Predictive value of MRI in the localization, staging, volume estimation, assessment of aggressiveness, and guidance of radiotherapy and biopsies in prostate cancer

A non-invasive 25-Gene PLNM-Score urine test for detection of prostate cancer pelvic lymph node metastasis

BACKGROUND

Prostate cancer patients with pelvic lymph node metastasis (PLNM) have poor prognosis. Based on EAU guidelines, patients with >5% risk of PLNM by nomograms often receive pelvic lymph node dissection (PLND) during prostatectomy. However, nomograms have limited accuracy, so large numbers of false positive patients receive unnecessary surgery with potentially serious side effects. It is important to accurately identify PLNM, yet current tests, including imaging tools are inaccurate. Therefore, we intended to develop a gene expression-based algorithm for detecting PLNM.

METHODS

An advanced random forest machine learning algorithm screening was conducted to develop a classifier for identifying PLNM using urine samples collected from a multi-center retrospective cohort (n = 413) as training set and validated in an independent multi-center prospective cohort (n = 243). Univariate and multivariate discriminant analyses were performed to measure the ability of the algorithm classifier to detect PLNM and compare it with the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram score.

RESULTS

An algorithm named 25 G PLNM-Score was developed and found to accurately distinguish PLNM and non-PLNM with AUC of 0.93 (95% CI: 0.85-1.01) and 0.93 (95% CI: 0.87-0.99) in the retrospective and prospective urine cohorts respectively. Kaplan-Meier plots showed large and significant difference in biochemical recurrence-free survival and distant metastasis-free survival in the patients stratified by the 25 G PLNM-Score (log rank P < 0.001 and P < 0.0001, respectively). It spared 96% and 80% of unnecessary PLND with only 0.51% and 1% of PLNM missing in the retrospective and prospective cohorts respectively. In contrast, the MSKCC score only spared 15% of PLND with 0% of PLNM missing.

CONCLUSIONS

The novel 25 G PLNM-Score is the first highly accurate and non-invasive machine learning algorithm-based urine test to identify PLNM before PLND, with potential clinical benefits of avoiding unnecessary PLND and improving treatment decision-making.

Radiomic Machine-Learning Analysis of Multiparametric Magnetic Resonance Imaging in the Diagnosis of Clinically Significant Prostate Cancer: New Combination of Textural and Clinical Features

BACKGROUND

The aim of our study was to develop a radiomic tool for the prediction of clinically significant prostate cancer.

METHODS

From September 2020 to December 2021, 91 patients who underwent magnetic resonance imaging prostate fusion biopsy at our institution were selected. Prostate cancer aggressiveness was assessed by combining the three orthogonal planes-Llocal binary pattern the 3Dgray level co-occurrence matrix, and other first order statistical features with clinical (semantic) features. The 487 features were used to predict whether the Gleason score was clinically significant (≥7) in the final pathology. A feature selection algorithm was used to determine the most predictive features, and at the end of the process, nine features were chosen through a 10-fold cross validation.

RESULTS

The feature analysis revealed a detection accuracy of 83.5%, with a clinically significant precision of 84.4% and a clinically significant sensitivity of 91.5%. The resulting area under the curve was 80.4%.

CONCLUSIONS

Radiomic analysis allowed us to develop a tool that was able to predict a Gleason score of ≥7. This new tool may improve the detection rate of clinically significant prostate cancer and overcome the limitations of the subjective interpretation of magnetic resonance imaging, reducing the number of useless biopsies.

Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images

Background

The 3D U-Net model has been proved to perform well in the automatic organ segmentation. The aim of this study is to evaluate the feasibility of the 3D U-Net algorithm for the automated detection and segmentation of lymph nodes (LNs) on pelvic diffusion-weighted imaging (DWI) images.

Methods

A total of 393 DWI images of patients suspected of having prostate cancer (PCa) between January 2019 and December 2020 were collected for model development. Seventy-seven DWI images from another group of PCa patients imaged between January 2021 and April 2021 were collected for temporal validation. Segmentation performance was assessed using the Dice score, positive predictive value (PPV), true positive rate (TPR), and volumetric similarity (VS), Hausdorff distance (HD), the Average distance (AVD), and the Mahalanobis distance (MHD) with manual annotation of pelvic LNs as the reference. The accuracy with which the suspicious metastatic LNs (short diameter > 0.8 cm) were detected was evaluated using the area under the curve (AUC) at the patient level, and the precision, recall, and F1-score were determined at the lesion level. The consistency of LN staging on an hold-out test dataset between the model and radiologist was assessed using Cohen’s kappa coefficient.

Results

In the testing set used for model development, the Dice score, TPR, PPV, VS, HD, AVD and MHD values for the segmentation of suspicious LNs were 0.85, 0.82, 0.80, 0.86, 2.02 (mm), 2.01 (mm), and 1.54 (mm) respectively. The precision, recall, and F1-score for the detection of suspicious LNs were 0.97, 0.98 and 0.97, respectively. In the temporal validation dataset, the AUC of the model for identifying PCa patients with suspicious LNs was 0.963 (95% CI: 0.892–0.993). High consistency of LN staging (Kappa = 0.922) was achieved between the model and expert radiologist.

Conclusion

The 3D U-Net algorithm can accurately detect and segment pelvic LNs based on DWI images.

Integration of clinicopathologic identification and deep transferrable image feature representation improves predictions of lymph node metastasis in prostate cancer

Background

Accurate identification of pelvic lymph node metastasis (PLNM) in patients with prostate cancer (PCa) is crucial for determining appropriate treatment options. Here, we built a PLNM-Risk calculator to obtain a precisely informed decision about whether to perform extended pelvic lymph node dissection (ePLND).

Methods

The PLNM-Risk calculator was developed in 280 patients and verified internally in 71 patients and externally in 50 patients by integrating a set of radiologists’ interpretations, clinicopathological factors and newly refined imaging indicators from MR images with radiomics machine learning and deep transfer learning algorithms. Its clinical applicability was compared with Briganti and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms.

Findings

The PLNM-Risk achieved good diagnostic discrimination with areas under the receiver operating characteristic curve (AUCs) of 0.93 (95% CI, 0.90-0.96), 0.92 (95% CI, 0.84-0.97) and 0.76 (95% CI, 0.62-0.87) in the training/validation, internal test and external test cohorts, respectively. If the number of ePLNDs missed was controlled at < 2%, PLNM-Risk provided both a higher number of ePLNDs spared (PLNM-Risk 59.6% vs MSKCC 44.9% vs Briganti 38.9%) and a lower number of false positives (PLNM-Risk 59.3% vs MSKCC 70.1% and Briganti 72.7%). In follow-up, patients stratified by the PLNM-Risk calculator showed significantly different biochemical recurrence rates after surgery.

Interpretation

The PLNM-Risk calculator offers a noninvasive clinical biomarker to predict PLNM for patients with PCa. It shows improved accuracy of diagnosis support and reduced overtreatment burdens for patients with findings suggestive of PCa.

Funding

This work was supported by the Key Research and Development Program of Jiangsu Province (BE2017756) and the Suzhou Science and Technology Bureau-Science and Technology Demonstration Project (SS201808).

An Audit of Surgical Neck Explorations for Penetrating Neck Injuries in Northwestern Nigeria: Experience from a Teaching Hospital

Background:

Penetrating neck injury is a major trauma mechanism present in about 5%–10% of trauma patients with an estimated mortality of 3%–10%. The management of these injuries is dependent on the anatomical level of injury.

Objectives:

The objective of the study was to document the clinical and operative findings as well as the treatment outcome among our patients who underwent neck exploration for penetrating neck injuries.

Materials and Methods:

A retrospective review of patients who had neck exploration for penetrating neck injury between January 2012 and December 2018 was done.

