Zhao Y, Haworth A, Williams S, et al. Homogeneous and heterogeneous boosting in prostate radiotherapy: Treatment planning and target dosimetry comparison. Radiother Oncol. 208:110916. https://doi.org/10.1016/j.radonc.2025.110916
Tadimalla S, Deng J, Barker PB, et al. MRI for biology-guided radiation therapy: Are we there yet? A summary of the 2024 ISMRM member-initiated session. Magn Reson Med. 2025 https://doi.org/10.1002/mrm.30616
Wang, Y.-F., Tadimalla, S., Thiruthaneeswaran, N. et al (2025). Longitudinal quantitative MRI in prostate cancer after radiation therapy with and without androgen deprivation therapy. Magnetic Resonance Imaging https://doi.org/10.1016/j.mri.2025.110431
Wang, Y. F., Tadimalla, S., Holloway, L et al (2025). Anatomical zone and tissue type impacts the repeatability of quantitative MRI parameters and radiomic features for longitudinal monitoring of treatment response in the prostate. MAGMA https://doi.org/10.1007/s10334-025-01231-9
2024
Zhao Y, Haworth A, Reynolds HM, et al (2024) Towards optimal heterogeneous prostate radiotherapy dose prescriptions based on patient-specific or population-based biological features.Med Phys https://doi.org/10.1002/mp.16936
2023
Poder, J., Radvan, S., Howie, A. et al (2023). Viability of focal dose escalation to prostate cancer intraprostatic lesions using HDR prostate brachytherapy. Brachytherapy https://doi.org/10.1016/j.brachy.2023.09.001
Zhao Y, Haworth A, Rowshanfarzad P, Ebert MA. Focal Boost in Prostate Cancer Radiotherapy: A Review of Planning Studies and Clinical Trials. Cancers. 2023; 15(19):4888. https://doi.org/10.3390/cancers15194888
Chan TH, Haworth A, Wang A, et al (2023) Detecting localised prostate cancer using radiomic features in PSMA PET and multiparametric MRI for biologically targeted radiation therapy. EJNMMI Res https://doi.org/10.1186/s13550-023-00984-5
Montazerolghaem M, Sun Y, Sasso G, Haworth A (2023) U-Net architecture for prostate segmentation: The impact of loss function on system performance. Bioengineering https://doi.org/10.3390/bioengineering10040412
Zhao Y, Haworth A, Reynolds HM, et al (2023) Patient-specific voxel-level dose prescriptions for prostate cancer radiotherapy considering tumor cell density and grade distribution. Med Phys https://doi.org/10.1002/mp.16264
Davoudi, F., Moradi, A., Becker, T. M. et al (2023). Genomic and Phenotypic Biomarkers for Precision Medicine Guidance in Advanced Prostate Cancer. Current Treatment Options in Oncology https://doi.org/10.1007/s11864-023-01121-z
2022
Tadimalla S, Wang W, and Haworth A (2022) Role of functional MRI in liver SBRT: Current use and future directions. Cancers https://doi.org/10.3390/cancers14235860
Reynolds HM, Tadimalla S, Wang YF, et al (2022) Semi-quantitative and quantitative dynamic contrast-enhanced (DCE) MRI parameters as prostate cancer imaging biomarkers for biologically targeted radiation therapy. Cancer Imaging https://doi.org/10.1186/s40644-022-00508-9
Finnegan RN, Reynolds HM, Ebert MA et al (2022) A statistical, voxelised model of prostate cancer for biologically optimised radiotherapy. Phys. Imaging Radiat. Oncol. https://doi.org/10.1016/j.phro.2022.02.011
2021
Thiruthaneeswaran, N., Bibby, B. A. S. et al (2021). Lost in application: Measuring hypoxia for radiotherapy optimisation. European Journal of Cancer https://doi.org/10.1016/j.ejca.2021.01.039
Di Re, A. M., Sun, Y. et al (2021). MRI radiomics in the prediction of therapeutic response to neoadjuvant therapy for locoregionally advanced rectal cancer: a systematic review. Expert Review of Anticancer Therapy https://doi.org/10.1080/14737140.2021.1860762
Wang YF, Tadimalla S, Hayden AJ, et al (2021) Artificial Intelligence and imaging biomarkers for prostate radiation therapy during and after treatment. J Med Imaging Radiat Oncol https://doi.org/10.1111/1754-9485.13242
Wang YF, Tadimalla S, Rai R, et al (2021) Quantitative MRI: Defining repeatability, reproducibility and accuracy for prostate cancer imaging biomarker development. Magn Reson Imaging https://doi.org/10.1016/j.mri.2020.12.018
Chlap, P., Min, H., Vandenberg, N. et al (2021). A review of medical image data augmentation techniques for deep learning applications. Journal of Medical Imaging and Radiation Oncology https://doi.org/10.1111/1754-9485.13261
2020
Her EJ, Ebert MA, Kennedy AM, et al (2020) Standard versus hypofractionated intensity-modulated radiotherapy for prostate cancer: assessing the impact on dose modulation and normal tissue effects when using patient-specific cancer biology. Phys Med Biol.
