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Textural analysis of multiparametric MRI detects transition zone prostate cancer

Sidhu, HS, Benigno, S, Ganeshan, B, Dikaios, Nikolaos, Johnston, EW, Allen, Clare, Kirkham, Alex, Groves, Ashley M, Ahmed, Hashim U, Emberton, Mark , Taylor, Stuart A, Halligan, Steve and Punwani, Shonit (2016) Textural analysis of multiparametric MRI detects transition zone prostate cancer European Radiology, 27 (6). pp. 2348-2358.

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Objectives To evaluate multiparametric-MRI (mpMRI) derived histogram textural-analysis parameters for detection of transition zone (TZ) prostatic tumour. Methods Sixty-seven consecutive men with suspected prostate cancer underwent 1.5T mpMRI prior to template-mappingbiopsy (TPM). Twenty-six men had ‘significant’ TZ tumour. Two radiologists in consensus matched TPM to the single axial slice best depicting tumour, or largest TZ diameter for those with benign histology, to define single-slice whole TZ-regions-ofinterest (ROIs). Textural-parameter differences between singleslice whole TZ-ROI containing significant tumour versus benign/insignificant tumour were analysed using Mann Whitney U test. Diagnostic accuracy was assessed by receiver operating characteristic area under curve (ROC-AUC) analysis cross-validated with leave-one-out (LOO) analysis. Results ADC kurtosis was significantly lower (p < 0.001) in TZ containing significant tumour with ROC-AUC 0.80 (LOO-AUC 0.78); the difference became non-significant following exclusion of significant tumour from single-slice whole TZ-ROI (p = 0.23). T1-entropy was significantly lower (p = 0.004) in TZ containing significant tumour with ROCAUC 0.70 (LOO-AUC 0.66) and was unaffected by excluding significant tumour from TZ-ROI (p = 0.004). Combining these parameters yielded ROC-AUC 0.86 (LOO-AUC 0.83). Conclusion Textural features of the whole prostate TZ can discriminate significant prostatic cancer through reduced kurtosis of the ADC-histogram where significant tumour is included in TZ-ROI and reduced T1 entropy independent of tumour inclusion.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Sidhu, HS
Benigno, S
Ganeshan, B
Johnston, EW
Allen, Clare
Kirkham, Alex
Groves, Ashley M
Ahmed, Hashim U
Emberton, Mark
Taylor, Stuart A
Halligan, Steve
Punwani, Shonit
Date : 12 September 2016
DOI : 10.1007/s00330-016-4579-9
Copyright Disclaimer : Copyright The Author(s) 2016. This article is published with open access at
Uncontrolled Keywords : MagneticResonance Imaging .Prostate .Cancer . Diagnosis . Image Processing
Depositing User : Melanie Hughes
Date Deposited : 23 May 2017 16:01
Last Modified : 16 Jan 2019 18:52

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