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

The detection rate for prostate cancer in systematic and targeted prostate biopsy in biopsy-naive patients, according to the localization of the lesion at the mpMRI: A single-center retrospective observational study

OBJECTIVE

Evaluate the detection rates of systematic, targeted and combined cores at biopsy according to tumor positions in biopsy-naïve patients.

MATERIAL AND METHODS

A retrospective analysis of a single-center patient cohort (n = 501) that underwent transrectal prostate biopsy between January 2017 and December 2019 was performed. Multi-parametric MRI was executed as a prebiopsy investigation. Biopsy protocol included, for each patient, 12 systematic cores plus 3 to 5 targeted cores per lesion identified at the mpMRI. Pearson and McNemar chi-squared tests were used for statistical analysis to compare tumor location-related detection rates of systematic, targeted and combined (systematic + targeted) cores at biopsy.

RESULTS

Median age of patients was 70 years (IQR 62-72), with a median PSA of 8.5 ng/ml (IQR 5.7-15.6). Positive biopsies were obtained in 67.7% of cases. Overall, targeted cores obtained higher detection rates compared to systematic cores (54.3% vs. 43.1%, p < 0.0001). Differences in detection rates were, however, higher for tumors located at the apex (61.1% vs. 26.3%, p < 0.05) and anteriorly (44.4% vs. 19.3%, p < 0.05). Targeted cores similarly obtained higher detection rates in the posterior zone of the prostate gland for clinically significant prostate cancer. A poor agreement was reported between targeted and systematic cores for the apex and anterior zone of the prostate with, respectively κ = 0.028 and κ = -0.018.

CONCLUSION

A combined approach of targeted and systematic biopsy delivers the highest detection rate in prostate cancer (PCa). The location of the tumor could however greatly influence overall detection rates, indicating the possibility to omit (as for the base or posterior zone of the gland) or add (as for the apex or anterior zone of the gland) further targeted cores.

Diagnostic Performance of Prostate-specific Antigen Density for Detecting Clinically Significant Prostate Cancer in the Era of Magnetic Resonance Imaging: A Systematic Review and Meta-analysis

CONTEXT

There has been a dramatic increase in the use of prostate magnetic resonance imaging (MRI) in the diagnostic workup. With prostate volume calculated from MRI, prostate-specific antigen density (PSAD) now is a ready-to-use parameter for prostate cancer (PCa) risk stratification before prostate biopsy, especially among patients with negative MRI or equivocal lesions.

OBJECTIVE

In this review, we aimed to evaluate the diagnostic performance of PSAD for clinically significant prostate cancer (CSPCa) among patients who received MRI before prostate biopsy.

EVIDENCE ACQUISITION

Two investigators performed a systematic review according of the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) criteria. Studies (published between January 1, 2012, and December 31, 2021) reporting the diagnostic performance (outcomes) of PSAD (intervention) for CSPCa among men who received prebiopsy prostate MRI and subsequent prostate biopsy (patients), using biopsy pathology as the gold standard (comparison), were eligible for inclusion.

EVIDENCE SYNTHESIS

A total of 1536 papers were identified in PubMed, Scopus, and Embase. Of these, 248 studies were reviewed in detail and 39 were qualified. The pooled sensitivity (SENS) and specificity (SPEC) for diagnosing CSPCa among patients with positive MRI were, respectively, 0.87 and 0.35 for PSAD of 0.1 ng/ml/ml, 0.74 and 0.61 for PSAD of 0.15 ng/ml/ml, and 0.51 and 0.81 for PSAD of 0.2 ng/ml/ml. The pooled SENS and SPEC for diagnosing CSPCa among patients with negative MRI were, respectively, 0.85 and 0.36 for PSAD of 0.1 ng/ml/ml, 0.60 and 0.66 for PSAD of 0.15 ng/ml/ml, and 0.33 and 0.84 for PSAD of 0.2 ng/ml/ml. The pooled SENS and SPEC among patients with Prostate Imaging Reporting and Data System (PI-RADS) 3 or Likert 3 lesions were, respectively, 0.87 and 0.39 for PSAD of 0.1 ng/ml/ml, 0.61 and 0.69 for PSAD of 0.15 ng/ml/ml, and 0.42 and 0.82 for PSAD of 0.2 ng/ml/ml. The post-test probability for CSPCa among patients with negative MRI was 6% if PSAD was <0.15 ng/ml/ml and dropped to 4% if PSAD was <0.10 ng/ml/ml.