Results:

Thirty-five patients all of whom had surgical neck exploration were included. The age ranged from 15 to 62 years with a male: female of 7.8:1. The mean age was 30.7 years with standard deviation of ± 12.5 years and the peak age of occurrence of 20–29 years. The mechanism of injury was commonly arrow injury in 9 (25.7%) and suicidal cutthroat in 7 (20%) patients. Thirty-two (91.4%) patients presented with stable vital signs. Zone II neck injuries were most prevalent, seen in 23 (65.7%) patients. Laryngeal injury in 7 (20%) and soft-tissue injury in 7 (20%) of the patients were the most common intraoperative findings. The complication rate of 17.1% with a mortality rate of 2.9% was recorded. There was a statistically significant association between the presence of vascular injury and the development of complications after exploration (Chi-square = 5.666, P = 0.017). It was also a significant positive predictor of complication following neck exploration (odds ratio = 0.017, P = 0.048).

Conclusion:

Male young adults were most involved, commonly from arrow and stab injuries. Although laryngeal and soft-tissue injuries were predominant, vascular injuries were most associated with postoperative complications.

Correlation between Body Mass Index and Gleason Score in Men with Prostate Cancer in Southeastern Nigeria

Introduction:

Prostate cancer has an increasing global burden. The clinical course varies from an indolent disease to a rapidly aggressive cancer. It is associated with higher mortality in less developed nations due to late presentation. The Gleason scoring system for prostatic adenocarcinoma has prognostic implications in diagnosed cases. Obesity has been associated with the evolution of many cancers including prostate cancer. There are conflicting reports on the relationship between obesity, as measured by body mass index (BMI), and prostate cancer aggressiveness, as measured by Gleason score. This study is aimed to determine if a correlation exists between BMI and Gleason score in men with prostate cancer.

Methodology:

This was a prospective, hospital-based, cross-sectional study involving consecutive patients with prostate cancer. Clinical evaluation including anthropometry, digital rectal examination, and relevant investigations were done for each patient and data collected with pro forma. This was followed by prostate needle biopsy and those diagnosed with adenocarcinoma of the prostate had their Gleason grades and scores obtained. Data were analyzed statistically using Spearman Correlation.

Results:

The mean age of the patients was 69.54 ± 8.61 years (range 47–83 years). The BMI ranged from 16.98 to 36.45 kg/m², with a mean of 27.03 ± 5.03 kg/m². Twenty-six of the patients (36.1%) were overweight and 34.7% were obese. The mean total prostate-specific antigen was 118.65 ± 84.43 ng/ml, with a range of 31–406 ng/ml. The modal Gleason score was 9 with a range of 4–10. There was a strong positive correlation between BMI and Gleason score (r = 0.817, P = 0.0003).

Conclusion:

The BMI of patients with prostate cancer correlated positively with their Gleason score.

Value of clinical parameters and MRI with PI-RADSV2 in predicting seminal vesicle invasion of prostate cancer

OBJECTIVE

To investigate the usefulness of magnetic resonance imaging (MRI) with Prostate Imaging Reporting and Data System version 2 (PI-RADS_V2) and clinical parameters in predicting seminal vesicle invasion (SVI).

MATERIAL AND METHODS

In this retrospective study, we identified 569 prostate cancer patients who underwent radical prostatectomy with MRI before surgery. SVI was interpreted with PI-RADS_V2. Clinical parameters such as the prostate-specific antigen (PSA) and Gleason score (GS) were analyzed for the prediction of SVI. Logistic regression models and receiver operating characteristic (ROC) curves were used to evaluate SVI based on clinical parameters and MRI with PI-RADS_V2.

RESULTS

The median age at presentation was 67 years (43-85 years). The median PSA level was 6.1 ng/mL (2.2-72.8 ng/mL). There were 113 patients with a biopsy GS of ≥ 8. A total of 34 patients (6.0%) were interpreted to have SVI by MRI of which 20 were true positive, and 52 patients (9.1%) had true SVI in the final pathologic analysis. In multivariable analysis, PSA (HR: 1.03, 95% CI: 1.00-1.07), biopsy GS ≥ 8 (HR: 4.14, 95% CI: 2.12-8.09), and MRI with PI-RADS_V2 (HR: 14.67, 95% CI: 6.34-33.93) were significantly associated with pathologic SVI. The area under the curve of the model based on the clinical parameters PSA and GS plus MRI (0.862) was significantly larger than that of the model based on clinical parameters alone (0.777, p < 0.001).

CONCLUSIONS

MRI with PI-RADS_V2 using the clinical parameters PSA and GS was effective in predicting SVI.

Histogram analysis in prostate cancer: a comparison of diffusion kurtosis imaging model versus monoexponential model

BACKGROUND

There is still little research about histogram analysis of diffusion kurtosis imaging (DKI) using in prostate cancer at present.

PURPOSE

To verify the utility of histogram analysis of DKI model in detection and assessment of aggressiveness of prostate cancer, compared with monoexponential model (MEM).

MATERIAL AND METHODS

Twenty-three patients were enrolled in this study. For DKI model and MEM, the D_app, K_app, and apparent diffusion coefficient (ADC) were obtained by using single-shot echo-planar imaging sequence. The pathologies were confirmed by in-bore magnetic resonance (MR)-guided biopsy. Regions of interest (ROI) were drawn manually in the position where biopsy needle was put. The mean values and histogram parameters in cancer and noncancerous foci were compared using independent-samples T test. Receiver operating characteristic curves were used to investigate the diagnostic efficiency. Spearman's test was used to evaluate the correlation of parameters and Gleason scores.

RESULTS

The mean, 10th, 25th, 50th, 75th, and 90th percentiles of ADC and D_app were significantly lower in prostate cancer than non-cancerous foci (P < 0.001). The mean, 50th, 75th, and 90th percentiles of K_app were significantly higher in prostate cancer (P < 0.05). There was no significant difference between the AUCs of two models (0.971 vs. 0.963, P > 0.05). With the increasing Gleason scores, the 10th ADC decreased (ρ = -0.583, P = 0.018), but the 90th K_app increased (ρ = 0.642, P = 0.007).

CONCLUSION

Histogram analysis of DKI model is feasible for diagnosing and grading prostate cancer, but it has no significant advantage over MEM.

Magnetic Resonance Imaging Assessment After Therapy in Prostate Cancer

Prostate cancer is the fifth leading cause of death worldwide. A variety of treatment options is available for localized prostate cancer and may range from active surveillance to focal therapy or whole gland treatment, that is, surgery or radiotherapy. Serum prostate-specific antigen levels are an important tool to monitor treatment success after whole gland treatment, unfortunately prostate-specific antigen is unreliable after focal therapy. Multiparametric magnetic resonance imaging of the prostate is rapidly gaining field in the management of prostate cancer and may play a crucial role in the evaluation of recurrent prostate cancer. This article will focus on postprocedural magnetic resonance imaging after different forms of local therapy in patients with prostate cancer.

PI-RADS 3 Lesions: Role of Prostate MRI Texture Analysis in the Identification of Prostate Cancer

PURPOSE

To determine the diagnostic performance of texture analysis of prostate MRI for the diagnosis of prostate cancer among Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions.

MATERIALS AND METHODS

Forty-three patients with at least 1 PI-RADS 3 lesion on prostate MRI performed between June 2016 and January 2019 were retrospectively included. Reference standard was pathological analysis of radical prostatectomy specimens or MRI-targeted biopsies. Texture analysis extraction of target lesions was performed on axial T2-weighted images and apparent diffusion coefficient (ADC) maps using a radiomic software. Lesions were categorized as prostate cancer (Gleason score [GS] ≥ 6), and no prostate cancer. Statistical analysis was performed using the generalized linear model (GLM) regression and the discriminant analysis (DA). AUROC with 95% confidence intervals were calculated to assess the diagnostic performance of standalone features and predictive models for the diagnosis of prostate cancer (GS ≥ 6) and clinically-significant prostate cancer (GS ≥ 7).

