Clinical value of prostate segmentation and volume determination on MRI in benign prostatic hyperplasia

The Most Accurate Technique and Formulation for Prostate Volume Estimation: A Comparative Analysis of Transrectal Ultrasonography, Magnetic Resonance Imaging, and Three-Dimensional Segmentation

Introduction: Prostate volume estimation is of great importance for patient evaluation in a urologist's clinical practice. The accuracy and superiority of the techniques used in volume calculation have always been the subject of debate. Therefore, we conducted a comparative analysis between the volumes derived from transrectal ultrasonography (TRUS), multiparametric prostate magnetic resonance imaging (MpMRI), and three-dimensional (3D)-constructed MpMRI images of patients, who underwent retropubic radical prostatectomy at our institution. Methods: The data of patients with preoperative TRUS and MpMRI who underwent radical prostatectomy (Rp) in our clinic between August 2021 and February 2023 were retrospectively reviewed. The prostatectomy specimens were taken to the pathology department without exposure to any fixative and measured with the water displacement method. All axial T2-weighted sequences were segmented by a single surgeon using 3D Slicer (v. 5.6.2) software, and all measurements were compared with the specimen volume measured at the pathology laboratory. Results: A total of 150 patients were included in this study. The median prostate volumes estimated by TRUS-ellipsoid, TRUS-bullet, MpMRI, and 3D segmentation were 43.45 cc (min.-max.: 15.1-122.6), 54.32 cc (min.-max.: 18.9-153.3), 44.05 cc (min.-max.: 15.4-128.9), and 43.11 cc (min.-max.: 14.3-110.6), respectively. The median Rp specimen volume measurement in the pathology department was 42 cc (min.-max.: 12-114). When the measurement techniques were compared between each other, it has been shown that the statistically significant difference was caused by TRUS-bullet measurement. No statistically significant difference between the other three measurement techniques as well as between them and the specimen volume measurements were detected. Conclusion: Consistent with the findings of previous studies, MpMRI has provided estimations closer to pathology measurements and 3D segmentation allows even more precise measurements. However, considering accessibility, reproducibility, time efficiency, and cost, TRUS-based measurements can be safely used in clinical practice, especially using the ellipsoid formula.

Prostate volume on computed tomography correlates well with magnetic resonance imaging measurements and is reproducible across rater training levels

BACKGROUND

Data are lacking for the accuracy of computed tomography (CT) in measuring prostate size, which can streamline care and prevent invasive procedures. We evaluate agreement and intra/inter-observer variability in prostate sizing between CT and magnetic resonance imaging (MRI) planimetry for a wide range of gland sizes.

METHODS

We retrospectively reviewed 700 patients who underwent MRI fusion biopsy at a single institution and identified 89 patients that had a CT within 2 years of the MRI. Six reviewers from different training levels were categorized as student, resident, or attending and each measured prostate size on CT by the prolate ellipse method. Bland-Altman analysis determined the degree of agreement between CT and MRI. Inter- and intra-observer reliability was calculated for CT.

RESULTS

Mean CT volume was higher than MRI volume in the < 60 g group (51.5 g vs. 44.5 g, p = 0.004), but not in the ≥ 60 g group (101 g vs. 100 g, p = 0.458). The bias for overestimation of prostate volume by CT was 4.1 g across prostate volumes, but the proportional agreement between modalities improved with size. The Pearson correlation coefficient between CT/MRI and inter/intra-rater reliability for CT increased in the ≥ 60 g vs. the < 60 g group for all training levels.

CONCLUSIONS

Our data show that there is greater clinical utility for prostate size estimation by CT than previously established, particularly for larger glands where accurate size estimation may influence therapeutic decisions. In larger glands, prostate size estimation by CT is also reproducible across various training levels.

Peripheral zone thickness in preoperative MRI is predictive of Trifecta achievement after Holmium laser enucleation of the prostate (HoLEP)

PURPOSE

To investigate various anatomical features of the prostate using preoperative MRI and patients' clinical factors to identify predictors of successful Holmium:YAG laser enucleation of the prostate (HoLEP).

METHODS

71 patients who had received HoLEP and undergone a 3.0-T prostate MRI scan within 6 months before surgery were retrospectively enrolled. MRI features (e.g., total prostate and transitional zone volume, peripheral zone thickness [PZT], BPH patterns, prostatic urethral angle, intravesical prostatic protrusion, etc.) and clinical data (e.g., age, body mass index, surgical technique, etc.) were analyzed using univariable and multivariable logistic regression to identify predictors of successful HoLEP. Successful HoLEP was defined as achieving the Trifecta, characterized by the contemporary absence of postoperative complications within 3 months, a 3-month postoperative maximum flow rate (Qmax) > 15 mL/s, and no urinary incontinence at 3 months postoperatively.

RESULTS

Trifecta achievement at 3 months post-surgery was observed in 37 (52%) patients. Patients with Trifecta achievement exhibited a lower preoperative IPSS-quality of life score (QoL) (4.1 vs. 4.5, P = 0.016) and a thinner preoperative peripheral zone thickness (PZT) on MRI (7.9 vs.10.3 mm, P < 0.001). In the multivariable regression analysis, a preoperative IPSS-QoL score < 5 (OR 3.98; 95% CI, 1.21-13.07; P = 0.017) and PZT < 9 mm (OR 11.51; 95% CI, 3.51-37.74; P < 0.001) were significant predictors of Trifecta achievement after HoLEP.

CONCLUSIONS

Alongside the preoperative QoL score, PZT measurement in prostate MRI can serve as an objective predictor of successful HoLEP. Our results underscore an additional utility of prostate MRI beyond its role in excluding concurrent prostate cancer.

What the urologist needs to know before radical prostatectomy: MRI effective support to pre-surgery planning

BACKGROUND

Radical prostatectomy (RP) is recommended in case of localized or locally advanced prostate cancer (PCa), but it can lead to side effects, including urinary incontinence (UI) and erectile dysfunction (ED). Magnetic resonance imaging (MRI) is recommended for PCa diagnosis and staging, but it can also improve preoperative risk-stratification.

PURPOSE

This nonsystematic review aims to provide an overview on factors involved in RP side effects, highlighting anatomical and pathological aspects that could be included in a structured report.

