Combined Analysis of Biparametric MRI and Prostate-Specific Antigen Density: Role in the Prebiopsy Diagnosis of Gleason Score 7 or Greater Prostate Cancer

Diagnostic performance of ADC and ADCratio in MRI-based prostate cancer assessment: A systematic review and meta-analysis

OBJECTIVES

To identify factors influencing the diagnostic performance of the quantitative imaging biomarkers ADC and ADCratio in prostate cancer (PCa) detection.

MATERIALS AND METHODS

A systematic literature search was conducted in Embase, Medline and Web of Science, for studies evaluating ADC values and ADCratio for PCa diagnosis, using the same patient cohorts and using histopathological references as ground truth. Pooled sensitivities, specificities, summary ROC curves and AUCs were calculated from constructed contingency data tables. Diagnostic performance (AUC) was quantitatively pooled using a bivariate mixed effects model. For identifying influencing factors, subgroup analysis, publication bias and heterogeneity assessment were investigated.

RESULTS

Thirteen studies, involving 1038 patients and 1441 lesions, were included. For ADC, the pooled sensitivity and specificity was 80% (95% CI: 74-85%) and 78% (95% CI: 70-85%), respectively. For ADCratio pooled sensitivity and specificity was 80% (95% CI: 74-84%) and 80% (95% CI: 71-87%). Summary ROC analysis revealed AUCs of 0.86 (95% CI: 0.83-0.89) and 0.86 (95% CI: 0.83-0.89), respectively. Meta-regression showed heterogeneity between both imaging biomarkers. Subgroup analysis showed that ADCratio improved diagnostic performance in comparison to ADC when including both peripheral and transitional zone lesions (AUC: 0.87 [95% CI: 0.84-0.90] and 0.82 [95% CI: 0.79-0.85], respectively).

CONCLUSION

Both ADC and ADCratio imaging biomarkers showed good and comparable diagnostic performance in PCa diagnosis. However, ADCratio shows better diagnostic performance than ADC in diagnosing transition zone cancers.

CLINICAL RELEVANCE STATEMENT

In quantitative MRI-based PCa diagnosis, the imaging biomarker ADCratio is useful in challenging MRI readings of lesions. Understanding the performance of quantitative imaging biomarkers better can aid diagnostic MRI protocols, enhancing the precision of PCa assessments.

KEY POINTS

MRI diffusion-weighted imaging-based ADC and ADCratio have comparable diagnostic performance in PCa assessment. In contrast to ADC, the ADCratio improves diagnostic performance, when assessing whole gland lesions. Compared to ADCratio, the ADC demonstrates enhanced diagnostic performance when evaluating peripheral zone lesions.

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.

A nomogram based on biparametric magnetic resonance imaging for detection of clinically significant prostate cancer in biopsy-naïve patients

PURPOSE

This study aimed to develop and validate a model based on biparametric magnetic resonance imaging (bpMRI) for the detection of clinically significant prostate cancer (csPCa) in biopsy-naïve patients.

METHOD

This retrospective study included 324 patients who underwent bpMRI and MRI targeted fusion biopsy (MRGB) and/or systematic biopsy, of them 217 were randomly assigned to the training group and 107 were assigned to the validation group. We assessed the diagnostic performance of three bpMRI-based scorings in terms of sensitivity and specificity. Subsequently, 3 models (Model 1, Model 2, and Model 3) combining bpMRI scorings with clinical variables were constructed and compared with each other using the area under the receiver operating characteristic (ROC) curves (AUC). The statistical significance of differences among these models was evaluated using DeLong's test.