RESULTS

The analysis of 46 PI-RADS 3 lesions (ie, 27 [58.7%] no prostate cancers; 19 [41.3%] prostate cancers) revealed 9 and 6 independent texture parameters significantly correlated with the final histopathological results on T2-weighted and ADC maps images, respectively. The resulting GLM and DA predictive models for the diagnosis of prostate cancer yielded an AUROC of 0.775 and 0.779 on T2-weighted images or 0.815 and 0.821 on ADC maps images. For the diagnosis of clinically-significant prostate cancer, the resulting GLM and DA predictive models for the diagnosis of prostate cancer yielded an AUROC of 0.769 and 0.817 on T2-weighted images or 0.749 and 0.744 on ADC maps images.

CONCLUSION

Texture analysis of PI-RADS 3 lesions on T2-weighted and ADC maps images helps identifying prostate cancer. The good diagnostic performance of the combination of multiple radiomic features for the diagnosis of prostate cancer may help predicting lesions where aggressive management may be warranted.

A machine learning-assisted decision-support model to better identify patients with prostate cancer requiring an extended pelvic lymph node dissection

OBJECTIVES

To develop a machine learning (ML)-assisted model to identify candidates for extended pelvic lymph node dissection (ePLND) in prostate cancer by integrating clinical, biopsy, and precisely defined magnetic resonance imaging (MRI) findings.

PATIENTS AND METHODS

In all, 248 patients treated with radical prostatectomy and ePLND or PLND were included. ML-assisted models were developed from 18 integrated features using logistic regression (LR), support vector machine (SVM), and random forests (RFs). The models were compared to the Memorial SloanKettering Cancer Center (MSKCC) nomogram using receiver operating characteristic-derived area under the curve (AUC) calibration plots and decision curve analysis (DCA).

RESULTS

A total of 59/248 (23.8%) lymph node invasions (LNIs) were identified at surgery. The predictive accuracy of the ML-based models, with (+) or without (-) MRI-reported LNI, yielded similar AUCs (RFs⁺ /RFs^- : 0.906/0.885; SVM⁺ /SVM^- : 0.891/0.868; LR⁺ /LR^- : 0.886/0.882) and were higher than the MSKCC nomogram (0.816; P < 0.001). The calibration of the MSKCC nomogram tended to underestimate LNI risk across the entire range of predicted probabilities compared to the ML-assisted models. The DCA showed that the ML-assisted models significantly improved risk prediction at a risk threshold of ≤80% compared to the MSKCC nomogram. If ePLNDs missed was controlled at <3%, both RFs⁺ and RFs^- resulted in a higher positive predictive value (51.4%/49.6% vs 40.3%), similar negative predictive value (97.2%/97.8% vs 97.2%), and higher number of ePLNDs spared (56.9%/54.4% vs 43.9%) compared to the MSKCC nomogram.

CONCLUSIONS

Our ML-based model, with a 5-15% cutoff, is superior to the MSKCC nomogram, sparing ≥50% of ePLNDs with a risk of missing <3% of LNIs.

Immunotherapy and the Interventional Oncologist: Challenges and Opportunities-A Society of Interventional Oncology White Paper

Interventional oncology is a subspecialty field of interventional radiology that addresses the diagnosis and treatment of cancer and cancer-related problems by using targeted minimally invasive procedures performed with image guidance. Immuno-oncology is an innovative area of cancer research and practice that seeks to help the patient's own immune system fight cancer. Both interventional oncology and immuno-oncology can potentially play a pivotal role in cancer management plans when used alongside medical, surgical, and radiation oncology in the care of cancer patients.

False positives in PIRADS (V2) 3, 4, and 5 lesions: relationship with reader experience and zonal location

PURPOSE

To investigate the effect of reader experience and zonal location on the occurrence of false positives (FPs) in PIRADS (V2) 3, 4, and 5 lesions on multiparametric (MP)-MRI of the prostate.

MATERIALS AND METHODS

This retrospective study included 139 patients who had consecutively undergone an MP-MRI of the prostate in combination with a transrectal ultrasound MRI fusion-guided biopsy between 2014 and 2017. MRI exams were prospectively read by a group of inexperienced radiologists (cohort 1; 54 patients) and an experienced radiologist (cohort 2; 85 patients). Multivariable logistic regression analysis was performed to determine the association of experience of the radiologist and zonal location with a FP reading. FP rates were compared between readings by inexperienced and experienced radiologists according to zonal location, using Chi-square (χ²) tests.

RESULTS

A total of 168 lesions in 139 patients were detected. Median patient age was 68 years (Interquartile range (IQR) 62.5-73), and median PSA was 10.9 ng/mL (IQR 7.6-15.9) for the entire patient cohort. According to multivariable logistic regression, inexperience of the radiologist was significantly (P = 0.044, odds ratio 1.927, 95% confidence interval [CI] 1.017-3.651) and independently associated with a FP reading, while zonal location was not (P = 0.202, odds ratio 1.444, 95% CI 0.820-2.539). In the transition zone (TZ), the FP rate of the inexperienced radiologists 59% (17/29) was significantly higher (χ²P = 0.033) than that of the experienced radiologist 33% (13/40).

CONCLUSION

Inexperience of the radiologist is significantly and independently associated with a FP reading, while zonal location is not. Inexperienced radiologists have a significantly higher FP rate in the TZ.

Automatic classification of tissues on pelvic MRI based on relaxation times and support vector machine

Tissue segmentation and classification in MRI is a challenging task due to a lack of signal intensity standardization. MRI signal is dependent on the acquisition protocol, the coil profile, the scanner type, etc. While we can compute quantitative physical tissue properties independent of the hardware and the sequence parameters, it is still difficult to leverage these physical properties to segment and classify pelvic tissues. The proposed method integrates quantitative MRI values (T1 and T2 relaxation times and pure synthetic weighted images) and machine learning (Support Vector Machine (SVM)) to segment and classify tissues in the pelvic region, i.e.: fat, muscle, prostate, bone marrow, bladder, and air. Twenty-two men with a mean age of 30±14 years were included in this prospective study. The images were acquired with a 3 Tesla MRI scanner. An inversion recovery-prepared turbo spin echo sequence was used to obtain T1-weighted images at different inversion times with a TR of 14000 ms. A 32-echo spin echo sequence was used to obtain the T2-weighted images at different echo times with a TR of 5000 ms. T1 and T2 relaxation times, synthetic T1- and T2-weighted images and anatomical probabilistic maps were calculated and used as input features of a SVM for segmenting and classifying tissues within the pelvic region. The mean SVM classification accuracy across subjects was calculated for the different tissues: prostate (94.2%), fat (96.9%), muscle (95.8%), bone marrow (91%) and bladder (82.1%) indicating an excellent classification performance. However, the segmentation and classification for air (within the rectum) may not always be successful (mean SVM accuracy 47.5%) due to the lack of air data in the training and testing sets. Our findings suggest that SVM can reliably segment and classify tissues in the pelvic region.

Prostate Cancer Differentiation and Aggressiveness: Assessment With a Radiomic-Based Model vs. PI-RADS v2

Background

Multiparametric MRI (mp‐MRI) combined with machine‐aided approaches have shown high accuracy and sensitivity in prostate cancer (PCa) diagnosis. However, radiomics‐based analysis has not been thoroughly compared with Prostate Imaging and Reporting and Data System version 2 (PI‐RADS v2) scores.

Purpose

To develop and validate a radiomics‐based model for differentiating PCa and assessing its aggressiveness compared with PI‐RADS v2 scores.

