Trends in Gleason score: concordance between biopsy and prostatectomy over 15 years

Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models

OBJECTIVE

To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort.

METHODS

Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015-2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow.

RESULTS

The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging.

CONCLUSIONS

Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status.

CLINICAL RELEVANCE STATEMENT

The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment.

KEY POINTS

• Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances. • The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic. •Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.

Value of 68Ga-labeled bombesin antagonist (RM2) in the detection of primary prostate cancer comparing with [18F]fluoromethylcholine PET-CT and multiparametric MRI-a phase I/II study

OBJECTIVES

The bombesin derivative RM2 is a GRPr antagonist with strong binding affinity to prostate cancer (PCa). In this study, the impact of [⁶⁸Ga]Ga-RM2 positron emission tomography-computed tomography (PET-CT) for the detection of primary PCa was compared with that of [¹⁸F]FCH PET-CT and multiparametric magnetic resonance imaging (mpMRI).

METHODS

This phase I/II study was conducted in 30 biopsy-positive PCa subjects. The patients were stratified into high (10 patients), intermediate (10 patients), and low risk (10 patients) for extraglandular metastases as defined by National Comprehensive Cancer Network (NCCN) criteria (NCCN Clinical Practice Guidelines in Oncology, 2016). The prostate gland was classified in 12 anatomic segments for data analysis of the imaging modalities as well as histopathologic findings. The segment with the highest radiotracer uptake was defined as the "index lesion." All cases were scheduled to undergo prostatectomy with pelvic lymph node (LN) dissection in intermediate- and high-risk patients. Intraprostatic and pelvic nodal [⁶⁸Ga]Ga-RM2 and [¹⁸F]FCH PET-CT findings were correlated with mpMRI and histopathologic results.

RESULTS

Of the 312 analyzed regions, 120 regions (4 to 8 lesions per patient) showed abnormal findings in the prostate gland. In a region-based analysis, overall sensitivity and specificity of [⁶⁸Ga]Ga-RM2 PET-CT in the detection of primary tumor were 74% and 90%, respectively, while it was 60% and 80% for [¹⁸F]FCH PET-CT and 72% and 89% for mpMRI. Although the overall sensitivity of [⁶⁸Ga]Ga-RM2 PET-CT was higher compared to that of [¹⁸F]FCH PET-CT and mpMRI, the statistical analysis showed only significant difference between [⁶⁸Ga]Ga-RM2 PET-CT and [¹⁸F]FCH PET-CT in the intermediate-risk group (p = 0.01) and [⁶⁸Ga]Ga-RM2 PET-CT and mpMRT in the high-risk group (p = 0.03). In the lesion-based analysis, there was no significant difference between SUVmax of [⁶⁸Ga]Ga-RM2 and [¹⁸F]FCH PET-CT in the intraprostatic malignant lesions ([⁶⁸Ga]Ga-RM2: mean SUVmax: 5.98 ± 4.13, median: 4.75; [¹⁸F]FCH: mean SUVmax: 6.08 ± 2.74, median: 5.5; p = 0.13).

CONCLUSIONS

[⁶⁸Ga]Ga-RM2 showed promising PET tracer for the detection of intraprostatic PCa in a cohort of patients with different risk stratifications. However, significant differences were only found between [⁶⁸Ga]Ga-RM2 PET-CT and [¹⁸F]FCH PET-CT in the intermediate-risk group and [⁶⁸Ga]Ga-RM2 PET-CT and mpMRT in the high-risk group. In addition, GRP-R-based imaging seems to play a complementary role to choline-based imaging for full characterization of PCa extent and biopsy guidance in low- and intermediate-metastatic-risk PCa patients and has the potential to discriminate them from those at higher risks.

KEY POINTS

• [⁶⁸Ga]Ga-RM2 is a promising PET tracer with a high detection rate for intraprostatic PCa especially in intermediate-risk prostate cancer patients. • GRPr-based imaging seems to play a complementary role to choline-based or PSMA-based PET/CT imaging in selected low- and intermediate-risk PCa patients for better characterization and eventually biopsy guidance of prostate cancer disease.