RESULTS

In the training group, 68 of 217 patients had pathologically proven csPCa. The sensitivity and specificity for Scoring 1 were 64.7% (95% CI 52.2%-75.9%) and 80.5% (95% CI 73.3%-86.6%); for Scoring 2 were 86.8% (95% CI 76.4%-93.8%) and 73.2% (95% CI 65.3%-80.1%); and for Scoring 3 were 61.8% (95% CI 49.2%-73.3%) and 80.5% (95% CI 73.3%-86.6%), respectively. Multivariable regression analysis revealed that scorings based on bpMRI, age, and prostate-specific antigen density (PSAD) were independent predictors of csPCa. The AUCs for the 3 models were 0.88 (95% CI 0.83-0.93), 0.90 (95% CI 0.85-0.94), and 0.88 (95% CI 0.83-0.93), respectively. Model 2 showed significantly higher performance than Model 1 (P = 0.03) and Model 3 (P < 0.01).

CONCLUSION

All three scorings had favorite diagnostic accuracy. While in conjunction with age and PSAD the prediction power was significantly improved, and the Model 2 that based on Scoring 2 yielded the highest performance.

Texture analysis of multiparametric magnetic resonance imaging for differentiating clinically significant prostate cancer in the peripheral zone

BACKGROUND

Texture analysis (TA) provides additional tissue heterogeneity data that may assist in differentiating peripheral zone(PZ) lesions in multiparametric magnetic resonance imaging (mpMRI). This study investigates the role of magnetic resonance imaging texture analysis (MRTA) in detecting clinically significant prostate cancer (csPCa) in the PZ.

METHODS

This retrospective study included 80 consecutive patients who had an mpMRI and a prostate biopsy for suspected prostate cancer. Two radiologists in consensus interpreted mpMRI and performed texture analysis based on their histopathology. The first-, second-, and higher-order texture parameters were extracted from mpMRI and were compared between groups. Univariate and multivariate logistic regression analyses were performed using the texture parameters to determine the independent predictors of csPCa. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance of the texture parameters.

RESULTS

: In the periferal zone, 39 men had csPCa, while 41 had benign lesions or clinically insignificant prostate cancer (cisPCa). Themajority of texture parameters showed statistically significant differences between the groups. Univariate ROC analysis showed that the ADC mean and ADC median were the best variables in differentiating csPCa (p < 0.001). The first-order logistic regression model (mean + entropy) based on the ADC maps had a higher AUC value (0.996; 95% CI: 0.989-1) than other texture-based logistic regression models (p < 0.001).

DISCUSSION

MRTA is useful in differentiating csPCa from other lesions in the PZ. Consequently, the first-order multivariate regressionmodel based on ADC maps had the highest diagnostic performance in differentiating csPCa.

Prostate gland volume estimation: anteroposterior diameters measured on axial versus sagittal ultrasonography and magnetic resonance images

PURPOSE

The aim of this study was to evaluate the accuracy of prostate volume estimates calculated from the ellipsoid formula using the anteroposterior (AP) diameter measured on axial and sagittal images obtained through ultrasonography (US) and magnetic resonance imaging (MRI).

METHODS

This retrospective study included 456 patients with transrectal US and MRI from two university hospitals. Two radiologists independently measured the prostate gland diameters on US and MRI: AP diameters on axial and sagittal images, transverse, and longitudinal diameters on midsagittal images. The volume estimates, volumeax and volumesag, were calculated from the ellipsoid formula by using the AP diameter on axial and sagittal images, respectively. The prostate volume extracted from MRI-based whole-gland segmentation was considered the gold standard. The intraclass correlation coefficient (ICC) was used to evaluate the inter-method agreement between volumeax and volumesag, and agreement with the gold standard. The Wilcoxon signedrank test was used to analyze the differences between the volume estimates and the gold standard.

RESULTS

The prostate gland volume estimates showed excellent inter-method agreement, and excellent agreement with the gold standard (ICCs >0.9). Compared with the gold standard, the volume estimates were significantly larger on MRI and significantly smaller on US (P<0.001). The volume difference (segmented volume-volume estimate) was greater in patients with larger prostate glands, especially on US.

CONCLUSION

Volumeax and volumesag showed excellent inter-method agreement and excellent agreement with the gold standard on both US and MRI. However, prostate volume was overestimated on MRI and underestimated on US.