Study Type

Retrospective.

Population

In all, 182 patients with biopsy‐proven PCa and 199 patients with a biopsy‐proven absence of cancer were enrolled in our study.

Field Strength/Sequence

Conventional and diffusion‐weighted MR images (b values = 0, 1000 sec/mm²) were acquired on a 3.0T MR scanner.

Assessment

A total of 396 features and 385 features were extracted from apparent diffusion coefficient (ADC) images and T₂WI, respectively. A predictive model was constructed for differentiating PCa from non‐PCa and high‐grade from low‐grade PCa. The diagnostic performance of each radiomics‐based model was compared with that of the PI‐RADS v2 scores.

Statistical Tests

A radiomics‐based predictive model was constructed by logistic regression analysis. 70% of the patients were assigned to the training group, and the remaining were assigned to the validation group. The diagnostic efficacy was analyzed with receiver operating characteristic (ROC) in both the training and validation groups.

Results

For PCa versus non‐PCa, the validation model had an area under the ROC curve (AUC) of 0.985, 0.982, and 0.999 with T₂WI, ADC, and T₂WI&ADC features, respectively. For low‐grade versus high‐grade PCa, the validation model had an AUC of 0.865, 0.888, and 0.93 with T₂WI, ADC, and T₂WI&ADC features, respectively. PI‐RADS v2 had an AUC of 0.867 in differentiating PCa from non‐PCa and an AUC of 0.763 in differentiating high‐grade from low‐grade PCa.

Data Conclusion

Both the T₂WI‐ and ADC‐based radiomics models showed high diagnostic efficacy and outperformed the PI‐RADS v2 scores in distinguishing cancerous vs. noncancerous prostate tissue and high‐grade vs. low‐grade PCa.

Level of Evidence: 3

Technical Efficacy: Stage 2

J. Magn. Reson. Imaging 2019;49:875–884.

Prostate-Specific Membrane Antigen PET Imaging in Prostate Cancer: Opportunities and Challenges

The aim of this systematic review was to describe the characteristics of prostate-specific membrane antigen (PSMA)-targeting PET and their clinical applications in prostate cancer patients. There have been major strides in the design, synthesis of PSMA-targeting PET tracers over the past several years. PSMA-targeting PET tracers can be categorized, according to positron emitters and targeting strategies for the PSMA. The majority of PSMA PET studies has been focused on patients with biochemical recurrence, but additional values of PSMA PET have also been investigated for use in primary staging, treatment planning, response evaluation, and PSMA radioligand therapy. PSMA PET is expected to bring improvements in the management of patients, but the impact of improved diagnosis by PSMA on overall survival remains unanswered. Many challenges still await PSMA PET to expedite the use in the clinical practice. At this early stage, prospective multicenter trials are needed to validate the effectiveness and usefulness of PSMA PET.

Progression and treatment rates using an active surveillance protocol incorporating image-guided baseline biopsies and multiparametric magnetic resonance imaging monitoring for men with favourable-risk prostate cancer

OBJECTIVE

To assess early outcomes since the introduction of an active surveillance (AS) protocol incorporating multiparametric magnetic resonance imaging (mpMRI)-guided baseline biopsies and image-based surveillance.

PATIENTS AND METHODS

A new AS protocol mandating image-guided baseline biopsies, annual mpMRI and 3-monthly prostate-specific antigen (PSA) testing, but which retained protocol re-biopsies, was tested. Pathological progression, treatment conversion and triggers for non-protocol biopsy were recorded prospectively.

RESULTS

Data from 157 men enrolled in the AS protocol (median age 64 years, PSA 6.8 ng/mL, follow-up 39 months) were interrogated. A total of 12 men (7.6%) left the AS programme by choice. Of the 145 men who remained, 104 had re-biopsies either triggered by a rise in PSA level, change in mpMRI findings or by protocol. Overall, 23 men (15.9%) experienced disease progression; pathological changes were observed in 20 men and changes in imaging results were observed in three men. Of these 23 men, 17 switched to treatment, giving a conversion rate of 11.7% (<4% per year). Of the 20 men with pathological progression, this was detected in four of them after a PSA increase triggered a re-biopsy, while in 10 men progression was detected after an mpMRI change. Progression was detected in six men, however, solely after a protocol re-biopsy without prior PSA or mpMRI changes. Using PSA and mpMRI changes alone to detect progression was found to have a sensitivity and specificity of 70.0% and 81.7%, respectively.

CONCLUSION

Our AS protocol, with thorough baseline assessment and imaging-based surveillance, showed low rates of progression and treatment conversion. Changes in mpMRI findings were the principle trigger for detecting progression by imaging alone or pathologically; however, per protocol re-biopsy still detected a significant number of pathological progressions without mpMRI or PSA changes.

An imaging-based approach predicts clinical outcomes in prostate cancer through a novel support vector machine classification

Preoperatively predict the probability of Prostate cancer (PCa) biochemical recurrence (BCR) is of definite clinical relevance. The purpose of this study was to develop an imaging-based approach in the prediction of 3-years BCR through a novel support vector machine (SVM) classification. We collected clinicopathologic and MR imaging datasets in 205 patients pathologically confirmed PCa after radical prostatectomy. Univariable and multivariable analyses were used to assess the association between MR findings and 3-years BCR, and modeled the imaging variables and follow-up data to predict 3-year PCa BCR using SVM analysis. The performance of SVM was compared with conventional Logistic regression (LR) and D'Amico risk stratification scheme by area under the receiver operating characteristic curve (Az) analysis. We found that SVM had significantly higher Az (0.959 vs. 0.886; p = 0.007), sensitivity (93.3% vs. 83.3%; p = 0.025), specificity (91.7% vs. 77.2%; p = 0.009) and accuracy (92.2% vs. 79.0%; p = 0.006) than LR analysis. Performance of popularized D'Amico scheme was effectively improved by adding MRI-derived variables (Az: 0.970 vs. 0.859, p < 0.001; sensitivity: 91.7% vs. 86.7%, p = 0.031; specificity: 94.5% vs. 78.6%, p = 0.001; and accuracy: 93.7% vs. 81.0%, p = 0.007). Additionally, beside pathological Gleason score (hazard ratio [HR] = 1.560, p = 0.008), surgical-T3b (HR = 4.525, p < 0.001) and positive surgical margin (HR = 1.314, p = 0.007), apparent diffusion coefficient (HR = 0.149, p = 0.035) was the only independent imaging predictor of time to PSA failure. Therefore, We concluded that imaging-based approach using SVM was superior to LR analysis in predicting PCa outcome. Adding MR variables improved the performance of D'Amico scheme.

Evaluation of periprostatic neurovascular fibers before and after radical prostatectomy by means of 1.5 T MRI diffusion tensor imaging

OBJECTIVE

To evaluate if diffusion tensor imaging (DTI) is able to detect changes of periprostatic neurovascular fibers (PNFs) before and after radical prostatectomy (RP), and if these changes are related to post-surgical urinary incontinence and erectile dysfunction.

METHODS

22 patients (mean age 62.6 years) with biopsy-proven prostate cancer underwent 1.5 T DTI before and after RP. The number, fractional anisotropy (FA) values and length of PNFs before and after RP were compared using Student's t-test. Each patient filled out two questionnaires before and after RP, one for the evaluation of urinary continence (ICIQ-SF) and one for the evaluation of erectile function (IIEF-5). The ratios of the number, FA values and length of PNFs before and after RP (DTI B-A RATIOs) and the ratios between the scores obtained before and after RP for both ICIQ-SF and IIEF-2 (ICIQ-SF B-A RATIOs and IIEF-2 B-A RATIOs) were calculated to perform the Kendall's τ-test between them.