EVIDENCE SYNTHESIS

Considering UI evaluation, MR can investigate membranous urethra length (MUL), prostate volume, the urethral sphincter complex, and the presence of prostate median lobe. Longer MUL measurement based on MRI is linked to a higher likelihood of achieving continence restoration. For ED assessment, MRI and diffusion tensor imaging identify the neurovascular bundle and they can aid in surgery planning. Finally, MRI can precisely describe extra-prostatic extension, prostate apex characteristics and lymph-node involvement, providing valuable preoperative information for PCa treatment.

CONCLUSIONS

Anatomical principals structures involved in RP side effects can be assessed with MR. A standardized MR report detailing these structures could assist urologists in planning optimal and tailored surgical techniques, reducing complications, and improving patients' care.

Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future.

Comparison of prostate volume measured by transabdominal ultrasound and MRI with the radical prostatectomy specimen volume: a retrospective observational study

BACKGROUND

Few studies have compared the use of transabdominal ultrasound (TAUS) and magnetic resonance imaging (MRI) to measure prostate volume (PV). In this study, we evaluate the accuracy and reliability of PV measured by TAUS and MRI.

METHODS

A total of 106 patients who underwent TAUS and MRI prior to radical prostatectomy were retrospectively analyzed. The TAUS-based and MRI-based PV were calculated using the ellipsoid formula. The specimen volume measured by the water-displacement method was used as a reference standard. Correlation analysis and intraclass correlation coefficients (ICC) were performed to compare different measurement methods and Bland Altman plots were drawn to assess the agreement.

RESULTS

There was a high degree of correlation and agreement between the specimen volume and PV measured with TAUS (r = 0.838, p < 0.01; ICC = 0.83) and MRI (r = 0.914, p < 0.01; ICC = 0.90). TAUS overestimated specimen volume by 2.4ml, but the difference was independent of specimen volume (p = 0.19). MRI underestimated specimen volume by 1.7ml, the direction and magnitude of the difference varied with specimen volume (p < 0.01). The percentage error of PV measured by TAUS and MRI was within ± 20% in 65/106(61%) and 87/106(82%), respectively. In patients with PV greater than 50 ml, MRI volume still correlated strongly with specimen volume (r = 0.837, p < 0.01), while TAUS volume showed only moderate correlation with specimen (r = 0.665, p < 0.01) or MRI volume (r = 0.678, p < 0.01).

CONCLUSIONS

This study demonstrated that PV measured by MRI and TAUS is highly correlated and reliable with the specimen volume. MRI might be a more appropriate choice for measuring the large prostate.

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.

Comparison of malignancy and spatial distribution between latent and clinical prostate cancer: an 8-year biopsy study

Background

Current prostate cancer (PCa) screening may detect nonprogressive lesion, leading to overdiagnosis and overtreatment. The purpose of the present study is to investigate whether the tumor pathological origin of latent prostate cancer (lPCa) and clinical prostate cancer (cPCa) are consistent, and to verify the current clinically significant prostate cancer criteria.

Methods

Prostate specimens were obtained from postmortem autopsy between 2014 and 2021 and patients who went through radical prostatectomy from 2013 to 2021. The pathological characteristics and spatial distribution of the lPCa group and cPCa group were compared and analyzed through SPSS software with P < 0.05 representing statistical significant.

Results

In lPCa group, a total of 45 tumor lesions from 24 lPCa cases were included, 54.2% of lPCa patients were ISUP ≥ 2, 12.5% had tumor volume ≥ 0.5 ml, and 16.7% had extraprostatic extension (EPE). In cPCa group, there were a total of 429 tumor lesions in 126 cases, 92.1% of cPCa patients were ISUP ≥ 2, and 82.5% had tumor volume of ≥ 0.5 ml. 36.3% had EPE. LPCa and cPCa have the same spatial distribution characteristics, and no significant difference was detected between the anterior and posterior zone. Peripheral zone tumors were significantly more common than transitional zone tumors. Tumors in apical 1/3 and middle 1/3 were significantly more common than basal 1/3.

Conclusion

The malignancy of cPCa is significantly higher than that of lPCa, and the spatial distribution of cPCa and lPCa is consistent. ISUP grade 2 is not sufficient to determine clinical significance of tumor.

Tasks for artificial intelligence in prostate MRI

The advent of precision medicine, increasing clinical needs, and imaging availability among many other factors in the prostate cancer diagnostic pathway has engendered the utilization of artificial intelligence (AI). AI carries a vast number of potential applications in every step of the prostate cancer diagnostic pathway from classifying/improving prostate multiparametric magnetic resonance image quality, prostate segmentation, anatomically segmenting cancer suspicious foci, detecting and differentiating clinically insignificant cancers from clinically significant cancers on a voxel-level, and classifying entire lesions into Prostate Imaging Reporting and Data System categories/Gleason scores. Multiple studies in all these areas have shown many promising results approximating accuracies of radiologists. Despite this flourishing research, more prospective multicenter studies are needed to uncover the full impact and utility of AI on improving radiologist performance and clinical management of prostate cancer. In this narrative review, we aim to introduce emerging medical imaging AI paper quality metrics such as the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and Field-Weighted Citation Impact (FWCI), dive into some of the top AI models for segmentation, detection, and classification.

Application of machine learning in liver transplantation: a review

BACKGROUND

Machine learning (ML) has been increasingly applied in the health-care and liver transplant setting. The demand for liver transplantation continues to expand on an international scale, and with advanced aging and complex comorbidities, many challenges throughout the transplantation decision-making process must be better addressed. There exist massive datasets with hidden, non-linear relationships between demographic, clinical, laboratory, genetic, and imaging parameters that conventional methods fail to capitalize on when reviewing their predictive potential. Pre-transplant challenges include addressing efficacies of liver segmentation, hepatic steatosis assessment, and graft allocation. Post-transplant applications include predicting patient survival, graft rejection and failure, and post-operative morbidity risk.

AIM

In this review, we describe a comprehensive summary of ML applications in liver transplantation including the clinical context and how to overcome challenges for clinical implementation.

METHODS

Twenty-nine articles were identified from Ovid MEDLINE, MEDLINE Epub Ahead of Print and In-Process and Other Non-Indexed Citations, Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials.

CONCLUSION

ML is vastly interrogated in liver transplantation with promising applications in pre- and post-transplant settings. Although challenges exist including site-specific training requirements, the demand for more multi-center studies, and optimization hurdles for clinical interpretability, the powerful potential of ML merits further exploration to enhance patient care.