Prebiopsy bpMRI and hematological parameter-based risk scoring model for predicting outcomes in biopsy-naive men with PSA 4-20 ng/mL

Excessive prostate biopsy is a common problem for clinicians. Although some hematological and bi-parametric magnetic resonance imaging (bpMRI) parameters might help increase the rate of positive prostate biopsies, there is a lack of studies on whether their combination can further improve clinical detection efficiency. We retrospectively enrolled 394 patients with PSA levels of 4-20 ng/mL who underwent prebiopsy bpMRI during 2010-2021. Based on bpMRI and hematological indicators, six models and a nomogram were constructed to predict the outcomes of biopsy. Furthermore, we constructed and evaluated a risk scoring model based on the nomogram. Age, prostate-specific antigen (PSA) density (PSAD), systemic immune-inflammation index, cystatin C level, and the Prostate Imaging Reporting and Data System (PI-RADS) v2.1 score were significant predictors of prostate cancer (PCa) on multivariable logistic regression analyses (P < 0.05) and the five parameters were used to construct the XYFY nomogram. The area under the receiver operating characteristic (ROC) curve (AUC) of the nomogram was 0.916. Based on the nomogram, a risk scoring model (XYFY risk model) was constructed and then we divided the patients into low-(XYFY score: < 95), medium-(XYFY score: 95-150), and, high-risk (XYFY score: > 150) groups. The predictive values for diagnosis of PCa and clinically-significant PCa among the three risk groups were 3.0%(6/201), 41.8%(51/122), 91.5%(65/71); 0.5%(1/201), 19.7%(24/122), 60.6%(43/71), respectively. In conclusion, in this study, we used hematological and bpMRI parameters to establish and internally validate a XYFY risk scoring model for predicting the biopsy outcomes for patients with PSA levels of 4-20 ng/mL and this risk model would support clinical decision-making and reduce excessive biopsies.

Combination of biparametric magnetic resonance imaging with prostate-specific antigen density to stratify the risk of significant prostate cancer: Initial biopsy and long-term follow-up results

OBJECTIVES

To assess whether the combination of biparametric magnetic resonance imaging with prostate-specific antigen density can properly stratify the risk of significant prostate cancer in patients undergoing prostate biopsies and how this approach affects the detection of prostate cancer during follow-up in patients who do not undergo prostate biopsy.

METHODS

In total, 411 biopsy-naïve patients who had elevated prostate-specific antigen levels and then underwent biparametric magnetic resonance imaging for suspicious prostate cancer were analyzed: 203 patients underwent prostate biopsies, whereas 208 patients did not. Significant prostate cancer detection rates stratified by the combination of Prostate Imaging Reporting and Data System score and prostate-specific antigen density were assessed in patients who underwent prostate biopsies. The cumulative incidence of prostate cancer detection during the follow-up was assessed in patients who omitted biopsy.

RESULTS

The negative predictive value for significant prostate cancer was 89% for Prostate Imaging Reporting and Data System scores 1-3, which increased to 97% when prostate-specific antigen density <0.15 ng/ml/cm³ was combined. Among patients who did not undergo biopsy, patients with Prostate Imaging Reporting and Data System scores 1-3 plus prostate-specific antigen density <0.15 ng/ml/cm³ included significantly less cases in which significant prostate cancer was detected during the follow-up, compared with the others (3.2% versus 17% at 36 months).

CONCLUSIONS

Restriction of prostate biopsies to patients with Prostate Imaging Reporting and Data System scores 4-5 or prostate-specific antigen density ≥0.15 ng/ml/cm³ proved to be the good biopsy strategy, effectively balancing risks and benefits.

The Role of PSA Density among PI-RADS v2.1 Categories to Avoid an Unnecessary Transition Zone Biopsy in Patients with PSA 4-20 ng/mL

Objective

To evaluate the role of prostate-specific antigen density (PSAD) in different Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) categories to avoid an unnecessary biopsy in transition zone (TZ) patients with PSA ranging from 4 to 20 ng/mL.