RESULTS

There was a statistically significant decrease of the number of PNFs after RP at base, midgland, and apex (p < 0.01) and of FA values at midgland (p < 0.05), with positive statistically significant correlation between the DTI B-A RATIOs of the number of PNFs and IIEF-2 B-A RATIOs (p < 0.05, ρ = 0.47).

CONCLUSION

DTI was able to detect that the decrease of the number of the PNFs after RP was statistically related to the post-surgical erectile dysfunction (p < 0.05). Advances in knowledge: This work demonstrates that: (1) 1.5 T MRI DTI is able to detect the decrease of the number and of the FA of PNFs after prostatectomy; (2) the decrease of the number of PNFs after prostatectomy is related with the post-surgical erectile dysfunction; (3) 1.5 T MRI DTI has demonstrated to be a reproducible technique in detecting the changes of the PNFs induced by RP, with high interobserver agreement.

Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer

OBJECTIVE

To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa).

METHODS

This IRB-approved study included 54 patients with PCa undergoing multi-parametric (mp) MRI before prostatectomy. Imaging analysis was performed on 54 tumours, 47 normal peripheral (PZ) and 48 normal transitional (TZ) zone based on histological-radiological correlation. Mp-MRI was scored via PI-RADS, and quantified by measuring radiomic features. Predictive model was developed using a novel support vector machine trained with: (i) radiomics, (ii) PI-RADS scores, (iii) radiomics and PI-RADS scores. Paired comparison was made via ROC analysis.

RESULTS

For PCa versus normal TZ, the model trained with radiomics had a significantly higher area under the ROC curve (Az) (0.955 [95% CI 0.923-0.976]) than PI-RADS (Az: 0.878 [0.834-0.914], p < 0.001). The Az between them was insignificant for PCa versus PZ (0.972 [0.945-0.988] vs. 0.940 [0.905-0.965], p = 0.097). When radiomics was added, performance of PI-RADS was significantly improved for PCa versus PZ (Az: 0.983 [0.960-0.995]) and PCa versus TZ (Az: 0.968 [0.940-0.985]).

CONCLUSION

Machine learning analysis of MR radiomics can help improve the performance of PI-RADS in clinically relevant PCa.

KEY POINTS

• Machine-based analysis of MR radiomics outperformed in TZ cancer against PI-RADS. • Adding MR radiomics significantly improved the performance of PI-RADS. • DKI-derived Dapp and Kapp were two strong markers for the diagnosis of PCa.

Prebiopsy mp-MRI Can Help to Improve the Predictive Performance in Prostate Cancer: A Prospective Study in 1,478 Consecutive Patients

Purpose: To investigate whether prebiopsy multi-parametric (mp) MRI can help to improve predictive performance in prostate cancer.Experimental Design: Based on a support vector machine (SVM) analysis, we prospectively modeled clinical data (age, PSA, digital rectal examination, transrectal ultrasound, PSA density, and prostate volume) and mp-MRI findings [Prostate Imaging and Reporting and Data System (PI-RADS) score and tumor-node-metastasis stage] in 985 men to predict the risk of prostate cancer. The new nomogram was validated in 493 patients treated at the same institution. Multivariable Cox regression analyses assessed the association between input variables and risk of prostate cancer, and area under the receiver operating characteristic curve (Az) analyzed the predictive ability.Results: At 5-year follow-up period, 34.3% of patients had systemic progression of prostate cancer. Nomogram (SVM-MRI) predicting 5-year prostate cancer rate trained with clinical and mp-MRI data was accurate and discriminating with an externally validated Az of 0.938, positive predictive value (PPV) of 77.4%, and negative predictive value of 91.5%. The improvement was significant (P < 0.001) compared with the nomogram trained with clinical data. When stratified by PSA, SVM-MRI nomogram had high PPV (93.6%) in patients with PSA > 20 ng/mL, with intermediate to low PPV in PSA 10 to 20 ng/mL (64%), PSA 4 to 10 ng/mL (55.8%), and PSA 0 to 4 ng/mL (29%). PI-RADS score (Cox HR, 2.112; P < 0.001), PSA level (HR, 1.435; P < 0.001), and age (HR, 1.012; P = 0.043) were independent predictors of prostate cancer.Conclusions: Featured with low false positive rate, mp-MRI could be the first investigation of a man with a raised PSA before prostate biopsy. Clin Cancer Res; 23(14); 3692-9. ©2017 AACR.

DWI-associated entire-tumor histogram analysis for the differentiation of low-grade prostate cancer from intermediate-high-grade prostate cancer

PURPOSE

To investigate diagnostic efficiency of DWI using entire-tumor histogram analysis in differentiating the low-grade (LG) prostate cancer (PCa) from intermediate-high-grade (HG) PCa in comparison with conventional ROI-based measurement.

METHODS

DW images (b of 0-1400 s/mm(2)) from 126 pathology-confirmed PCa (diameter >0.5 cm) in 110 patients were retrospectively collected and processed by mono-exponential model. The measurement of tumor apparent diffusion coefficients (ADCs) was performed with using histogram-based and ROI-based approach, respectively. The diagnostic ability of ADCs from two methods for differentiating LG-PCa (Gleason score, GS ≤ 6) from HG-PCa (GS > 6) was determined by ROC regression, and compared by McNemar's test.

RESULTS

There were 49 LG-tumor and 77 HG-tumor at pathologic findings. Histogram-based ADCs (mean, median, 10th and 90th) and ROI-based ADCs (mean) showed dominant relationships with ordinal GS of Pca (ρ = -0.225 to -0.406, p < 0.05). All above imaging indices reflected significant difference between LG-PCa and HG-PCa (all p values <0.01). Histogram 10th ADCs had dominantly high Az (0.738), Youden index (0.415), and positive likelihood ratio (LR+, 2.45) in stratifying tumor GS against mean, median and 90th ADCs, and ROI-based ADCs. Histogram mean, median, and 10th ADCs showed higher specificity (65.3%-74.1% vs. 44.9%, p < 0.01), but lower sensitivity (57.1%-71.3% vs. 84.4%, p < 0.05) than ROI-based ADCs in differentiating LG-PCa from HG-PCa.

CONCLUSIONS

DWI-associated histogram analysis had higher specificity, Az, Youden index, and LR+ for differentiation of PCa Gleason grade than ROI-based approach.

Pilot Study of the Use of Hybrid Multidimensional T2-Weighted Imaging-DWI for the Diagnosis of Prostate Cancer and Evaluation of Gleason Score

OBJECTIVE

The objective of our study was to evaluate the role of a hybrid T2-weighted imaging-DWI sequence for prostate cancer diagnosis and differentiation of aggressive prostate cancer from nonaggressive prostate cancer.

MATERIALS AND METHODS

Twenty-one patients with prostate cancer who underwent preoperative 3-T MRI and prostatectomy were included in this study. Patients underwent a hybrid T2-weighted imaging-DWI examination consisting of DW images acquired with TEs of 47, 75, and 100 ms and b values of 0 and 750 s/mm(2). The apparent diffusion coefficient (ADC) and T2 were calculated for cancer and normal prostate ROIs at each TE and b value. Changes in ADC and T2 as a function of increasing the TE and b value, respectively, were analyzed. A new metric termed "PQ4" was defined as the percentage of voxels within an ROI that has increasing T2 with increasing b value and has decreasing ADC with increasing TE.

RESULTS

ADC values were significantly higher in normal ROIs than in cancer ROIs at all TEs (p < 0.0001). With increasing TE, the mean ADC increased 3% in cancer ROIs and increased 12% in normal ROIs. T2 was significantly higher in normal ROIs than in cancer ROIs at both b values (p ≤ 0.0002). The mean T2 decreased with increasing b value in cancer ROIs (ΔT2 = -17 ms) and normal ROIs (ΔT2 = -52 ms). PQ4 clearly differentiated normal ROIs from prostate cancer ROIs (p = 0.0004) and showed significant correlation with Gleason score (ρ = 0.508, p < 0.0001).

