-021-01658-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 09/20/2021] [Accepted: 09/22/2021] [Indexed: 01/18/2023]
Abstract
PURPOSE To evaluate the correlation of radiomic features in pelvic [2-deoxy-2-18F]fluoro-D-glucose positron emission tomography/magnetic resonance imaging and computed tomography ([18F]FDG PET/MRI and [18F]FDG PET/CT) in patients with primary cervical cancer (CCa). PROCEDURES Nineteen patients with histologically confirmed primary squamous cell carcinoma of the cervix underwent same-day [18F]FDG PET/MRI and PET/CT. Two nuclear medicine physicians performed a consensus reading in random order. Free-hand regions of interest covering the primary cervical tumors were drawn on PET, contrast-enhanced pelvic CT, and pelvic MR (T2 weighted and ADC) images. Several basic imaging features, standard uptake values (SUVmean, SUVmax, and SUVpeak), total lesion glycolysis (TLG), metabolic tumor volume (MTV), and more advanced texture analysis features were calculated. Pearson's correlation test was used to assess the correlation between each pair of features. Features were compared between local and metastatic tumors, and their role in predicting metastasis was evaluated by receiver operating characteristic curves. RESULTS For a total of 101 extracted features, 1104/5050 pairs of features showed a significant correlation (ρ ≥ 0.70, p < 0.05). There was a strong correlation between 190/484 PET pairs of features from PET/MRI and PET/CT, 91/418 pairs of CT and PET from PET/CT, 79/418 pairs of T2 and PET from PET/MRI, and 50/418 pairs of ADC and PET from PET/MRI. Significant difference was seen between eight features in local and metastatic tumors including MTV, TLG, and entropy on PET from PET/CT; MTV and TLG on PET from PET/MRI; compactness and entropy on T2; and entropy on ADC images. CONCLUSIONS We demonstrated strong correlation of many extracted radiomic features between PET/MRI and PET/CT. Eight radiomic features calculated on PET/CT and PET/MRI were significantly different between local and metastatic CCa. This study paves the way for future studies to evaluate the diagnostic and predictive potential of radiomics that could guide clinicians toward personalized patients care.
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Affiliation(s)
- Shadi A Esfahani
- Department of Radiology, Division of Abdominal Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Boston and Harvard Medical School, Boston, MA, USA
| | - Angel Torrado-Carvajal
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Boston and Harvard Medical School, Boston, MA, USA.,Medical Image Analysis and Biometry Lab, Universidad Rey Juan Carlos, Madrid, Spain
| | - Barbara Juarez Amorim
- Division of Nuclear Medicine, State University of Campinas (UNICAMP), Campinas, Brazil
| | - David Groshar
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.,Department of Nuclear Medicine and Radiology, Assuta Medical Centers, Tel-Aviv, Israel
| | - Liran Domachevsky
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.,Department of Nuclear Medicine and Radiology, Assuta Medical Centers, Tel-Aviv, Israel
| | - Hanna Bernstine
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.,Department of Nuclear Medicine and Radiology, Assuta Medical Centers, Tel-Aviv, Israel
| | - Dan Stein
- Department of Nuclear Medicine and Radiology, Assuta Medical Centers, Tel-Aviv, Israel
| | - Debra Gervais
- Department of Radiology, Division of Abdominal Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Onofrio A Catalano
- Department of Radiology, Division of Abdominal Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. .,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Boston and Harvard Medical School, Boston, MA, USA.
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Wang YF, Tadimalla S, Hayden AJ, Holloway L, Haworth A. Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment. J Med Imaging Radiat Oncol 2021; 65:612-626. [PMID: 34060219 DOI: 10.1111/1754-9485.13242] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/18/2021] [Accepted: 05/02/2021] [Indexed: 12/15/2022]
Abstract
Magnetic resonance imaging (MRI) is increasingly used in the management of prostate cancer (PCa). Quantitative MRI (qMRI) parameters, derived from multi-parametric MRI, provide indirect measures of tumour characteristics such as cellularity, angiogenesis and hypoxia. Using Artificial Intelligence (AI), relevant information and patterns can be efficiently identified in these complex data to develop quantitative imaging biomarkers (QIBs) of tumour function and biology. Such QIBs have already demonstrated potential in the diagnosis and staging of PCa. In this review, we explore the role of these QIBs in monitoring treatment response during and after PCa radiotherapy (RT). Recurrence of PCa after RT is not uncommon, and early detection prior to development of metastases provides an opportunity for salvage treatments with curative intent. However, the current method of monitoring treatment response using prostate-specific antigen levels lacks specificity. QIBs, derived from qMRI and developed using AI techniques, can be used to monitor biological changes post-RT providing the potential for accurate and early diagnosis of recurrent disease.
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Affiliation(s)
- Yu-Feng Wang
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Sirisha Tadimalla
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
| | - Amy J Hayden
- Sydney West Radiation Oncology, Westmead Hospital, Wentworthville, New South Wales, Australia
- Faculty of Medicine, Western Sydney University, Sydney, New South Wales, Australia
- Faculty of Medicine, Health & Human Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Lois Holloway
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
- Liverpool and Macarthur Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia
| | - Annette Haworth
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
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