Radiomics Analysis on [68Ga]Ga-PSMA-11 PET and MRI-ADC for the Prediction of Prostate Cancer ISUP Grades: Preliminary Results of the BIOPSTAGE Trial

Simple Summary

Radiomics analysis is used on magnetic resonance imaging – apparent diffusion coefficient (MRI-ADC) maps and [68Ga]Ga-PSMA-11 PET uptake maps to assess unique tumor traits not visible to the naked eye and predict histology-proven ISUP grades in a cohort of 28 patients. Our study’s main goal is to report imaging features that can distinguish patients with low ISUP grades from those with higher grades (ISUP one+) by employing logistic regression statistical models based on MRI-ADC and 68Ga-PSMA data, as well as assess the features’ stability under small contouring variations. Our findings reveal that MRI-ADC and [⁶⁸Ga]Ga-PSMA-11 PET imaging features-based models are equivalent and complementary for predicting low ISUP grade patients. These models can be employed in broader studies to confirm their ISUP grade prediction ability and eventually impact clinical workflow by reducing overdiagnosis of indolent, early-stage PCa.

Abstract

Prostate cancer (PCa) risk categorization based on clinical/PSA testing results in a substantial number of men being overdiagnosed with indolent, early-stage PCa. Clinically non-significant PCa is characterized as the presence of ISUP grade one, where PCa is found in no more than two prostate biopsy cores.MRI-ADC and [⁶⁸Ga]Ga-PSMA-11 PET have been proposed as tools to predict ISUP grade one patients and consequently reduce overdiagnosis. In this study, Radiomics analysis is applied to MRI-ADC and [⁶⁸Ga]Ga-PSMA-11 PET maps to quantify tumor characteristics and predict histology-proven ISUP grades. ICC was applied with a threshold of 0.6 to assess the features’ stability with variations in contouring. Logistic regression predictive models based on imaging features were trained on 31 lesions to differentiate ISUP grade one patients from ISUP two+ patients. The best model based on [⁶⁸Ga]Ga-PSMA-11 PET returned a prediction efficiency of 95% in the training phase and 100% in the test phase whereas the best model based on MRI-ADC had an efficiency of 100% in both phases. Employing both imaging modalities, prediction efficiency was 100% in the training phase and 93% in the test phase. Although our patient cohort was small, it was possible to assess that both imaging modalities add information to the prediction models and show promising results for further investigations.

Overcoming Drug Resistance in Advanced Prostate Cancer by Drug Repurposing

Prostate cancer (PCa) is the second most common cancer in men. Common treatments include active surveillance, surgery, or radiation. Androgen deprivation therapy and chemotherapy are usually reserved for advanced disease or biochemical recurrence, such as castration-resistant prostate cancer (CRPC), but they are not considered curative because PCa cells eventually develop drug resistance. The latter is achieved through various cellular mechanisms that ultimately circumvent the pharmaceutical’s mode of action. The need for novel therapeutic approaches is necessary under these circumstances. An alternative way to treat PCa is by repurposing of existing drugs that were initially intended for other conditions. By extrapolating the effects of previously approved drugs to the intracellular processes of PCa, treatment options will expand. In addition, drug repurposing is cost-effective and efficient because it utilizes drugs that have already demonstrated safety and efficacy. This review catalogues the drugs that can be repurposed for PCa in preclinical studies as well as clinical trials.

TRUS-Guided Target Biopsy for a PI-RADS 3–5 Index Lesion to Reduce Gleason Score Underestimation: A Propensity Score Matching Analysis

Background

Magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS)-guided cognitive or image fusion biopsy is performed to target a prostate imaging reporting and data system (PI-RADS) 3–5 lesion. Biopsy Gleason score (GS) is frequently underestimated compared to prostatectomy GS. However, it is still unclear about how many cores on target are necessary to reduce undergrading and if additional cores around the target may improve grade prediction on surgical specimen.

Purpose

To determine the number of target cores and targeting strategy to reduce GS underestimation.

Materials and Methods

Between May 2017 and April 2020, a total of 385 patients undergoing target cognitive or image fusion biopsy of PI-RADS 3–5 index lesions and radical prostatectomies (RP) were 2:1 matched with propensity score using multiple variables and divided into the 1–4 core (n = 242) and 5–6 core (n = 143) groups, which were obtained with multiple logistic regression with restricted cubic spline curve. Target cores of 1–3 and 4–6 were sampled from central and peripheral areas, respectively. Pathologic outcomes and target cores were retrospectively assessed to analyze the GS difference or changes between biopsy and RP with Wilcoxon signed-rank test.