Materials and Methods

In this retrospective and single-center study, 333 biopsy-naïve patients with TZ lesions who underwent biparametric magnetic resonance imaging (bp-MRI) were analyzed from January 2016 to March 2020. Multivariate logistic regression analyses were performed to determine independent predictors of clinically significant prostate cancer (cs-PCa). The receiver operating characteristic (ROC) curve was used to compare diagnostic performance.

Results

PI-RADS v2.1 and PSAD were the independent predictors for TZ cs-PCa in patients with PSA 4-20 ng/mL. 0.9% (2/213), 10.0% (7/70), and 48.0% (24/50) of PI-RADS v2.1 score 1-2, 3, and 4-5 had TZ cs-PCa. However, for patients with PI-RADS v2.1 score 1-2, there were no obvious changes in the detection of TZ cs-PCa (0.8% (1/129), 1.3% (1/75), and 0.0% (0/9)) combining with different PSAD stratification (PSAD < 0.15, 0.15-0.29, and ≥0.30 ng/mL/mL). For patients with PI-RADS v2.1 score ≥ 3, the TZ cs-PCa detection rate significantly varied according to different PSAD stratification. A PI-RADS v2.1 score 3 and PSAD < 0.15 and 0.15-0.29 ng/mL/mL had 8.6% (3/35) and 3.7% (1/27) of TZ cs-PCa, while a PI-RADS v2.1 score 3 and PSAD ≥ 0.30 ng/mL/mL had a higher TZ cs-PCa detection rate (37.5% (3/8)). A PI-RADS v2.1 score 4-5 and PSAD <0.15 ng/mL/mL had no cs-PCa (0.0% (0/9)). In contrast, a PI-RADS v2.1 score 4-5 and PSAD 0.15-0.29 and ≥0.30 ng/mL/mL had the highest cs-PCa detection rate (50.0% (10/20), 66.7% (14/21)). It showed the highest AUC in the combination of PI-RADS v2.1 and PSAD (0.910), which was significantly higher than PI-RADS v2.1 (0.889, P = 0.039) or PSAD (0.803, P < 0.001).

Conclusions

For TZ patients with PSA 4-20 ng/mL, PI-RADS v2.1 score ≤ 2 can avoid an unnecessary biopsy regardless of PSAD. PI-RADS v2.1 score ≥ 3 may avoid an unnecessary biopsy after combining with PSAD. PI-RADS v2.1 combined with PSAD could significantly improve diagnostic performance.

A systematic review and meta-analysis of the diagnostic accuracy of biparametric prostate MRI for prostate cancer in men at risk

INTRODUCTION

Multiparametric magnetic resonance imaging (mpMRI), the use of three multiple imaging sequences, typically T2-weighted, diffusion weighted (DWI) and dynamic contrast enhanced (DCE) images, has a high sensitivity and specificity for detecting significant cancer. Current guidance now recommends its use prior to biopsy. However, the impact of DCE is currently under debate regarding test accuracy. Biparametric MRI (bpMRI), using only T2 and DWI has been proposed as a viable alternative. We conducted a contemporary systematic review and meta-analysis to further examine the diagnostic performance of bpMRI in the diagnosis of any and clinically significant prostate cancer.

METHODS

A systematic review of the literature from 01/01/2017 to 06/07/2019 was performed by two independent reviewers using predefined search criteria. The index test was biparametric MRI and the reference standard whole-mount prostatectomy or prostate biopsy. Quality of included studies was assessed by the QUADAS-2 tool. Statistical analysis included pooled diagnostic performance (sensitivity; specificity; AUC), meta-regression of possible covariates and head-to-head comparisons of bpMRI and mpMRI where both were performed in the same study.