Bisphenol A exposure triggers the malignant transformation of prostatic hyperplasia in beagle dogs via cfa-miR-204/KRAS axis

The prostatic toxicity of bisphenol A (BPA) exposure is mainly associated with hormonal disturbances, thus interfering with multiple signal pathways and increasing the susceptibility to prostatic lesions. This study concentrates predominantly on the potential effect and mechanisms of low-dose BPA exposure on prostates in adult beagle dogs. The dogs were orally given BPA (2, 6, 18 μg/kg/day) and vehicle for 8 weeks, followed by blood collection and dissection. The ascended organ coefficient and volume of prostates, thickened epithelium, as well as histopathological observation have manifested that BPA exposure could trigger the aberrant prostatic hyperplasia in beagle dogs. Hormone level detection revealed that the ratios of estradiol (E₂) to testosterone (T) (E₂/T) and prolactin (PRL) to T (PRL/T) were up-regulated in the serum from BPA group. Based on microRNA (miRNA) microarray screening and functional enrichment analysis, BPA might facilitate the progression of prostate tumorigenesis in beagle dogs via cfa-miR-204 and its downstream target KRAS oncogene. Subsequently, the overexpression of KRAS, CDKN1A, MAPK1, VEGFA, BCL2 and PTGS2 was validated. These findings provide a series of underlying targets for preventing the initiation and metastasis of BPA-induced prostatic hyperplasia and tumorigenesis, while the regulatory relationship headed with KRAS requires further investigation.

Deep learning-based artificial intelligence applications in prostate MRI: brief summary

Prostate cancer (PCa) is the most common cancer type in males in the Western World. MRI has an established role in diagnosis of PCa through guiding biopsies. Due to multistep complex nature of the MRI-guided PCa diagnosis pathway, diagnostic performance has a big variation. Developing artificial intelligence (AI) models using machine learning, particularly deep learning, has an expanding role in radiology. Specifically, for prostate MRI, several AI approaches have been defined in the literature for prostate segmentation, lesion detection and classification with the aim of improving diagnostic performance and interobserver agreement. In this review article, we summarize the use of radiology applications of AI in prostate MRI.

Prostate volume prediction on MRI: tools, accuracy and variability

OBJECTIVE

A reliable estimation of prostate volume (PV) is essential to prostate cancer management. The objective of our multi-rater study was to compare intra- and inter-rater variability of PV from manual planimetry and ellipsoid formulas.

METHODS

Forty treatment-naive patients who underwent prostate MRI were selected from a local database. PV and corresponding PSA density (PSAd) were estimated on 3D T2-weighted MRI (3 T) by 7 independent radiologists using the traditional ellipsoid formula (TEF), the newer biproximate ellipsoid formula (BPEF), and the manual planimetry method (MPM) used as ground truth. Intra- and inter-rater variability was calculated using the mixed model-based intraclass correlation coefficient (ICC).

RESULTS

Mean volumes were 67.00 (± 36.61), 66.07 (± 35.03), and 64.77 (± 38.27) cm³ with the TEF, BPEF, and MPM methods, respectively. Both TEF and BPEF overestimated PV relative to MPM, with the former presenting significant differences (+ 1.91 cm³, IQ = [- 0.33 cm³, 5.07 cm³], p val = 0.03). Both intra- (ICC > 0.90) and inter-rater (ICC > 0.90) reproducibility were excellent. MPM had the highest inter-rater reproducibility (ICC = 0.999). Inter-rater PV variation led to discrepancies in classification according to the clinical criterion of PSAd > 0.15 ng/mL for 2 patients (5%), 7 patients (17.5%), and 9 patients (22.5%) when using MPM, TEF, and BPEF, respectively.

CONCLUSION

PV measurements using ellipsoid formulas and MPM are highly reproducible. MPM is a robust method for PV assessment and PSAd calculation, with the lowest variability. TEF showed a high degree of concordance with MPM but a slight overestimation of PV. Precise anatomic landmarks as defined with the BPEF led to a more accurate PV estimation, but also to a higher variability.

KEY POINTS

• Manual planimetry used for prostate volume estimation is robust and reproducible, with the lowest variability between readers. • Ellipsoid formulas are accurate and reproducible but with higher variability between readers. • The traditional ellipsoid formula tends to overestimate prostate volume.

Deep Learning Enables Prostate MRI Segmentation: A Large Cohort Evaluation With Inter-Rater Variability Analysis

Whole-prostate gland (WPG) segmentation plays a significant role in prostate volume measurement, treatment, and biopsy planning. This study evaluated a previously developed automatic WPG segmentation, deep attentive neural network (DANN), on a large, continuous patient cohort to test its feasibility in a clinical setting. With IRB approval and HIPAA compliance, the study cohort included 3,698 3T MRI scans acquired between 2016 and 2020. In total, 335 MRI scans were used to train the model, and 3,210 and 100 were used to conduct the qualitative and quantitative evaluation of the model. In addition, the DANN-enabled prostate volume estimation was evaluated by using 50 MRI scans in comparison with manual prostate volume estimation. For qualitative evaluation, visual grading was used to evaluate the performance of WPG segmentation by two abdominal radiologists, and DANN demonstrated either acceptable or excellent performance in over 96% of the testing cohort on the WPG or each prostate sub-portion (apex, midgland, or base). Two radiologists reached a substantial agreement on WPG and midgland segmentation (κ = 0.75 and 0.63) and moderate agreement on apex and base segmentation (κ = 0.56 and 0.60). For quantitative evaluation, DANN demonstrated a dice similarity coefficient of 0.93 ± 0.02, significantly higher than other baseline methods, such as DeepLab v3+ and UNet (both p values < 0.05). For the volume measurement, 96% of the evaluation cohort achieved differences between the DANN-enabled and manual volume measurement within 95% limits of agreement. In conclusion, the study showed that the DANN achieved sufficient and consistent WPG segmentation on a large, continuous study cohort, demonstrating its great potential to serve as a tool to measure prostate volume.

Evaluation of prostate volume in mpMRI: comparison of the recommendations of PI-RADS v2 and PI-RADS v2.1

PURPOSE

We aimed to evaluate the prostate volumes calculated as recommended in the PI-RADS v2 and PI-RADS v2.1 guidelines, intraobserver and interobserver variability, and the agreement between the two measurement methods.

METHODS

Prostate mpMRI examinations of 114 patients were evaluated retrospectively. T2-weighted sequences in the axial and sagittal planes were used for the measurement of the prostate volume. The measurements were performed by two independent observers as recommended in the PI-RADS v2 and PI-RADS v2.1 guidelines. Both observers conducted the measurements twice and the average values were obtained. In order to prevent bias, the observers carried out measurements at one-week intervals. In order to assess intraobserver variability, observers repeated the measurements again at one-week intervals. The prostate volume was calculated using the ellipsoid formula (W×H×L×0.52).