CONCLUSION

Hybrid MRI measures the response of ADC and T2 to changing TEs and b values, respectively. This approach shows promise for detecting prostate cancer and determining its aggressiveness noninvasively.

New RESOLVE-Based Diffusional Kurtosis Imaging in MRI-Visible Prostate Cancer: Effect of Reduced b Value on Image Quality and Diagnostic Effectiveness

OBJECTIVE

The purpose of this article was to investigate whether a new readout segmentation of long variable echo-trains (RESOLVE)-based diffusional kurtosis imaging (DKI) with reduced b value technique can affect image quality and diagnostic effectiveness in MRI-visible prostate cancer (PCA).

SUBJECTS AND METHODS

Prostatic RESOLVE DKI (0-1400 s/mm2) was prospectively performed for 12 volunteers. The optimal protocol was then performed in 108 MRI-visible PCAs to determine whether it can compete against a preferred b-value set (0-2000 s/mm(2)) regarding image quality and diagnostic effectiveness. Images were interpreted by two independent radiologists using the prostate imaging reporting and data system (PI-RADS). Readers' concordance and diagnostic effectiveness were tested with the Fleiss kappa and area under the ROC curve (Az) analyses.

RESULTS

A b value of 1400 s/mm(2) generated a larger apparent diffusion coefficient of gaussian distribution (Dapp) (1.35 ± 0.31 vs 1.30 ± 0.30 mm(2)/s; p < 0.001) and apparent kurtosis coefficient (Kapp) (1.11 ± 0.26 vs 1.00 ± 0.21; p < 0.001) in PCA than did a b value of 2000 s/mm(2). Interreader agreement using PI-RADS was relatively low when Dapp and Kapp maps were excluded from image interpretations (κ = 0.39-0.41 vs κ = 0.66-0.68 with Dapp and Kapp maps). Interreader agreement in staging PCA was relatively high (κ > 0.80) and was not influenced by reducing the b value. The power of Dapp and Kapp to differentiate PCA from normal tissue (Az = 0.97-0.98), tissue with a Gleason score less than or equal to 3 + 4 from tissue with a Gleason score greater than 3 + 4 (Az = 0.77-0.82), and PCA stage lower than pT3 from stage pT3 and higher PCA (Az = 0.70-0.75) was not significantly degraded by reducing the b value.

CONCLUSION

We found that b values significantly influenced image quality, PI-RADS score, and DKI outputs but did not degrade the diagnostic effectiveness of DKI parameters to detect and classify PCA.

Tumor radio-sensitivity assessment by means of volume data and magnetic resonance indices measured on prostate tumor bearing rats

PURPOSE

Radiation therapy is one of the most common treatments in the fight against prostate cancer, since it is used to control the tumor (early stages), to slow its progression, and even to control pain (metastasis). Although many factors (e.g., tumor oxygenation) are known to influence treatment efficacy, radiotherapy doses and fractionation schedules are often prescribed according to the principle "one-fits-all," with little personalization. Therefore, the authors aim at predicting the outcome of radiation therapy a priori starting from morphologic and functional information to move a step forward in the treatment customization.

METHODS

The authors propose a two-step protocol to predict the effects of radiation therapy on individual basis. First, one macroscopic mathematical model of tumor evolution was trained on tumor volume progression, measured by caliper, of eighteen Dunning R3327-AT1 bearing rats. Nine rats inhaled 100% O2 during irradiation (oxy), while the others were allowed to breathe air. Second, a supervised learning of the weight and biases of two feedforward neural networks was performed to predict the radio-sensitivity (target) from the initial volume and oxygenation-related information (inputs) for each rat group (air and oxygen breathing). To this purpose, four MRI-based indices related to blood and tissue oxygenation were computed, namely, the variation of signal intensity ΔSI in interleaved blood oxygen level dependent and tissue oxygen level dependent (IBT) sequences as well as changes in longitudinal ΔR1 and transverse ΔR2(*) relaxation rates.

RESULTS

An inverse correlation of the radio-sensitivity parameter, assessed by the model, was found with respect the ΔR2(*) (-0.65) for the oxy group. A further subdivision according to positive and negative values of ΔR2(*) showed a larger average radio-sensitivity for the oxy rats with ΔR2(*)<0 and a significant difference in the two distributions (p < 0.05). Finally, a leave-one-out procedure yielded a radio-sensitivity error lower than 20% in both neural networks.

CONCLUSIONS

While preliminary, these specific results suggest that subjects affected by the same pathology can benefit differently from the same irradiation modalities and support the usefulness of IBT in discriminating between different responses.

Abnormal findings on multiparametric prostate magnetic resonance imaging predict subsequent biopsy upgrade in patients with low risk prostate cancer managed with active surveillance

PURPOSE

To determine the ability of multiparametric MR imaging to predict disease progression in patients with prostate cancer managed by active surveillance.

METHODS

Sixty-four men with biopsy-proven prostate cancer managed by active surveillance were included in this HIPPA compliant, IRB approved study. We reviewed baseline MR imaging scans for the presence of a suspicious findings on T2-weighted imaging, MR spectroscopic imaging (MRSI), and diffusion-weighted MR imaging (DWI). The Gleason grades at subsequent biopsy were recorded. A Cox proportional hazard model was used to determine the predictive value of MR imaging for Gleason grades, and the model performance was described using Harrell's C concordance statistic and 95% confidence intervals (CIs).

RESULTS

The Cox model that incorporated T2-weighted MR imaging, DWI, and MRSI showed that only T2-weighted MR imaging and DWI are independent predictors of biopsy upgrade (T2; HR = 2.46; 95% CI 1.36-4.46; P = 0.003-diffusion; HR = 2.76; 95% CI 1.13-6.71; P = 0.03; c statistic = 67.7%; 95% CI 61.1-74.3). There was an increasing rate of Gleason score upgrade with a greater number of concordant findings on multiple MR sequences (HR = 2.49; 95% CI 1.72-3.62; P < 0.001).

CONCLUSIONS

Abnormal results on multiparametric prostate MRI confer an increased risk for Gleason score upgrade at subsequent biopsy in men with localized prostate cancer managed by active surveillance. These results may be of help in appropriately selecting candidates for active surveillance.

Histogram analysis of diffusion kurtosis magnetic resonance imaging in differentiation of pathologic Gleason grade of prostate cancer

OBJECTIVE

To investigate diagnostic performance of diffusion kurtosis imaging with histogram analysis for stratifying pathologic Gleason grade of prostate cancer (PCa).

MATERIALS AND METHODS

This retrospective study was approved by the institutional review board, and written informed consent was waived. A total of 110 patients pathologically confirmed as having PCa (diameter>0.5 cm) underwent preoperative diffusion-weighted magnetic resonance imaging (b value of 0-2,100 s/mm(2)) at 3T. Data were postprocessed by monoexponential and diffusion kurtosis models for quantitation of apparent diffusion coefficients (ADCs), apparent diffusion for Gaussian distribution (D(app)), and apparent kurtosis coefficient (K(app)). The measurement was based on an entire-tumor histogram analysis approach. The ability of imaging indices for differentiating low-grade (LG) PCa (Gleason score [GS]≤6) from intermediate-/high-grade (HG: GS>6) PCa was analyzed by receiver operating characteristic regression.