CONCLUSIONS

In this systematic review, we quantitatively evaluated the diagnosis performance of PSAD for CSPCa in combination with prostate MRI. It demonstrated a complementary performance and predictive value, especially among patients with negative MRI and PI-RADS 3 or Likert 3 lesions. Integration of PSAD into decision-making for prostate biopsy may facilitate improved risk-adjusted care.

PATIENT SUMMARY

Prostate-specific antigen density is a ready-to-use parameter in the era of increased magnetic resonance imaging (MRI) use in clinically significant prostate cancer (CSPCa) diagnosis. Findings suggest that the chance of having CSPCa was very low (4% or 6% for those with negative prebiopsy MRI or Prostate Imaging Reporting and Data System (Likert) score 3 lesion, respectively, if the PSAD was <0.10 ng/ml/ml), which may lower the need for biopsy in these patients.

Modelling the compression of a soft ellipsoid fingertip

A purely mechanical-driven haptic feedback system was developed for amputees by [G. Shi et al., IEEE Trans. Haptics, 2020, 13, 204-210]. The fingertip ellipsoid modulates the compression force and transmits it to the feedback actuator when the finger interacts with an object. In this paper, the haptic feedback system has been modelled using finite deformation theory. For the ellipsoid fingertip, the compression behaviour between two rigid, flat surfaces has been studied and can predict the force-indentation trend and deformed shape of the membrane with the contact area. For the feedback actuator, the model for the flat membrane is developed with elastic theory, in which the deformation resulting in contact area increase has been studied. The model has been validated with experimental results, which consists of the fingertip ellipsoid membrane being compressed by a rigid surface and the feedback actuator being pressurised. The results of force-indentation, pressure-indentation and the deformation of the membrane from ellipsoid modelling lay within the experimental data and fit the non-linear trend well. The results from modelling the feedback actuator have the same trend as the experimental data in the force-pressure relationship. The haptic feedback system is consistent as a functional tactile sensor after validation. We present the modelling and validation of the proposed model for the mechanical driven haptic feedback system.

Improving Prostatic Preoperative Volume Estimation and Planning before Laser Enucleation

We aimed to validate a formula for improving the estimation of prostatic volume by abdominal ultrasound (AUS) prior to transurethral laser enucleation. A total of 293 patients treated for benign prostate hyperplasia (BPH) by laser enucleation from 2019−2022 were included. The preoperative AUS volume was adjusted by the formula 1.082 × Age + 0.523 × AUS − 53.845, which was based on specimens retrieved by suprapubic prostatectomy. The results were compared to the weight of the tissue removed by laser enucleation as determined by the intraclass correlation coefficient test (ICC). The potential impact of preoperative planning on operating time was calculated. The ICC between the adjusted volumes and the enucleated tissue weights was 0.86 (p < 0.001). The adjusted volume was more accurate than the AUS volume (weight-to-volume ratio of 0.84 vs. 0.7, p < 0.001) and even more precise for prostates weighing >80 g. The median operating time was 90 min. The adjusted volume estimation resulted in an overall shorter expected preoperative operating time by a median of 21 min (24%) and by a median of 40 min in prostates weighing >80 g. The adjustment formula accurately predicts prostate volume before laser enucleation procedures and may significantly improve preoperative planning, the matching of a surgeon’s level of expertise, and the management of patients’ expectations.

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.

Prostate MRI percentage tumor involvement or "PI-RADS percent" as a predictor of adverse surgical pathology

BACKGROUND

This study assesses magnetic resonance imaging (MRI) prostate % tumor involvement or "PI-RADs percent" as a predictor of adverse pathology (AP) after surgery for localized prostate cancer (PCa). Two separate variables, "All PI-RADS percent" (APP) and "Highest PI-RADS percent" (HPP), are defined as the volume of All PI-RADS 3-5 score lesions on MRI and the volume of the Highest PI-RADS 3-5 score lesion each divided by TPV, respectively.

METHOD

An analysis was done of an IRB approved prospective cohort of 557 patients with localized PCa who had targeted biopsy of MRI PIRADs 3-5 lesions followed by RARP from April 2015 to May 2020 performed by a single surgeon at a single center. AP was defined as ISUP GGG ≥3, pT stage ≥T3 and/or LNI. Univariate and multivariable analyses were used to evaluate APP and HPP at predicting AP with other clinical variables such as Age, PSA at surgery, Race, Biopsy GGG, mpMRI ECE and mpMRI SVI. Internal and External Validation demonstrated predicted probabilities versus observed probabilities.

