Comparison of PSA-density of the transition zone and whole gland for risk stratification of men with suspected prostate cancer: A retrospective MRI-cohort study

Transition-zone PSA-density calculated from MRI deep learning prostate zonal segmentation model for prediction of clinically significant prostate cancer

PURPOSE

To develop a deep learning (DL) zonal segmentation model of prostate MR from T2-weighted images and evaluate TZ-PSAD for prediction of the presence of csPCa (Gleason score of 7 or higher) compared to PSAD.

METHODS

1020 patients with a prostate MRI were randomly selected to develop a DL zonal segmentation model. Test dataset included 20 cases in which 2 radiologists manually segmented both the peripheral zone (PZ) and TZ. Pair-wise Dice index was calculated for each zone. For the prediction of csPCa using PSAD and TZ-PSAD, we used 3461 consecutive MRI exams performed in patients without a history of prostate cancer, with pathological confirmation and available PSA values, but not used in the development of the segmentation model as internal test set and 1460 MRI exams from PI-CAI challenge as external test set. PSAD and TZ-PSAD were calculated from the segmentation model output. The area under the receiver operating curve (AUC) was compared between PSAD and TZ-PSAD using univariate and multivariate analysis (adjusts age) with the DeLong test.

RESULTS

Dice scores of the model against two radiologists were 0.87/0.87 and 0.74/0.72 for TZ and PZ, while those between the two radiologists were 0.88 for TZ and 0.75 for PZ. For the prediction of csPCa, the AUCs of TZPSAD were significantly higher than those of PSAD in both internal test set (univariate analysis, 0.75 vs. 0.73, p < 0.001; multivariate analysis, 0.80 vs. 0.78, p < 0.001) and external test set (univariate analysis, 0.76 vs. 0.74, p < 0.001; multivariate analysis, 0.77 vs. 0.75, p < 0.001 in external test set).

CONCLUSION

DL model-derived zonal segmentation facilitates the practical measurement of TZ-PSAD and shows it to be a slightly better predictor of csPCa compared to the conventional PSAD. Use of TZ-PSAD may increase the sensitivity of detecting csPCa by 2-5% for a commonly used specificity level.

Decreased prostate MRI cancer detection rate due to moderate to severe susceptibility artifacts from hip prosthesis

OBJECTIVES

To evaluate the impact of susceptibility artifacts from hip prosthesis on cancer detection rate (CDR) in prostate MRI.

MATERIALS AND METHODS

This three-center retrospective study included prostate MRI studies for patients without known prostate cancer between 2017 and 2021. Exams with hip prosthesis were searched on MRI reports. The degree of susceptibility artifact on diffusion-weighted images was retrospectively categorized into mild, moderate, and severe (> 66%, 33-66%, and < 33% of the prostate volume are evaluable) by blind reviewers. CDR was defined as the number of exams with Gleason score ≥7 detected by MRI (PI-RADS ≥3) divided by the total number of exams. For each artifact grade, control exams without hip prosthesis were matched (1:6 match), and CDR was compared. The degree of CDR reduction was evaluated with ratio, and influential factors were evaluated by expanding the equation.

RESULTS

Hip arthroplasty was present in 548 (4.8%) of the 11,319 MRI exams. CDR of the cases and matched control exams for each artifact grade were as follows: mild (n = 238), 0.27 vs 0.25, CDR ratio = 1.09 [95% CI: 0.87-1.37]; moderate (n = 143), 0.18 vs 0.27, CDR ratio = 0.67 [95% CI: 0.46-0.96]; severe (n = 167), 0.22 vs 0.28, CDR ratio = 0.80 [95% CI: 0.59-1.08]. When moderate and severe artifact grades were combined, CDR ratio was 0.74 [95% CI: 0.58-0.93]. CDR reduction was mostly attributed to the increased frequency of PI-RADS 1-2.

CONCLUSION

With moderate to severe susceptibility artifacts from hip prosthesis, CDR was decreased to 74% compared to the matched control.

