Prostate volumes derived from MRI and volume-adjusted serum prostate-specific antigen: correlation with Gleason score of prostate cancer

Machine learning-based analysis of 68Ga-PSMA-11 PET/CT images for estimation of prostate tumor grade

Early diagnosis of prostate cancer, the most common malignancy in men, can improve patient outcomes. Since the tissue sampling procedures are invasive and sometimes inconclusive, an alternative image-based method can prevent possible complications and facilitate treatment management. We aim to propose a machine-learning model for tumor grade estimation based on 68 Ga-PSMA-11 PET/CT images in prostate cancer patients. This study included 90 eligible participants out of 244 biopsy-proven prostate cancer patients who underwent staging 68Ga-PSMA-11 PET/CT imaging. The patients were divided into high and low-intermediate groups based on their Gleason scores. The PET-only images were manually segmented, both lesion-based and whole prostate, by two experienced nuclear medicine physicians. Four feature selection algorithms and five classifiers were applied to Combat-harmonized and non-harmonized datasets. To evaluate the model's generalizability across different institutions, we performed leave-one-center-out cross-validation (LOOCV). The metrics derived from the receiver operating characteristic curve were used to assess model performance. In the whole prostate segmentation, combining the ANOVA algorithm as the feature selector with Random Forest (RF) and Extra Trees (ET) classifiers resulted in the highest performance among the models, with an AUC of 0.78 and 083, respectively. In the lesion-based segmentation, the highest AUC was achieved by MRMR feature selector + Linear Discriminant Analysis (LDA) and Logistic Regression (LR) classifiers (0.76 and 0.79, respectively). The LOOCV results revealed that both the RF_ANOVA and ET_ANOVA models showed high levels of accuracy and generalizability across different centers, with an average AUC value of 0.87 for the ET_ANOVA combination. Machine learning-based analysis of radiomics features extracted from 68Ga-PSMA-11 PET/CT scans can accurately classify prostate tumors into low-risk and intermediate- to high-risk groups.

The Impact of Prostate Volume on Prostate Cancer Detection: Comparing Magnetic Resonance Imaging with Transrectal Ultrasound in Biopsy-naïve Men

Background and objective

This study aimed to determine the difference in prostate volume (PV) derived from transrectal ultrasound (TRUS) and multiparametric magnetic resonance imaging (mpMRI), and to further investigate the role of TRUS prostate-specific antigen density (PSAD) and mpMRI-PSAD in prostate cancer (PCa) detection in biopsy-naïve men.

Methods

Patients who underwent an initial prostate biopsy within 3 mo after mpMRI between January 2016 and December 2021 were analyzed retrospectively. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of both TRUS-PSAD and mpMRI-PSAD for PCa detection were calculated and compared. The Pearson correlation coefficient, Bland-Altman plot, and receiver operating characteristic curve were also utilized to explore the interests of this study.

Key findings and limitations

The median prostate-specific antigen level of 875 patients was 9.79 (interquartile range [IQR]: 7.09-13.50) ng/ml. The median mpMRI-PV and TRUS-PV were 41.92 (IQR: 29.29-60.73) and 41.04 (IQR: 29.24-57.27) ml, respectively, demonstrating a strong linear correlation (r = 0.831, 95% confidence interval: 0.809, 0.850; p < 0.01) and sufficient agreement. No significant difference was observed in terms of the sensitivity, specificity, PPV, and NPV between TRUS-PSAD and mpMRI-PSAD for any PCa and clinically significant PCa (csPCa) detection. The overall discriminative ability of TRUS-PSAD for detecting PCa or non-PCa, as well as csPCa and non-csPCa, was comparable with that of mpMRI-PSAD, and similar results were also observed in the subsequent analysis stratified by mpMRI-PV quartiles, prostate-specific antigen level, and age. The limitations include the retrospective and single-center nature and a lack of follow-up information.

Conclusions and clinical implications

TRUS-PV and MRI-PV exhibited a strong linear correlation and reached sufficient agreement. The efficiency of TRUS-PSAD and mpMRI-PSAD for PCa detection was comparable. TRUS could be used for PV estimation and dynamic monitoring of PSAD, and TRUS-PSAD could effectively guide clinical decision-making and optimize diagnostic strategies.

Patient summary

In this work, prostate volume (PV) derived from transrectal ultrasound (TRUS) exhibited a strong linear correlation with the PV derived from multiparametric magnetic resonance imaging (mpMRI). The efficiency of TRUS prostate-specific antigen density (PSAD) and mpMRI-PSAD for the detection of prostate cancer was comparable. TRUS could be used for PV estimation and TRUS-PSAD could help in clinical decision-making and optimizing diagnostic strategies.

Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer

PURPOSE

Bone metastasis is a significant contributor to morbidity and mortality in advanced prostate cancer, and early diagnosis is challenging due to its insidious onset. The use of machine learning to obtain prognostic information from pathological images has been highlighted. However, there is a limited understanding of the potential of early prediction of bone metastasis through the feature combination method from various sources. This study presents a method of integrating multimodal data to enhance the feasibility of early diagnosis of bone metastasis in prostate cancer.