RESULTS

Forty-four articles were included in the analysis. The pooled sensitivity for any cancer detection was 0.84 (95% CI, 0.80-0.88), specificity 0.75 (95% CI, 0.68-0.81) for bpMRI. The summary ROC curve yielded a high AUC value (AUC = 0.86). The pooled sensitivity for clinically significant prostate cancer was 0.87 (95% CI, 0.78-0.93), specificity 0.72 (95% CI, 0.56-0.84) and the AUC value was 0.87. Meta-regression analysis revealed no difference in the pooled diagnostic estimates between bpMRI and mpMRI.

CONCLUSIONS

This meta-analysis on contemporary studies shows that bpMRI offers comparable test accuracies to mpMRI in detecting prostate cancer. These data are broadly supportive of the bpMRI approach but heterogeneity does not allow definitive recommendations to be made. There is a need for prospective multicentre studies of bpMRI in biopsy naïve men.

Biparametric Magnetic Resonance Imaging-Derived Nomogram to Detect Clinically Significant Prostate Cancer by Targeted Biopsy for Index Lesion

BACKGROUND

Currently, it is necessary to investigate how to combine biparametric magnetic resonance imaging (bpMRI) with various clinical parameters for the detection of clinically significant prostate cancer (csPCa).

PURPOSE

To develop a multivariate prebiopsy nomogram using clinical and bpMRI parameters for estimating the probability of csPCa.

STUDY TYPE

Retrospective, single-center study.

SUBJECTS

Two hundred and twenty-six patients who underwent targeted biopsy (TBx) for the MRI-suspected index lesion because of clinical suspicions of PCa.

FIELD STRENGTH/SEQUENCE

A 3 T MRI including turbo spin-echo T₂ -weighted and diffusion-weighted single-shot echo-planar imaging sequences.

ASSESSMENT

Prebiopsy clinical and bpMRI parameters were patient age, biopsy history (biopsy-naïve or repeated biopsy status), prostate-specific antigen density (PSAD), Prostate Imaging-Reporting and Data System version 2.1 (PI-RADSv2.1), and apparent diffusion coefficient ratio (ADCR). ADCR was defined as mean ADC of the index lesion divided by mean ADC of the contralateral prostatic region. A multivariate prebiopsy nomogram for csPCa (i.e. Gleason sum ≥7) was developed. Area under the curve (AUC) of each parameter and prebiopsy nomogram was assessed. Five-fold cross-validation was performed for robust estimation of performance of the prebiopsy nomogram.

STATISTICAL TESTS

Logistic regression, receiver-operating curve, and 5-fold cross-validation. P-value < 0.05 was considered statistically significant.

RESULTS

Proportion of csPCa was 31.9% (72/226). The AUCs of age, biopsy-naïve status, PSAD, PI-RADSv2.1, ADCR, and prebiopsy nomogram were 0.657 (95% confidence interval [CI], 0.580-0.733), 0.593 (95% CI, 0.525-0.660), 0.762 (95% CI, 0.697-0.826), 0.824 (95% CI, 0.770-0.878), 0.829 (95% CI, 0.769-0.888), and 0.906 (95% CI, 0.863-0.948), respectively: AUC of nomogram was significantly different than that of individual parameter. In the 5-fold cross-validation, the mean AUC of the prebiopsy nomogram for csPCa was 0.888 (95% CI, 0.786-0.983).

DATA CONCLUSIONS

This multivariate prebiopsy nomogram using clinical and bpMRI parameters may help estimate the probability of csPCa in patients undergoing TBx. ADCR seems to enhance the role of bpMRI in detecting csPCa.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

A nomogram based on PI-RADS v2.1 and clinical indicators for predicting clinically significant prostate cancer in the transition zone

Background

This study attempted to develop a nomogram for predicting clinically significant prostate cancer (cs-PCa) in the transition zone (TZ) with the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) score based on biparametric magnetic resonance imaging (bp-MRI) and clinical indicators.

Methods

We retrospectively reviewed 383 patients with suspicious prostate lesions in the TZ as a training cohort and 128 patients as the validation cohort from January 2015 to March 2020. Multivariable logistic regression analysis was performed to determine independent predictors for building a nomogram, and the performance of the nomogram was assessed by the area under the receiver operating characteristic curve (AUC), the calibration curve and decision curve.

