Dataset Information

Multiparametric Magnetic Resonance Imaging Grades the Aggressiveness of Prostate Cancer.

ABSTRACT: We sought to find further evidence showing the increase in PCa aggressiveness as PI-RADS score increases from four surrogates of PCa aggressiveness: i. prostate biopsy GG (≤3 vs. >3), ii. type of pathology in surgical specimens (favourable vs. unfavourable), iii. clinical stage (localised vs. advanced), and risk of recurrence of localised PCa after primary treatment (low-intermediate vs. high). A group of 692 PCa patients were diagnosed after 3-T multiparametric MRI (mpMRI) and guided and/or systematic biopsies, showing csPCa (GG ≥ 2) in 547 patients (79%) and insignificant PCa (iPCa) in 145 (21%). The csPCa rate increased from 32.4% in PI-RADS < 3 to 95.5% in PI-RADS 5 (p < 0.001). GG ≥ 3 was observed in 7.6% of PCa with PI-RADS < 3 and 32.6% in those with PI-RADS > 3 (p < 0.001). Unfavourable pathology was observed in 38.9% of PCa with PI-RAD < 3 and 68.3% in those with PI-RADS > 3 (p = 0.030). Advanced disease was not observed in PCa with PI-RADS ≤ 3, while it existed in 12.7% of those with PI-RADS > 3 (p < 0.001). High-risk recurrence localised PCa was observed in 9.5% of PCa with PI-RADS < 3 and 35% in those with PI-RADS > 3 (p = 0.001). The PI-RADS score was an independent predictor of all surrogates of PCa aggressiveness as PSA density. We confirmed that mpMRI grades PCa aggressiveness.

SUBMITTER: Morote J

PROVIDER: S-EPMC8997549 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Multiparametric Magnetic Resonance Imaging Grades the Aggressiveness of Prostate Cancer.

Morote Juan J Borque-Fernando Angel A Triquell Marina M Celma Anna A Regis Lucas L Mast Richard R de Torres Inés M IM Semidey María E ME Santamaría Anna A Planas Jacques J Esteban Luis M LM Trilla Enrique E

Cancers 20220405 7

We sought to find further evidence showing the increase in PCa aggressiveness as PI-RADS score increases from four surrogates of PCa aggressiveness: i. prostate biopsy GG (≤3 vs. >3), ii. type of pathology in surgical specimens (favourable vs. unfavourable), iii. clinical stage (localised vs. advanced), and risk of recurrence of localised PCa after primary treatment (low-intermediate vs. high). A group of 692 PCa patients were diagnosed after 3-T multiparametric MRI (mpMRI) and guided and/or sys ...[more]

PMID: 35406600

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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: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:Background and objectiveMultiparametric magnetic resonance imaging (mpMRI)-guided prostate biopsies have become the standard of care. This study aims to analyze changes in the distribution of detected prostate cancer (PCa) risk groups in Switzerland during the adoption period of a multiparametric magnetic resonance imaging (mpMRI)-guided prostate biopsy.MethodsThis ecological study analyzed prostate biopsies from a tertiary hospital and PCa diagnoses from the National Agency for Cancer Registration in Switzerland between January 2005 and December 2019. A survey assessed mpMRI uptake in Swiss urological centers. PCa risk group proportions were calculated and compared for the entire period and annually.Key findings and limitationsA total of 4890 biopsies in the hospital dataset and 74 747 national PCa cases were analyzed. Before mpMRI availability, 72.6% of hospital biopsies were PCa negative, with detected cases including 46.4% low risk (LR), 30.5% intermediate risk (IR), and 23.2% high risk (HR). After the availability of mpMRI, 45.7% were PCa negative, with 24.6% LR, 49.0% IR, and 26.5% HR. National mpMRI uptake began in 2008, surpassing 95% by 2019. In 2005, 3448 PCa cases were recorded across 14 cantons, with 46.3% LR, 33.1% IR, and 20.6% HR. By 2019, 6868 cases were registered in 23 cantons, with 28.0% LR, 48.9% IR, and 23.2% HR.Conclusions and clinical implicationsAfter implementation of mpMRI, fewer negative prostate biopsy results were observed. Furthermore, a shift was detected in the distribution of risk groups, with an increase in the proportion of IR cases and a decline in LR cases alongside the uptake of mpMRI. The proportion of HR cases remained essentially constant over time. Further research is needed to determine whether this reflects improved stratification or an artifact of the changed diagnostic pathway.Patient summaryIn this study, we examined how the detected prostate cancer grades changed during the adoption of multiparametric magnetic resonance imaging (mpMRI)-guided prostate biopsies in Switzerland between 2005 and 2019. After mpMRI, fewer negative biopsies occurred and the distribution of prostate cancer grades changed, with more intermediate-risk and fewer low-risk cancers identified, while high-risk cases remained stable.

Project description:Background: To develop a radiomics model based on multiparametric MRI (mpMRI) for preoperative prediction of extraprostatic extension (EPE) in patients with prostate cancer (PCa). Methods: Ninety-five pathology-confirmed PCa patients with 115 lesions (49 positive and 66 negative) were retrospectively enrolled. A 3.0T MR scanner was used to perform T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCE). Radiomics features extracted from T2WI, DWI, apparent diffusion coefficient (ADC), and DCE were used to build a radiomics model. Patients' clinical and pathological variables were also obtained to build a clinical model. The radiomics model and clinical model were further integrated to build a combined nomogram. All lesions were randomly divided into the training group (82 lesions) and the validation group (33 lesions). A least absolute shrinkage and selection operator (LASSO) regression algorithm was applied to build the radiomics model. The diagnostic performance of different models was assessed by calculating the area under the curve (AUC) and compared using the Delong test. The calibration curve and decision curve analyses were used to assess the calibration and clinical usefulness of the radiomics model. Results: The AUC values for the radiomics model in the training and validation group were 0.919 and 0.865, respectively, with a good calibration performance. The decision curve analysis confirmed the clinical utility of the radiomics model. The accuracy, sensitivity, and specificity were 81.8, 71.4, and 89.5% in the validation group. In the validation group, the radiomics model outperformed the clinical model (AUC = 0.658, P = 0.020), and was comparable with the combined nomogram (AUC = 0.857, P = 0.644). Conclusion: The radiomics model based on mpMRI could different EPE and non-EPE lesions with satisfactory diagnostic performance, and this model might assist in predicting EPE before prostatectomy.

Dataset Information

Multiparametric Magnetic Resonance Imaging Grades the Aggressiveness of Prostate Cancer.

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Multiparametric Magnetic Resonance Imaging Grades the Aggressiveness of Prostate Cancer.

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