RESULTS

Intraclass correlation coefficient (ICC) revealed almost perfect agreement between the first and second observers for the measurements according to both PI-RADS v2 (0.93) and PI-RADS v2.1 (0.96) guidelines. The measurements were repeated by both observers. According to the ICC values, there was excellent agreement between the first and second measurements with respect to both PI-RADS v2 and PI-RADS v2.1 for first (0.94 and 0.96, respectively) and second observer (0.94 and 0.97, respectively). For both observers, the differences had a random, homogeneous distribution, and there was no clear relationship between the differences and mean values.

CONCLUSION

The ellipsoid formula is a reliable method for rapid assessment of prostate volume, with excellent intra- and interobserver agreement and no need for expert training. For the height measurement, the recommendations of the PIRADS v2.1 guideline seem to provide more consistently reproducible results.

MRI Evaluation of Patients Before and After Interventions for Benign Prostatic Hyperplasia: An Update

Transurethral resection of the prostate is the most commonly performed procedure for the management of patients with lower urinary tract symptoms attributed to benign prostatic hyperplasia (BPH). However, in recent years, various minimally invasive surgical therapies have been introduced to treat BPH. These include laser-based procedures such as holmium laser enucleation of the prostate and photoselective vaporization of the prostate as well as thermal ablation procedures such as water vapor thermal therapy (Rezūm), all of which result in volume reduction of periurethral prostatic tissue. In comparison, a permanent metallic device (UroLift) can be implanted to pull open the prostatic urethra without an associated decrease in prostate size, and selective catheter-directed prostate artery embolization results in a global decrease in prostate size. The goal of this article is to familiarize radiologists with the underlying anatomic changes that occur in BPH as visualized on MRI and to describe the appearance of the prostate on MRI performed after these procedures. Complications encountered on imaging after these procedures are also discussed. Although MRI is not currently used in the routine preprocedural evaluation of BPH, emerging data support a role for MRI in predicting postprocedure outcomes.

The Comparison of Imaging and Clinical Methods to Estimate Prostate Volume: A Single-Centre Retrospective Study

BACKGROUND

Prostate volume (PV) is a useful tool in risk stratification, diagnosis, and follow-up of numerous prostatic diseases including prostate cancer and benign prostatic hypertrophy. There is currently no accepted ideal PV measurement method.

OBJECTIVE

This study compares multiple means of PV estimation, including digital rectal examination (DRE), transrectal ultrasound (TRUS), and magnetic resonance imaging (MRI), and radical prostatectomy specimens to determine the best volume measurement style.

METHODS

A retrospective, observational, single-site study with patients identified using an institutional database was performed. A total of 197 patients who underwent robot-assisted radical prostatectomy were considered. Data collected included age, serum PSA at the time of the prostate biopsy, clinical T stage, Gleason score, and PVs for each of the following methods: DRE, TRUS, MRI, and surgical specimen weight (SPW) and volume.

RESULTS

A paired t test was performed, which reported a statistically significant difference between PV measures (DRE, TRUS, MRI ellipsoid, MRI bullet, SP ellipsoid, and SP bullet) and the actual prostate weight. Lowest differences were reported for SP ellipsoid volume (M = -2.37; standard deviation [SD] = 10.227; t[167] = -3.011; and p = 0.003), MRI ellipsoid volume (M = -4.318; SD = 9.53; t[167] = -5.87; and p = 0.000), and MRI bullet volume (M = 5.31; SD = 10.77; t[167] = 6.387; and p = 0.000).

CONCLUSION

The PV obtained by MRI has proven to correlate with the PV obtained via auto-segmentation software as well as actual SPW, while also being more cost-effective and time-efficient. Therefore, demonstrating that MRI estimated the PV is an adequate method for use in clinical practice for therapeutic planning and patient follow-up.

Quantitative Characterization of the Prostatic Urethra Using MRI: Implications for Lower Urinary Tract Symptoms in Patients with Benign Prostatic Hyperplasia

INTRODUCTION

The aim of this study was to perform a quantitative assessment of the prostate anatomy with a focus on the relation of prostatic urethral anatomic variation to urinary symptoms.

METHODS

This retrospective study involved patients undergoing magnetic resonance imaging for prostate cancer who were also assessed for lower urinary tract symptoms. Volumetric segmentations were utilized to derive the in vivo prostatic urethral length and urethral trajectory in coronal and sagittal planes using a piece-wise cubic spline function to derive the angle of the urethra within the prostate. Association of anatomical factors with urinary symptoms was evaluated using ordinal univariable and multivariable logistic regression with IPSS score cutoffs of ≤7, 8-19, and >20 to define mild, moderate, and severe symptoms, respectively.

RESULTS

A total of 423 patients were included. On univariable analysis, whole prostate volume, transition zone volume, prostatic urethral length, urethral angle, and retrourethral volume were all significantly associated with worse urinary symptoms. On multivariable analysis prostatic urethral length was associated with urinary symptoms with a normalized odds ratio of 1.5 (95% confidence interval 1.0-2.2, p = 0.04). In a subset analysis of patients on alpha blockers, maximal urethral angle, transition zone volume as well as urethral length were all associated with worse urinary symptoms.

CONCLUSION

Multiple parameters were associated with worse urinary symptoms on univariable analysis, but only prostatic urethral length was associated with worse urinary symptoms on multivariable analysis. This study demonstrates the ability of quantitative assessment of prostatic urethral anatomy to predict lower urinary tract symptoms.

Computer vision applied to dual-energy computed tomography images for precise calcinosis cutis quantification in patients with systemic sclerosis

Background

Although treatments have been proposed for calcinosis cutis (CC) in patients with systemic sclerosis (SSc), a standardized and validated method for CC burden quantification is necessary to enable valid clinical trials. We tested the hypothesis that computer vision applied to dual-energy computed tomography (DECT) finger images is a useful approach for precise and accurate CC quantification in SSc patients.

