Journal article
Automatic stratification of prostate tumour aggressiveness using multiparametric MRI: a horizontal comparison of texture features
Y Sun, HM Reynolds, D Wraith, S Williams, ME Finnegan, C Mitchell, D Murphy, A Haworth
Acta Oncologica | TAYLOR & FRANCIS LTD | Published : 2019
Open access
Abstract
Background: Previous studies have identified apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI) can stratify prostate cancer into high- and low-grade disease (HG and LG, respectively). In this study, we consider the improvement of incorporating texture features (TFs) from T2-weighted (T2w) multiparametric magnetic resonance imaging (mpMRI) relative to mpMRI alone to predict HG and LG disease. Material and methods:In vivo mpMRI was acquired from 30 patients prior to radical prostatectomy. Sequences included T2w imaging, DWI and dynamic contrast enhanced (DCE) MRI. In vivo mpMRI data were co-registered with ‘ground truth’ histology. Tumours were delineated on the histol..
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Awarded by Department of Health, Australian Government
Funding Acknowledgements
This study was supported by NHMRC [grant 1126955], PdCCRS [grant 628592] with the following funding partners: Prostate Cancer Foundation of Australia (PCFA), the Radiation Oncology Section of the Australian Government of Health and Aging and Cancer Australia. Yu Sun is funded by the Melbourne International Research Scholarship, the Movember Young Investigator Grant through PCFA and CTx Top-up Funding. Hayley Reynolds is funded by a Movember Young Investigator Grant awarded through PCFA's Research Program.