Dataset Information

Radiologists' Contribution to Variation in Detecting Clinically Significant Prostate Cancer in Men With Prostate MRI.

ABSTRACT:

Objective

Assess radiologists' contribution to variation in clinically significant prostate cancer (csPCa) detection in patients with elevated prostate-specific antigen (PSA) and multiparametric MRI (mpMRI).

Methods

This institutional review board-approved, retrospective cohort study was performed at a tertiary, academic, National Cancer Institute-designated Comprehensive Cancer Center with a multidisciplinary prostate cancer program. Men undergoing mpMRI examinations from January 1, 2015, to December 31, 2019, with elevated PSA (≥4 ng/mL) and biopsy within 6 months pre- or post-MRI or prostatectomy within 6 months post-mpMRI were included. Univariate and multivariable hierarchical logistic regression assessed impact of patient, provider, mpMRI examination, mpMRI report, and pathology factors on the diagnosis of Grade Group ≥ 2 csPCa.

Results

Study cohort included 960 MRIs in 928 men, mean age 64.0 years (SD ± 7.4), and 59.8% (555 of 928) had csPCa. Interpreting radiologist was not significant individually (P > .999) or combined with mpMRI ordering physician and physician performing biopsy or prostatectomy (P = .41). Prostate Imaging Reporting and Data System (PI-RADS) category 2 (odds ratio [OR] 0.18, P = .04), PI-RADS category 4 (OR 2.52, P < .001), and PI-RADS category 5 (OR 4.99, P < .001) assessment compared with no focal lesion; PSA density of 0.1 to 0.15 ng/mL/cc (OR 2.46, P < .001), 0.15 to 0.2 ng/mL/cc (OR 2.77, P < .001), or ≥0.2 ng/mL/cc (OR 4.52, P < .001); private insurance (reference = Medicare, OR 0.52, P = .001), and unambiguous extraprostatic extension on mpMRI (OR 2.94, P = .01) were independently associated with csPCa. PI-RADS 3 assessment (OR 1.18, P = .56), age (OR 0.99, P = .39), and African American race (OR 0.90, P = .75) were not.

Discussion

Although there is known in-practice variation in radiologists' interpretation of mpMRI, in our multidisciplinary prostate cancer program we found no significant radiologist-attributable variation in csPCa detection.

SUBMITTER: Naik S

PROVIDER: S-EPMC10189741 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Radiologists' Contribution to Variation in Detecting Clinically Significant Prostate Cancer in Men With Prostate MRI.

Naik Sachin S Burk Kristine S KS Budiawan Elvira E Lacson Ronilda R Lee Leslie K LK Fennessy Fiona M FM Tempany Clare C Cole Alexander P AP Trinh Quoc-Dien QD Kibel Adam S AS Khorasani Ramin R

Journal of the American College of Radiology : JACR 20221013 12

<h4>Objective</h4>Assess radiologists' contribution to variation in clinically significant prostate cancer (csPCa) detection in patients with elevated prostate-specific antigen (PSA) and multiparametric MRI (mpMRI).<h4>Methods</h4>This institutional review board-approved, retrospective cohort study was performed at a tertiary, academic, National Cancer Institute-designated Comprehensive Cancer Center with a multidisciplinary prostate cancer program. Men undergoing mpMRI examinations from January ...[more]