METHODS AND MATERIALS

Overall, 211 patients diagnosed with prostate cancer (PCa) at Gansu Provincial Hospital between January 2017 and February 2023 were included in this study. The patients were randomized (8:2) into a training group (n = 169) and a validation group (n = 42). The region of interest (ROI) were segmented from the three magnetic resonance imaging (MRI) sequences (T2WI, DWI, and ADC), and pathological features were extracted from tissue sections (hematoxylin and eosin [H&E] staining, 10 × 20). A deep learning (DL) model using ResNet 50 was employed to extract deep transfer learning (DTL) features. The least absolute shrinkage and selection operator (LASSO) regression method was utilized for feature selection, feature construction, and reducing feature dimensions. Different machine learning classifiers were used to build predictive models. The performance of the models was evaluated using receiver operating characteristic curves. The net clinical benefit was assessed using decision curve analysis (DCA). The goodness of fit was evaluated using calibration curves. A joint model nomogram was eventually developed by combining clinically independent risk factors.

RESULTS

The best prediction models based on DTL and pathomics features showed area under the curve (AUC) values of 0.89 (95% confidence interval [CI], 0.799-0.989) and 0.85 (95% CI, 0.714-0.989), respectively. The AUC for the best prediction model based on radiomics features and combining radiomics features, DTL features, and pathomics features were 0.86 (95% CI, 0.735-0.979) and 0.93 (95% CI, 0.854-1.000), respectively. Based on DCA and calibration curves, the model demonstrated good net clinical benefit and fit.

CONCLUSION

Multimodal radiomics and pathomics serve as valuable predictors of the risk of bone metastases in patients with primary PCa.

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.

Diagnostic accuracy of MRI-based PSA density for detection of prostate cancer among the Thai population

Background

The PSAD calculating by the serum PSA level divided by prostate volume had more specificity and accuracy than the serum PSA level for detection of prostate cancer.

Methods

MRI examinations of 319 patients who had suspected prostate cancer between January 2014 and December 2019 were retrospectively reviewed. Prostate volumes were measured by MRI images and PSAD values were calculated. The accuracy and optimal cutoff points of MRI-based PSAD were evaluated using receiver operating characteristic curves (ROC curves). Correlations between the MRI-based PSAD and Gleason scores were also analyzed to predict prognosis of prostate cancer.

Results

Overall, of 154 patients were included in this study, 59 patients (38.31%) were diagnosed with prostate cancer. The optimal cutoff point of PSAD was 0.16 (81.40% sensitivity, 54.70% specificity, 52.70% PPV, 82.50% NPV), and the AUC was 0.680 (95% CI: 0.609–0.751). In subgroup analyses, the optimal cutoff point of PSAD in patients with serum PSA 4–10 ng/ml was 0.16 (61.10% sensitivity, 76.00% specificity) and for > 10 ng/ml was 0.30 (68.30% sensitivity, 64.30% specificity). Furthermore, there was a statistically significant correlation between PSAD and Gleason scores (p-value 0.014).

Conclusions

The optimal cutoff point of MRI-based PSAD was 0.16 which was relatively different from international consensus.

Combining prostate-specific antigen density with prostate imaging reporting and data system score version 2.1 to improve detection of clinically significant prostate cancer: A retrospective study

Globally, Prostate cancer (PCa) is the second most common cancer in the male population worldwide, but clinically significant prostate cancer (CSPCa) is more aggressive and causes to more deaths. The authors aimed to construct the risk category based on Prostate Imaging Reporting and Data System score version 2.1 (PI-RADS v2.1) in combination with Prostate-Specific Antigen Density (PSAD) to improve CSPCa detection and avoid unnecessary biopsy. Univariate and multivariate logistic regression and receiver-operating characteristic (ROC) curves were performed to compare the efficacy of the different predictors. The results revealed that PI-RADS v2.1 score and PSAD were independent predictors for CSPCa. Moreover, the combined factor shows a significantly higher predictive value than each single variable for the diagnosis of CSPCa. According to the risk stratification model constructed based on PI-RADS v2.1 score and PSAD, patients with PI-RADS v2.1 score of ≤2, or PI-RADS V2.1 score of 3 and PSA density of &lt;0.15 ng/mL2, can avoid unnecessary of prostate biopsy and does not miss clinically significant prostate cancer.

Diagnostic Accuracy of Predictive Models in Prostate Cancer: A Systematic Review and Meta-Analysis

Aim

Accurate diagnosis of prostate cancer (PCa) has a fundamental role in clinical and patient care. Recent advances in diagnostic testing and marker lead to standardized interpretation and increased prescription by clinicians to improve the detection of clinically significant PCa and select patients who strictly require targeted biopsies.

Methods

In this study, we present a systematic review of the overall diagnostic accuracy of each testing panel regarding the panel details. In this meta-analysis, using a structured search, Web of Science and PubMed databases were searched up to 23 September 2019 with no restrictions and filters. The study's outcome was the AUC and 95% confidence interval of prediction models. This index was reported as an overall and based on the WHO region and models with/without MRI.

Results

The thirteen final articles included 25,691 people. The overall AUC and 95% CI in thirteen studies were 0.78 and 95% CI: 0.73–0.82. The weighted average AUC in the countries of the Americas region was 0.73 (95% CI: 0.70–0.75), and in European countries, it was 0.80 (95% CI: 0.72–0.88). In four studies with MRI, the average weighted AUC was 0.88 (95% CI: 0.86–0.90), while in other articles where MRI was not a parameter in the diagnostic model, the mean AUC was 0.73 (95% CI: 0.70–0.76).

Conclusions

The present study's findings showed that MRI significantly improved the detection accuracy of prostate cancer and had the highest discrimination to distinguish candidates for biopsy.