Results

The median of target cores was 3 and 6 in the 1–4 core and 5–6 core groups, respectively (p < 0.001). Restricted cubic spline curve showed that GS upgrade was significantly reduced from the 5th core and there was no difference between 5th and 6th cores. Among the matched patients, 35.4% (136/385; 95% confidence interval, 0.305–0.403) had a GS upgrade after RP. The GS upgrades in the 1–4 core and 5–6 core groups were observed in 40.6% (98/242, 0.343–0.470) and 26.6% (38/143, 0.195–0.346), respectively (p = 0.023). Although there was no statistical difference between the matched groups in terms of RP GS (p = 0.092), the 5–6 core group had significantly higher biopsy GS (p = 0.006) and lower GS change from biopsy to RP (p = 0.027).

Conclusion

Five or more target cores sampling from both periphery and center of an index tumor contribute to reduce GS upgrade.

Hundred years of transperineal prostate biopsy

The earliest recorded efforts to biopsy prostate, in the early 20th century, were made through transperineal (TP) approach, with open perineal prostate biopsy (PBx) being considered the gold standard for prostate cancer (PCa) diagnosis in that era. Later, to minimize morbidity and increase diagnostic accuracy, several technical modifications and transrectal ultrasound (TRUS) assistance were incorporated. However, in the 1980s, the transrectal (TR) approach became the predominant PBx method following the introduction of TRUS-TR PBx with sextant sampling, providing a convenient and efficacious method for prostate sampling. With modernization of PCa diagnosis, a recent resurgence of the TP PBx has been observed, driven primarily by TR drawbacks of infectious complications and sampling limitations. TP PBx is rapidly emerging as the new PBx standard, being officially recommended as the initial approach for biopsy in Europe and is increasingly being conducted and studied in the United States. The modern era of TP PBx is based on the improvements in local anesthesia techniques, TP access systems, and robotic assistance. These modifications and advancements have improved the ease of use, patient comfort, and diagnostic outcomes with TP PBx. Herein, we present a history of the evolution of TP PBx spanning over 100 years and explore the basis of the technique that merits future utilization.

Image-Guided Targeted Prostate Biopsies

Prostate cancer is the second most common cancer in the United States. Screening for prostate cancer has increased through the usage of prostate specific antigen and biopsies. Traditionally, prostate biopsies are done using transrectal ultrasound with 10-12 cores obtained in a sextant pattern. Advances in prostate imaging with multiparametric magnetic resonance imaging has led to image guided targeted prostate biopsies. This can be done with cognitive fusion, MRI-fusion, and in-bore MRI. This article will review the indications, techniques, and outcomes for targeted image guided prostate biopsies using in-bore MRI and MRI fusion.

A Fully Automatic Artificial Intelligence System Able to Detect and Characterize Prostate Cancer Using Multiparametric MRI: Multicenter and Multi-Scanner Validation

In the last years, the widespread use of the prostate-specific antigen (PSA) blood examination to triage patients who will enter the diagnostic/therapeutic path for prostate cancer (PCa) has almost halved PCa-specific mortality. As a counterpart, millions of men with clinically insignificant cancer not destined to cause death are treated, with no beneficial impact on overall survival. Therefore, there is a compelling need to develop tools that can help in stratifying patients according to their risk, to support physicians in the selection of the most appropriate treatment option for each individual patient. The aim of this study was to develop and validate on multivendor data a fully automated computer-aided diagnosis (CAD) system to detect and characterize PCas according to their aggressiveness. We propose a CAD system based on artificial intelligence algorithms that a) registers all images coming from different MRI sequences, b) provides candidates suspicious to be tumor, and c) provides an aggressiveness score of each candidate based on the results of a support vector machine classifier fed with radiomics features. The dataset was composed of 131 patients (149 tumors) from two different institutions that were divided in a training set, a narrow validation set, and an external validation set. The algorithm reached an area under the receiver operating characteristic (ROC) curve in distinguishing between low and high aggressive tumors of 0.96 and 0.81 on the training and validation sets, respectively. Moreover, when the output of the classifier was divided into three classes of risk, i.e., indolent, indeterminate, and aggressive, our method did not classify any aggressive tumor as indolent, meaning that, according to our score, all aggressive tumors would undergo treatment or further investigations. Our CAD performance is superior to that of previous studies and overcomes some of their limitations, such as the need to perform manual segmentation of the tumor or the fact that analysis is limited to single-center datasets. The results of this study are promising and could pave the way to a prediction tool for personalized decision making in patients harboring PCa.

