Clinical applications of multiparametric MRI within the prostate cancer diagnostic pathway.
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ABSTRACT:
SUBMITTER: Dickinson L
PROVIDER: S-EPMC4274606 | biostudies-literature | 2013 Apr
REPOSITORIES: biostudies-literature
Publications
Clinical applications of multiparametric MRI within the prostate cancer diagnostic pathway.
Dickinson Louise L Ahmed Hashim U HU Allen Clare C Barentsz Jelle O JO Carey Brendan B Futterer Jurgen J JJ Heijmink Stijn W SW Hoskin Peter P Kirkham Alex P AP Padhani Anwar R AR Raj Persad Ch M ChM van der Meulen Jan J Villers Arnauld A Emberton Mark M
Urologic oncology 20130401 3
Similar Datasets
Project description: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 is to investigate the association of multiparametric (mp)MRI quantitative features with prostate cancer risk gene expression profiles in mpMRI-guided biopsies tissues.
2016-06-07 | GSE80683 | GEO
Project description:BackgroundPositron emission tomography (PET) using different positron-emitting radiopharmaceuticals has emerged as a promising new metabolic diagnostic tool for the evaluation of a variety of malignant diseases. Thus, we investigated the diagnostic efficacy of F-18-Fluorocholine positron emission tomography/computed tomography (PET/CT) and multiparametric magnetic resonance imaging (mpMRI) for the detection and localization of tumors within the prostate with the correlating histopathology as the standard of reference.MethodsForty patients with histologically proven prostate cancer underwent both F-18-Fluorocholine PET/CT and mpMRI before robot-assisted laparoscopic radical prostatectomy (RARP). The maximum standard uptake values and the tumor-to-background ratio were measured on a sextant basis. In brief, the sextants were defined as right apex, right middle, right base, left apex, left middle, and left base. For each tumor region, the correlation of the tumor localization based on the sextant in both F-18-Fluorocholine PET/CT and mpMRI scans with the histopathological results was determined.ResultsThe correlation between both imaging modalities and RARP pathology representing (1) all cancer and (2) clinically significant cancer defined as a ≥ International Society of Urological Pathology grade of 2 showed that the sensitivity and the area under the curve (AUC) were higher for mpMRI than for F-18-Fluorocholine PET/CT. In contrast, F-18-Fluorocholine PET/CT had relatively higher specificity than mpMRI. Importantly, we found a very high AUC value of over 0.8 in both imaging modalities.ConclusionmpMRI had results superior to F-18-Fluorocholine PET/CT in assessing intraprostatic tumor localization. However, F-18-Fluorocholine PET/CT showed superiority in terms of specificity. Thus, using both modalities in conjunction could provide better treatment planning.
| S-EPMC9520420 | biostudies-literature
Project description:PurposeTo compare the diagnostic performance and image quality of a deep-learning-assisted ultra-fast biparametric MRI (bpMRI) with the conventional multiparametric MRI (mpMRI) for the diagnosis of clinically significant prostate cancer (csPCa).MethodsThis prospective single-center study enrolled 123 biopsy-naïve patients undergoing conventional mpMRI and additionally ultra-fast bpMRI at 3 T between 06/2023-02/2024. Two radiologists (R1: 4 years and R2: 3 years of experience) independently assigned PI-RADS scores (PI-RADS v2.1) and assessed image quality (mPI-QUAL score) in two blinded study readouts. Weighted Cohen's Kappa (κ) was calculated to evaluate inter-reader agreement. Diagnostic performance was analyzed using clinical data and histopathological results from clinically indicated biopsies.ResultsInter-reader agreement was good for both mpMRI (κ = 0.83) and ultra-fast bpMRI (κ = 0.87). Both readers demonstrated high sensitivity (≥94 %/≥91 %, R1/R2) and NPV (≥96 %/≥95 %) for csPCa detection using both protocols. The more experienced reader mostly showed notably higher specificity (≥77 %/≥53 %), PPV (≥62 %/≥45 %), and diagnostic accuracy (≥82 %/≥65 %) compared to the less experienced reader. There was no significant difference in the diagnostic performance of correctly identifying csPCa between both protocols (p > 0.05). The ultra-fast bpMRI protocol had significantly better image quality ratings (p < 0.001) and achieved a reduction in scan time of 80 % compared to conventional mpMRI.ConclusionDeep-learning-assisted ultra-fast bpMRI protocols offer a promising alternative to conventional mpMRI for diagnosing csPCa in biopsy-naïve patients with comparable inter-reader agreement and diagnostic performance at superior image quality. However, reader experience remains essential for diagnostic performance.