Construction and Validation of a Clinical Predictive Nomogram for Improving the Cancer Detection of Prostate Naive Biopsy Based on Chinese Multicenter Clinical Data

Objectives

Prostate biopsy is a common approach for the diagnosis of prostate cancer (PCa) in patients with suspicious PCa. In order to increase the detection rate of prostate naive biopsy, we constructed two effective nomograms for predicting the diagnosis of PCa and clinically significant PCa (csPCa) prior to biopsy.

Materials and Methods

The data of 1,428 patients who underwent prostate biopsy in three Chinese medical centers from January 2018 to June 2021 were used to conduct this retrospective study. The KD cohort, which consisted of 701 patients, was used for model construction and internal validation; the DF cohort, which consisted of 385 patients, and the ZD cohort, which consisted of 342 patients, were used for external validation. Independent predictors were selected by univariate and multivariate binary logistic regression analysis and adopted for establishing the predictive nomogram. The apparent performance of the model was evaluated via internal validation and geographically external validation. For assessing the clinical utility of our model, decision curve analysis was also performed.

Results

The results of univariate and multivariate logistic regression analysis showed prostate-specific antigen density (PSAD) (P<0.001, OR:2.102, 95%CI:1.687-2.620) and prostate imaging-reporting and data system (PI-RADS) grade (P<0.001, OR:4.528, 95%CI:2.752-7.453) were independent predictors of PCa before biopsy. Therefore, a nomogram composed of PSAD and PI-RADS grade was constructed. Internal validation in the developed cohort showed that the nomogram had good discrimination (AUC=0.804), and the calibration curve indicated that the predicted incidence was consistent with the observed incidence of PCa; the brier score was 0.172. External validation was performed in the DF and ZD cohorts. The AUC values were 0.884 and 0.882, in the DF and ZD cohorts, respectively. Calibration curves elucidated greatly predicted the accuracy of PCa in the two validation cohorts; the brier scores were 0.129 in the DF cohort and 0.131 in the ZD cohort. Decision curve analysis showed that our model can add net benefits for patients. A separated predicted model for csPCa was also established and validated. The apparent performance of our nomogram for PCa was also assessed in three different PSA groups, and the results were as good as we expected.

Conclusions

In this study, we put forward two simple and convenient clinical predictive models comprised of PSAD and PI-RADS grade with excellent reproducibility and generalizability. They provide a novel calculator for the prediction of the diagnosis of an individual patient with suspicious PCa.

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.

Significance of the prostate central gland and total gland volume ratio in the diagnosis of prostate cancer patients in the prostate specific antigen grey zone

Objective

To explore the significance of the prostate central gland to total gland volume ratio (PVc/PV) in the diagnosis of prostate cancer (PCa) in patients with prostate specific antigen (PSA) levels in the grey zone (4–10 ng/ml).

Methods

This retrospective study enrolled patients that had undergone prostate biopsy. The volume of the prostate and the central prostate gland were measured. The differences in PSA, the ratio of free to total PSA (f/tPSA), PSA density (PSAD) and PVc/PV between the PCa and non-PCa groups were compared. Receiver operating characteristic curve analysis for PCa and clinically significant PCa (csPCa) diagnosis were calculated according to PSA (reference), f/tPSA, PSAD and PVc/PV.

Results

This study enrolled 136 patients. There was no significant difference in PSA and f/tPSA between the PCa and non-PCa groups, while there were significant differences in PSAD and PVc/PV. The area under the curve values of PVc/PV for PCa or csPCa diagnosis were 0.876 and 0.933, respectively; and for PSAD, they were 0.705 and 0.790, respectively. These were significantly different compared with the PSA curve, whereas f/tPSA showed no significant difference from the PSA curve.

Conclusion

PVc/PV could be a predictor of PCa when PSA is between 4–10 ng/ml.

A Radiomics nomogram for predicting bone metastasis in newly diagnosed prostate cancer patients

PURPOSE

To establish and validate a radiomics nomogram for predicting bone metastasis (BM) in patients with newly diagnosed prostate cancer (PCa).

METHOD

One-hundred and sixteen patients (training cohort: n = 81; validation cohort: n = 35) who underwent prostate MR imaging and confirmed by pathology with newly diagnosed PCa from January 2014 to January 2019 were enrolled. Radiomic features were extracted from diffusion-weighted, axial T2-weighted fat suppression, and dynamic contrast-enhanced T1-weighted MRI of each patient. Dimension reduction, feature selection, and radiomics feature construction were performed using the least absolute shrinkage and selection operator (LASSO) regression. Combined with independent clinical risk factors, a multivariate logistic regression model was used to establish a radiomics nomogram. Nomogram calibration and discrimination were evaluated in training cohort and verified in the validation cohort. Finally, the clinical usefulness of the nomogram was estimated through decision curve analysis (DCA).

RESULTS

Radiomics signature consisting of 12 selected features was significantly correlated with bone status (P < 0.001 for both training and validation sets). The radiomics nomogram combined a radiomics signature from multiparametric MR images with independent clinic risk factors. The model showed good discrimination and calibration in the training cohort (AUC 0.93, 95% CI, 0.86 to 0.99) and the validation cohort (AUC 0.92, 95% CI, 0.84 to 0.99). DCA also demonstrated the clinical use of the radiomics model.

CONCLUSION

The radiomics nomogram, which incorporates the multiparametric MRI-based radiomics signature and clinical risk factors, can be conveniently used to promote individualized prediction of BM in patients with newly diagnosed PCa.

