Dataset Information

Optimising the Diagnosis of Prostate Cancer in the Era of Multiparametric Magnetic Resonance Imaging: A Cost-effectiveness Analysis Based on the Prostate MR Imaging Study (PROMIS).

ABSTRACT:

Background

The current recommendation of using transrectal ultrasound-guided biopsy (TRUSB) to diagnose prostate cancer misses clinically significant (CS) cancers. More sensitive biopsies (eg, template prostate mapping biopsy [TPMB]) are too resource intensive for routine use, and there is little evidence on multiparametric magnetic resonance imaging (MPMRI).

Objective

To identify the most effective and cost-effective way of using these tests to detect CS prostate cancer.

Design, setting, and participants

Cost-effectiveness modelling of health outcomes and costs of men referred to secondary care with a suspicion of prostate cancer prior to any biopsy in the UK National Health Service using information from the diagnostic Prostate MR Imaging Study (PROMIS).

Intervention

Combinations of MPMRI, TRUSB, and TPMB, using different definitions and diagnostic cut-offs for CS cancer.

Outcome measurements and statistical analysis

Strategies that detect the most CS cancers given testing costs, and incremental cost-effectiveness ratios (ICERs) in quality-adjusted life years (QALYs) given long-term costs.

Results and limitations

The use of MPMRI first and then up to two MRI-targeted TRUSBs detects more CS cancers per pound spent than a strategy using TRUSB first (sensitivity = 0.95 [95% confidence interval {CI} 0.92-0.98] vs 0.91 [95% CI 0.86-0.94]) and is cost effective (ICER = £7,076 [€8350/QALY gained]). The limitations stem from the evidence base in the accuracy of MRI-targeted biopsy and the long-term outcomes of men with CS prostate cancer.

Conclusions

An MPMRI-first strategy is effective and cost effective for the diagnosis of CS prostate cancer. These findings are sensitive to the test costs, sensitivity of MRI-targeted TRUSB, and long-term outcomes of men with cancer, which warrant more empirical research. This analysis can inform the development of clinical guidelines.

Patient summary

We found that, under certain assumptions, the use of multiparametric magnetic resonance imaging first and then up to two transrectal ultrasound-guided biopsy is better than the current clinical standard and is good value for money.

SUBMITTER: Faria R

PROVIDER: S-EPMC5718727 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Publications

Optimising the Diagnosis of Prostate Cancer in the Era of Multiparametric Magnetic Resonance Imaging: A Cost-effectiveness Analysis Based on the Prostate MR Imaging Study (PROMIS).

Faria Rita R Soares Marta O MO Spackman Eldon E Ahmed Hashim U HU Brown Louise C LC Kaplan Richard R Emberton Mark M Sculpher Mark J MJ

European urology 20170919 1

<h4>Background</h4>The current recommendation of using transrectal ultrasound-guided biopsy (TRUSB) to diagnose prostate cancer misses clinically significant (CS) cancers. More sensitive biopsies (eg, template prostate mapping biopsy [TPMB]) are too resource intensive for routine use, and there is little evidence on multiparametric magnetic resonance imaging (MPMRI).<h4>Objective</h4>To identify the most effective and cost-effective way of using these tests to detect CS prostate cancer.<h4>Desig ...[more]