Diffusion-weighted imaging in prostate cancer

Diffusion-weighted imaging (DWI), a key component in multiparametric MRI (mpMRI), is useful for tumor detection and localization in clinically significant prostate cancer (csPCa). The Prostate Imaging Reporting and Data System versions 2 and 2.1 (PI-RADS v2 and PI-RADS v2.1) emphasize the role of DWI in determining PIRADS Assessment Category in each of the transition and peripheral zones. In addition, several recent studies have demonstrated comparable performance of abbreviated biparametric MRI (bpMRI), which incorporates only T2-weighted imaging and DWI, compared with mpMRI with dynamic contrast-enhanced MRI. Therefore, further optimization of DWI is essential to achieve clinical application of bpMRI for efficient detection of csPC in patients with elevated PSA levels. Although DWI acquisition is routinely performed using single-shot echo-planar imaging, this method suffers from such as susceptibility artifact and anatomic distortion, which remain to be solved. In this review article, we will outline existing problems in standard DWI using the single-shot echo-planar imaging sequence; discuss solutions that employ newly developed imaging techniques, state-of-the-art technologies, and sequences in DWI; and evaluate the current status of quantitative DWI for assessment of tumor aggressiveness in PC.

Quantitative diffusion-weighted imaging and dynamic contrast-enhanced MR imaging for assessment of tumor aggressiveness in prostate cancer at 3T

PURPOSE

To compare diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MR imaging (DCE-MRI) for characterization of prostate cancer (PC).

METHODS

104 PC patients who underwent prostate multiparametric MRI at 3T including DWI and DCE-MRI before MRI-guided biopsy or radical prostatectomy. Apparent diffusion coefficient (ADC) with histogram analysis (mean, 0-25th percentile, skewness, and kurtosis), intravoxel incoherent motion model including D and f; stretched exponential model including distributed diffusion coefficient (DDC) and a; and permeability parameters including K^trans, K_ep, and V_e were obtained from a region of interest placed on the dominant tumor of each patient.

RESULTS

ADC_mean, ADC_0-25, D, DDC, and V_e were significantly lower and K_ep was significantly higher in GS ≥ 3 + 4 tumors (n = 89) than in GS = 3 + 3 tumors (n = 15), and also in GS ≥ 4 + 3 tumors (n = 57) than in GS ≤ 3 + 4 tumors (n = 47) (P < 0.001 to P = 0.040). f was significantly lower in GS ≥ 4 + 3 tumors than in GS ≤ 3 + 4 tumors (P = 0.022), but there was no significant difference between GS = 3 + 3 tumors and GS ≥ 3 + 4 tumors, or between the remaining metrics in both comparisons. In metrics with area under the curve (AUC) >0.80, there was a significant difference in AUC between ADC_0-25 and D, and DDC for separating GS ≤ 3 + 4 tumors from GS ≥ 4 + 3 tumors (P = 0.040 and P = 0.022, respectively). There were no significant differences between metrics with AUC > 0.80 for separating GS = 3 + 3 tumors from GS ≥ 3 + 4 tumors. ADC_0-25 had the highest correlation with Gleason grade (ρ = -0.625, P < 0.001).

CONCLUSIONS

DWI and DCE-MRI showed no apparent clinical superiority of non-Gaussian models or permeability MRI over the mono-exponential model for assessment of tumor aggressiveness in PC.

Optimized Application of 68Ga-Prostate-Specific Membrane Antigen-617 Whole-Body PET/CT and Pelvic PET/MR in Prostate Cancer Initial Diagnosis and Staging

Purpose: To analyze ⁶⁸Ga-PSMA-617 PET/CT or PET/MR and delayed PET/MR images in patients diagnosed with or suspicion of prostate cancer, and to explore the optimal use of PET/CT and PET/MR for initial diagnosis and staging in prostate cancer.

Methods: Images from conventional scan by ⁶⁸Ga-PSMA whole-body PET/CT or PET/MR followed by delayed pelvic PET/MR were retrospectively analyzed. Prostatic ⁶⁸Ga-PSMA uptake was measured as SUVmax1 (conventional scan 1 h post injection) and SUVmax2 (delayed scan 3 h post injection). Age, PSA levels, and SUVmax were compared between benign and malignant cases. The correlation of SUVmax1 and SUVmax2 was analyzed. Diagnostic performance was evaluated by ROC analysis.