Methods

De-identified 2-dimensional (2D) DECT images from SSc patients with clinically evident lesser finger CC lesions were obtained. An expert musculoskeletal radiologist confirmed accurate manual segmentation (subtraction) of the phalanges for each image as a gold standard, and a U-Net Convolutional Neural Network (CNN) computer vision model for segmentation of healthy phalanges was developed and tested. A validation study was performed in an independent dataset whereby two independent radiologists manually measured the longest length and perpendicular short axis of each lesion and then calculated an estimated area by assuming the lesion was elliptical using the formula long axis/2 × short axis/2 × π, and a computer scientist used a region growing technique to calculate the area of CC lesions. Spearman’s correlation coefficient, Lin’s concordance correlation coefficient with 95% confidence intervals (CI), and a Bland-Altman plot (Stata V 15.1, College Station, TX) were used to test for equivalence between the radiologists’ and the CNN algorithm-generated area estimates.

Results

Forty de-identified 2D DECT images from SSc patients with clinically evident finger CC lesions were obtained and divided into training (N = 30 with image rotation × 3 to expand the set to N = 120) and test sets (N = 10). In the training set, five hundred epochs (iterations) were required to train the CNN algorithm to segment phalanges from adjacent CC, and accurate segmentation was evaluated using the ten held-out images. To test model performance, CC lesional area estimates calculated by two independent radiologists and a computer scientist were compared (radiologist 1 vs. radiologist 2 and radiologist 1 vs. computer vision approach) using an independent test dataset comprised of 31 images (8 index finger and 23 other fingers). For the two radiologists’, and the radiologist vs. computer vision measurements, Spearman’s rho was 0.91 and 0.94, respectively, both p < 0.0001; Lin’s concordance correlation coefficient was 0.91 (95% CI 0.85–0.98, p < 0.001) and 0.95 (95% CI 0.91–0.99, p < 0.001); and Bland-Altman plots demonstrated a mean difference between radiologist vs. radiologist, and radiologist vs. computer vision area estimates of − 0.5 mm² (95% limits of agreement − 10.0–9.0 mm²) and 1.7 mm² (95% limits of agreement − 6.0–9.5 mm², respectively.

Conclusions

We demonstrate that CNN quantification has a high degree of correlation with expert radiologist measurement of finger CC area measurements. Future work will include segmentation of 3-dimensional (3D) images for volumetric and density quantification, as well as validation in larger, independent cohorts.

Which is the best radiological imaging method for predicting actual prostate weight?

In this study, we compared the weight of the prostate specimen removed after robotic radical prostatectomy with the prostate weight measured pre-operatively by four different imaging modalities. Pre-operative prostate weight before robotic radical prostatectomy was measured by Transabdominal Ultrasonography (TAUS), Transrectal Ultrasonography (TRUS), Abdominal Tomography (CT) and MultiparametricProstate Magnetic Resonance imaging (mpMRI). Of the 170 patients enrolled in the study, the mean age was 65.2 ± 7.08 (46-84) years and mean prostate-specific antigen (PSA) 9.6 ± 7.7 (1.8-50). The mean post-operative actual prostate weight was 63.1 ± 30 gr. The mean pre-operative prostate volumes measured by TAUS, TRUS, CT and MPMRI were 64.5 ± 28.5, 49.1 ± 30.6, 54.5 ± 30.5 and 68.7 ± 31.7 ml, respectively (p < .001). Post-operative actual prostate weight correlated with prostate weight measured by TAUS, TRUS, CT and mpMRI (r coefficient 0.776, 0.802, 0.768 and 0.825 respectively). The best of these was mpMRI. Although prostate weight measured by different imaging methods has a high correlation to predict actual prostate weight, actual prostate weight is best predicted by measurements with mpMRI. However, errors and deviations that may occur with these imaging methods should be taken into consideration.

Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends

Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML), a branch of artificial intelligence, can rapidly and accurately analyze mpMRI images. ML could provide better standardization and consistency in identifying prostate lesions and enhance prostate carcinoma management. This review summarizes ML applications to prostate mpMRI and focuses on prostate organ segmentation, lesion detection and segmentation, and lesion characterization. A literature search was conducted to find studies that have applied ML methods to prostate mpMRI. To date, prostate organ segmentation and volume approximation have been well executed using various ML techniques. Prostate lesion detection and segmentation are much more challenging tasks for ML and were attempted in several studies. They largely remain unsolved problems due to data scarcity and the limitations of current ML algorithms. By contrast, prostate lesion characterization has been successfully completed in several studies because of better data availability. Overall, ML is well situated to become a tool that enhances radiologists' accuracy and speed.

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.

Locally advanced prostate cancer imaging findings and implications for treatment from the surgical perspective

The anatomy of the prostate is reviewed in the context of discussing the staging of prostate cancer and patterns of tumor spread. The utility of prostate magnetic resonance imaging along with new advancements in tumor staging are discussed specifically in locally advanced disease. What should be included in the radiology report carries a substantial weight to formulate the urologist's decision in regards to the selection of surgical candidates, preoperative planning and avoiding postoperative complications.

Measurement of Prostate Volume with MRI (A Guide for the Perplexed): Biproximate Method with Analysis of Precision and Accuracy

To review the anatomic basis of prostate boundary selection on T2-weighted magnetic resonance imaging (MRI). To introduce an alternative 3D ellipsoid measuring technique that maximizes precision, report the intra- and inter-observer reliability, and to advocate it's use for research involving multiple observers. We demonstrate prostate boundary anatomy using gross pathology and MRI examples. This provides background for selecting key boundary marks when measuring prostate volume. An alternative ellipsoid volume method is then proposed using these boundaries in an attempt to improve inter-observer precision. An IRB approved retrospective study of 140 patients with elevated serum prostate specific antigen levels and/or abnormal digital rectal examinations was done with T2-weighted MRI applying a new (Biproximate) technique. Measurements were made by 2 examiners, correlated with each other for inter-observer precision and correlated with an expert observer for accuracy. Correlation statistics, linear regression analysis, and tests of means were applied using p ≤ 0.05 as the threshold for significance. Inter-observer correlation (precision) was 0.95 between observers. Correlation between these observers and the expert (accuracy) was 0.94 and 0.97 respectively. Intra-observer correlation for the expert was 0.98. Means for inter-rater reliability and accuracy were all the same (p = 0.001). We conclude that using more precise reproducible landmarks with biproximate technique, precision and accuracy of total prostate volume is found to be demonstrated.

Inter-imaging accuracy of computed tomography, magnetic resonance imaging, and transrectal ultrasound in measuring prostate volume compared to the anatomic prostatic weight

OBJECTIVE

To evaluate the accuracy of transrectal ultrasound (TRUS), computed tomography (CT), and magnetic resonance imaging (MRI) compared to the reference standard of the post-surgical anatomic prostatic weight (APW).