RESULTS

There were 49 LG tumors and 77 HG tumors at pathologic findings. HG-PCa had significantly lower ADCs, lower diffusion kurtosis diffusivity (D(app)), and higher kurtosis coefficient (K(app)) in mean, median, 10th, and 90th percentile, with higher D(app) in skewness and kurtosis than LG-PCa (P< 0.05). The 10th ADCs, the 10th D(app), and the 90th K(app) showed relatively higher area under receiver operating characteristic curve (Az), Youden index, and positive likelihood ratio in stratifying aggressiveness of PCa against other indices. The 90th K(app) showed relatively higher correlation (ρ>0.6) with ordinal GS of PCa; significantly higher Az, sensitivity, and specificity (0.889, 74.1%, and 93.9%, respectively) than the 10th D(app) did (0.765, 61.0%, and 79.6%, respectively; P<0.05); and higher Az and specificity than the 10th ADCs did (0.738 and 71.4%, respectively; P<0.05) in differentiating LG-PCa from HG-PCa.

CONCLUSIONS

It demonstrated a good reliability of histogram diffusion kurtosis imaging for stratifying pathologic GS of PCa. The 90th K(app) had better diagnostic performance in differentiating LG-PCa from HG-PCa.

High-risk prostate cancer-classification and therapy

Approximately 15% of patients with prostate cancer are diagnosed with high-risk disease. However, the current definitions of high-risk prostate cancer include a heterogeneous group of patients with a range of prognoses. Some have the potential to progress to a lethal phenotype that can be fatal, while others can be cured with treatment of the primary tumour alone. The optimal management of this patient subgroup is evolving. A refined classification scheme is needed to enable the early and accurate identification of high-risk disease so that more-effective treatment paradigms can be developed. We discuss several principles established from clinical trials, and highlight other questions that remain unanswered. This Review critically evaluates the existing literature focused on defining the high-risk population, the management of patients with high-risk prostate cancer, and future directions to optimize care.

Potential of hybrid ¹⁸F-fluorocholine PET/MRI for prostate cancer imaging

PURPOSE

To report the first results of hybrid (18)F-fluorocholine PET/MRI imaging for the detection of prostate cancer.

METHODS

This analysis included 26 consecutive patients scheduled for prostate PET/MRI before radical prostatectomy. The examinations were performed on a hybrid whole-body PET/MRI scanner. The MR acquisitions which included T2-weighted, diffusion-weighted and dynamic contrast-enhanced sequences were followed during the same session by whole-body PET scans. Parametric maps were constructed to measure normalized T2-weighted intensity (nT2), apparent diffusion coefficient (ADC), volume transfer constant (K (trans)), extravascular extracellular volume fraction (v e) and standardized uptake values (SUV). With pathology as the gold standard, ROC curves were calculated using logistic regression for each parameter and for the best combination with and without PET to obtain a MR model versus a PETMR model.

RESULTS

Of the 26 patients initially selected, 3 were excluded due to absence of an endorectal coil (2 patients) or prosthesis artefacts (1 patient). In the whole prostate, the area under the curve (AUC) for SUVmax, ADC, nT2, K (trans) and v e were 0.762, 0.756, 0.685, 0.611 and 0.529 with a best threshold at 3.044 for SUVmax and 1.075 × 10(-3) mm(2)/s for ADC. The anatomical distinction between the transition zone and the peripheral zone showed the potential of the adjunctive use of PET. In the peripheral zone, the AUC of 0.893 for the PETMR model was significantly greater (p = 0.0402) than the AUC of 0.84 for the MR model only. In the whole prostate, no relevant correlation was observed between ADC and SUVmax. The SUVmax was not affected by the Gleason score.

CONCLUSION

The performance of a hybrid whole-body (18)F-fluorocholine PET/MRI scan in the same session combined with a prostatic MR examination did not interfere with the diagnostic accuracy of the MR sequences. The registration of the PET data and the T2 anatomical MR sequence data allowed precise localization of hypermetabolic foci in the prostate. While in the transition zone the adenomatous hyperplasia interfered with cancer detection by PET, the quantitative analysis tool performed well for cancer detection in the peripheral zone.

MR imaging of the prostate and adjacent anatomic structures before, during, and after ejaculation: qualitative and quantitative evaluation

PURPOSE

To determine the possibility of obtaining high-quality magnetic resonance (MR) images before, during, and immediately after ejaculation and detecting measurable changes in quantitative MR imaging parameters after ejaculation.

MATERIALS AND METHODS

In this prospective, institutional review board-approved, HIPAA-compliant study, eight young healthy volunteers (median age, 22.5 years), after providing informed consent, underwent MR imaging while masturbating to the point of ejaculation. A 1.5-T MR imaging unit was used, with an eight-channel surface coil and a dynamic single-shot fast spin-echo sequence. In addition, a quantitative MR imaging protocol that allowed calculation of T1, T2, and apparent diffusion coefficient (ADC) values was applied before and after ejaculation. Volumes of the prostate and seminal vesicles (SV) were calculated by using whole-volume segmentation on T2-weighted images, both before and after ejaculation. Pre- and postejaculation changes in quantitative MR parameters and measured volumes were evaluated by using the Wilcoxon signed rank test with Bonferroni adjustment.

RESULTS

There was no significant change in prostate volumes on pre- and postejaculation images, while the SV contracted by 41% on average (median, 44.5%; P = .004). No changes before and after ejaculation were observed in T1 values or in T2 and ADC values in the central gland, while T2 and ADC values were significantly reduced in the peripheral zone by 12% and 14%, respectively (median, 13% and 14.5%, respectively; P = .004).

CONCLUSION

Successful dynamic MR imaging of ejaculation events and the ability to visualize internal sphincter closure, passage of ejaculate, and significant changes in SV volumes were demonstrated. Significant changes in peripheral zone T2 and ADC values were observed.

Prostate cancer detection and diagnosis: the role of MR and its comparison with other diagnostic modalities--a radiologist's perspective

It is now universally recognized that many prostate cancers are over-diagnosed and over-treated. The European Randomized Study of Screening for Prostate Cancer from 2009 evidenced that, to save one man from death from prostate cancer, over 1400 men need to be screened, and 48 need to undergo treatment. The detection of prostate cancer is traditionally based on digital rectal examination (DRE) and the measurement of serum prostate-specific antigen (PSA), followed by ultrasound-guided biopsy. The primary role of imaging for the detection and diagnosis of prostate cancer has been transrectal ultrasound (TRUS) guidance during biopsy. Traditionally, MRI has been used primarily for the staging of disease in men with biopsy-proven cancer. It has a well-established role in the detection of T3 disease, planning of radiation therapy, especially three-dimensional conformal or intensity-modulated external beam radiation therapy, and planning and guiding of interstitial seed implant or brachytherapy. New advances have now established that prostate MRI can accurately characterize focal lesions within the gland, an ability that has led to new opportunities for improved cancer detection and guidance for biopsy. Two new approaches to prostate biopsy are under investigation. Both use pre-biopsy MRI to define potential targets for sampling, and the biopsy is performed either with direct real-time MR guidance (in-bore) or MR fusion/registration with TRUS images (out-of-bore). In-bore and out-of-bore MRI-guided prostate biopsies have the advantage of using the MR target definition for the accurate localization and sampling of targets or suspicious lesions. The out-of-bore method uses combined MRI/TRUS with fusion software that provides target localization and increases the sampling accuracy of TRUS-guided biopsies by integrating prostate MRI information with TRUS. Newer parameters for each imaging modality, such as sonoelastography or shear wave elastography, contrast-enhanced ultrasound and MRI elastography, show promise to further enrich datasets.

Simultaneous 18F choline positron emission tomography/magnetic resonance imaging of the prostate: initial results

PURPOSE

The purposes of this study were to evaluate the feasibility of simultaneous 18F choline positron emission tomography (PET) and magnetic resonance imaging (MRI) of the prostate and to present the first clinical results of the method.

