Dataset Information

Biparametric Quantitative MRI for Prostate Cancer Detection.

ABSTRACT:

Objectives

This study sought to prospectively investigate a novel quantitative biparametric prostate magnetic resonance imaging (MRI) protocol to detect prostate cancer (PCa) in biopsy-naïve men. Secondarily, this study reports the accuracy of fractional order calculus (FROC) diffusion and quantitative T2 compared with the Prostate Imaging Reporting & Data System (PI-RADS).

Methods

This prospective pilot study (NCT04175730) enrolled 50 prostate biopsy-naïve men who met eligibility criteria. All men received 3T MRI with T2 and diffusion-weighted imaging (DWI) (b-values: 50-4,000 s/mm2). Men with PI-RADS lesions ≥3 underwent targeted and systematic prostate biopsy, omitting systematic biopsy cores in peripheral zone lesions. DWI series images were fit to signal decay to calculate ADC (mm2/s) and the FROC model for coefficient DF (mm2/s). The primary end point was detection of Gleason grade group ≥2 (GG≥2) PCa. Receiver operating characteristic regression and area under the curve (AUC) were reported.

Results

Forty-eight men underwent MRI and biopsy. Mean age was 61.5 years (56-68), 29% were White, 52% were African American, mean PSA was 6.0 ng/mL (4.9-8.0), and mean PSA density was 0.14 ng/mL2. In total, 61 PI-RADS ≥3 lesions were targeted for biopsy. GG≥2 PC was found in 7% (1/14) of PI-RADS 3 lesions, 28% (10/36) of PI-RADS 4 lesions, and 36% (4/11) of PI-RADS 5 lesions. The AUC for detection of GG≥2 PC was 0.63 (0.5-0.76) for PI-RADS, 0.82 (0.68-0.96) for ADC, and 0.87 (0.77-0.97) for the FROC model.

Conclusion

This small prospective pilot study demonstrates the feasibility of a novel quantitative biparametic MRI protocol to detect prostate cancer in biopsy-naïve men.

SUBMITTER: Uyanik M

PROVIDER: S-EPMC10691659 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Publications

Biparametric Quantitative MRI for Prostate Cancer Detection.

Uyanik Meltem M Vigneswaran Hari T HT Hale Graham R GR Gann Peter P Magin Richard R Abern Michael R MR

Topics in magnetic resonance imaging : TMRI 20231115 6

<h4>Objectives</h4>This study sought to prospectively investigate a novel quantitative biparametric prostate magnetic resonance imaging (MRI) protocol to detect prostate cancer (PCa) in biopsy-naïve men. Secondarily, this study reports the accuracy of fractional order calculus (FROC) diffusion and quantitative T2 compared with the Prostate Imaging Reporting & Data System (PI-RADS).<h4>Methods</h4>This prospective pilot study (NCT04175730) enrolled 50 prostate biopsy-naïve men who met eligibility ...[more]

