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Prostate cancer heterogeneity: texture analysis score based on multiple magnetic resonance imaging sequences for detection, stratification and selection of lesions at time of biopsy
Author(s) -
Orczyk Clement,
Villers Arnauld,
Rusinek Henry,
Lepennec Vincent,
Bazille Céline,
Giganti Francesco,
Mikheev Artem,
Bernaudin Myriam,
Emberton Mark,
Fohlen Audrey,
Valable Samuel
Publication year - 2019
Publication title -
bju international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.773
H-Index - 148
eISSN - 1464-410X
pISSN - 1464-4096
DOI - 10.1111/bju.14603
Subject(s) - medicine , magnetic resonance imaging , mcnemar's test , prostate cancer , biopsy , nuclear medicine , confidence interval , radiology , cancer , statistics , mathematics
Objective To undertake an early proof‐of‐concept study on a novel, semi‐automated texture‐based scoring system in order to enhance the association between magnetic resonance imaging (MRI) lesions and clinically significant prostate cancer (SPCa). Patients and Methods With ethics approval, 536 imaging volumes were generated from 20 consecutive patients who underwent multiparametric MRI (mp MRI) at time of biopsy. Volumes of interest ( VOI s) included zonal anatomy segmentation and suspicious MRI lesions for cancer (Likert Scale score >2). Entropy (E), measuring heterogeneity, was computed from VOI s and plotted as a multiparametric score defined as the entropy score ( ES ) = E ADC + E K trans + E Ve + E T2 WI . 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 three‐dimensions. Diagnostic accuracy for detection of SPC a, defined as Gleason score ≥3 + 4 or >3 mm of any grade of cancer on a single core, was assessed using receiver operating characteristics, correlation, and descriptive statistics. The proportion of cancerous lesions detected by ES and visual scoring ( VS ) were statistically compared using the paired McNemar test. Results Any cancer (Gleason score 6–8) was found in 12 of the 20 (60%) patients, with a median PSA level of 8.22 ng/mL. SPC a (mean [95% confidence interval, CI] ES = 17.96 [0.72] NATural information unit [NAT]) had a significantly higher ES than non‐ SPC a (mean [95% CI] ES = 15.33 [0.76] NAT ). The ES correlated with Gleason score ( r s = 0.568, P = 0.033) and maximum cancer core length (ρ = 0.781; P < 0.001). The area under the curve for the ES (0.89) and VS (0.91) were not significantly different ( P = 0.75) for the detection of SPC a amongst MRI lesions. Best ES estimated numerical threshold of 16.61 NAT led to a sensitivity of 100% and negative predictive value of 100%. The proportion of MRI lesions that were found to be positive for SPC a using this ES threshold (54%) was significantly higher ( P < 0.001) than using the VS (24% of score 3, 4, 5) in a paired analysis using the McNemar test. In all, 53% of MRI lesions would have avoided biopsy sampling without missing significant disease. Conclusion Capturing heterogeneity of prostate cancer across multiple MRI sequences with the ES yielded high performances for the detection and stratification of SP Ca. The ES outperformed the VS in predicting positivity of lesions, holding promise in the selection of targets for biopsy and calling for further understanding of this association.