CLINICAL RELEVANCE STATEMENT

Moderate to severe susceptibility artifacts from hip prosthesis may cause a non-negligible CDR reduction in prostate MRI. Expanding indications for systematic prostate biopsy may be considered when PI-RADS 1-2 was assigned.

KEY POINTS

• We proposed cancer detection rate as a diagnostic performance metric in prostate MRI. • With moderate to severe susceptibility artifacts secondary to hip arthroplasty, cancer detection rate decreased to 74% compared to the matched control. • Expanding indications for systematic prostate biopsy may be considered when PI-RADS 1-2 is assigned.

Quantitative Evaluation of Apparent Diffusion Coefficient Values, ISUP Grades and Prostate-Specific Antigen Density Values of Potentially Malignant PI-RADS Lesions

The aim of this study was to demonstrate the correlation between ADC values and the ADC/PSAD ratio for potentially malignant prostate lesions classified into ISUP grades and to determine threshold values to differentiate benign lesions (noPCa), clinically insignificant (nsPCa) and clinically significant prostate cancer (csPCa). We enrolled a total of 403 patients with 468 prostate lesions, of which 46 patients with 50 lesions were excluded for different reasons. Therefore, 357 patients with a total of 418 prostate lesions remained for the final evaluation. For all lesions, ADC values were measured; they demonstrated a negative correlation with ISUP grades (p < 0.001), with a significant difference between csPCa and a combined group of nsPCa and noPCa (ns-noPCa, p < 0.001). The same was true for the ADC/PSAD ratio, but only the ADC/PSAD ratio proved to be a significant discriminator between nsPCa and noPCa (p = 0.0051). Using the calculated threshold values, up to 31.6% of biopsies could have been avoided. Furthermore, the ADC/PSAD ratio, with the ability to distinguish between nsPCa and noPCa, offers possible active surveillance without prior biopsy.

Clinical and prostate multiparametric magnetic resonance imaging findings as predictors of general and clinically significant prostate cancer risk: A retrospective single-center study

Background

To evaluate the predictive values of Prostate Imaging Reporting and Data System version 2 (PI-RADS v2), prostate-specific antigen (PSA) level, PSA density (PSAD), digital rectal examination findings, and prostate volume, individually and in combination, for the detection of prostate cancer (PCa) in biopsy-naive patients.

Methods

We retrospectively analyzed 630 patients who underwent transrectal systematic prostate biopsy following prostate multiparametric magnetic resonance imaging. A standard 12-core biopsy procedure was performed. Univariate and multivariate analyses were performed to determine the significant predictors of clinically significant cancer but not PCa.

Results

The median age, PSA level, and PSAD were 70 years, 8.6 ng/mL, and 0.18 ng/mL/mL, respectively. A total of 374 (59.4%) of 630 patients were biopsy-positive for PCa, and 241 (64.4%) of 374 were diagnosed with clinically significant PCa (csPCa). The PI-RADS v2 score and PSAD were independent predictors of PCa and csPCa. The PI-RADS v2 score of 5 regardless of the PSAD value, or PI-RADS v2 score of 4 plus a PSAD of <0.3 ng/mL/mL, was associated with the highest csPCa detection rate (36.1%-82.1%). Instead, the PI-RADS v2 score of <3 and PSAD of <0.3 ng/mL/mL yielded the lowest risk of csPCa.

Conclusion

The combination of the PI-RADS v2 score and PSAD could prove to be a helpful and reliable diagnostic tool before performing prostate biopsies. Patients with a PI-RADS v2 score of <3 and PSAD of <0.3 ng/mL/mL could potentially avoid a prostate biopsy.

Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI

OBJECTIVES

Prostate volume (PV) in combination with prostate specific antigen (PSA) yields PSA density which is an increasingly important biomarker. Calculating PV from MRI is a time-consuming, radiologist-dependent task. The aim of this study was to assess whether a deep learning algorithm can replace PI-RADS 2.1 based ellipsoid formula (EF) for calculating PV.