PMID: 38051029

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Project description:ImportanceMultiparametric magnetic resonance imaging (MRI) enhances detection and risk stratification for significant prostate cancer but is time-consuming (approximately 40 minutes) and expensive. Rapid and simpler (approximately 15-minute) biparametric MRI (bpMRI) using fewer scan sequences could be implemented as a prostate MRI triage test on a larger scale before performing biopsies.ObjectivesTo assess the diagnostic accuracy and negative predictive value (NPV) of a novel bpMRI method in biopsy-naive men in detecting and ruling out significant prostate cancer in confirmatory biopsies.Design, setting, and participantsA single-institutional, paired, prospective cohort study of biopsy-naive men with clinical suspicion of prostate cancer from November 1, 2015, to June 15, 2017.InterventionsAll patients underwent bpMRI (T2-weighted and diffusion-weighted imaging) followed by standard transrectal ultrasound-guided biopsies (all men) and targeted biopsies of men with suspicious bpMRI findings.Main outcomes and measuresSuspicion grades of bpMRI, biopsy results, and NPV of bpMRI were evaluated for detection of or ruling out significant prostate cancer (Gleason score ≥4 + 3 or maximum cancerous core length >50% for Gleason score 3 + 4). We compared the diagnostic performance of standard biopsies in all men vs standard plus targeted (combined) biopsies restricted to men with suspicious bpMRI findings. The reference standard was combined biopsy results from all men.ResultsA total of 1020 men were enrolled, with a median age of 67 years (interquartile range, 61-71 years) and a median prostate-specific antigen level of 8.0 ng/mL (interquartile range, 5.7-13.0 ng/mL). Combined biopsies detected any and significant prostate cancer in 655 of 1020 men (64%) and 404 of 1020 men (40%), respectively. Restricting combined biopsies to men with suspicious bpMRI findings meant 305 of 1020 men (30%) with low-suspicious bpMRIs could avoid prostate biopsies (biopsy in 715 men with suspicious bpMRIs vs all 1020 men who required standard biopsies [70%]; P < .001). Significant prostate cancer diagnoses were improved by 11% (396 vs 351 men; P < .001), and insignificant prostate cancer diagnoses were reduced by 40% (173 vs 288 men; P < .001) compared with our current diagnostic standard, standard biopsies alone in all men. The NPV of bpMRI findings in ruling out significant prostate cancer was 97% (95% CI, 95%-99%).Conclusions and relevanceLow-suspicion bpMRI has a high NPV in ruling out significant prostate cancer in biopsy-naive men. Using a simple and rapid bpMRI method as a triage test seems to improve risk stratification and may be used to exclude aggressive disease and avoid unnecessary biopsies with its inherent risks. Future studies are needed to fully explore its role in clinical prostate cancer management.

Project description:ObjectivesTo evaluate the feasibility of automatic longitudinal analysis of consecutive biparametric MRI (bpMRI) scans to detect clinically significant (cs) prostate cancer (PCa).MethodsThis retrospective study included a multi-center dataset of 1513 patients who underwent bpMRI (T2 + DWI) between 2014 and 2020, of whom 73 patients underwent at least two consecutive bpMRI scans and repeat biopsies. A deep learning PCa detection model was developed to produce a heatmap of all PIRADS ≥ 2 lesions across prior and current studies. The heatmaps for each patient's prior and current examination were used to extract differential volumetric and likelihood features reflecting explainable changes between examinations. A machine learning classifier was trained to predict from these features csPCa (ISUP > 1) at the current examination according to biopsy. A classifier trained on the current study only was developed for comparison. An extended classifier was developed to incorporate clinical parameters (PSA, PSA density, and age). The cross-validated diagnostic accuracies were compared using ROC analysis. The diagnostic performance of the best model was compared to the radiologist scores.ResultsThe model including prior and current study (AUC 0.81, CI: 0.69, 0.91) resulted in a higher (p = 0.04) diagnostic accuracy than the current only model (AUC 0.73, CI: 0.61, 0.84). Adding clinical variables further improved diagnostic performance (AUC 0.86, CI: 0.77, 0.93). The diagnostic performance of the surveillance AI model was significantly better (p = 0.02) than of radiologists (AUC 0.69, CI: 0.54, 0.81).ConclusionsOur proposed AI-assisted surveillance of prostate MRI can pick up explainable, diagnostically relevant changes with promising diagnostic accuracy.Key points• Sequential prostate MRI scans can be automatically evaluated using a hybrid deep learning and machine learning approach. • The diagnostic accuracy of our csPCa detection AI model improved by including clinical parameters.