MATERIAL AND METHODS

A total of 349 patients from two institutions were included. The CT and MRI dimensions, and TRUS-reported prostate volumes (PV) were obtained. The prolate ellipsoid formula was used to calculate PV. Cross-sectional measurements were evaluated and compared to the reported post-surgical pathology measurements and calculated pathology volume (path PV). A basic statistical analysis was performed using the Pearson correlation, Bland-Altman analysis, and Passing-Bablok regression.

RESULTS

A total of 198 patients were included in the MRI group, 118 in the CT group, 295 in the TRUS group, and 51 in the all-inclusive common cohort. The MRI PV demonstrated a good to excellent correlation with the APW (r=0.79). The CT PV demonstrated a good correlation with APW (r=0.78). The TRUS PV showed a correlation with APW (r=0.67). The correlations identified in each individual group held true in the common cohort as well. The path PV showed an excellent correlation with APW (r=0.87), followed by MRI PV (r=0.81), then CT PV (r=0.73), and lastly TRUS PV (r=0.71).

CONCLUSION

MRI and CT are equally effective in assessing the PV, and they can be readily utilized to guide the benign prostatic hyperplasia (BPH) management without repeating in-office TRUS. This is not only cost-effective, but also eliminates patient anxiety and discomfort.

Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors

PURPOSE

To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well-established semi-automatic (interactive) methods for determining liver volume in living liver transplant donors at computerized tomography (CT) imaging.

METHODS

A total of 12 (6 semi-, 6 full-automatic) methods are evaluated. The semi-automatic segmentation algorithms are based on both traditional iterative models including watershed, fast marching, region growing, active contours and modern techniques including robust statistical segmenter and super-pixels. These methods entail some sort of interaction mechanism such as placing initialization seeds on images or determining a parameter range. The automatic methods are based on deep learning and they include three framework templates (DeepMedic, NiftyNet and U-Net) the first two of which are applied with default parameter sets and the last two involve adapted novel model designs. For 20 living donors (6 training and 12 test datasets), a group of imaging scientists and radiologists created ground truths by performing manual segmentations on contrast material-enhanced CT images. Each segmentation is evaluated using five metrics (i.e. volume overlap and relative volume errors, average/RMS/maximum symmetrical surface distances). The results are mapped to a scoring system and a final grade is calculated by taking their average. Accuracy and repeatability were evaluated using slice by slice comparisons and volumetric analysis. Diversity and complementarity are observed through heatmaps. Majority voting and Simultaneous Truth and Performance Level Estimation (STAPLE) algorithms are utilized to obtain the fusion of the individual results.

RESULTS

The top four methods are determined to be automatic deep models having 79.63, 79.46 and 77.15 and 74.50 scores. Intra-user score is determined as 95.14. Overall, deep automatic segmentation outperformed interactive techniques on all metrics. The mean volume of liver of ground truth is found to be 1409.93 mL ± 271.28 mL, while it is calculated as 1342.21 mL ± 231.24 mL using automatic and 1201.26 mL ± 258.13 mL using interactive methods, showing higher accuracy and less variation on behalf of automatic methods. The qualitative analysis of segmentation results showed significant diversity and complementarity enabling the idea of using ensembles to obtain superior results. The fusion of automatic methods reached 83.87 with majority voting and 86.20 using STAPLE that are only slightly less than fusion of all methods that achieved 86.70 (majority voting) and 88.74 (STAPLE).

CONCLUSION

Use of the new deep learning based automatic segmentation algorithms substantially increases the accuracy and repeatability for segmentation and volumetric measurements of liver. Fusion of automatic methods based on ensemble approaches exhibits best results almost without any additional time cost due to potential parallel execution of multiple models.

Ulmus macrocarpa Hance improves benign prostatic hyperplasia by regulating prostatic cell apoptosis

ETHNOPHARMACOLOGICAL RELEVANCE

Ulmus macrocarpa Hance (UMH), of the family Ulmaceae, is a deciduous tree, widely distributed throughout Korea. UMH has been used as a traditional oriental medicine in Korea for the treatment of urological disorders, including bladder outlet obstruction (BOO), lower urinary tract syndrome (LUTS), diuresis, and hematuria. To date, its possible protective effects against benign prostatic hyperplasia (BPH) have not been analyzed.

AIM OF THE STUDY

This study investigated the effects of UMH on the development of BPH using a rat model of testosterone propionate (TP)-induced BPH.

MATERIALS AND METHODS

BPH was induced by daily subcutaneous injections of testosterone propionate (TP) for four weeks. UMH was administrated daily by oral gavage at a dose of 150 mg/kg during the four weeks of TP injections. Animals were sacrificed, and their prostates were weighed and subjected to histopathological examination, TUNEL assay, and western blot analysis.

RESULTS

Treatment of BPH-model rats with UMH significantly reduced prostate weight, serum testosterone concentration and dihydrotestosterone (DHT) concentration in prostate tissue. TP-induced prostatic hyperplasia and the expression of proliferating cell nuclear antigen (PCNA) were significantly attenuated in UMH-treated rats. In addition, UMH administration markedly induced the activation of caspases-3, - 8, and - 9 in prostate tissues of BPH rats, accompanied by upregulation of expression of Fas, Fas-associated protein with death domain (FADD), and Fas ligand (FasL) and a reduction in the ratio of B-cell lymphoma 2 (Bcl-2) to Bcl-2-associated X protein (Bax).

CONCLUSIONS

UMH effectively inhibited the proliferation and promoted the apoptosis of prostate cells, suggesting it may be useful for the treatment of BPH.

A semiautomatic approach for prostate segmentation in MR images using local texture classification and statistical shape modeling

Segmentation of the prostate in magnetic resonance (MR) images has many applications in image-guided treatment planning and procedures such as biopsy and focal therapy. However, manual delineation of the prostate boundary is a time-consuming task with high inter-observer variation. In this study, we proposed a semiautomated, three-dimensional (3D) prostate segmentation technique for T2-weighted MR images based on shape and texture analysis. The prostate gland shape is usually globular with a smoothly curved surface that could be accurately modeled and reconstructed if the locations of a limited number of well-distributed surface points are known. For a training image set, we used an inter-subject correspondence between the prostate surface points to model the prostate shape variation based on a statistical point distribution modeling. We also studied the local texture difference between prostate and non-prostate tissues close to the prostate surface. To segment a new image, we used the learned prostate shape and texture characteristics to search for the prostate border close to an initially estimated prostate surface. We used 23 MR images for training, and 14 images for testing the algorithm performance. We compared the results to two sets of experts' manual reference segmentations. The measured mean ± standard deviation of error values for the whole gland were 1.4 ± 0.4 mm, 8.5 ± 2.0 mm, and 86 ± 3% in terms of mean absolute distance (MAD), Hausdorff distance (HDist), and Dice similarity coefficient (DSC). The average measured differences between the two experts on the same datasets were 1.5 mm (MAD), 9.0 mm (HDist), and 83% (DSC). The proposed algorithm illustrated a fast, accurate, and robust performance for 3D prostate segmentation. The accuracy of the algorithm is within the inter-expert variability observed in manual segmentation and comparable to the best performance results reported in the literature.