METHODS

Eight different measures of PV were retrospectively collected for each of 124 patients who underwent radical prostatectomy and preoperative MRI of the prostate (multicenter and multi-scanner MRI's 1.5 and 3 T). Agreement between volumes obtained from the deep learning algorithm (PV_DL) and ellipsoid formula by two radiologists (PV_EF1 and PV_EF2) was evaluated against the reference standard PV obtained by manual planimetry by an expert radiologist (PV_MPE). A sensitivity analysis was performed using a prostatectomy specimen as the reference standard. Inter-reader agreement was evaluated between the radiologists using the ellipsoid formula and between the expert and inexperienced radiologists performing manual planimetry.

RESULTS

PV_DL showed better agreement and precision than PV_EF1 and PV_EF2 using the reference standard PV_MPE (mean difference [95% limits of agreement] PV_DL: -0.33 [-10.80; 10.14], PV_EF1: -3.83 [-19.55; 11.89], PV_EF2: -3.05 [-18.55; 12.45]) or the PV determined based on specimen weight (PV_DL: -4.22 [-22.52; 14.07], PV_EF1: -7.89 [-30.50; 14.73], PV_EF2: -6.97 [-30.13; 16.18]). Inter-reader agreement was excellent between the two experienced radiologists using the ellipsoid formula and was good between expert and inexperienced radiologists performing manual planimetry.

CONCLUSION

Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI.

KEY POINTS

• A commercially available deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI. • The deep-learning algorithm was previously untrained on this heterogenous multicenter day-to-day practice MRI data set.

A semi-automated software program to assess the impact of second reads in prostate MRI for equivocal lesions: results from a UK tertiary referral centre

PURPOSE

To investigate the utility of a prostate magnetic resonance imaging (MRI) second read using a semi-automated software program in the one-stop clinic, where patients undergo multiparametric MRI, review and biopsy planning in one visit. We looked at concordance between readers for patients with equivocal scans and the possibility for biopsy deferral in this group.

METHODS

We present data from 664 consecutive patients. Scans were reported by seven different expert genitourinary radiologists using dedicated software (MIM®) and a Likert scale. All scans were rescored by another expert genitourinary radiologist using a customised workflow for second reads that includes annotated biopsy contours for accurate visual targeting. The number of scans in which a biopsy could have been deferred using biopsy results and prostate specific antigen density was assessed. Gleason score ≥ 3 + 4 was considered clinically significant disease. Concordance between first and second reads for equivocal scans (Likert 3) was evaluated.

RESULTS

A total of 209/664 (31%) patients scored Likert 3 on first read, 128 of which (61%) were concordant after second read. 103/209 (49%) of patients with Likert 3 scans were biopsied, with clinically significant disease in 31 (30%) cases. Considering Likert 3 scans that were both downgraded and biopsied using the workflow-generated biopsy contours, 25/103 (24%) biopsies could have been deferred.

CONCLUSIONS

Implementing a semi-automated workflow for accurate lesion contouring and targeting biopsies is helpful during the one-stop clinic. We observed a reduction of indeterminate scans after second reading and almost a quarter of biopsies could have been deferred, reducing the potential biopsy-related side effects.

Serum levels of chromogranin are not predictive of poorly differentiated prostate cancer: Results from a multicenter radical prostatectomy cohort

BACKGROUND

Recently a possible link between elevated Chromogranin A (CgA) levels and poorly differentiated prostate cancer has been proposed. The aim of our study was to explore the association of CgA levels and the risk of poorly differentiated prostate cancer (PCa) in men undergoing radical retropubic prostatectomy (RRP).

MATERIALS AND METHODS

From 2012 onwards, 335 consecutive men undergoing RRP for PCa at three centers in Italy were enrolled into a prospective database. Body mass index (BMI) was calculated before RRP. Blood samples were collected and tested for total prostate-specific antigen (PSA) levels and chromogranin A (CgA). We evaluated the association between serum levels of CgA and upstaging and upgrading using logistic regression analyses.