PMID: 36244674

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Project description:BackgroundWith the widespread adoption of prostatic-specific antigen (PSA) screening, the detection rates of prostate cancer (PCa) have increased. Due to the low specificity and high false-positive rate of serum PSA levels, it was difficult to diagnose PCa accurately. To improve the diagnosis of PCa and clinically significant prostate cancer (CSPCa), we established novel models on the basis of the prostate health index (PHI) and multiparametric magnetic resonance imaging (mpMRI) in the Asian population.MethodsWe retrospectively collected the clinical indicators of patients with TPSA at 4-20 ng/ml. Furthermore, mpMRI was performed using a 3.0-T scanner and reported in the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS). Univariable and multivariable logistic analyses were performed to construct the models. The performance of different models based on PSA derivatives, PHI derivatives, PI-RADS, and a combination of PHI derivatives and PI-RADS was evaluated.ResultsAmong the 128 patients, 47 (36.72%) patients were diagnosed with CSPCa and 81 (63.28%) patients were diagnosed with non-CSPCa. Of the 81 (63.28%) patients, 8 (6.25%) patients were diagnosed with Gleason Grade 1 PCa and 73 (57.03%) patients were diagnosed with non-PCa. In the analysis of the receiver operator characteristic (ROC) curves in TPSA 4-20 ng/ml, the multivariable model for PCa was significantly larger than that for the model based on the PI-RADS (p = 0.004) and that for the model based on the PHI derivatives (p = 0.031) in diagnostic accuracy. The multivariable model for CSPCa was significantly larger than that for the model based on the PI-RADS (p = 0.003) and was non-significantly larger than that for the model based on the PHI derivatives (p = 0.061) in diagnostic accuracy. For PCa in TPSA 4-20 ng/ml, a multivariable model achieved the optimal diagnostic value at four levels of predictive variables. For CSPCa in TPSA 4-20 ng/ml, the multivariable model achieved the optimal diagnostic value at a sensitivity close to 90% and 80%.ConclusionsThe models combining PHI derivatives and PI-RADS performed better in detecting PCa and CSPCa than the models based on either PHI or PI-RADS.

Project description:Standardized reporting of multiparametric prostate MRI (mpMRI) is widespread and follows international standards (Pi-RADS). However, quantitative measurements from mpMRI are not widely comparable. Although T2 mapping sequences can provide repeatable quantitative image measurements and extract reliable imaging biomarkers from mpMRI, they are often time-consuming. We therefore investigated the value of quantitative measurements on a highly accelerated T2 mapping sequence, in order to establish a threshold to differentiate benign from malignant lesions. For this purpose, we evaluated a novel, highly accelerated T2 mapping research sequence that enables high-resolution image acquisition with short acquisition times in everyday clinical practice. In this retrospective single-center study, we included 54 patients with clinically indicated MRI of the prostate and biopsy-confirmed carcinoma (n = 37) or exclusion of carcinoma (n = 17). All patients had received a standard of care biopsy of the prostate, results of which were used to confirm or exclude presence of malignant lesions. We used the linear mixed-effects model-fit by REML to determine the difference between mean values of cancerous tissue and healthy tissue. We found good differentiation between malignant lesions and normal appearing tissue in the peripheral zone based on the mean T2 value. Specifically, the mean T2 value for tissue without malignant lesions was (151.7 ms [95% CI: 146.9-156.5 ms] compared to 80.9 ms for malignant lesions [95% CI: 67.9-79.1 ms]; p < 0.001). Based on this assessment, a limit of 109.2 ms is suggested. Aditionally, a significant correlation was observed between T2 values of the peripheral zone and PI-RADS scores (p = 0.0194). However, no correlation was found between the Gleason Score and the T2 relaxation time. Using REML, we found a difference of -82.7 ms in mean values between cancerous tissue and healthy tissue. We established a cut-off-value of 109.2 ms to accurately differentiate between malignant and non-malignant prostate regions. The addition of T2 mapping sequences to routine imaging could benefit automated lesion detection and facilitate contrast-free multiparametric MRI of the prostate.