Noninvasive Prediction of High-Grade Prostate Cancer via Biparametric MRI Radiomics

BACKGROUND

Gleason score (GS) is a histologic prognostic factor and the basis of treatment decision-making for prostate cancer (PCa). Treatment regimens between lower-grade (GS ≤7) and high-grade (GS >7) PCa differ largely and have great effects on cancer progression.

PURPOSE

To investigate the use of different sequences in biparametric MRI (bpMRI) of the prostate gland for noninvasively distinguishing high-grade PCa.

STUDY TYPE

Retrospective.

POPULATION

In all, 489 patients (training cohort: N = 326; test cohort: N = 163) with PCa between June 2008 and January 2018.

FIELD STRENGTH/SEQUENCE

3.0T, pelvic phased-array coils, bpMRI including T₂ -weighted imaging (T₂ WI) and diffusion-weighted imaging (DWI); apparent diffusion coefficient map extracted from DWI.

ASSESSMENT

The whole prostate gland was delineated. Radiomic features were extracted and selected using the Kruskal-Wallis test, the minimum redundancy-maximum relevance, and the sequential backward elimination algorithm. Two single-sequence radiomic (T₂ WI, DWI) and two combined (T₂ WI-DWI, T₂ WI-DWI-Clinic) models were respectively constructed and validated via logistic regression.

STATISTICAL TESTS

The Kruskal-Wallis test and chi-squared test were utilized to evaluate the differences among variable groups. P < 0.05 determined statistical significance. The area under the receiver operating characteristic curve (AUC), specificity, sensitivity, and accuracy were used to evaluate model performance. The Delong test was conducted to compare the differences between the AUCs of all models.

RESULT

All radiomic models showed significant (P < 0.001) predictive performances. Between the single-sequence radiomic models, the DWI model achieved the most encouraging results, with AUCs of 0.801 and 0.787 in the training and test cohorts, respectively. For the combined models, the T₂ WI-DWI models acquired an AUC of 0.788, which was almost the same with DWI in the test cohort, and no significant difference was found between them (training cohort: P = 0.199; test cohort: P = 0.924).

DATA CONCLUSION

Radiomics based on bpMRI can noninvasively identify high-grade PCa before the operation, which is helpful for individualized diagnosis of PCa.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1102-1109.

Three-Dimensional Convolutional Neural Network for Prostate MRI Segmentation and Comparison of Prostate Volume Measurements by Use of Artificial Neural Network and Ellipsoid Formula

OBJECTIVE. The purposes of this study were to assess the performance of a 3D convolutional neural network (CNN) for automatic segmentation of prostates on MR images and to compare the volume estimates from the 3D CNN with those of the ellipsoid formula. MATERIALS AND METHODS. The study included 330 MR image sets that were divided into 260 training sets and 70 test sets for automated segmentation of the entire prostate. Among these, 162 training sets and 50 test sets were used for transition zone segmentation. Assisted by manual segmentation by two radiologists, the following values were obtained: estimates of ground-truth volume (V_GT), software-derived volume (V_SW), mean of V_GT and V_SW (V_AV), and automatically generated volume from the 3D CNN (V_NET). These values were compared with the volume calculated with the ellipsoid formula (V_EL). RESULTS. The Dice similarity coefficient for the entire prostate was 87.12% and for the transition zone was 76.48%. There was no significant difference between V_NET and V_AV (p = 0.689) in the test sets of the entire prostate, whereas a significant difference was found between V_EL and V_AV (p < 0.001). No significant difference was found among the volume estimates in the test sets of the transition zone. Overall intraclass correlation coefficients between the volume estimates were excellent (0.887-0.995). In the test sets of entire prostate, the mean error between V_GT and V_NET (2.5) was smaller than that between V_GT and V_EL (3.3). CONCLUSION. The fully automated network studied provides reliable volume estimates of the entire prostate compared with those obtained with the ellipsoid formula. Fast and accurate volume measurement by use of the 3D CNN may help clinicians evaluate prostate disease.

MRI-Based Prostate-Specific Antigen Density Predicts Gleason Score Upgrade in an Active Surveillance Cohort

OBJECTIVE. Elevated prostate-specific antigen density (PSAD) based on transrectal ultrasound (TRUS) measurements has been shown to be strongly associated with clinically significant disease and to predict progression on active surveillance (AS) for men with disease that is at a low stage or grade. We hypothesized that elevated MRI PSAD is similarly associated with increased risk of progression on subsequent biopsy. MATERIALS AND METHODS. In this retrospective study, men with Gleason score of 3+3 on diagnostic TRUS-guided biopsy who were managed with AS, had undergone MRI, and had at least one additional biopsy were included. MRI PSAD was calculated using prostate volume on MRI and prostate-specific antigen level temporally closest to the MRI. Multivariable logistics regression models were used to evaluate the association between MRI PSAD and predictors of upgrade on serial biopsy. RESULTS. A total of 166 patients were identified, of whom 74 (44.6%) were upgraded to a Gleason score of 7 or higher on subsequent biopsy. Lesions with Prostate Imaging Reporting and Data System (PI-RADS) scores of 4 and 5 more commonly had MRI PSAD of 0.15 ng/mL² or higher (51.93% vs 22.22%, p = 0.01) than lesions with PI-RADS scores of 1-3. Median MRI PSAD was significantly higher in the upgraded group compared with the group that was not upgraded (0.15 ng/mL² vs 0.11 ng/mL², p = 0.01). MRI PSAD was significantly associated with increased odds of upgrading on subsequent biopsy (log transformation; odds ratio, 1.9 [95% CI, 1.2-2.8]; p = 0.01) after adjusting for age and length of follow-up. CONCLUSION. MRI PSAD was significantly associated with Gleason score upgrading on subsequent biopsy for men initially diagnosed with Gleason 3+3 disease. Although this result is intuitive, to our knowledge it has not been previously shown. As MRI utilization increases, MRI PSAD can aid in risk stratification for men managed with AS.