RESULTS

Median age and preoperative PSA levels were 65 years (interquartile range [IQR]: 60-69) and 7.2 ng/ml (IQR: 5.3-10.4), respectively. Median BMI was 26.1 kg/m² (IQR: 24-29) with 56 (16%) obese (BMI ≥ 30 kg/m² ). Median CgA levels were 51 (39/71). Overall, 129/335 (38,5%) presented an upstaging, and 99/335 (30%) presented an upgrading. CgA was not a predictor of upstaging or upgrading on RP.

CONCLUSIONS

In our multicenter cohort of patients, CgA is not a predictor of poorly differentiated PCa on radical prostatectomy. According to our experience, CgA should not be considered a reliable marker to predict poorly differentiated or advanced prostate cancer.

Evaluation of transabdominal and transperineal ultrasound-derived prostate specific antigen (PSA) density and clinical utility compared to MRI prostate volumes: A feasibility study

Purpose

To investigate the accuracy of surface-based ultrasound-derived PSA-density (US-PSAD) versus gold-standard MRI-PSAD as a risk-stratification tool.

Methods

Single-centre prospective study of patients undergoing MRI for suspected prostate cancer (PCa). Four combinations of US-volumes were calculated using transperineal (TP) and transabdominal (TA) views, with triplanar measurements to calculate volume and US-PSAD. Intra-class correlation coefficient (ICC) was used to compare US and MRI volumes. Categorical comparison of MRI-PSAD and US-PSAD was performed at PSAD cut-offs <0.15, 0.15–0.20, and >0.20 ng/mL² to assess agreement with MRI-PSAD risk-stratification decisions.

Results

64 men were investigated, mean age 69 years and PSA 7.0 ng/mL. 36/64 had biopsy-confirmed prostate cancer (18 Gleason 3+3, 18 Gleason ≥3+4). Mean MRI-derived gland volume was 60 mL, compared to 56 mL for TA-US, and 65 mL TP-US. ICC demonstrated good agreement for all US volumes with MRI, with highest agreement for transabdominal US, followed by combined TA/TP volumes. Risk-stratification decisions to biopsy showed concordant agreement between triplanar MRI-PSAD and ultrasound-PSAD in 86–91% and 92–95% at PSAD thresholds of >0.15 ng/mL² and >0.12 ng/mL², respectively. Decision to biopsy at threshold >0.12 ng/mL², demonstrated sensitivity ranges of 81–100%, specificity 85–100%, PPV 86–100% and NPV 83–100%. Transabdominal US provided optimal sensitivity of 100% for this clinical decision, with specificity 85%, and transperineal US provided optimal specificity of 100%, with sensitivity 87%.

Conclusion

Transperineal-US and combined TA-TP US-derived PSA density values compare well with standard MRI-derived values and could be used to provide accurate PSAD at presentation and inform the need for further investigations.

New Diagnostic Model for Clinically Significant Prostate Cancer in Biopsy-Naïve Men With PIRADS 3

Purpose

The aim of this study was to explore a new model of clinical decision-making to predict the occurrence of clinically significant prostate cancer (csPCa).

Patients and Methods

The demographic and clinical characteristics of 152 patients were recorded. Prostate-specific antigen (PSA), PSA density (PSAD), adjusted PSAD of peripheral zone (aPSADPZ), and peripheral zone volume ratio (PZ ratio) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The calibration and discrimination abilities of new nomograms were verified with calibration curve and area under the ROC curve (AUC). The clinical benefits of these models were evaluated by decision curve analysis and clinical impact curves.

Results

The AUCs of PSA, PSAD, aPSADPZ, and PZ ratio were 0.521, 0.645, 0.745, and 0.717 for prostate cancer (PCa) diagnosis, while the corresponding values were 0.590, 0.678, 0.780, and 0.731 for csPCa diagnosis, respectively. All nomograms displayed higher net benefit and better overall calibration than the scenarios for predicting the occurrence of csPCa. The new model significantly improved the diagnostic accuracy of csPCa (0.865 vs. 0.741, p = 0.0284) compared with the base model. In addition, the new model was better than the base model for predicting csPCa in the low or medium probability while the number of patients with csPCa predicted by the new model was in good agreement with the actual number of patients with csPCa in the high-risk threshold.