PMID: 28935163

Similar Datasets

Project description:BackgroundAll risk stratification strategies in cancer overlook a spectrum of disease. The Prostate MR Imaging Study (PROMIS) provides a unique opportunity to explore cancers that are overlooked by multiparametric magnetic resonance imaging (mpMRI).ObjectiveTo summarise attributes of cancers that are systematically overlooked by mpMRI.Design, setting, and participantsPROMIS tested performance of mpMRI and transrectal ultrasonography (TRUS)-guided biopsy, using 5 mm template mapping (TPM) biopsy as the reference standard.Outcome measurements and statistical analysisOutcomes were overall and maximum Gleason scores, maximum cancer core length (MCCL), and prostate-specific antigen density (PSAD). Cancer attributes were compared between cancers that were overlooked and those that were detected.Results and limitationsOf men with cancer, 7% (17/230; 95% confidence interval [CI] 4.4-12%) had significant disease overlooked by mpMRI according to definition 1 (Gleason ≥ 4 + 3 of any length or MCCL ≥ 6 mm of any grade) and 13% (44/331; 95% CI 9.8-17%) according to definition 2 (Gleason ≥ 3 + 4 of any length or MCCL ≥ 4 mm). In comparison, TRUS-guided biopsy overlooked 52% (119/230; 95% CI 45-58%) of significant disease by definition 1 and 40% (132/331; 95% CI 35-45%) by definition 2. Prostate cancers undetected by mpMRI had significantly lower overall and maximum Gleason scores (p = 0.0007; p < 0.0001) and shorter MCCL (median difference: 3 mm [5 vs 8 mm], p < 0.0001; 95% CI 1-3) than cancers that were detected. No tumours with overall Gleason score > 3 + 4 (Gleason Grade Groups 3-5; 95% CI 0-6.4%) or maximum Gleason score > 4 + 3 (Gleason Grade Groups 4-5; 95% CI 0-8.0%) on TPM biopsy were undetected by mpMRI. Application of a PSAD threshold of 0.15 reduced the proportion of men with undetected cancer to 5% (12/230; 95% CI 2.7-8.9%) for definition 1 and 9% (30/331; 95% CI 6.2-13%) for definition 2. Application of a PSAD threshold of 0.10 reduced the proportion of men with undetected disease to 3% (6/230; 95% CI 1.0-5.6%) for definition 1 cancer and to 3% (11/331; 95% CI 1.7-5.9%) for definition 2 cancer. Limitations were post hoc analysis and uncertain significance of undetected lesions.ConclusionsOverall, a small proportion of cancers are overlooked by mpMRI, with estimates ranging from 4.4% (lower boundary of 95% CI for definition 1) to 17% (upper boundary of 95% CI for definition 2). Prostate cancers undetected by mpMRI are of lower grade and shorter length than cancers that are detected.Patient summaryProstate cancers that are undetected by magnetic resonance imaging (MRI) are smaller and less aggressive than those that are detected, and none of the most aggressive cancers are overlooked by MRI.

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:BackgroundFalse positive multiparametric magnetic resonance imaging (mpMRI) phenotypes prompt unnecessary biopsies. The Prostate MRI Imaging Study (PROMIS) provides a unique opportunity to explore such phenotypes in biopsy-naïve men with raised prostate-specific antigen (PSA) and suspected cancer.ObjectiveTo compare mpMRI lesions in men with/without significant cancer on transperineal mapping biopsy (TPM).Design, setting, and participantsPROMIS participants (n=235) underwent mpMRI followed by a combined biopsy procedure at University College London Hospital, including 5-mm TPM as the reference standard. Patients were divided into four mutually exclusive groups according to TPM findings: (1) no cancer, (2) insignificant cancer, (3) definition 2 significant cancer (Gleason ≥3+4 of any length and/or maximum cancer core length ≥4mm of any grade), and (4) definition 1 significant cancer (Gleason ≥4+3 of any length and/or maximum cancer core length ≥6mm of any grade).Outcome measurements and statistical analysisIndex and/or additional lesions present in 178 participants were compared between TPM groups in terms of number, conspicuity, volume, location, and radiological characteristics.Results and limitationsMost lesions were located in the peripheral zone. More men with significant cancer had two or more lesions than those without significant disease (67% vs 37%; p< 0.001). In the former group, index lesions were larger (mean volume 0.68 vs 0.50 ml; p< 0.001, Wilcoxon test), more conspicuous (Likert 4-5: 79% vs 22%; p< 0.001), and diffusion restricted (mean apparent diffusion coefficient [ADC]: 0.73 vs 0.86; p< 0.001, Wilcoxon test). In men with Likert 3 index lesions, log2PSA density and index lesion ADC were significant predictors of definition 1/2 disease in a logistic regression model (mean cross-validated area under the receiver-operator characteristic curve: 0.77 [95% confidence interval: 0.67-0.87]).ConclusionsSignificant cancer-associated MRI lesions in biopsy-naïve men have clinical-radiological differences, with lesions seen in prostates without significant disease. MRI-calculated PSA density and ADC could predict significant cancer in those with indeterminate MRI phenotypes.Patient summaryMagnetic resonance imaging (MRI) lesions that mimic prostate cancer but are, in fact, benign prompt unnecessary biopsies in thousands of men with raised prostate-specific antigen. In this study we found that, on closer look, such false positive lesions have different features from cancerous ones. This means that doctors could potentially develop better tools to identify cancer on MRI and spare some patients from unnecessary biopsies.

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.

Dataset Information

Optimising the Diagnosis of Prostate Cancer in the Era of Multiparametric Magnetic Resonance Imaging: A Cost-effectiveness Analysis Based on the Prostate MR Imaging Study (PROMIS).

Background

Objective

Design, setting, and participants

Intervention

Outcome measurements and statistical analysis

Results and limitations

Conclusions

Patient summary

Publications

Optimising the Diagnosis of Prostate Cancer in the Era of Multiparametric Magnetic Resonance Imaging: A Cost-effectiveness Analysis Based on the Prostate MR Imaging Study (PROMIS).

Similar Datasets

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