MATERIALS AND METHODS

From March 2012 to October 2012, a total of 15 consecutive patients were examined with simultaneous 18F choline PET/MRI. At the time of the examination, 8 patients had histologically proven prostate cancer, 2 patients had repeated prostate biopsies with negative results, and 5 patients had suspected prostate cancer with an elevated or rising prostate specific antigene level but did not have a prostate biopsy. Sequence protocol comprised T2-weighted high-resolution images and diffusion-weighted images of the prostate in addition to PET imaging. Image quality was assessed by 2 radiologists, and the PET images were evaluated qualitatively and quantitatively.

RESULTS

Simultaneous PET/MRI of the prostate was accomplished successfully in all patients. The method proved to be robust without technical failure, and the image quality was rated to be diagnostic in all examinations except in 1 diffusion-weighted imaging (DWI) data set that was judged to be nondiagnostic because of susceptibility artifacts. High-resolution T2-weighted images allowed exact correlation of elevated focal or diffuse choline uptake to suspicious T2-weighted lesions of the prostate. A high accordance was found between PET and DWI. However, PET-positive lesions were found in 3 patients wherein DWI did not indicate tumor in suspicious T2-weighted lesions.

CONCLUSIONS

Simultaneous positron emission tomography/magnetic resonance imaging of the prostate has the advantage of combining high-resolution prostate images, functional studies, and metabolic/molecular imaging. The PET component adds diagnostic confidence to the MRI-based parameters in identifying and localizing tumor in the prostate.

Hybrid multidimensional T(2) and diffusion-weighted MRI for prostate cancer detection

PURPOSE

To study the dependence of apparent diffusion coefficient (ADC) and T2 on echo time (TE) and b-value, respectively, in normal prostate and prostate cancer, using two-dimensional MRI sampling, referred to as "hybrid multidimensional imaging."

MATERIALS AND METHODS

The study included 10 patients with biopsy-proven prostate cancer who underwent 3 Tesla prostate MRI. Diffusion-weighted MRI (DWI) data were acquired at b = 0, 750, and 1500 s/mm(2) . For each b-value, data were acquired at TEs of 47, 75, and 100 ms. ADC and T2 were measured as a function of b-value and TE, respectively, in 15 cancer and 10 normal regions of interest (ROIs). The Friedman test was used to test the significance of changes in ADC as a function of TE and of T2 as a function of b-value.

RESULTS

In normal prostate ROIs, the ADC at TE of 47 ms is significantly smaller than ADC at TE of 100 ms (P = 0.0003) and T2 at b-value of 0 s/mm(2) is significantly longer than T2 at b-value of 1500 s/mm(2) (P = 0.001). In cancer ROIs, average ADC and T2 values do not change as a function of TE and b-value, respectively. However, in many cancer pixels, there are large decreases in the ADC as a function of TE and large increases in T2 as a function of b-value. Cancers are more conspicuous in ADC maps at longer TEs.

CONCLUSION

Parameters derived from hybrid imaging that depend on coupled/associated values of ADC and T2 may improve the accuracy of MRI in diagnosing prostate cancer.

3 Tesla multiparametric MRI for GTV-definition of Dominant Intraprostatic Lesions in patients with Prostate Cancer--an interobserver variability study

PURPOSE

To evaluate the interobserver variability of gross tumor volume (GTV) - delineation of Dominant Intraprostatic Lesions (DIPL) in patients with prostate cancer using published MRI criteria for multiparametric MRI at 3 Tesla by 6 different observers.

MATERIAL AND METHODS

90 GTV-datasets based on 15 multiparametric MRI sequences (T2w, diffusion weighted (DWI) and dynamic contrast enhanced (DCE)) of 5 patients with prostate cancer were generated for GTV-delineation of DIPL by 6 observers. The reference GTV-dataset was contoured by a radiologist with expertise in diagnostic imaging of prostate cancer using MRI. Subsequent GTV-delineation was performed by 5 radiation oncologists who received teaching of MRI-features of primary prostate cancer before starting contouring session. GTV-datasets were contoured using Oncentra Masterplan® and iplan® Net. For purposes of comparison GTV-datasets were imported to the Artiview® platform (Aquilab®), GTV-values and the similarity indices or Kappa indices (KI) were calculated with the postulation that a KI > 0.7 indicates excellent, a KI > 0.6 to < 0.7 substantial and KI > 0.5 to < 0.6 moderate agreement. Additionally all observers rated difficulties of contouring for each MRI-sequence using a 3 point rating scale (1 = easy to delineate, 2 = minor difficulties, 3 = major difficulties).

RESULTS

GTV contouring using T2w (KI-T2w = 0.61) and DCE images (KI-DCE = 0.63) resulted in substantial agreement. GTV contouring using DWI images resulted in moderate agreement (KI-DWI = 0.51). KI-T2w and KI-DCE was significantly higher than KI-DWI (p = 0.01 and p = 0.003). Degree of difficulty in contouring GTV was significantly lower using T2w and DCE compared to DWI-sequences (both p < 0.0001). Analysis of delineation differences revealed inadequate comparison of functional (DWI, DCE) to anatomical sequences (T2w) and lack of awareness of non-specific imaging findings as a source of erroneous delineation.

CONCLUSIONS

Using T2w and DCE sequences at 3 Tesla for GTV-definition of DIPL in prostate cancer patients by radiation oncologists with knowledge of MRI features results in substantial agreement compared to an experienced MRI-radiologist, but for radiotherapy purposes higher KI are desirable, strengthen the need for expert surveillance. DWI sequence for GTV delineation was considered as difficult in application.

Image-guided robotic interventions for prostate cancer

Robotic prostatectomy is a common surgical treatment for men with prostate cancer, with some studies estimating that 80% of prostatectomies now performed in the USA are done so robotically. Despite the technical advantages offered by robotic systems, functional and oncological outcomes of prostatectomy can still be improved further. Alternative minimally invasive treatments that have also adopted robotic platforms include brachytherapy and high-intensity focused ultrasonography (HIFU). These techniques require real-time image guidance--such as ultrasonography or MRI--to be truly effective; issues with software compatibility as well as image registration and tracking currently limit such technologies. However, image-guided robotics is a fast-growing area of research that combines the improved ergonomics of robotic systems with the improved visualization of modern imaging modalities. Although the benefits of a real-time image-guided robotic system to improve the precision of surgical interventions are being realized, the clinical usefulness of many of these systems remains to be seen.

Performance of multiparametric magnetic resonance imaging in the evaluation and management of clinically low-risk prostate cancer

OBJECTIVE

The purpose of this article is to review the multiparametric magnetic resonance imaging (mMRI) of the prostate and MR-guided prostate biopsy, and their role in the evaluation and management of men with low-risk prostate cancer.

METHODS

We performed a literature review based on the MEDLINE database search for publications on the role of mMRI (a) in detection and localization of prostate cancer, prediction of tumor aggressiveness and progression and (b) in guiding targeted prostate biopsy.

RESULTS

The mMRI, particularly diffusion-weighted imaging with T2-weighted imaging, is a useful tool for tumor localization in low-risk prostate cancer as it can detect lesions that are more likely missed on extended biopsy schemes and can identify clinically significant disease requiring definitive treatment. The MR-guided biopsy of the most suspicious lesions enables more accurate and safer approach to guide enrollment into the active surveillance program. However, the MR-guided biopsy is complex. The fusion of MRI data with transrectal ultrasound for the purpose of biopsy provides a more feasible technique with documented accurate sampling.

CONCLUSION

Although the mMRI is not routinely used for risk stratification and prognostic assessment in prostate cancer, it can provide valuable information to guide management of men with low-risk disease. Incorporation of mMRI into the workup and monitoring of patients with low-risk prostate cancer can help discriminate clinically significant disease from indolent disease. Targeted biopsy of MR-suspicious lesions enables accurate sampling of potentially aggressive tumors that may affect outcomes.