Results

The PI-RADS v2.1 score and prostate-specific antigen density (PSAD) were independent predictors of TZ cs-PCa. The prediction model had a significantly higher AUC (0.936) than the individual predictors (0.914 for PI-RADS v2.1 score, P=0.045, 0.842 for PSAD, P<0.001). The nomogram showed good discrimination (AUC of 0.936 in the training cohort and 0.963 in the validation cohort) and favorable calibration. When the PI-RADS v2.1 score was combined with PSAD, the diagnostic sensitivity and specificity were 80.7% and 93.8%, respectively, which were better than those of the PI-RADS v2.1 score (sensitivity, 74.2%; specificity, 92.5%) and PSAD (sensitivity, 66.1%; specificity, 88.2%).

Conclusions

The newly constructed nomogram exhibits satisfactory predictive accuracy and consistency for TZ cs-PCa. PI-RADS v2.1 based on bp-MRI is a strong predictor in the detection of TZ cs-PCa. Adding PSAD to PI-RADS v2.1 could improve its diagnostic performance, thereby avoiding unnecessary biopsies.

Diagnostic Performance of PI-RADS v2, Proposed Adjusted PI-RADS v2 and Biparametric Magnetic Resonance Imaging for Prostate Cancer Detection: A Preliminary Study

Purpose: To evaluate the diagnostic performance of PI-RADS v2, proposed adjustments to PI-RADS v2 (PA PI-RADS v2) and biparametric magnetic resonance imaging (MRI) for prostate cancer detection. Methods: A retrospective cohort of 224 patients with suspected prostate cancer was included from January 2016 to November 2018. All the patients underwent a multi-parametric MR scan before biopsy. Two radiologists independently evaluated the MR examinations using PI-RADS v2, PA PI-RADS v2, and a biparametric MRI protocol, respectively. Receiver operating characteristic (ROC) curves for the three different protocols were drawn. Results: In total, 90 out of 224 cases (40.18%) were pathologically diagnosed as prostate cancer. The area under the ROC curves (AUC) for diagnosing prostate cancers by biparametric MRI, PI-RADS v2, and PA PI-RADS v2 were 0.938, 0.935, and 0.934, respectively. For cancers in the peripheral zone (PZ), the diagnostic sensitivity was 97.1% for PI-RADS v2/PA PI-RADS v2 and 96.2% for biparametric MRI. Moreover, the specificity was 84.0% for biparametric MRI and 58.0% for PI-RADS v2/PA PI-RADS v2. For cancers in the transition zone (TZ), the diagnostic sensitivity was 93.4% for PA PI-RADS v2 and 88.2% for biparametric MRI/PI-RADS v2. Furthermore, the specificity was 95.4% for biparametric MRI/PI-RADS v2 and 78.0% for PA PI-RADS v2. Conclusions: The overall diagnostic performance of the three protocols showed minimal differences. For lesions assessed as being category 3 using the biparametric MRI protocol, PI-RADS v2, or PA PI-RADS v2, it was thought prostate cancer detection could be improved. Attention should be paid to false positive results when PI-RADS v2 or PA PI-RADS v2 are used.

Why Is a b-value Range of 1500-2000 s/mm² Optimal for Evaluating Prostatic Index Lesions on Synthetic Diffusion-Weighted Imaging?

Objective

It is uncertain why a b-value range of 1500–2000 s/mm² is optimal. This study was aimed at qualitatively and quantitatively analyzing the optimal b-value range of synthetic diffusion-weighted imaging (sDWI) for evaluating prostatic index lesions.