How Accurately Can Prostate Gland Imaging Measure the Prostate Gland Volume? Results of a Systematic Review

AIM

The measurement of the volume of the prostate gland can have an influence on many clinical decisions. Various imaging methods have been used to measure it. Our aim was to conduct the first systematic review of their accuracy.

METHODS

The literature describing the accuracy of imaging methods for measuring the prostate gland volume was systematically reviewed. Articles were included if they compared volume measurements obtained by medical imaging with a reference volume measurement obtained after removal of the gland by radical prostatectomy. Correlation and concordance statistics were summarised.

RESULTS

28 articles describing 7768 patients were identified. The imaging methods were ultrasound, computed tomography, and magnetic resonance imaging (US, CT, and MRI). Wide variations were noted but most articles about US and CT provided correlation coefficients that lay between 0.70 and 0.90, while those describing MRI seemed slightly more accurate at 0.80-0.96. When concordance was reported, it was similar; over- and underestimation of the prostate were variably reported. Most studies showed evidence of at least moderate bias and the quality of the studies was highly variable.

DISCUSSION

The reported correlations were moderate to high in strength indicating that imaging is sufficiently accurate when quantitative measurements of prostate gland volume are required. MRI was slightly more accurate than the other methods.

Characterization of prostate cancer using diffusion tensor imaging: A new perspective

PURPOSE

This study is aimed at evaluating the potential role of quantitative magnetic resonance diffusion tensor imaging (DTI) and tractography parameters in the detection and characterization of peripheral zone prostate cancer with a particular attention for fiber tract density.

MATERIALS AND METHODS

DTI was acquired from eleven high risk, transrectal ultrasound (TRUS)-guided biopsy proven prostate cancers with perineural invasion (histological Gleason score ≥ 7) on a 3 T magnet. Twenty parameters derived from DTI were quantified in cancer and healthy regions of the prostate. In addition, fiber tract density in normal versus cancer tissues was also calculated using DTI tractography. Support vector machine with a radial basis function kernel and area under receiver operator characteristic (ROC) were used to describe and compare the diagnostic performance of combined fractional anisotropy (FA) and mean diffusivity (MD) and other statistically significant DTI parameters. Spearman correlation analysis between DTI parameters and Gleason scores was conducted.

RESULTS

Eighteen DTI parameters yielded statistically significant differences between cancer and healthy regions (p-value < 0.05). The ROC curve of all statistically significant DTI parameters between cancer and healthy regions was higher than the area under ROC curve using FA + MD alone (95% confidence interval = 0.988, range = 0.975-1.00) vs (95% confidence interval = 0.935, range = 0.898-0.999), respectively (p-value < 0.05). Fiber tract density was also found to be higher in cancer than in healthy tissues (+38.22%, p-value = 0.010) and may be related to the increase in nerve and vascular density reported in prostate cancer. The linear and relative anisotropy were highly correlated with Gleason score (Spearman correlation factor r = 0.655, p-value = 0.001 and r = 0.667, p-value < 0.001, respectively).

CONCLUSIONS

DTI has the potential to provide imaging biomarkers in the detection and characterization of prostate cancer. Novel quantitative parameters derived from DTI and DTI tractography, including fiber tract density, support the use of DTI in the assessment of high grade prostate cancer.

Peripheral zone volume ratio (PZ-ratio) is relevant with biopsy results and can increase the accuracy of current diagnostic modality

The current diagnostic modality of prostate cancer based on prostate specific antigen (PSA) and systematic biopsy is far from ideal in terms of over-diagnosing indolent prostate cancer and missing significant ones. Thus we integrated the peripheral zone volume ratio (PZ-ratio) for diagnostic refinement. This retrospective study included 247 consecutive patients who underwent initial transrectal ultrasound-guided systematic prostate biopsy from April 2014 to November 2015. Prostate volume was determined by semi-automatic contour on axial T2 weighted magnetic resonance imaging (MRI). PZ-ratio was inversely correlated with age (r = −0.36, p <0.0001). Adding PZ-ratio and MRI findings to the current predictive model (age, PSA density, percent-free PSA) significantly increased diagnostic accuracy in all patients (AUC: 0.871 vs. 0.812, p = 0.0059), but not in patient subgroup with PSA 4–10 ng/ml (AUC: 0.863 vs. 0.803, p = 0.12). The new model also significantly reduced the number of unnecessary biopsies while missing less significant cancers at a probability threshold of 25%. PZ-ratio is a potential tool in predicting biopsy results, and when added alone or in combination with MRI findings, the diagnostic accuracy can be further enhanced.

3D Printed Organ Models with Physical Properties of Tissue and Integrated Sensors

The design and development of novel methodologies and customized materials to fabricate patient-specific 3D printed organ models with integrated sensing capabilities could yield advances in smart surgical aids for preoperative planning and rehearsal. Here, we demonstrate 3D printed prostate models with physical properties of tissue and integrated soft electronic sensors using custom-formulated polymeric inks. The models show high quantitative fidelity in static and dynamic mechanical properties, optical characteristics, and anatomical geometries to patient tissues and organs. The models offer tissue-mimicking tactile sensation and behavior and thus can be used for the prediction of organ physical behavior under deformation. The prediction results show good agreement with values obtained from simulations. The models also allow the application of surgical and diagnostic tools to their surface and inner channels. Finally, via the conformal integration of 3D printed soft electronic sensors, pressure applied to the models with surgical tools can be quantitatively measured.

