Dataset Information

Genomic Evaluation of Multiparametric Magnetic Resonance Imaging-visible and -nonvisible Lesions in Clinically Localised Prostate Cancer.

ABSTRACT:

Background

The prostate cancer (PCa) diagnostic pathway is undergoing a radical change with the introduction of multiparametric magnetic resonance imaging (mpMRI), genomic testing, and different prostate biopsy techniques. It has been proposed that these tests should be used in a sequential manner to optimise risk stratification.

Objective

To characterise the genomic, epigenomic, and transcriptomic features of mpMRI-visible and -nonvisible PCa in clinically localised disease.

Design, setting, and participants

Multicore analysis of fresh prostate tissue sampled immediately after radical prostatectomy was performed for intermediate- to high-risk PCa.

Intervention

Low-pass whole-genome, exome, methylation, and transcriptome profiling of patient tissue cores taken from microscopically benign and cancerous areas in the same prostate. Circulating free and germline DNA was assessed from the blood of five patients.

Outcome measurement and statistical analysis

Correlations between preoperative mpMRI and genomic characteristics of tumour and benign prostate samples were assessed. Gene profiles for individual tumour cores were correlated with existing genomic classifiers currently used for prognostication.

Results and limitations

A total of 43 prostate cores (22 tumour and 21 benign) were profiled from six whole prostate glands. Of the 22 tumour cores, 16 were tumours visible and six were tumours nonvisible on mpMRI. Intratumour genomic, epigenomic, and transcriptomic heterogeneity was found within mpMRI-visible lesions. This could potentially lead to misclassification of patients using signatures based on copy number or RNA expression. Moreover, three of the six cores obtained from mpMRI-nonvisible tumours harboured one or more genetic alterations commonly observed in metastatic castration-resistant PCa. No circulating free DNA alterations were found. Limitations include the small cohort size and lack of follow-up.

Conclusions

Our study supports the continued use of systematic prostate sampling in addition to mpMRI, as avoidance of systematic biopsies in patients with negative mpMRI may mean that clinically significant tumours harbouring genetic alterations commonly seen in metastatic PCa are missed. Furthermore, there is inconsistency in individual genomics when genomic classifiers are applied.

Patient summary

Our study shows that tumour heterogeneity within prostate tumours visible on multiparametric magnetic resonance imaging (mpMRI) can lead to misclassification of patients if only one core is used for genomic analysis. In addition, some cancers that were missed by mpMRI had genomic aberrations that are commonly seen in advanced metastatic prostate cancer. Avoiding biopsies in mpMRI-negative cases may mean that such potentially lethal cancers are missed.

SUBMITTER: Parry MA

PROVIDER: S-EPMC6472613 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Publications

Genomic Evaluation of Multiparametric Magnetic Resonance Imaging-visible and -nonvisible Lesions in Clinically Localised Prostate Cancer.

Parry Marina A MA Srivastava Shambhavi S Ali Adnan A Cannistraci Alessio A Antonello Jenny J Barros-Silva João Diogo JD Ubertini Valentina V Ramani Vijay V Lau Maurice M Shanks Jonathan J Nonaka Daisuke D Oliveira Pedro P Hambrock Thomas T Leong Hui Sun HS Dhomen Nathalie N Miller Crispin C Brady Ged G Dive Caroline C Clarke Noel W NW Marais Richard R Baena Esther E

European urology oncology 20180905 1

<h4>Background</h4>The prostate cancer (PCa) diagnostic pathway is undergoing a radical change with the introduction of multiparametric magnetic resonance imaging (mpMRI), genomic testing, and different prostate biopsy techniques. It has been proposed that these tests should be used in a sequential manner to optimise risk stratification.<h4>Objective</h4>To characterise the genomic, epigenomic, and transcriptomic features of mpMRI-visible and -nonvisible PCa in clinically localised disease.<h4>D ...[more]