Conclusions

This study demonstrates that aPSADPZ has a higher predictive accuracy for csPCa diagnosis than the conventional indicators. Including aPSADPZ, PZ ratio, and age can improve csPCa diagnosis and avoid unnecessary biopsies.

Comparison of PI-RADS Versions 2.0 and 2.1 for MRI-based Calculation of the Prostate Volume

RATIONALE AND OBJECTIVES

Prostate gland volume (PGV) should be routinely included in MRI reports of the prostate. The recently updated Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 includes a change in the recommended measurement method for PGV compared to version 2.0. The purpose of this study was to evaluate the agreement of MRI-based PGV calculations with the volumetric manual slice-by-slice prostate segmentation as a reference standard using the linear measurements per PI-RADS versions 2.0 and 2.1. Furthermore, to assess inter-reader agreement for the different measurement approaches, determine the influence of an enlarged transition zone on measurement accuracy and to assess the value of the bullet formula for PGV calculation.

MATERIALS AND METHODS

Ninety-five consecutive treatment-naive patients undergoing prostate MRI were retrospectively analyzed. Prostates were manually contoured and segmented on axial T2-weighted images. Four different radiologists independently measured the prostate in three dimensions according to PI-RADS v2.0 and v2.1, respectively. MRI-based PGV was calculated using the ellipsoid and bullet formulas. Calculated volumes were compared to the reference manual segmentations using Wilcoxon signed-rank test. Inter-reader agreement was calculated using intraclass correlation coefficient (ICC).

RESULTS

Inter-reader agreement was excellent for the ellipsoid and bullet formulas using PI-RADS v2.0 (ICC 0.985 and 0.987) and v2.1 (ICC 0.990 and 0.994), respectively. The median difference from the reference standard using the ellipsoid formula derived PGV was 0.4 mL (interquartile range, -3.9 to 5.1 mL) for PI-RADS v2.0 (p = 0.393) and 2.6 mL (interquartile range, -1.6 to 7.3 mL) for v2.1 (p < 0.001) with a median difference of 2.2 mL. The bullet formula overestimated PGV by a median of 13.3 mL using PI-RADS v2.0 (p < 0.001) and 16.0 mL using v2.1 (p < 0.001). In the presence of an enlarged transition zone the PGV tended to be higher than the reference standard for PI-RADS v2.0 (median difference of 4.7 mL; p = 0.018) and for v2.1 (median difference of 5.7 mL, p < 0.001) using the ellipsoid formula.

CONCLUSION

Inter-reader agreement was excellent for the calculated PGV for both methods. PI-RADS v2.0 measurements with the ellipsoid formula yielded the most accurate volume estimates. The differences between PI-RADS v2.0 and v2.1 were statistically significant although small in absolute numbers but may be of relevance in specific clinical scenarios like prostate-specific antigen density calculation. These findings validate the use of the ellipsoid formula and highlight that the bullet formula should not be used for prostate volume estimation due to systematic overestimation.

Prostate Volume Estimation on MRI: Accuracy and Effects of Ellipsoid and Bullet-Shaped Measurements on PSA Density

RATIONALE AND OBJECTIVES

PSA density (PSAd), an important decision-making parameter for patients with suspected prostate cancer (PCa), is dependent on magnetic resonance imaging prostate volume (PV) estimation. We aimed to compare the accuracy of the ellipsoid and bullet-shaped formulas with manual whole-gland segmentation as reference standard and to evaluate the corresponding PSAd diagnostic accuracy in predicting clinically significant PCa.