Materials and Methods

This retrospective study included 92 patients who underwent DWI and targeted biopsy for magnetic resonance imaging (MRI)-suggested index lesions. We generated sDWI at a b-value range of 1000–3000 s/mm² using dedicated software and true DWI data at b-values of 0, 100, and 1000 s/mm². We hypothesized that lesion conspicuity would be best when the background (i.e., MRI-suggested benign prostatic [bP] and periprostatic [pP] regions) signal intensity (SI) is suppressed and becomes homogeneous. To prove this hypothesis, we performed both qualitative and quantitative analyses. For qualitative analysis, two independent readers analyzed the b-value showing the best visual conspicuity of an MRI-suggested index lesion. For quantitative analysis, the readers assessed the b-value showing the same bP and pP region SI. The 95% confidence interval (CI) or interquartile range of qualitatively and quantitatively selected optimal b-values was assessed, and the mean difference between qualitatively and quantitatively selected b-values was investigated.

Results

The 95% CIs of optimal b-values from qualitative and quantitative analyses were 1761–1805 s/mm² and 1640–1771 s/mm² (median, 1790 s/mm² vs. 1705 s/mm²; p = 0.003) for reader 1, and 1835–1895 s/mm² and 1705–1841 s/mm² (median, 1872 s/mm² vs. 1763 s/mm²; p = 0.022) for reader 2, respectively. Interquartile ranges of qualitatively and quantitatively selected optimal b-values were 1735–1873 s/mm² and 1573–1867 s/mm² for reader 1, and 1775–1945 s/mm² and 1591–1955 s/mm² for reader 2, respectively. Bland–Altman plots consistently demonstrated a mean difference of less than 100 s/mm² between qualitatively and quantitatively selected optimal b-values.

Conclusion

b-value range showing a homogeneous background signal may be optimal for evaluating prostatic index lesions on sDWI. Our qualitative and quantitative data consistently recommend b-values of 1500–2000 s/mm².

MRI combined with PSA density in detecting clinically significant prostate cancer in patients with PSA serum levels of 4∼10ng/mL: Biparametric versus multiparametric MRI

PURPOSE

To compare the performance of biparametric magnetic resonance imaging (bpMRI) to that of multiparametric MRI (mpMRI) in combination with prostate-specific antigen density (PSAD) in detecting clinically significant prostate cancer (csPCa) in patients with PSA serum levels of 4∼10ng/mL.

MATERIALS AND METHODS

A total of 123 men (mean age, 66.3±8.9 [SD]; range: 42-83 years) with PSA serum levels of 4∼10ng/mL with suspected csPCa were included. All patients underwent mpMRI at 3 Tesla and transrectal ultrasound-guided prostate biopsy in their clinical workup and were followed-up for >1 year when no csPCa was found at initial biopsy. The mpMRI images were reinterpreted according to the Prostate Imaging Reporting and Data System (PI-RADS, v2.1) twice in two different sessions using either mpMRI sequences or bpMRI sequences. The patients were divided into 2 groups according to whether csPCa was detected. The PI-RADS (mpMRI or bpMRI) categories and PSAD were used in combination to detect csPCa. Receiver operating characteristic (ROC) curve and decision curve analyses were performed to compare the efficacy of the different models (mpMRI, bpMRI, PSAD, mpMRI+PSAD and bpMRI+PSAD).

RESULTS

Thirty-seven patients (30.1%, 37/123) had csPCa. ROC analysis showed that bpMRI (AUC=0.884 [95% confidence interval (CI): 0.814-0.935]) outperformed mpMRI (AUC=0.867 [95% CI: 0.794-0.921]) (P=0.035) and that bpMRI and mpMRI performed better than PSAD (0.682 [95% CI: 0.592-0.763]) in detecting csPCa; bpMRI+PSAD (AUC=0.907 [95% CI: 0.841-0.952]) performed similarly to mpMRI+PSAD (AUC=0.896 [95% CI: 0.828-0.944]) (P=0.151) and bpMRI (P=0.224). The sensitivity and specificity were 81.1% (95% CI: 64.8-92.0%) and 88.4% (95% CI: 79.7-94.3%), respectively for bpMRI, and 83.8% (95% CI: 68.0-93.8%) and 80.2% (95% CI: 70.2-88.0%), respectively for mpMRI (P>0.999 for sensitivity and P=0.016 for specificity). Among the 5 decision models, the decision curve analysis showed that all models (except for PSAD) achieved a high net benefit.