Her EJ, Haworth A, Reynolds HM, et al (2020) Voxel-level biological optimisation of prostate IMRT using patient-specific tumour location and clonogen density derived from mpMRI. Radiat Oncol 15:1–13. https://doi.org/10.1186/s13014-020-01568-6
Her EJ, Haworth A, Rowshanfarzad P, Ebert MA (2020) Progress towards Patient-Specific, Spatially-Continuous Radiobiological Dose Prescription and Planning in Prostate Cancer IMRT : An Overview. Cancers (Basel) 12:1–17. https://doi.org/doi:10.3390/cancers12040854
2019
Sun Y, Reynolds HM, Wraith D, et al (2019) Automatic stratification of prostate tumour aggressiveness using multiparametric MRI: a horizontal comparison of texture features. Acta Oncol (Madr) 58:1118–1126. https://doi.org/10.1080/0284186X.2019.1598576
Sun Y, Williams S, Byrne D, et al (2019) Association analysis between quantitative MRI features and hypoxia-related genetic profiles in prostate cancer : a pilot study. Br J Radiol 92:20190373. https://doi.org/10. 1259/ bjr. 20190373
Haworth A, Sun Y, Ebert M, et al (2019) Use of contemporary prostate brachytherapy approaches in clinical trials. In: Journal of Physics: Conference Series. p 12010
Reynolds HM, Williams S, Jackson P, et al (2019) Voxel-wise correlation of positron emission tomography/computed tomography with multiparametric magnetic resonance imaging and histology of the prostate using a sophisticated registration framework. BJU Int 123:1020–1030. https://doi.org/10.1111/bju.14648
Sun Y, Reynolds HM, Parameswaran B, et al (2019) Multiparametric MRI and radiomics in prostate cancer: a review. Australas Phys Eng Sci Med 42:3–25. https://doi.org/10.1007/s13246-019-00730-z
2018
Her EJ, Reynolds HM, Mears C, et al (2018) Radiobiological parameters in a tumour control probability model for prostate cancer LDR brachytherapy. Phys Med Biol 63:135011. https://doi.org//10.1088/1361-6560/aac814
Liu J, Dwyer T, Marriott K, et al (2018) Understanding the Relationship between Interactive Optimisation and Visual Analytics in the Context of Prostate Brachytherapy. IEEE Trans Vis Comput Graph 24:319–329. https://doi.org/10.1109/TVCG.2017.2744418
Sun Y, Reynolds HM, Wraith D, et al (2018) Voxel-wise prostate cell density prediction using multiparametric magnetic resonance imaging and machine learning. Acta Oncol (Madr) 57:1540–1546. https://doi.org/10.1080/0284186X.2018.1468084
2017 and prior
Betts JM, Mears C, Reynolds HM, et al (2017) Prostate cancer focal brachytherapy: Improving treatment plan robustness using a convolved dose rate model. In: Procedia Computer Science. pp 1522–1531
Ghasab MAJ, Paplinski AP, Betts JM, et al (2017) Automatic 3D modelling for prostate cancer brachytherapy. In: Image Processing (ICIP), 2017 IEEE International Conference on. IEEE, pp 4452–4456
Haworth A, Williams S (2017) Focal therapy for prostate cancer: the technical challenges. J Contemp Brachytherapy 4:383–389. https://doi.org/10.5114/jcb.2017.69809