| S-EPMC11791330 | biostudies-literature
Project description:ObjectiveTo compare the diagnostic outcomes of the current approach of transrectal ultrasound (TRUS)-guided biopsy in men with suspected prostate cancer to an alternative approach using multiparametric MRI (mpMRI), followed by MRI-targeted biopsy if positive.DesignClinical decision analysis was used to synthesise data from recently emerging evidence in a format that is relevant for clinical decision making.PopulationA hypothetical cohort of 1000 men with suspected prostate cancer.InterventionsmpMRI and, if positive, MRI-targeted biopsy compared with TRUS-guided biopsy in all men.Outcome measuresWe report the number of men expected to undergo a biopsy as well as the numbers of correctly identified patients with or without prostate cancer. A probabilistic sensitivity analysis was carried out using Monte Carlo simulation to explore the impact of statistical uncertainty in the diagnostic parameters.ResultsIn 1000 men, mpMRI followed by MRI-targeted biopsy 'clinically dominates' TRUS-guided biopsy as it results in fewer expected biopsies (600 vs 1000), more men being correctly identified as having clinically significant cancer (320 vs 250), and fewer men being falsely identified (20 vs 50). The mpMRI-based strategy dominated TRUS-guided biopsy in 86% of the simulations in the probabilistic sensitivity analysis.ConclusionsOur analysis suggests that mpMRI followed by MRI-targeted biopsy is likely to result in fewer and better biopsies than TRUS-guided biopsy. Future research in prostate cancer should focus on providing precise estimates of key diagnostic parameters.
| S-EPMC4067835 | biostudies-literature
Project description:PurposeTo assess the diagnostic performance of prostate MRI by estimating the proportion of clinically significant prostate cancer (csPCa) in patients without prostate pathology.Materials and methodsThis three-center retrospective study included prostate MRI examinations performed for clinical suspicion of csPCa (Grade group ≥ 2) between 2018 and 2022. Examinations were divided into two groups: pathological diagnosis within 1 year after the MRI (post-MRI pathology) is present and absent. Risk prediction models were developed using the extracted eleven common predictive variables from the patients with post-MRI pathology. Then, the csPCa proportion in the patients without post-MRI pathology was estimated by applying the model. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values (PPV/NPV) of prostate MRI in diagnosing csPCa were subsequently calculated for patients with and without post-MRI prostate pathology (estimated statistics) with a positive threshold of PI-RADS ≥ 3.ResultsOf 12,191 examinations enrolled (mean age, 65.7 years ± 8.4 [standard deviation]), PI-RADS 1-2 was most frequently assigned (55.4%) with the lowest pathological confirmation rate of 14.0-18.2%. Post-MRI prostate pathology was found in 5670 (46.5%) examinations. The estimated csPCa proportions across facilities were 12.6-15.3%, 18.4-31.4%, 45.7-69.9%, and 75.4-88.3% in PI-RADS scores of 1-2, 3, 4, and 5, respectively. The estimated (observed) performance statistics were as follows: AUC, 0.78-0.81 (0.76-0.79); sensitivity, 76.6-77.3%; specificity, 67.5-78.6%; PPV, 49.8-66.6% (52.0-67.7%); and NPV, 84.4-87.2% (82.4-86.6%).ConclusionWe proposed a method to estimate the probabilities harboring csPCa for patients who underwent prostate MRI examinations, which allows us to understand the PI-RADS diagnostic performance with several metrics.Clinical relevance statementThe reported estimated performance metrics are expected to aid in understanding the true diagnostic value of PI-RADS in the entire prostate MRI population performed with clinical suspicion of prostate cancer.Key pointsCalculating performance metrics only from patients who underwent prostate biopsy may be biased due to biopsy selection criteria, especially in PI-RADS 1-2. The estimated area under the receiver operating characteristic curve of PI-RADS in the entire prostate MRI population ranged from 0.78 to 0.81 at three facilities. The estimated statistics are expected to help us understand the true PI-RADS performance and serve as a reference for future studies.
