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  • 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. Medical Physics https://doi.org/10.1002/mp.16264
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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