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Prostate cancer heterogeneity: texture analysis score based on multiple MRI sequences for detection, stratification and selection of lesions at time of biopsy

Orczyk, C; Villers, A; Rusinek, H; Lepennec, V; Bazille, C; Giganti, F; Mikheev, A; ... Valable, S; + view all (2018) Prostate cancer heterogeneity: texture analysis score based on multiple MRI sequences for detection, stratification and selection of lesions at time of biopsy. BJU International 10.1111/bju.14603. (In press).

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Abstract

PURPOSE: To undertake an early proof of concept study on a novel, semi-automated texture-based scoring system in order to enhance the association between MRI lesions and clinically significant cancer. PATIENTS AND METHODS: With ethics approval, 536 imaging volumes were generated from 20 consecutive patients who underwent mpMRI at time of biopsy. Volumes of interest (VOIs) included zonal anatomy segmentation and suspicious MRI lesion for cancer (Likert scale score greater than 2). Entropy (E), measuring heterogeneity, was computed from VOIs and plotted as a multiparametric score defined as Entropy Score (ES) = E ADC+ E Ktrans + E Ve+ E T2WI. The reference test that was used to define the ground truth comprised systematic saturation biopsies coupled with MRI targeted sampling. This generated 422 cores in all that were individually labelled and oriented in 3D. Diagnostic accuracy for detection of clinically significant prostate cancer (SPCa), defined as Gleason score of 3+4 (or higher) or more than 3mm of any grade of cancer on a single core, was assessed using Receiver Operating Characteristics, correlation and descriptive statistics. Proportion of cancerous lesions detected by ES and Visual Scoring (VS) were statistically compared using paired McNemar test. RESULTS: Any cancer (Gleason Score 6 to 8) was found in 12 of the 20 (60%) patients with a median PSA of 8.22ng/ml. SPCa (ES=17.96 ±0.72 NAT; CI 95%) showed a significant higher ES than non-SPCa (ES=15.33 ±0.76 NAT). ES correlated with Gleason Score (rs =0.5683, p=0.033) and maximum cancer core length (ρ = 0.781; p=0.0009). The Area Under the Curve for ES (0.89) and visual scoring (VS) (0.91) were not significantly different (p=0.75) for detection of SPCa among MRI lesions. Best ES estimated numerical threshold of 16.61 NATural information unit (NAT) led to a sensitivity of 100% and negative predictive value of 100%. The proportion of MRI lesion which found to positive for SPCa using this ES threshold (54%) was significantly higher (p<0.001) than those using VS (24% of score 3,4,5) in a paired analysis using McNemar test. 53% of MRI lesion would have avoided biopsy sampling without missing significant disease. CONCLUSION: Capturing heterogeneity of PCa across multiple MRI sequences with ES yielded high performances for the detection and stratification of SPca. ES outperformed visual scoring in predicting positivity of lesions, holding promise in the selection of targets for biopsy and calling for further understanding of this association. This article is protected by copyright. All rights reserved.

Type: Article
Title: Prostate cancer heterogeneity: texture analysis score based on multiple MRI sequences for detection, stratification and selection of lesions at time of biopsy
Location: England
DOI: 10.1111/bju.14603
Publisher version: https://doi.org/10.1111/bju.14603
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: MRI, biopsy, detection, entropy, histogram, image processing, pharmacokinetic model, prostate cancer, radionomics, stratification, texture analysis
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci
URI: http://discovery.ucl.ac.uk/id/eprint/10061148
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