PMID: 30929837

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Project description:PurposeThe clinical utility of multiparametric magnetic resonance imaging (mpMRI) for the detection and localization of prostate cancer (PCa) has been evaluated and validated. However, the implementation of mpMRI into the clinical practice remains some burden of cost and availability for patients and society. We aimed to predict the results of prostate mpMRI using the clinical parameters and multivariable model to reduce unnecessary mpMRI scans.MethodsWe retrospectively identified 784 men who underwent mpMRI scans and subsequent prostate biopsy between 2016 and 2020 according to the inclusion criterion. The cohort was split into a training cohort of 548 (70%) patients and a validation cohort of 236 (30%) patients. Clinical parameters including age, prostate-specific antigen (PSA) derivates, and prostate volume (PV) were assessed as the predictors of mpMRI results. The mpMRI results were divided into groups according to the reports: "negative", "equivocal", and "suspicious" for the presence of PCa.ResultsUnivariate analysis showed that the total PSA (tPSA), free PSA (fPSA), PV, and PSA density (PSAD) were significant predictors for suspicious mpMRI (P < 0.05). The PSAD (AUC = 0.77) and tPSA (AUC = 0.74) outperformed fPSA (AUC = 0.68) and PV (AUC = 0.62) in the prediction of the mpMRI results. The multivariate model (AUC = 0.80) had a similar diagnostic accuracy with PSAD (P = 0.108), while higher than tPSA (P = 0.024) in predicting the mpMRI results. The multivariate model illustrated a better calibration and substantial improvement in the decision curve analysis (DCA) at a threshold above 20%. Using the PSAD with a 0.13 ng/ml2 cut-off could spare the number of mpMRI scans by 20%, keeping a 90% sensitivity in the prediction of suspicious MRI-PCa and missing three (3/73, 4%) clinically significant PCa cases. At the same sensitivity level, the multivariate model with a 32% cut-off could spare the number of mpMRI scans by 27%, missing only one (1/73, 1%) clinically significant PCa case.ConclusionOur multivariate model could reduce the number of unnecessary mpMRI scans without comprising the diagnostic ability of clinically significant PCa. Further prospective validation is required.

Project description:ContextThere is a lack of comprehensive data regarding the factors that influence the diagnostic accuracy of multiparametric magnetic resonance imaging (mpMRI) to detect and localize clinically significant prostate cancer (csPCa).ObjectiveTo systematically review the current literature assessing the factors influencing the variability of mpMRI performance in csPCa diagnosis.Evidence acquisitionA computerized bibliographic search of Medline/PubMed database was performed for all studies assessing magnetic field strength, use of an endorectal coil, assessment system used by radiologists and inter-reader variability, experience of radiologists and urologists, use of a contrast agent, and use of computer-aided diagnosis (CAD) tools in relation to mpMRI diagnostic accuracy.Evidence synthesisA total of 77 articles were included. Both radiologists' reading experience and urologists'/radiologists' biopsy experience were the main factors that influenced diagnostic accuracy. Therefore, it is mandatory to indicate the experience of the interpreting radiologists and biopsy-performing urologists to support the reliability of the findings. The most recent Prostate Imaging Reporting and Data System (PI-RADS) guidelines are recommended for use as the main assessment system for csPCa, given the simplified and standardized approach as well as its particular added value for less experienced radiologists. Biparametric MRI had similar accuracy to mpMRI; however, biparametric MRI performed better with experienced readers. The limited data available suggest that the combination of CAD and radiologist readings may influence diagnostic accuracy positively.ConclusionsMultiple factors affect the accuracy of mpMRI and MRI-targeted biopsy to detect and localize csPCa. The high heterogeneity across the studies underlines the need to define the experience of radiologists and urologists, implement quality control, and adhere to the most recent PI-RADS assessment guidelines. Further research is needed to clarify which factors impact the accuracy of the MRI pathway and how.Patient summaryWe systematically reported the factors influencing the accuracy of multiparametric magnetic resonance imaging (mpMRI) in detecting clinically significant prostate cancer (csPCa). These factors are significantly related to each other, with the experience of the radiologists being the dominating factor. In order to deliver the benefits of mpMRI to diagnose csPCa, it is necessary to develop expertise for both radiologists and urologists, implement quality control, and adhere to the most recent Prostate Imaging Reporting and Data System assessment guidelines.