RESULTS

AP was reported in 44.5% (n = 248) of patients. Multivariable regression showed both APP (odds ratio [OR]: 1.10, 95% confidence interval [CI]: 1.04-1.14, p = 0.0007) and HPP (OR: 1.10; 95% CI: 1.04-1.16; p = 0.0007) were significantly associated with AP with individual area under the operating curves (AUCs) of 0.6142 and 0.6229, respectively, and AUCs of 0.8129 and 0.8124 when incorporated in models including preoperative PSA and highest biopsy GGG.

CONCLUSIONS

Increasing PI-RADS Percent was associated with a higher risk of AP, and both APP and HPP may have clinical utility as predictors of AP in GGG 1 and 2 patients being considered for AS.

PATIENT SUMMARY

Using PIRADs percent to predict AP for presurgical patients may help risk stratification, and for low and low volume intermediate risk patients, may influence treatment decisions.

A New Preclinical Decision Support System Based on PET Radiomics: A Preliminary Study on the Evaluation of an Innovative 64Cu-Labeled Chelator in Mouse Models

The 64Cu-labeled chelator was analyzed in vivo by positron emission tomography (PET) imaging to evaluate its biodistribution in a murine model at different acquisition times. For this purpose, nine 6-week-old female Balb/C nude strain mice underwent micro-PET imaging at three different time points after 64Cu-labeled chelator injection. Specifically, the mice were divided into group 1 (acquisition 1 h after [64Cu] chelator administration, n = 3 mice), group 2 (acquisition 4 h after [64Cu]chelator administration, n = 3 mice), and group 3 (acquisition 24 h after [64Cu] chelator administration, n = 3 mice). Successively, all PET studies were segmented by means of registration with a standard template space (3D whole-body Digimouse atlas), and 108 radiomics features were extracted from seven organs (namely, heart, bladder, stomach, liver, spleen, kidney, and lung) to investigate possible changes over time in [64Cu]chelator biodistribution. The one-way analysis of variance and post hoc Tukey Honestly Significant Difference test revealed that, while heart, stomach, spleen, kidney, and lung districts showed a very low percentage of radiomics features with significant variations (p-value < 0.05) among the three groups of mice, a large number of features (greater than 60% and 50%, respectively) that varied significantly between groups were observed in bladder and liver, indicating a different in vivo uptake of the 64Cu-labeled chelator over time. The proposed methodology may improve the method of calculating the [64Cu]chelator biodistribution and open the way towards a decision support system in the field of new radiopharmaceuticals used in preclinical imaging trials.

AutoProstate: Towards Automated Reporting of Prostate MRI for Prostate Cancer Assessment Using Deep Learning

Multiparametric magnetic resonance imaging (mpMRI) of the prostate is used by radiologists to identify, score, and stage abnormalities that may correspond to clinically significant prostate cancer (CSPCa). Automatic assessment of prostate mpMRI using artificial intelligence algorithms may facilitate a reduction in missed cancers and unnecessary biopsies, an increase in inter-observer agreement between radiologists, and an improvement in reporting quality. In this work, we introduce AutoProstate, a deep learning-powered framework for automatic MRI-based prostate cancer assessment. AutoProstate comprises of three modules: Zone-Segmenter, CSPCa-Segmenter, and Report-Generator. Zone-Segmenter segments the prostatic zones on T2-weighted imaging, CSPCa-Segmenter detects and segments CSPCa lesions using biparametric MRI, and Report-Generator generates an automatic web-based report containing four sections: Patient Details, Prostate Size and PSA Density, Clinically Significant Lesion Candidates, and Findings Summary. In our experiment, AutoProstate was trained using the publicly available PROSTATEx dataset, and externally validated using the PICTURE dataset. Moreover, the performance of AutoProstate was compared to the performance of an experienced radiologist who prospectively read PICTURE dataset cases. In comparison to the radiologist, AutoProstate showed statistically significant improvements in prostate volume and prostate-specific antigen density estimation. Furthermore, AutoProstate matched the CSPCa lesion detection sensitivity of the radiologist, which is paramount, but produced more false positive detections.

Three-dimensional measurement and analysis of benign prostatic hyperplasia

Background

The volume and thickness of intravesical prostatic protrusion and other characteristics of benign prostatic hyperplasia have not been investigated. We determine the effects of age and prostate volume on anatomical features of benign prostatic hyperplasia using three-dimensional measurement in this study.