Magnetic Resonance Imaging-Based Prostate-Specific Antigen Density for Prediction of Gleason Score Upgrade in Patients With Low-Risk Prostate Cancer on Initial Biopsy

OBJECTIVE

The aim of this study was to assess the utility of prostate-specific antigen density (PSAD) calculated using magnetic resonance imaging for predicting Gleason score (GS) upgrade in patients with low-risk prostate cancer on biopsy.

METHODS

Seventy-three patients were divided into 2 groups according to the concordance between biopsy and prostatectomy GS: group 1 (6/6) and group 2 (6/≥7). Magnetic resonance imaging-based PSAD, prostate volume, prostate-specific antigen (PSA), and age were compared between the 2 groups. Logistic regression and receiver operating characteristic curve analysis were performed.

RESULTS

Gleason score was upgraded in 40 patients. Patients in group 2 had significantly higher PSAD and PSA values and smaller prostate volume than did those in group 1. Prostate-specific antigen density of 0.26 ng/mL per cm or higher, PSA of 7.63 ng/mL or higher, and prostate volume of 25.1 cm or less were related to GS upgrade, with area-under-the-curve values of 0.765, 0.721, and 0.639, respectively.

CONCLUSIONS

Magnetic resonance imaging-based PSAD could help in predicting postoperative GS upgrade in patients with low-risk prostate cancer.

Performance characteristics of prostate-specific antigen density and biopsy core details to predict oncological outcome in patients with intermediate to high-risk prostate cancer underwent robot-assisted radical prostatectomy

Background

Many urologic surgeons refer to biopsy core details for decision making in cases of localized prostate cancer (PCa) to determine whether an extended resection and/or lymph node dissection should be performed. Furthermore, recent reports emphasize the predictive value of prostate-specific antigen density (PSAD) for further risk stratification, not only for low-risk PCa, but also for intermediate- and high-risk PCa. This study focused on these parameters and compared respective predictive impact on oncologic outcomes in Japanese PCa patients.

Methods

Two-hundred and fifty patients with intermediate- and high-risk PCa according to the National Comprehensive Cancer Network (NCCN) classification, that underwent robot-assisted radical prostatectomy at a single institution, and with observation periods of longer than 6 months were enrolled. None of the patients received hormonal treatments including antiandrogens, luteinizing hormone-releasing hormone analogues, or 5-alpha reductase inhibitors preoperatively. PSAD and biopsy core details, including the percentage of positive cores and the maximum percentage of cancer extent in each positive core, were analyzed in association with unfavorable pathologic results of prostatectomy specimens, and further with biochemical recurrence. The cut-off values of potential predictive factors were set through receiver-operating characteristic curve analyses.

Results

In the entire cohort, a higher PSAD, the percentage of positive cores, and maximum percentage of cancer extent in each positive core were independently associated with advanced tumor stage ≥ pT3 and an increased index tumor volume > 0.718 ml. NCCN classification showed an association with a tumor stage ≥ pT3 and a Gleason score ≥8, and the attribution of biochemical recurrence was also sustained. In each NCCN risk group, these preoperative factors showed various associations with unfavorable pathological results. In the intermediate-risk group, the percentage of positive cores showed an independent predictive value for biochemical recurrence. In the high-risk group, PSAD showed an independent predictive value.

Conclusions

PSAD and biopsy core details have different performance characteristics for the prediction of oncologic outcomes in each NCCN risk group. Despite the need for further confirmation of the results with a larger cohort and longer observation, these factors are important as preoperative predictors in addition to the NCCN classification for a urologic surgeon to choose a surgical strategy.

Electronic supplementary material

The online version of this article (doi:10.1186/s12894-017-0238-y) contains supplementary material, which is available to authorized users.

Role of MRI in the Risk Assessment of Primary Prostate Cancer

A successful paradigm shift toward personalized management strategies for patients with prostate cancer (PCa) is heavily dependent on the availability of noninvasive diagnostic tools capable of accurately establishing the true extent of disease at the time of diagnosis and estimating the risk of subsequent disease progression and related mortality. Although there is still considerable scope for improvement in its diagnostic, predictive, and prognostic capabilities, multiparametric prostate magnetic resonance imaging (MRI) is currently regarded as the imaging modality of choice for local staging of PCa. A negative MRI, that is, the absence of any MRI-visible intraprostatic lesion, has a high negative predictive value for the presence of clinically significant PCa and can substantiate the consideration of active surveillance as a preferred initial management approach. MRI-derived quantitative and semi-quantitative parameters can be utilized to noninvasively characterize MRI-visible prostate lesions and identify those patients who are most likely to benefit from radical treatment, and differentiate them from patients with benign or indolent prostate pathology that may also be visible on MRI. This literature review summarizes current strategies how MRI can be used to determine a tailored management strategy for an individual patient.