Determination of Prostate Volume: A Comparison of Contemporary Methods

RATIONALE AND OBJECTIVES

Prostate volume (PV) determination provides important clinical information. We compared PVs determined by digital rectal examination (DRE), transrectal ultrasound (TRUS), magnetic resonance imaging (MRI) with or without three-dimensional (3D) segmentation software, and surgical prostatectomy weight (SPW) and volume (SPV).

MATERIALS AND METHODS

This retrospective review from 2010 to 2016 included patients who underwent radical prostatectomy ≤1 year after multiparametric prostate MRI. PVs from DRE and TRUS were obtained from urology clinic notes. MRI-based PVs were calculated using bullet and ellipsoid formulas, automated 3D segmentation software (MRI-A3D), manual segmentation by a radiologist (MRI-R3D), and a third-year medical student (MRI-S3D). SPW and SPV were derived from pathology reports. Intraclass correlation coefficients compared the relative accuracy of each volume measurement.

RESULTS

Ninety-nine patients were analyzed. Median PVs were DRE 35 mL, TRUS 35 mL, MRI-bullet 49 mL, MRI-ellipsoid 39 mL, MRI-A3D 37 mL, MRI-R3D 36 mL, MRI-S3D 36 mL, SPW 54 mL, SPV-bullet 47 mL, and SPV-ellipsoid 37 mL. SPW and bullet formulas had consistently large PV, and formula-based PV had a wider spread than PV based on segmentation. Compared to MRI-R3D, the intraclass correlation coefficient was 0.91 for MRI-S3D, 0.90 for MRI-ellipsoid, 0.73 for SPV-ellipsoid, 0.72 for MRI-bullet, 0.71 for TRUS, 0.70 for SPW, 0.66 for SPV-bullet, 0.38 for MRI-A3D, and 0.33 for DRE.

CONCLUSIONS

With MRI-R3D measurement as the reference, the most reliable methods for PV estimation were MRI-S3D and MRI-ellipsoid formula. Automated segmentations must be individually assessed for accuracy, as they are not always truly representative of the prostate anatomy. Manual segmentation of the prostate does not require expert training.

Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging

PURPOSE

We propose an approach of 3D convolutional neural network to segment the prostate in MR images.

METHODS

A 3D deep dense multi-path convolutional neural network that follows the framework of the encoder-decoder design is proposed. The encoder is built based upon densely connected layers that learn the high-level feature representation of the prostate. The decoder interprets the features and predicts the whole prostate volume by utilizing a residual layout and grouped convolution. A set of sub-volumes of MR images, centered at the prostate, is generated and fed into the proposed network for training purpose. The performance of the proposed network is compared to previously reported approaches.

RESULTS

Two independent datasets were employed to assess the proposed network. In quantitative evaluations, the proposed network achieved 95.11 and 89.01 Dice coefficients for the two datasets. The segmentation results were robust to variations in MR images. In comparison experiments, the segmentation performance of the proposed network was comparable to the previously reported approaches. In qualitative evaluations, the segmentation results by the proposed network were well matched to the ground truth provided by human experts.

CONCLUSIONS

The proposed network is capable of segmenting the prostate in an accurate and robust manner. This approach can be applied to other types of medical images.

Utility of MR Angiography in the Identification of Prostatic Artery Origin Prior to Prostatic Artery Embolization

In 17 patients who underwent prostate artery embolization for treatment of lower urinary tract symptoms, the accuracy of preprocedural magnetic resonance (MR) angiography was retrospectively compared with intraprocedural digital subtraction angiography (DSA) in the identification of prostatic artery origin. Of 34 vessels, 26 MR angiography identified origins (76.5%) were confirmed by DSA at the time of embolization. Although image postprocessing is required, the ability of MR angiography to accurately identify prostatic artery origins prior to embolization is useful in treatment planning and can obviate the need for separate computed tomographic angiography, thus reducing both radiation dose and time demand on patients.

Dynamic tomographic studies of interscapular feline injection-site sarcoma: essential or useless practice?

Objectives Feline injection-site sarcomas (FISSs) are soft tissue tumours typically characterised by an interscapular location and highly infiltrative behaviour. CT is considered the modality of choice for FISS staging and double positioning (dynamic approach) was reported to successfully detect the exact extent of infiltration into the muscles. The aim of the present study was to investigate the utility of the dynamic approach in feline patients referred for preoperative staging of interscapular FISS. Methods Tumour volume estimates were compared between the ellipsoid and the semi-automated segmentation methods. Two radiologists blinded to the patient coding used images from each position to assess the extent of muscular infiltration. The distance between the neoplasm and the adjacent skeletal structures (scapulae, spinous processes) was recorded in both positions by a single radiologist. Results Fifty-nine of 84 neoplasms invaded the adjacent muscular structures, with up to 15 muscles infiltrated. Between the extended and flexed position the average estimated numbers of muscles infiltrated were 1.9 (extended) and 1.84 (flexed) for observer A and 1.89 (extended) and 1.85 (flexed) for observer B. Good agreement between observers was established, with higher tumour volumes detected via the ellipsoid method. Moreover, tumours with smaller volumes showed slightly decreased muscular infiltration. Marked difference in the recorded distance between the skeletal structures and the neoplasm in the two different positions was established (mean ± SD difference spinous processes: 9.74 ± 9.57 mm; mean ± SD difference scapulae: 15.15 ± 11.76 mm). Conclusions and relevance A dynamic approach should be used for a complete evaluation of the invasiveness of FISS along with appropriate methodology for tumour volume measurement, which could potentially alter the tomographic estimation of the real dimension of the neoplasms.

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.