CONCLUSION

In patients with PSA serum levels of 4∼10ng/mL, bpMRI and bpMRI combined with PSAD achieve better performance than mpMRI in detecting csPCa; bpMRI has a higher specificity than mpMRI, which could decrease unnecessary biopsy, and may serve as a potential alternative to mpMRI to optimize clinical workup.

Investigating the equivalent performance of biparametric compared to multiparametric MRI in detection of clinically significant prostate cancer

PURPOSE

PIRADS v2 stipulates that dynamic contrast-enhanced (DCE) imaging be used to categorize diffusion-weighted-imaging (DWI) score 3 (DWI 3) peripheral zone (PZ) lesions as PIRADS score 3 (PIRADS 3; DCE -) or PIRADS 4 (DCE +). It's controversial for the value of DCE in improving clinically significant prostate cancer (csPCa) detection. We aimed to figure out whether DCE improves csPCa detection and explore new available measures to improve csPCa detection.

PATIENTS AND METHODS

We retrospectively enrolled 375 patients who underwent mp MRI before MRI/ultrasound (US) fusion-targeted biopsy (TB) with transperineal systematic biopsy (SB). All lesions were classified as DWI 3/DCE -, DWI 3/DCE +, DWI 4/PIRADS 4 lesions. Detection rates of csPCa for each lesion group were analyzed. The diagnostic performance of each approach was analyzed by receiver operating characteristics (ROC) analysis and decision curve analysis.

RESULTS

Totally, 109 DWI 3 or DWI 4 single lesions in PZ were analyzed (n = 109). The rates of csPCa detection for Group A, Group B, Group C is 10.3%, 13.9%, 55.9%, respectively (A vs. B, p = 0.625; B vs. C, p < 0.001). ROC analysis and decision curve analysis showed the method of combining Age, PSA Density (PSAD) and the mean apparent diffusion coefficient value (ADC_mean) outperforms individual approaches for csPCa detection.

CONCLUSION

For DWI 3 lesions in PZ, DCE sequence has not additional value for improving detection of csPCa. The integration of clinical characteristics and bpMRI parameter improves the detection of csPCa.

Clinically significant prostate cancer detection on MRI: A radiomic shape features study

PURPOSE

Prostate multiparametric MRI (mpMRI) is the imaging modality of choice for detecting clinically significant prostate cancer (csPCa). Among various parameters, lesion maximum diameter and volume are currently considered of value to increase diagnostic accuracy. Quantitative radiomics allows for the extraction of more advanced shape features. Our aim was to assess which shape features derived from MRI index lesions correlate with csPCa presence.

MATERIALS AND METHODS

We retrospectively enrolled 75 consecutive subjects, who underwent mpMRI on a 3 T scanner, divided based on MRI index lesion Gleason Score in a csPCa group (GS > 3 + 4, n = 41) and a non-csPCa one (n = 34). Ten shape features were extracted both from axial T2-weighted and ADC maps images, after lesion tridimensional segmentation. Univariable and multivariable logistic analysis were used to evaluate the relationship between shape features and csPCa. Diagnostic performance was assessed measuring the area under the curve of the receiver operating characteristic (ROC) analysis. Diagnostic accuracy, sensitivity, and specificity were determined using the best cut-off on each ROC. A P value < 0.05 was considered statistically significant.

RESULTS

Univariable analysis demonstrated that almost every shape feature was statistically significant between csPCa e non-csPCa groups. However, multivariable analysis revealed that the parameter defined as surface area to volume ratio (SAVR), especially when extracted from ADC maps is the strongest independent predictor of csPCa among tested shape features.

CONCLUSION

The radiomic shape feature SAVR, extracted from ADC maps after index lesion segmentation, appears as a promising tool for csPCa detection.