Project description:Texture features based on the spatial relationship of pixels, known as the gray-level co-occurrence matrix (GLCM), may play an important role in providing the accurate classification of suspected prostate cancer. The purpose of this study was to use quantitative imaging parameters of pre-biopsy multiparametric magnetic resonance imaging (mpMRI) for the prediction of clinically significant prostate cancer. This was a prospective study, recruiting 200 men suspected of having prostate cancer. Participants were imaged using a protocol-based 3T MRI in the pre-biopsy setting. Radiomics parameters were extracted from the T2WI and ADC texture features of the gray-level co-occurrence matrix were delineated from the region of interest. Radical prostatectomy histopathology was used as a reference standard. A Kruskal-Wallis test was applied first to identify the significant radiomic features between the three groups of Gleason scores (i.e., G1, G2 and G3). Subsequently, the Holm-Bonferroni method was applied to correct and control the probability of false rejections. We compared the probability of correctly predicting significant prostate cancer between the explanatory GLCM radiomic features, PIRADS and PSAD, using the area under the receiver operation characteristic curves. We identified the significant difference in radiomic features between the three groups of Gleason scores. In total, 12 features out of 22 radiomics features correlated with the Gleason groups. Our model demonstrated excellent discriminative ability (C-statistic = 0.901, 95%CI 0.859-0.943). When comparing the probability of correctly predicting significant prostate cancer between explanatory GLCM radiomic features (Sum Variance T2WI, Sum Entropy T2WI, Difference Variance T2WI, Entropy ADC and Difference Variance ADC), PSAD and PIRADS via area under the ROC curve, radiomic features were 35.0% and 34.4% more successful than PIRADS and PSAD, respectively, in correctly predicting significant prostate cancer in our patients (p < 0.001). The Sum Entropy T2WI score had the greatest impact followed by the Sum Variance T2WI. Quantitative GLCM texture analyses of pre-biopsy MRI has the potential to be used as a non-invasive imaging technique to predict clinically significant cancer in men suspected of having prostate cancer.

Project description:PurposeA pre-biopsy decision aid is needed to counsel men with a clinical suspicion for clinically significant prostate cancer (csPCa), despite normal prostate magnetic resonance imaging (MRI).MethodsA risk calculator (RC) for csPCa (International Society of Urological Pathology grade group (ISUP) ⩾ 2) presence in men with a negative-MRI (Prostate Imaging-Reporting and Data System (PI-RADS) ⩽ 2) was developed, and its performance was compared with RCs of the European Randomized Study of Screening for Prostate Cancer (ERSPC), Prostate Biopsy Collaborative Group (PBCG), and Prospective Loyola University mpMRI (PLUM). All biopsy-naïve and prior negative biopsy men with a negative-MRI followed by systematic prostate biopsy were included from October 2015 to September 2021. The RC was developed using multivariable logistic regression with the following parameters: age (years), family history of PCa (first- or second-degree family member), ancestry (African Caribbean/other), digital rectal exam (benign/malignant), MRI field strength (1.5/3.0 Tesla), prior negative biopsy status, and prostate-specific antigen (PSA) density (ng/ml/cc). Performance of RCs was compared using receiver operating characteristic (ROC) curve analysis.ResultsA total of 232 men were included for analysis, of which 18.1% had csPCa. Parameters associated with csPCa were family history of PCa (p < 0.0001), African Caribbean ancestry (p = 0.005), PSA density (p = 0.002), prior negative biopsy (p = 0.06), and age at biopsy (p = 0.157). The area under the curve (AUC) of the developed RC was 0.76 (95% CI 0.68-0.85). This was significantly better than the RCs of the ERSPC (AUC: 0.59; p = 0.001) and PBCG (AUC: 0.60; p = 0.002), yet similar to PLUM (AUC: 0.69; p = 0.09).ConclusionThe developed RC (Prostate Biopsy Cohort Amsterdam ('PROBA' RC), integrated predictors for csPCa at prostate biopsy in negative-MRI men and outperformed other widely used RCs. These findings require external validation before introduction in daily practice.

Dataset Information

Radiologists' Contribution to Variation in Detecting Clinically Significant Prostate Cancer in Men With Prostate MRI.

Objective

Methods

Results

Discussion

Publications

Radiologists' Contribution to Variation in Detecting Clinically Significant Prostate Cancer in Men With Prostate MRI.

Similar Datasets

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