Abstract
BackgroundIdentification of imaging traits to discriminate clinically significant prostate cancer is challenging due to the multi focal nature of the disease. The difficulty in obtaining a consensus by the Prostate Imaging and Data Systems (PI-RADS) scores coupled with disagreements in interpreting multi-parametric Magnetic Resonance Imaging (mpMRI) has resulted in increased variability in reporting findings and evaluating the utility of this imaging modality in detecting clinically significant prostate cancer. This study assess the ability of radiological traits (semantics) observed on multi-parametric Magnetic Resonance images (mpMRI) to discriminate clinically significant prostate cancer.MethodsWe obtained multi-parametric MRI studies from 103 prostate cancer patients with 167 targeted biopsies from a single institution. The study was approved by our Institutional Review Board (IRB) for retrospective analysis. The biopsy location had been identified and marked by a clinical radiologist for targeted biopsy based on initial study interpretation. Using the target locations, two study radiologists independently re-evaluated the scans and scored 16 semantic traits on a point scale (up to 5 levels) based on mpMRI images. The semantic traits describe size, shape, and border characteristics of the prostate lesion, as well as presence of disease around lymph nodes (lymphadenopathy). We built a linear classifier model on these semantic traits and related to pathological outcome to identify clinically significant tumors (Gleason Score ≥ 7). The discriminatory ability of the predictors was tested using cross validation method randomly repeated and ensemble values were reported. We then compared the performance of semantic predictors with the PI-RADS predictors.ResultsWe found several semantic features individually discriminated high grade Gleason score (ADC-intensity, Homogeneity, early-enhancement, T2-intensity and extraprostatic extention), these univariate predictors had an average area under the receiver operator characteristics (AUROC) ranging from 0.54 to 0.68. Multivariable semantic predictors with three features (ADC-intensity; T2-intensity, enhancement homogenicity) had an average AUROC of 0.7 [0.43, 0.94]. The PI-RADS based predictor had average AUROC of 0.6 [0.47, 0.75].ConclusionWe find semantics traits are related to pathological findings with relatively higher reproducibility between radiologists. Multivariable predictors formed on these traits shows higher discriminatory ability compared to PI-RADS scores.
Highlights
Identification of imaging traits to discriminate clinically significant prostate cancer is challenging due to the multi focal nature of the disease
The inclusion criteria were as follows: a) availability of at least one targeted biopsy by the UroNav fusion system identified on the original interpretation, b) availability of multi-parametric Magnetic Resonance Imaging (mpMRI) sequences (T2WI, Dynamic Contrast Enhancement Images (DCE), diffusionweighted MRI (DWI)/ Apparent Diffusion Coefficient of Multi-parametric Magnetic Resonant Imaging (MRI) (ADC)) suitable for Prostate Imaging Reporting and Data System (PI-RADS) scoring, and c) no image related limitations; i.e., post-biopsy hemorrhage, motion artifacts, et al Table 1 Clinical characteristic of the study cohort a
We find the area under the receiver operator characteristics (AUROC) along with sensitivity, specificity, positive predictive value, and negative predictive value for the multivariable pairs of interest
Summary
Identification of imaging traits to discriminate clinically significant prostate cancer is challenging due to the multi focal nature of the disease. The difficulty in obtaining a consensus by the Prostate Imaging and Data Systems (PI-RADS) scores coupled with disagreements in interpreting multi-parametric Magnetic Resonance Imaging (mpMRI) has resulted in increased variability in reporting findings and evaluating the utility of this imaging modality in detecting clinically significant prostate cancer. This study assess the ability of radiological traits (semantics) observed on multi-parametric Magnetic Resonance images (mpMRI) to discriminate clinically significant prostate cancer. Given the heterogeneous and multifocal nature of prostate cancer, both indolent and clinically significant tumors may be found in the same gland. Ultrasound-MRI fusion guided needle biopsies have been shown to improve precision in identifying, targeting and sampling prostate lesions of interest [5, 6]
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