Dataset Information

Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies.

ABSTRACT: Standard clinicopathological variables are inadequate for optimal management of prostate cancer patients. While genomic classifiers have improved patient risk classification, the multifocality and heterogeneity of prostate cancer can confound pre-treatment assessment. The objective was to investigate the association of multiparametric (mp)MRI quantitative features with prostate cancer risk gene expression profiles in mpMRI-guided biopsies tissues.Global gene expression profiles were generated from 17 mpMRI-directed diagnostic prostate biopsies using an Affimetrix platform. Spatially distinct imaging areas ('habitats') were identified on MRI/3D-Ultrasound fusion. Radiomic features were extracted from biopsy regions and normal appearing tissues. We correlated 49 radiomic features with three clinically available gene signatures associated with adverse outcome. The signatures contain genes that are over-expressed in aggressive prostate cancers and genes that are under-expressed in aggressive prostate cancers. There were significant correlations between these genes and quantitative imaging features, indicating the presence of prostate cancer prognostic signal in the radiomic features. Strong associations were also found between the radiomic features and significantly expressed genes. Gene ontology analysis identified specific radiomic features associated with immune/inflammatory response, metabolism, cell and biological adhesion. To our knowledge, this is the first study to correlate radiogenomic parameters with prostate cancer in men with MRI-guided biopsy.

SUBMITTER: Stoyanova R

PROVIDER: S-EPMC5288193 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies.

Stoyanova Radka R Pollack Alan A Takhar Mandeep M Lynne Charles C Parra Nestor N Lam Lucia L C LL Alshalalfa Mohammed M Buerki Christine C Castillo Rosa R Jorda Merce M Ashab Hussam Al-Deen HA Kryvenko Oleksandr N ON Punnen Sanoj S Parekh Dipen J DJ Abramowitz Matthew C MC Gillies Robert J RJ Davicioni Elai E Erho Nicholas N Ishkanian Adrian A

Oncotarget 20160801 33

Standard clinicopathological variables are inadequate for optimal management of prostate cancer patients. While genomic classifiers have improved patient risk classification, the multifocality and heterogeneity of prostate cancer can confound pre-treatment assessment. The objective was to investigate the association of multiparametric (mp)MRI quantitative features with prostate cancer risk gene expression profiles in mpMRI-guided biopsies tissues.Global gene expression profiles were generated fr ...[more]

PMID: 27438142

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Project description:PURPOSE:To assess the ability of (N-[N-[(S)-1,3-dicarboxypropyl]carbamoyl]-4-F-fluorobenzyl-L-cysteine) (F-DCFBC), a prostate-specific membrane antigen-targeted PET agent, to detect localized prostate cancer lesions in correlation with multiparametric MRI (mpMRI) and histopathology. METHODS:This Health Insurance Portability and Accountability Act of 1996-compliant, prospective, institutional review board-approved study included 13 evaluable patients with localized prostate cancer (median age, 62.8 years [range, 51-74 years]; median prostate-specific antigen, 37.5 ng/dL [range, 3.26-216 ng/dL]). Patients underwent mpMRI and F-DCFBC PET/CT within a 3 months' window. Lesions seen on mpMRI were biopsied under transrectal ultrasound/MRI fusion-guided biopsy, or a radical prostatectomy was performed. F-DCFBC PET/CT and mpMRI were evaluated blinded and separately for tumor detection on a lesion basis. For PET image analysis, MRI and F-DCFBC PET images were fused by using software registration; imaging findings were correlated with histology, and uptake of F-DCFBC in tumors was compared with uptake in benign prostatic hyperplasia nodules and normal peripheral zone tissue using the 80% threshold SUVmax. RESULTS:A total of 25 tumor foci (mean size, 1.8 cm; median size, 1.5 cm; range, 0.6-4.7 cm) were histopathologically identified in 13 patients. Sensitivity rates of F-DCFBC PET/CT and mpMRI were 36% and 96%, respectively, for all tumors. For index lesions, the largest tumor with highest Gleason score, sensitivity rates of F-DCFBC PET/CT and mpMRI were 61.5% and 92%, respectively. The average SUVmax for primary prostate cancer was higher (5.8 ± 4.4) than that of benign prostatic hyperplasia nodules (2.1 ± 0.3) or that of normal prostate tissue (2.1 ± 0.4) at 1 hour postinjection (P = 0.0033). CONCLUSIONS:The majority of index prostate cancers are detected with F-DCFBC PET/CT, and this may be a prognostic indicator based on uptake and staging. However, for detecting prostate cancer with high sensitivity, it is important to combine prostate-specific membrane antigen PET/CT with mpMRI.

Project description:Background: Multiparametric magnetic resonance imaging (mpMRI) has emerged as a non-invasive modality to diagnose and monitor prostate cancer. Quantitative metrics on the regions of abnormality have shown to be useful descriptors to discriminate clinically significant cancers. In this study, we evaluate the reproducibility of quantitative imaging features using repeated mpMRI on the same patients. Methods: We retrospectively obtained the deidentified records of patients, who underwent two mpMRI scans within 2 weeks of the first baseline scan. The patient records were obtained as deidentified data (including imaging), obtained through the TCIA (The Cancer Imaging Archive) repository and analyzed in our institution with an institutional review board-approved Health Insurance Portability and Accountability Act-compliant retrospective study protocol. Indicated biopsied regions were used as a marker for our study radiologists to delineate the regions of interest. We extracted 307 quantitative features in each mpMRI modality [T2-weighted MR sequence image (T2w) and apparent diffusion coefficient (ADC) with b values of 0 and 1,400 mm/s2] across the two sequential scans. Concordance correlation coefficients (CCCs) were computed on the features extracted from sequential scans. Redundant features were removed by computing the coefficient of determination (R 2) among them and replaced with a feature that had the highest dynamic range within intercorrelated groups. Results: We have assessed the reproducibility of quantitative imaging features among sequential scans and found that habitat region characterization improves repeatability in ADC maps. There were 19 T2w features and two ADC features in radiologist drawn regions (native raw image), compared to 18 T2w and 15 ADC features in habitat regions (sphere), which were reproducible (CCC ?0.65) and non-redundant (R 2 ? 0.99). We also found that z-transformation of the images prior to feature extraction reduced the number of reproducible features with no detrimental effect. Conclusion: We have shown that there are quantitative imaging features that are reproducible across sequential prostate mpMRI acquisition at a preset level of filters. We also found that a habitat approach improves feature repeatability in ADC. A validated set of reproducible image features in mpMRI will allow us to develop clinically useful disease risk stratification, enabling the possibility of using imaging as a surrogate to invasive biopsies.

Dataset Information

Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies.

Publications

Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies.

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