Sun Y, Reynolds H, Wraith D, et al (2017) Predicting prostate tumour location from multiparametric MRI using Gaussian kernel support vector machines : a preliminary study. Australas Phys Eng Sci Med 40:39–49. https://doi.org/10.1007/s13246-016-0515-1
Haworth A (2016) Brachytherapy: a dying art or missed opportunity? Australas Phys Eng Sci Med 39:5–9
Haworth A, Mears C, Betts JM, et al (2016) A radiobiology-based inverse treatment planning method for optimisation of permanent l-125 prostate implants in focal brachytherapy. Phys Med Biol 61:430–444. https://doi.org/10.1088/0031-9155/61/1/430
Wilson C, Waterhouse D, Lane SE, et al (2016) Ten-year outcomes using low dose rate brachytherapy for localised prostate cancer: An update to the first Australian experience. J Med Imaging Radiat Oncol 60:531–538. https://doi.org/10.1111/1754-9485.12453
Betts JM, Mears C, Reynolds HM, et al (2015) Optimised robust treatment plans for prostate cancer focal brachytherapy. Procedia Comput Sci 51:914–923
DiFranco MD, Reynolds HM, Mitchell C, et al (2015) Performance assessment of automated tissue characterization for prostate H and E stained histopathology. In: Medical Imaging 2015: Digital Pathology. International Society for Optics and Photonics, p 94200M
Reynolds HM, Williams S, Zhang A, et al (2015) Development of a registration framework to validate MRI with histology for prostate focal therapy. Med Phys 42:7078. https://doi.org/10.1118/1.4935343
Weingant M, Reynolds HM, Haworth A, et al (2015) Ensemble prostate tumor classification in H&E whole slide imaging via stain normalization and cell density estimation. In: International Workshop on Machine Learning in Medical Imaging. Springer, pp 280–287
Haworth A, Paneghel A, Bressel M, et al (2014) Prostate bed radiation therapy: the utility of ultrasound volumetric imaging of the bladder. Clin Oncol 26:789–796
Reynolds HM, Williams S, Zhang AM, et al (2014) Cell density in prostate histopathology images as a measure of tumor distribution. Proc SPIE 9041:90410S. https://doi.org/10.1117/12.2043360
Yahya N, Ebert MA, Bulsara M, et al (2014) Impact of treatment planning and delivery factors on gastrointestinal toxicity: an analysis of data from the RADAR prostate radiotherapy trial. Radiat Oncol 9:282. https://doi.org/10.1186/s13014-014-0282-7
Haworth A, Williams S, Reynolds H, et al (2013) Validation of a radiobiological model for low-dose-rate prostate boost focal therapy treatment planning. Brachytherapy 12:628–636. https://doi.org/10.1016/j.brachy.2013.04.008
Ebert MA, Blight J, Price S, et al (2004) Multicentre analysis of treatment planning information: technical requirements, possible applications and a proposal. Australas Radiol 48:347–352
Haworth A, Ebert M, Waterhouse D, et al (2004) Assessment of i-125 prostate implants by tumor bioeffect. Int J Radiat Oncol 59:1405–1413. https://doi.org/10.1016/j.ijrobp.2004.01.047
Haworth A, Ebert M, Waterhouse D, et al (2004) Prostate implant evaluation using tumour control probability—the effect of input parameters. Phys Med Biol 49:3649–3664. https://doi.org/10.1088/0031-9155/49/16/012