Project description:There is a growing need to localize prostate cancers on magnetic resonance imaging (MRI) to facilitate the use of image guided biopsy, focal therapy, and active surveillance follow up. Our goal was to develop a decision support system (DSS) for detecting and localizing peripheral zone prostate cancers by using machine learning approach to calculate a cancer probability map from multiparametric MR images (MP-MRI).This IRB approved Health Insurance Portability and Accountability Act compliant retrospective study consisted of 31 patients (mean age and serum prostate specific antigen of 60.4 and 6.62 ng∕ml, respectively) who had MP-MRI at 3 T followed by radical prostatectomy. Seven patients were excluded due to technical issues with their MP-MRI (e.g., motion artifact, failure to perform all sequences). Cancer and normal regions were identified in the peripheral zone by correlating them to whole mount histology slides of the excised prostatectomy specimens. To facilitate the correlation, tissue blocks matching the MR slices were obtained using a MR-based patient-specific mold. Segmented regions on the MP-MRI were correlated to histopathology and used as training sets for the learning system that generated the cancer probability maps. Leave-one-patient-out cross-validation on the cancer and normal regions was performed to determine the learning system's efficacy, an evolutionary strategies approach (also known as a genetic algorithm) was used to find the optimal values for a set of parameters, and finally a cancer probability map was generated.For the 24 patients that were used in the study, 225 cancer and 264 noncancerous regions were identified from the region maps. The efficacy of DSS was first determined without optimizing support vector machines (SVM) parameters, where a region having a cancer probability greater than or equal to 50% was considered as a correct classification. The nonoptimized system had an f-measure of 85% and the Kappa coefficient of 71% (Rater's agreement, where raters are DSS and ground truth histology). The efficacy of the DSS after optimizing SVM parameters using a genetic algorithm had an f-measure of 89% and a Kappa coefficient of 80%. Thus, after optimization of the DSS there was a 4% increase in the f-measure and a 9% increase in the Kappa coefficient.This DSS provides a cancer probability map for peripheral zone prostate tumors based on endorectal MP-MRI. These cancer probability maps can potentially aid radiologists in accurately localizing peripheral zone prostate cancers for planning targeted biopsies, focal therapy, and follow up for active surveillance.

Project description:BackgroundExpert consensus recommends treatment of magnetic resonance imaging (MRI)-visible prostate cancer (PCa). Outcomes of partial-gland ablation (PGA) for MRI-invisible PCa remain unknown.ObjectiveTo compare recurrence-free survival, adverse events, and health-related quality of life (HRQoL) outcomes following cryoablation of MRI-visible vs invisible PCa.Design setting and participantsWe analyzed data for 75 men who underwent cryoablation therapy between January 2017 and January 2022. PCa identified on MRI-targeted and/or adjacent systematic biopsy cores was defined as MRI-visible, whereas PCa identified on systematic biopsy beyond the targeted zone was defined as MRI-invisible.Outcome measurements and statistical analysisThe primary outcome was recurrence at 12 mo after PGA, defined as the presence of clinically significant PCa (grade group [GG] ≥2) on surveillance biopsy. Adverse events were captured using the Clavien-Dindo classification and HRQoL was captured using the Expanded Prostate Cancer Index-Clinical Practice (EPIC-CP) tool.Results and limitationsOf the 58 men treated for MRI-visible and 17 treated for MRI-invisible lesions, 51 (88%) and 16 (94%), respectively, had at least one surveillance biopsy performed. There were no statistically significant differences in age, race, body mass index, biopsy GG, prostate-specific antigen, prostate volume, or treatment extent between the MRI-visible and MRI-invisible groups. Median follow-up was 44 mo (interquartile range 17-54) and did not significantly differ between the groups. The recurrence rate at 12 mo did not significantly differ between the groups (MRI-visible 39%, MRI-invisible 19%; p = 0.2), and log-rank survival analysis demonstrated no significant difference in recurrence-free survival (p = 0.15). Adverse event rates did not significantly differ (MRI-visible 29%, MRI-invisible 53%; p = 0.092); no man in the MRI-visible group had a Clavien-Dindo grade ≥III complication, while one subject in the MRI-invisible group had a Clavien-Dindo grade III complication. Median EPIC-CP urinary and sexual function scores were similar for the two groups at baseline and at 12 mo after PGA. Study limitations include the retrospective design and small sample size.ConclusionsWe observed similar cancer control, adverse event, and HRQoL outcomes for MRI-visible versus MRI-invisible PCa in the first comparison of partial-gland cryoablation. Longer follow-up and external validation of our findings are needed to inform patient selection for PGA for MRI-invisible PCa.Patient summaryPatients with prostate cancer lesions that are not visible on magnetic resonance imaging (MRI) scans who undergo partial gland ablation may have similar treatment outcomes compared to patients with cancer lesions that are visible on MRI.

Dataset Information

Genomic Evaluation of Multiparametric Magnetic Resonance Imaging-visible and -nonvisible Lesions in Clinically Localised Prostate Cancer.

Background

Objective

Design, setting, and participants

Intervention

Outcome measurement and statistical analysis

Results and limitations

Conclusions

Patient summary

Publications

Genomic Evaluation of Multiparametric Magnetic Resonance Imaging-visible and -nonvisible Lesions in Clinically Localised Prostate Cancer.

Similar Datasets

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