"Textural analysis of multiparametric MRI detects transition zone prostate cancer"

OBJECTIVES

To evaluate multiparametric-MRI (mpMRI) derived histogram textural-analysis parameters for detection of transition zone (TZ) prostatic tumour.

METHODS

Sixty-seven consecutive men with suspected prostate cancer underwent 1.5T mpMRI prior to template-mapping-biopsy (TPM). Twenty-six men had 'significant' TZ tumour. Two radiologists in consensus matched TPM to the single axial slice best depicting tumour, or largest TZ diameter for those with benign histology, to define single-slice whole TZ-regions-of-interest (ROIs). Textural-parameter differences between single-slice whole TZ-ROI containing significant tumour versus benign/insignificant tumour were analysed using Mann Whitney U test. Diagnostic accuracy was assessed by receiver operating characteristic area under curve (ROC-AUC) analysis cross-validated with leave-one-out (LOO) analysis.

RESULTS

ADC kurtosis was significantly lower (p < 0.001) in TZ containing significant tumour with ROC-AUC 0.80 (LOO-AUC 0.78); the difference became non-significant following exclusion of significant tumour from single-slice whole TZ-ROI (p = 0.23). T1-entropy was significantly lower (p = 0.004) in TZ containing significant tumour with ROC-AUC 0.70 (LOO-AUC 0.66) and was unaffected by excluding significant tumour from TZ-ROI (p = 0.004). Combining these parameters yielded ROC-AUC 0.86 (LOO-AUC 0.83).

CONCLUSION

Textural features of the whole prostate TZ can discriminate significant prostatic cancer through reduced kurtosis of the ADC-histogram where significant tumour is included in TZ-ROI and reduced T1 entropy independent of tumour inclusion.

KEY POINTS

• MR textural features of prostate transition zone may discriminate significant prostatic cancer. • Transition zone (TZ) containing significant tumour demonstrates a less peaked ADC histogram. • TZ containing significant tumour reveals higher post-contrast T1-weighted homogeneity. • The utility of MR texture analysis in prostate cancer merits further investigation.

Prostate volume estimations using magnetic resonance imaging and transrectal ultrasound compared to radical prostatectomy specimens

INTRODUCTION

We sought to evaluate the accuracy of prostate volume estimates in patients who received both a preoperative transrectal ultrasound (TRUS) and magnetic resonance imaging (MRI) in relation to the referent pathological specimen post-radical prostatectomy.

METHODS

Patients receiving both TRUS and MRI prior to radical prostatectomy at one academic institution were retrospectively analyzed. TRUS and MRI volumes were estimated using the prolate ellipsoid formula. TRUS volumes were collected from sonography reports. MRI volumes were estimated by two blinded raters and the mean of the two was used for analyses. Pathological volume was calculated using a standard fluid displacement method.

RESULTS

Three hundred and eighteen (318) patients were included in the analysis. MRI was slightly more accurate than TRUS based on interclass correlation (0.83 vs. 0.74) and absolute risk bias (higher proportion of estimates within 5, 10, and 20 cc of pathological volume). For TRUS, 87 of 298 (29.2%) prostates without median lobes differed by >10 cc of specimen volume and 22 of 298 (7.4%) differed by >20 cc. For MRI, 68 of 298 (22.8%) prostates without median lobes differed by >10 cc of specimen volume, while only 4 of 298 (1.3%) differed by >20 cc.

CONCLUSIONS

MRI and TRUS prostate volume estimates are consistent with pathological volumes along the prostate size spectrum. MRI demonstrated better correlation with prostatectomy specimen volume in most patients and may be better suited in cases where TRUS and MRI estimates are disparate. Validation of these findings with prospective, standardized ultrasound techniques would be helpful.

MR diffusion-weighted imaging-based subcutaneous tumour volumetry in a xenografted nude mouse model using 3D Slicer: an accurate and repeatable method

Accurate and repeatable measurement of the gross tumour volume(GTV) of subcutaneous xenografts is crucial in the evaluation of anti-tumour therapy. Formula and image-based manual segmentation methods are commonly used for GTV measurement but are hindered by low accuracy and reproducibility. 3D Slicer is open-source software that provides semiautomatic segmentation for GTV measurements. In our study, subcutaneous GTVs from nude mouse xenografts were measured by semiautomatic segmentation with 3D Slicer based on morphological magnetic resonance imaging(mMRI) or diffusion-weighted imaging(DWI)(b = 0,20,800 s/mm²) . These GTVs were then compared with those obtained via the formula and image-based manual segmentation methods with ITK software using the true tumour volume as the standard reference. The effects of tumour size and shape on GTVs measurements were also investigated. Our results showed that, when compared with the true tumour volume, segmentation for DWI(P = 0.060–0.671) resulted in better accuracy than that mMRI(P < 0.001) and the formula method(P < 0.001). Furthermore, semiautomatic segmentation for DWI(intraclass correlation coefficient, ICC = 0.9999) resulted in higher reliability than manual segmentation(ICC = 0.9996–0.9998). Tumour size and shape had no effects on GTV measurement across all methods. Therefore, DWI-based semiautomatic segmentation, which is accurate and reproducible and also provides biological information, is the optimal GTV measurement method in the assessment of anti-tumour treatments.

Whole prostate volume and shape changes with the use of an inflatable and flexible endorectal coil

Purpose. To determine to what extent an inflatable endorectal coil (ERC) affects whole prostate (WP) volume and shape during prostate MRI. Materials and Methods. 79 consecutive patients underwent T2W MRI at 3T first with a 6-channel surface coil and then with the combination of a 16-channel surface coil and ERC in the same imaging session. WP volume was assessed by manually contouring the prostate in each T2W axial slice. PSA density was also calculated. The maximum anterior-posterior (AP), left-right (LR), and craniocaudal (CC) prostate dimensions were measured. Changes in WP prostate volume, PSA density, and prostate dimensions were then evaluated. Results. In 79 patients, use of an ERC yielded no significant change in whole prostate volume (0.6 ± 5.7%, P = 0.270) and PSA density (−0.2 ± 5.6%, P = 0.768). However, use of an ERC significantly decreased the AP dimension of the prostate by −8.6 ± 7.8% (P < 0.001), increased LR dimension by 4.5 ± 5.8% (P < 0.001), and increased the CC dimension by 8.8 ± 6.9% (P < 0.001). Conclusion. Use of an ERC in prostate MRI results in the shape deformation of the prostate gland with no significant change in the volume of the prostate measured on T2W MRI. Therefore, WP volumes calculated on ERC MRI can be reliably used in clinical workflow.