MATERIALS AND METHODS

We retrospectively analysed 195 patients with suspected PCa who underwent magnetic resonance imaging and prostate biopsy. Patients with PCa were categorized according to ISUP score. PV and corresponding PSAd were calculated with manual segmentation (mPV and mPSAd) as well as with ellipsoid (ePV and ePSAd) and bullet-shaped (bPV and bPSAd) formulas. Inter and intra-reader reproducibility were assessed with Lin's concordance correlation coefficient and the intraclass correlation coefficient (ICC). A 2-way analysis of variance with post-hoc Bonferroni test was used for assessing PV differences. Predictive values of PSAd calculated with different methods for detecting clinically significant PCa were evaluated by receiver operating characteristic curve analysis and Youden's index.

RESULTS

Both intra (ρ = 0.99, ICC = 0.99) and inter-reader (ρ = 0.98, ICC = 0.98) reproducibility were excellent. No significant difference was found between ePV and reference standard (p = 1.00). bPV was significantly different from both (p = 0.00). PSAd (mPSAd/ePSAd cut-off ≥ 0.15, bPSAd cut-off ≥ 0.12) had sensitivity = 69-70%, specificity = 72-75%, areas under the curve = 0.757-0.760 (p = 0.70-0.88).

CONCLUSIONS

Our work shows that when using bullet-shaped formula, a different PSAd cut-off must be considered to avoid PCa under-diagnosis and inaccurate risk-stratification.

Deep Learning Whole-Gland and Zonal Prostate Segmentation on a Public MRI Dataset

BACKGROUND

Prostate volume, as determined by magnetic resonance imaging (MRI), is a useful biomarker both for distinguishing between benign and malignant pathology and can be used either alone or combined with other parameters such as prostate-specific antigen.

PURPOSE

This study compared different deep learning methods for whole-gland and zonal prostate segmentation.

STUDY TYPE

Retrospective.

POPULATION

A total of 204 patients (train/test = 99/105) from the PROSTATEx public dataset.

FIELD STRENGTH/SEQUENCE

A 3 T, TSE T₂ -weighted.

ASSESSMENT

Four operators performed manual segmentation of the whole-gland, central zone + anterior stroma + transition zone (TZ), and peripheral zone (PZ). U-net, efficient neural network (ENet), and efficient residual factorized ConvNet (ERFNet) were trained and tuned on the training data through 5-fold cross-validation to segment the whole gland and TZ separately, while PZ automated masks were obtained by the subtraction of the first two.

STATISTICAL TESTS

Networks were evaluated on the test set using various accuracy metrics, including the Dice similarity coefficient (DSC). Model DSC was compared in both the training and test sets using the analysis of variance test (ANOVA) and post hoc tests. Parameter number, disk size, training, and inference times determined network computational complexity and were also used to assess the model performance differences. A P < 0.05 was selected to indicate the statistical significance.

RESULTS

The best DSC (P < 0.05) in the test set was achieved by ENet: 91% ± 4% for the whole gland, 87% ± 5% for the TZ, and 71% ± 8% for the PZ. U-net and ERFNet obtained, respectively, 88% ± 6% and 87% ± 6% for the whole gland, 86% ± 7% and 84% ± 7% for the TZ, and 70% ± 8% and 65 ± 8% for the PZ. Training and inference time were lowest for ENet.

DATA CONCLUSION

Deep learning networks can accurately segment the prostate using T₂ -weighted images.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Biomarkers in the setting of benign prostatic hyperplasia-induced lower urinary tract symptoms: what an interventional radiologist needs to know

With increasing evidence to support prostate artery embolization (PAE) in the treatment of benign prostatic hyperplasia (BPH)-induced lower urinary tract symptoms (LUTS), Interventional Radiologists have begun to play an important role in the management of these patients. One area of knowledge needed when developing a PAE practice is knowledge of prostate-specific antigen (PSA) and other biomarkers utilized to detect prostate cancer in this population and what role they should play in the work up and follow-up of patients presenting with presumed BPH-induced LUTS. Furthermore, understanding how to evaluate presumed BPH-induced LUTS and stratify the risk of prostate cancer is an important skill to develop. The goal of this review is to provide Interventional Radiologists who have begun or aim to begin a PAE practice with the information they need to know regarding PSA levels and prostate cancer risk stratification for this patient population.