Developing a nomogram based on multiparametric magnetic resonance imaging for forecasting high-grade prostate cancer to reduce unnecessary biopsies within the prostate-specific antigen gray zone

BACKGROUND

Since 1980s the application of Prostate specific antigen (PSA) brought the revolution in prostate cancer diagnosis. However, it is important to underline that PSA is not the ideal screening tool due to its low specificity, which leads to the possible biopsy for the patient without High-grade prostate cancer (HGPCa). Therefore, the aim of this study was to establish a predictive nomogram for HGPCa in patients with PSA 4-10 ng/ml based on Prostate Imaging Reporting and Data System version 2 (PI-RADS v2), MRI-based prostate volume (PV), MRI-based PV-adjusted Prostate Specific Antigen Density (adjusted-PSAD) and other traditional classical parameters.

METHODS

Between January 2014 and September 2015, Of 151 men who were eligible for analysis were formed the training cohort. A prediction model for HGPCa was built by using backward logistic regression and was presented on a nomogram. The prediction model was evaluated by a validation cohort between October 2015 and October 2016 (n = 74). The relationship between the nomogram-based risk-score as well as other parameters with Gleason score (GS) was evaluated. All patients underwent 12-core systematic biopsy and at least one core targeted biopsy with transrectal ultrasonographic guidance.

RESULTS

The multivariate analysis revealed that patient age, PI-RADS v2 score and adjusted-PSAD were independent predictors for HGPCa. Logistic regression (LR) model had a larger AUC as compared with other parameters alone. The most discriminative cutoff value for LR model was 0.36, the sensitivity, specificity, positive predictive value and negative predictive value were 87.3, 78.4, 76.3, and 90.4%, respectively and the diagnostic performance measures retained similar values in the validation cohort (AUC 0.82 [95% CI, 0.76-0.89]). For all patients with HGPCa (n = 50), adjusted-PSAD and nomogram-based risk-score were positively correlated with the GS of HGPCa in PSA gray zone (r = 0.455, P = 0.002 and r = 0.509, P = 0.001, respectively).

CONCLUSION

The nomogram based on multiparametric magnetic resonance imaging (mp-MRI) for forecasting HGPCa is effective, which could reduce unnecessary prostate biopsies in patients with PSA 4-10 ng/ml and nomogram-based risk-score could provide a more robust parameter of assessing the aggressiveness of HGPCa in PSA gray zone.

Developing a new PI-RADS v2-based nomogram for forecasting high-grade prostate cancer

AIM

To establish a predictive nomogram for high-grade prostate cancer (HGPCa) in biopsy-naive patients based on the Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2), magnetic resonance imaging (MRI)-based prostate volume (PV), MRI-based PV-adjusted prostate-specific antigen density (PSAD), and other classical parameters.

MATERIAL AND METHODS

Between August 2014 and August 2015, 158 men who were eligible for analysis were included as the training cohort. A prediction model for HGPCa was built using backward logistic regression and was presented on a nomogram. The prediction model was evaluated by a validation cohort between September 2015 and March 2016 (n=89). Histology of all lesions was obtained with MRI-directed transrectal ultrasound (TRUS)-guided targeted and sectoral biopsy.

RESULTS

The multivariate analysis revealed that patient age, PI-RADS v2 score, and adjusted PSAD were independent predictors for HGPCa. The most discriminative cut-off value for the logistic regression model was 0.33; the sensitivity, specificity, positive predictive value, and negative predictive value were 83.3%, 87.4%, 88.4%, and 81.2%, respectively. The diagnostic performance measures retained similar values in the validation cohort (AUC=0.83).

CONCLUSION

The nomogram for forecasting HGPCa is effective and potentially reducing harm from unnecessary prostate biopsy and over-diagnosis.

MRI evaluation of benign prostatic hyperplasia: Correlation with international prostate symptom score

PURPOSE

To investigate the correlation between magnetic resonance imaging (MRI)-derived prostate parameters and benign prostatic hyperplasia (BPH) type with the International Prostate Symptom Score (IPSS).

MATERIALS AND METHODS

In all, 61 patients (median age, 60; range, 41-81 years) who underwent preoperative MRI and prostatectomy were included in this retrospective study. The MRI-based parameters including total prostate volume (TPV), transition zone (TZ) volume (TZV), TZ index, intravesical prostatic protrusion (IPP), the anterior fibromuscular stroma (AFMS) distance, prostatic urethral angle, bladder wall thickness, urethral wall thickness, urethral compression, urethral wall changes, and BPH type were correlated with total IPSS, IPSS-storage symptom (IPSS-ss), IPSS-voiding symptom (IPSS-vs), and responses to the individual IPSS questions using Spearman (ρ) or Pearson (r) correlation coefficients, one-way analysis of variance (ANOVA), and multiple linear regression.

RESULTS

TPV (r = 0.414, P = 0.001), TZV (r = 0.405, P = 0.001), IPP (r = 0.270, P = 0.04), and AFMS distance (r = 0.363, P = 0.004) correlated with total IPSS. In multiple linear regression analysis, TZV was the only predictor for total IPSS (P = 0.001), IPSS-ss (P < 0.001), IPSS-vs (P = 0.03), and the scores for the IPSS questions 1 (P = 0.03) and 4 (P = 0.001). TPV was a predictor of the scores for questions 2 (P = 0.003), 3 (P = 0.009), and 7 (P < 0.001).

CONCLUSION

Several MRI-derived prostate measurements (TPV, TZV, IPP, AFMS distance) correlated with total IPSS. TZV was the only predictor for total IPSS based on multiple regression analysis.