| S-EPMC11568117 | biostudies-literature
Project description:Purpose To compare the diagnostic accuracy of gallium 68 (68Ga)-labeled prostate-specific membrane antigen (PSMA)-11 PET/MRI with that of multiparametric MRI in the detection of prostate cancer. Materials and Methods The authors performed a retrospective study of men with biopsy-proven prostate cancer who underwent simultaneous 68Ga-PSMA-11 PET/MRI before radical prostatectomy between December 2015 and June 2017. The reference standard was whole-mount pathologic examination. Readers were blinded to radiologic and pathologic findings. Tumor localization was based on 30 anatomic regions. Region-specific sensitivity and specificity were calculated for PET/MRI and multiparametric MRI by using raw stringent and alternative neighboring approaches. Maximum standardized uptake value (SUVmax) in the tumor and Prostate Imaging Reporting and Data System (PI-RADS) version 2 grade were compared with tumor Gleason score. Generalized estimating equations were used to estimate population-averaged sensitivity and specificity and to determine the association between tumor characteristics and SUVmax or PI-RADS score. Results Thirty-two men (median age, 68 years; interquartile range: 62-71 years) were imaged. The region-specific sensitivities of PET/MRI and multiparametric MRI were 74% (95% confidence interval [CI]: 70%, 77%) and 50% (95% CI: 45%, 0.54%), respectively, with the alternative neighboring approach (P < .001 for both) and 73% (95% CI: 68%, 79%) and 69% (95% CI: 62%, 75%), respectively, with the population-averaged generalized estimating equation (P = .04). Region-specific specificity of PET/MRI was similar to that of multiparametric MRI with the alternative neighboring approach (88% [95% CI: 85%, 91%] vs 90% [95% CI: 87%, 92%], P = .99) and in population-averaged estimates (70% [95% CI: 64%, 76%] vs 70% [95% CI: 64%, 75%], P = .99). SUVmax was associated with a Gleason score of 7 and higher (odds ratio: 1.71 [95% CI: 1.27, 2.31], P < .001). Conclusion The sensitivity of gallium 68-labeled prostate-specific membrane antigen-11 PET/MRI in the detection of prostate cancer is better than that of multiparametric MRI. © RSNA, 2018 See also the editorial by Civelek in this issue.
| S-EPMC6283324 | biostudies-literature
Project description:IntroductionThis study was conducted to describe the changes in repeat multiparametric MRI (mpMRI) occurring in prostate cancer (PCa) patients during active surveillance (AS), and to study possible associations between mpMRI-related parameters in predicting prostate biopsy (Bx) Gleason score (GS) upgrading >3+3 and protocol-based treatment change (TC).Materials and methodsThe study cohort consisted of 76 AS patients with GS 3+3 PCa and at least two consecutive mpMRIs of the prostate performed between 2006-2015. Patients were followed according to the Prostate Cancer Research International Active Surveillance (PRIAS) protocol and an additional mpMRI. The primary end points were GS upgrading (GU) (>3+3) in protocol-based Bxs and protocol-based TC.ResultsOut of 76 patients, 53 (69%) had progression (PIRADS upgrade, size increase or new lesion[s]), while 18 (24%) had radiologically stable disease, and 5 (7%) had regression (PIRADS or size decrease, disappearance of lesion[s]) in repeat mpMRIs during AS. PIRADS scores of 4-5 in the initial mpMRI were associated with GU (p = 0.008) and protocol-based TC (p = 0.009). Tumour progression on repeat mpMRIs was associated with TC (p = 0.045) but not with GU (p = 1.00). PIRADS scores of 4-5 predict GU (sensitivity 0.80 [95% confidence interval (CI); 0.51-0.95, specificity 0.62 [95% CI; 0.52-0.77]) with PPV and NPV values of 0.34 (95% CI; 0.21-0.55) and 0.93 (95% CI; 0.80-0.98), respectively.ConclusionmpMRI is a useful tool not only to select but also to monitor PCa patients on AS.