Methods

This retrospective study included a total of 98 patients with benign prostatic hyperplasia. Three-dimensional models of prostate, central gland, peripheral zone, intravesical prostatic protrusion, prostatic urethra and bladder were reconstructed according to pelvic T2-weighted magnetic resonance imaging of these patients. The models were used to measure the intravesical prostatic protrusion volume, intravesical prostatic protrusion thickness, intravesical prostatic protrusion index, intravesical prostatic protrusion, prostate volume, peripheral zone volume, peripheral zone thickness, peripheral zone index, prostatic urethra thickness, the angle and distance of distal prostatic urethra with regard to coronal plane and sagittal plane and so on.

Results

Intravesical prostatic protrusion volume, intravesical prostatic protrusion thickness and peripheral zone volume of prostate volume >80 mL group were significantly higher than these in prostate volume <80 mL group (P<0.001, 0.01, 0.01, respectively). These parameters significantly increased with age (P<0.001, 0.01, 0.05, respectively). Peripheral zone index was significantly lower of prostate volume >80 mL group than these in prostate volume <80 mL group (P<0.05). Peripheral zone index significantly decreased with age (P<0.01). Intravesical prostatic protrusion index had no significant difference in all age groups. Peripheral zone thickness and prostatic urethra thickness had no significant difference in all groups. The distance and angle of distal prostatic urethra prostatic urethra with regard to coronal plane were significantly higher than these with regard to sagittal plane (both P<0.001).

Conclusions

The rearward slope of the prostatic urethra is greater than the left or right offset during the process of benign prostatic hyperplasia. Three-dimensional measurement provides good supports for further clinical and scientific research.

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.

Novel ultrasound-based volume estimation of prostatic benign enlargement to improve decision-making on surgical approach

Aim:

To assess the precision of preoperative ultrasonography (US)-determined prostate volume and to propose formulas for improving it.

Methods:

This retrospective study comprised 155 consecutive men who underwent open prostatectomy for benign prostatic hyperplasia (BPH) between 2013 and 2019. Preoperative prostate volume was estimated by either abdominal US (AUS) (n = 92) or transrectal US (TRUS) (n = 63), and was compared with the weight of surgically enucleated tissue at a conversion rate of 1 ml (US) = 1 g tissue. Statistical analysis was conducted and a novel formula for prostate volume was constructed.

Results:

The median prostate volumes by AUS and TRUS were 140 ml [interquartile ratio (IQR) 111–182] and 108 ml (IQR 93–120), respectively. Enucleated tissue weight was lower than the AUS assessment by a median difference of 50 g (IQR 28.7–75.7; p < 0.001), and lower than the TRUS assessment by a median difference of 27 g, IQR 10–43, p < 0.001). Using a cutoff of 80 ml, 30 (33%) AUS patients and 23 (36%) TRUS patients underwent unneeded open procedures. Mathematical calculations revealed two formulas that significantly adjusted for the actual weight: 1.082*Age + 0.523*AUS − 53.845 for AUS and 0.138*age + 2.22*prostate-specific antigen + 0.453*TRUS + 11.682 for TRUS (p < 0.001). These formulas increased the overall US prostate volume accuracy from 65% to 85%.

Conclusion:

Assessment of prostate volume by US is imprecise for decision-making of whether to perform open simple prostatectomy for BPH. Our novel formulas may enhance stratification of patients with prostatic enlargement to a more optimal surgical approach. Future studies in larger cohorts are needed to substantiate our results.

Investigating the Performance of Generative Adversarial Networks for Prostate Tissue Detection and Segmentation

The manual delineation of region of interest (RoI) in 3D magnetic resonance imaging (MRI) of the prostate is time-consuming and subjective. Correct identification of prostate tissue is helpful to define a precise RoI to be used in CAD systems in clinical practice during diagnostic imaging, radiotherapy and monitoring the progress of disease. Conditional GAN (cGAN), cycleGAN and U-Net models and their performances were studied for the detection and segmentation of prostate tissue in 3D multi-parametric MRI scans. These models were trained and evaluated on MRI data from 40 patients with biopsy-proven prostate cancer. Due to the limited amount of available training data, three augmentation schemes were proposed to artificially increase the training samples. These models were tested on a clinical dataset annotated for this study and on a public dataset (PROMISE12). The cGAN model outperformed the U-Net and cycleGAN predictions owing to the inclusion of paired image supervision. Based on our quantitative results, cGAN gained a Dice score of 0.78 and 0.75 on the private and the PROMISE12 public datasets, respectively.