Dataset Information

Evaluation of an Automated Analysis Tool for Prostate Cancer Prediction Using Multiparametric Magnetic Resonance Imaging.

ABSTRACT:

Objective

To evaluate the diagnostic performance of an automated analysis tool for the assessment of prostate cancer based on multiparametric magnetic resonance imaging (mpMRI) of the prostate.

Methods

A fully automated analysis tool was used for a retrospective analysis of mpMRI sets (T2-weighted, T1-weighted dynamic contrast-enhanced, and diffusion-weighted sequences). The software provided a malignancy prediction value for each image pixel, defined as Malignancy Attention Index (MAI) that can be depicted as a colour map overlay on the original images. The malignancy maps were compared to histopathology derived from a combination of MRI-targeted and systematic transperineal MRI/TRUS-fusion biopsies.

Results

In total, mpMRI data of 45 patients were evaluated. With a sensitivity of 85.7% (with 95% CI of 65.4-95.0), a specificity of 87.5% (with 95% CI of 69.0-95.7) and a diagnostic accuracy of 86.7% (with 95% CI of 73.8-93.8) for detection of prostate cancer, the automated analysis results corresponded well with the reported diagnostic accuracies by human readers based on the PI-RADS system in the current literature.

Conclusion

The study revealed comparable diagnostic accuracies for the detection of prostate cancer of a user-independent MAI-based automated analysis tool and PI-RADS-scoring-based human reader analysis of mpMRI. Thus, the analysis tool could serve as a detection support system for less experienced readers. The results of the study also suggest the potential of MAI-based analysis for advanced lesion assessments, such as cancer extent and staging prediction.

SUBMITTER: Roethke MC

PROVIDER: S-EPMC4959716 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Evaluation of an Automated Analysis Tool for Prostate Cancer Prediction Using Multiparametric Magnetic Resonance Imaging.

Roethke Matthias C MC Kuru Timur H TH Mueller-Wolf Maya B MB Agterhuis Erik E Edler Christopher C Hohenfellner Markus M Schlemmer Heinz-Peter HP Hadaschik Boris A BA

PloS one 20160725 7

<h4>Objective</h4>To evaluate the diagnostic performance of an automated analysis tool for the assessment of prostate cancer based on multiparametric magnetic resonance imaging (mpMRI) of the prostate.<h4>Methods</h4>A fully automated analysis tool was used for a retrospective analysis of mpMRI sets (T2-weighted, T1-weighted dynamic contrast-enhanced, and diffusion-weighted sequences). The software provided a malignancy prediction value for each image pixel, defined as Malignancy Attention Index ...[more]

PMID: 27454770

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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:ObjectivesTo investigate the role of urine spermine and spermine risk score in predicting prostate cancer (PCa) diagnoses in combination with multiparametric magnetic resonance imaging (mpMRI).MethodsThree hundred forty seven consecutive men with elevated prostate-specific antigen (PSA) with mpMRI examination were prospectively enrolled in this study. In 265 patients with PSA levels between 4 and20 ng/ml, pre-biopsy urine samples were analyzed for spermine levels with ultra-high performance liquid chromatography (UPLC-MS/MS). Transperineal image-guided prostate biopsies with 16-18 cores were performed. Logistic regressions were used to form different models for the prediction of the PCa, and the performances were compared using the area under the curve (AUC).ResultsThe median serum PSA level and prostate volume were 7.4 ng/mL and 33.9 mL, respectively. PCa and high-grade PCa (ISUP group ≥2, HGPCa) were diagnosed in 66.0% (175/265) and 132/265 (49.8%) cases, respectively. The urine spermine levels were significantly lower in men with PCa (0.87 vs. 2.20, P < 0.001). Multivariate analyses showed that age, PSA, PV, urine spermine level, and Prostate Imaging Reporting and Data System (PI-RADS) findings were independent predictors for PCa. The Spermine Risk Score is a multivariable model including PSA, age, prostate volume, and urine spermine. Adding the Spermine Risk Score to PI-RADS improved the AUC from 0.73 to 0.86 in PCa and from 0.72 to 0.83 in high grade PCa (HGPCa) prediction (both P < 0.001). At 90% sensitivity for HGPCa prediction using Spermine Risk Score, 31.1% of unnecessary biopsies could be avoided. In men with equivocal MRI PI-RADS score 3, the AUC for HGPCa prediction was 0.58, 0.79, and 0.87 for PSA, PSA density, and Spermine Risk Score, respectively.ConclusionUrine Spermine Risk Score, including mpMRI could accurately identify men at high risk of HGPCa and reduce unnecessary prostate biopsies. Spermine Risk Score could more accurately predict HGPCa than PSA density in men with MRI showing equivocal PI-RADS 3 lesions.

Project description:BackgroundThe 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.ObjectiveTo characterise the genomic, epigenomic, and transcriptomic features of mpMRI-visible and -nonvisible PCa in clinically localised disease.Design, setting, and participantsMulticore analysis of fresh prostate tissue sampled immediately after radical prostatectomy was performed for intermediate- to high-risk PCa.InterventionLow-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 analysisCorrelations 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 limitationsA 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.ConclusionsOur 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 summaryOur 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.

Dataset Information

Evaluation of an Automated Analysis Tool for Prostate Cancer Prediction Using Multiparametric Magnetic Resonance Imaging.

Objective

Methods

Results

Conclusion

Publications

Evaluation of an Automated Analysis Tool for Prostate Cancer Prediction Using Multiparametric Magnetic Resonance Imaging.

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