LEVEL OF EVIDENCE

3 J. Magn. Reson. Imaging 2017;45:917-925.

Predicting Gleason score using the initial serum total prostate-specific antigen in Black men with symptomatic prostate adenocarcinoma in Nigeria

Background

Men of Black African descent are known to have the highest incidence of prostate cancer. The disease is also more aggressive in this group possibly due to biologically more aggressive tumor or late presentation. Currently, serum prostate-specific antigen (PSA) assay plays a significant role in making the diagnosis of prostate cancer. However, the obtained value of serum PSA may not directly relate with the Gleason score (GS), a measure of tumor aggression in prostate cancer. This study explores the relationship between serum total PSA at presentation (iPSA) and GS.

Patients and methods

The iPSA of patients with histologically confirmed prostate cancer was compared with the obtained GS of the prostate biopsy specimens. The age of the patients at presentation and the prostate volumes were also analyzed with respect to the iPSA and GS. The data were analyzed retrospectively using IBM SPSS Version 20. Pearson correlation was used for numeric variables, whereas Fisher’s exact test was used for categorical variables. Significance was set at P≤0.05.

Results

There were 205 patients from January 2010 to November 2013 who satisfied the inclusion criteria. iPSA as well as age at presentation and prostate volume were not found to significantly correlate with the primary Gleason grade, the secondary Gleason grade, or the GS. However, the presence of distant metastasis was identified to significantly correlate positively with GS.

Conclusion

GS may not be confidently predicted by the iPSA. Higher iPSA does not correlate with higher GS and vice versa.

MRI-based prostate volume-adjusted prostate-specific antigen in the diagnosis of prostate cancer

PURPOSE

To determine whether prostate-specific antigen (PSA) levels adjusted by prostate and zonal volumes estimated from magnetic resonance imaging (MRI) improve the diagnosis of prostate cancer (PCa) and differentiation between patients who harbor high-Gleason-sum PCa and those without PCa.

MATERIALS AND METHODS

This retrospective study was Health Insurance Portability and Accountability Act (HIPAA)-compliant and approved by the Institutional Review Board of participating medical institutions. T2 -weighted MR images were acquired for 61 PCa patients and 100 patients with elevated PSA but without PCa. Computer methods were used to segment prostate and zonal structures and to estimate the total prostate and central-gland (CG) volumes, which were then used to calculate CG volume fraction, PSA density, and PSA density adjusted by CG volume. These quantities were used to differentiate patients with and without PCa. Area under the receiver operating characteristic curve (AUC) was used as the figure of merit.

RESULTS

The total prostate and CG volumes, CG volume fraction, and PSA density adjusted by the total prostate and CG volumes were statistically significantly different between patients with PCa and patients without PCa (P ≤ 0.007). AUC values for the total prostate and CG volumes, and PSA density adjusted by CG volume, were 0.68 ± 0.04, 0.68 ± 0.04, and 0.66 ± 0.04, respectively, and were significantly better than that of PSA (P < 0.02), for differentiation of PCa patients from patients without PCa.

CONCLUSION

The total prostate and CG volumes estimated from T2 -weighted MR images and PSA density adjusted by these volumes can improve the effectiveness of PSA for the diagnosis of PCa and differentiation of high-Gleason-sum PCa patients from patients without PCa.

Prostate volume and biopsy tumor length are significant predictors for classical and redefined insignificant cancer on prostatectomy specimens in Japanese men with favorable pathologic features on biopsy

Background

Gleason pattern 3 less often has molecular abnormalities and often behaves indolent. It is controversial whether low grade small foci of prostate cancer (PCa) on biopsy could avoid immediate treatment or not, because substantial cases harbor unfavorable pathologic results on prostatectomy specimens. This study was designed to identify clinical predictors for classical and redefined insignificant cancer on prostatectomy specimens in Japanese men with favorable pathologic features on biopsy.

Methods

Retrospective review of 1040 PCa Japanese patients underwent radical prostatectomy between 2006 and 2013. Of those, 170 patients (16.3%) met the inclusion criteria of clinical stage ≤ cT2a, Gleason score (GS) ≤ 6, up to two positive biopsies, and no more than 50% of cancer involvement in any core. The associations between preoperative data and unfavorable pathologic results of prostatectomy specimens, and oncological outcome were analyzed. The definition of insignificant cancer consisted of pathologic stage ≤ pT2, GS ≤ 6, and an index tumor volume < 0.5 mL (classical) or 1.3 mL (redefined).

Results

Pathologic stage ≥ pT3, upgraded GS, index tumor volume ≥ 0.5 mL, and ≥ 1.3 mL were detected in 25 (14.7%), 77 (45.3%), 83 (48.8%), and 53 patients (31.2%), respectively. Less than half of cases had classical (41.2%) and redefined (47.6%) insignificant cancer. The 5-year recurrence-free survival was 86.8%, and the insignificant cancers essentially did not relapse regardless of the surgical margin status. MRI-estimated prostate volume, tumor length on biopsy, prostate-specific antigen density (PSAD), and findings of magnetic resonance imaging were associated with the presence of classical and redefined insignificant cancer. Large prostate volume and short tumor length on biopsy remained as independent predictors in multivariate analysis.

Conclusions

Favorable features of biopsy often are followed by adverse pathologic findings on prostatectomy specimens despite fulfilling the established criteria. The finding that prostate volume is important does not simply mirror many other studies showing PSAD is important, and the clinical criteria for risk assessment before definitive therapy or active surveillance should incorporate these significant factors other than clinical T-staging or PSAD to minimize under-estimation of cancer in Japanese patients with low-risk PCa.