| S-EPMC5744936 | biostudies-literature
Project description:Prostate cancer (PCa) is a highly prevalent cancer type with a heterogeneous prognosis. An accurate assessment of tumor aggressiveness can pave the way for tailored treatment strategies, potentially leading to better outcomes. While tumor aggressiveness is typically assessed based on invasive methods (e.g., biopsy), radiogenomics, combining diagnostic imaging with genomic information can help uncover aggressive (imaging) phenotypes, which in turn can provide non-invasive advice on individualized treatment regimens. In this study, we carried out a parallel analysis on both imaging and transcriptomics data in order to identify features associated with clinically significant PCa (defined as an ISUP grade ≥ 3), subsequently evaluating the correlation between them. Textural imaging features were extracted from multi-parametric MRI sequences (T2W, DWI, and DCE) and combined with DCE-derived parametric pharmacokinetic maps obtained using magnetic resonance dispersion imaging (MRDI). A transcriptomic analysis was performed to derive functional features on transcription factors (TFs), and pathway activity from RNA sequencing data, here referred to as transcriptomic features. For both the imaging and transcriptomic features, different machine learning models were separately trained and optimized to classify tumors in either clinically insignificant or significant PCa. These models were validated in an independent cohort and model performance was used to isolate a subset of relevant imaging and transcriptomic features to be further investigated. A final set of 31 imaging features was correlated to 33 transcriptomic features obtained on the same tumors. Five significant correlations (p < 0.05) were found, of which, three had moderate strength (|r| ≥ 0.5). The strongest significant correlations were seen between a perfusion-based imaging feature-MRDI A median-and the activities of the TFs STAT6 (-0.64) and TFAP2A (-0.50). A higher-order T2W textural feature was also significantly correlated to the activity of the TF STAT6 (-0.58). STAT6 plays an important role in controlling cell proliferation and migration. Loss of the AP2alpha protein expression, quantified by TFAP2A, has been strongly associated with aggressiveness and progression in PCa. According to our findings, a combination of texture features extracted from T2W and DCE, as well as perfusion-based pharmacokinetic features, can be considered for the prediction of clinically significant PCa, with the pharmacokinetic MRDI A feature being the most correlated with the underlying transcriptomic information. These results highlight a link between quantitative imaging features and the underlying transcriptomic landscape of prostate tumors.
| S-EPMC10296397 | biostudies-literature
Project description: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.
| S-EPMC5288193 | biostudies-literature
Project description:The utilization of multi-parametric MRI (mpMRI) in clinical decisions regarding prostate cancer patients' management has recently increased. After biopsy, clinicians can assess risk using National Comprehensive Cancer Network (NCCN) risk stratification schema and commercially available genomic classifiers, such as Decipher. We built radiomics-based models to predict lesions/patients at low risk prior to biopsy based on an established three-tier clinical-genomic classification system. Radiomic features were extracted from regions of positive biopsies and Normally Appearing Tissues (NAT) on T2-weighted and Diffusion-weighted Imaging. Using only clinical information available prior to biopsy, five models for predicting low-risk lesions/patients were evaluated, based on: 1: Clinical variables; 2: Lesion-based radiomic features; 3: Lesion and NAT radiomics; 4: Clinical and lesion-based radiomics; and 5: Clinical, lesion and NAT radiomic features. Eighty-three mpMRI exams from 78 men were analyzed. Models 1 and 2 performed similarly (Area under the receiver operating characteristic curve were 0.835 and 0.838, respectively), but radiomics significantly improved the lesion-based performance of the model in a subset analysis of patients with a negative Digital Rectal Exam (DRE). Adding normal tissue radiomics significantly improved the performance in all cases. Similar patterns were observed on patient-level models. To the best of our knowledge, this is the first study to demonstrate that machine learning radiomics-based models can predict patients' risk using combined clinical-genomic classification.
| S-EPMC10647832 | biostudies-literature