Project description:BackgroundRadiomics or computer-extracted texture features derived from MRI have been shown to help quantitatively characterize prostate cancer (PCa). Radiomics have not been explored depth in the context of predicting biochemical recurrence (BCR) of PCa.PurposeTo identify a set of radiomic features derived from pretreatment biparametric MRI (bpMRI) that may be predictive of PCa BCR.Study typeRetrospective.SubjectsIn all, 120 PCa patients from two institutions, I1 and I2 , partitioned into training set D1 (N = 70) from I1 and independent validation set D2 (N = 50) from I2 . All patients were followed for ≥3 years.Sequence3T, T2 -weighted (T2 WI) and apparent diffusion coefficient (ADC) maps derived from diffusion-weighted sequences.AssessmentPCa regions of interest (ROIs) on T2 WI were annotated by two experienced radiologists. Radiomic features from bpMRI (T2 WI and ADC maps) were extracted from the ROIs. A machine-learning classifier (CBCR ) was trained with the best discriminating set of radiomic features to predict BCR (pBCR ).Statistical testsWilcoxon rank-sum tests with P < 0.05 were considered statistically significant. Differences in BCR-free survival at 3 years using pBCR was assessed using the Kaplan-Meier method and compared with Gleason Score (GS), PSA, and PIRADS-v2.ResultsDistribution statistics of co-occurrence of local anisotropic gradient orientation (CoLlAGe) and Haralick features from T2 WI and ADC were associated with BCR (P < 0.05) on D1 . CBCR predictions resulted in a mean AUC = 0.84 on D1 and AUC = 0.73 on D2 . A significant difference in BCR-free survival between the predicted classes (BCR + and BCR-) was observed (P = 0.02) on D2 compared to those obtained from GS (P = 0.8), PSA (P = 0.93) and PIRADS-v2 (P = 0.23).Data conclusionRadiomic features from pretreatment bpMRI can be predictive of PCa BCR after therapy and may help identify men who would benefit from adjuvant therapy.Level of evidence4 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;48:1626-1636.

Project description:ObjectiveTo assess the diagnostic value of fluorine 18 (18F)-labeled prostate-specific membrane antigen (PSMA)-1007 Positron emission tomography/Magnetic resonance imaging (PET/MRI) and compared with that of biparametric MRI (bpMRI) for the detection of prostate cancer (PCa).Materials and methodsThe study enrolled 29 patients with suspected PCa preoperatively who underwent 18F-PSMA-1007 PET/MRI and subsequent targeted biopsy for suspected PCa lesions. Two readers independently assessed the images of each suspected PCa lesion and determined their overall assessment category on bpMRI and 18F-PSMA-1007 PET/MRI. By using biopsy histopathology as the reference standard, the accuracies of 18F-PSMA-1007 PET/MRI and bpMRI for the detection of PCa lesion were determined. Furthermore, the receiver-operating characteristic (ROC) curves of their semi-quantitative parameters of the optimal standardized uptake value (SUVmax) and apparent diffusion coefficient (ADC) for detecting PCa lesions were derived, and their correlations with the International Society of Urological Pathology (ISUP) grade were reported.ResultsOf the 48 suspected PCa lesions in 29 patients, 38 were pathologically diagnosed with clinically significant PCa and 10 with nonprostate cancer (non-PCa) lesions. Compared with the pathological results, 18F-PSMA-1007 PET/MRI demonstrated much greater diagnostic accuracy (area under the curve, AUC), sensitivity, specificity, positive predictive value, and negative predictive value than bpMRI: 0.974 versus 0.711, 94.74% versus 92.11%, 100% versus 50%, 100% versus 87.50%, and 83.33% versus 62.50%, respectively. The semi-quantitative parameters of SUVmax demonstrated a higher AUC of 0.874 than that of ADC with 0.776 for detecting PCa. The ISUP grade was positively associated with SUVmax at spearman's rho correlation coefficient (Rho) = 0.539, p = 0), but not associated with ADC (Rho = -0.105, p = 0.529).ConclusionThe diagnostic value of 18F-PSMA-1007 PET/MRI for the detection of PCa is better than that of bpMRI, and a high SUVmax may indicate a lesion with a high ISUP grade.

Dataset Information

Biparametric Quantitative MRI for Prostate Cancer Detection.

Objectives

Methods

Results

Conclusion

Publications

Biparametric Quantitative MRI for Prostate Cancer Detection.

Similar Datasets

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