Results: Fifty-six patients with 41 prostate cancers and 15 benign prostate lesions were enrolled. Fifty-three patients had paired conventional and delayed scans. Age, tPSA, fPSA levels, and SUVmax were significantly different between benign and malignant cases. A good correlation was found between SUVmax1 and SUVmax2. There was significant difference between SUVmax1 and SUVmax2 in the malignant group (p = 0.001). SUVmax1 had superior diagnostic performance than SUVmax2, SUVmax difference and PSA levels, with a sensitivity of 85.4%, a specificity of 100% and an AUC of 0.956. A combination of SUVmax1 with nodal and/or distant metastases and MR PI-RADS V2 score had a sensitivity and specificity of 100%. Delayed pelvic PET/MR imaging in 33 patients were found to be redundant because these patients had nodal and/or distant metastases which can be easily detected by PET/CT. PET/MR provided incremental value in 8 patients at early-stage prostate cancer based on precise anatomical localization and changes in lesion signal provided by MR.

Conclusion: Combined ⁶⁸Ga-PSMA whole-body PET/CT and pelvic PET/MR can accurately differentiate benign prostate diseases from prostate cancer and accurately stage prostate cancer. Whole-body PET/CT is sufficient for advanced prostate cancer. Pelvic PET/MR contributes to diagnosis and accurate staging in early prostate cancer. Imaging at about 1 h after injection is sufficient in most patients.

ClinicalTrials.gov: NCT03756077. Registered 27 November 2018—Retrospectively registered, https://clinicaltrials.gov/show/NCT03756077.

Advanced Imaging Analysis in Prostate MRI: Building a Radiomic Signature to Predict Tumor Aggressiveness

Radiomics allows the extraction quantitative features from imaging, as imaging biomarkers of disease. The objective of this exploratory study is to implement a reproducible radiomic-pipeline for the extraction of a magnetic resonance imaging (MRI) signature for prostate cancer (PCa) aggressiveness. One hundred and two consecutive patients performing preoperative prostate multiparametric magnetic resonance imaging (mpMRI) and radical prostatectomy were enrolled. Multiparametric images, including T2-weighted (T2w), diffusion-weighted and dynamic contrast-enhanced images, were acquired at 1.5 T. Ninety-three imaging features (Ifs) were extracted from segmentation of index lesion. Ifs were ranked based on a stability rank and redundant Ifs were excluded. Using unsupervised hierarchical clustering, patients were grouped on the basis of similar radiomic patterns, whose association with Gleason Grade Group (GGG), extracapsular extension (ECE), and nodal involvement (pN) was tested. Signatures composed by IFs from T2w-images and Apparent Diffusion Coefficient (ADC) maps were tested for the prediction of GGG, ECE, and pN. T2w radiomic pattern was associated with pN, ECE, and GGG (p = 0.027, 0.05, 0.03) and ADC radiomic pattern was associated with GGG (p = 0.004). The best performance was reached by the signature combing IFs from multiparametric images (0.88, 0.89, and 0.84 accuracy for GGG, pN, and ECE). A reliable multiparametric MRI radiomic signature was extracted, potentially able to predict PCa aggressiveness, to be further validated on an independent sample.

Correlation between Prostate Imaging Reporting and Data System Version 2, Prostate-Specific Antigen Levels, and Local Staging in Biopsy-Proven Carcinoma Prostate: A Retrospective Study

Background:

Multi-parametric magnetic resonance imaging (mp-MRI) is a promising tool in the diagnosis of clinically significant prostate cancer. Morphologic assessment using T2-weighted (T2W) images and functional assessment with diffusion-weighted imaging is the cornerstone for the diagnosis of prostate cancer on mp-MRI.

Aim/Objectives:

The aim of this study is to evaluate the role of mp-MRI based prostate imaging reporting and data system version 2 (PI-RADS v2) for the assessment of prostate cancer and its correlation with serum prostate specific antigen (S.PSA) levels, local (T) staging on MRI and histopathology.

Materials and Methods:

The study was carried out from June 2019 to February 2020. All patients with raised S.PSA levels and abnormal digital rectal examination who underwent mp-MRI of the prostate were included. MRI findings were characterized on the basis of PI-RADS v2 grading. All the patients underwent biopsy and histopathology. The score was correlated with S.PSA levels and the local stage of disease on MRI. Statistical analysis was performed, and results interpreted.

Results:

Carcinoma prostate was reported in 32/33 cases on biopsy. A significant correlation was observed between PI-RADS v2 score and S.PSA Levels and between PI-RADS v2 score and T stage of disease in our study. MRI was highly sensitive (93.75%) and specific (100%) in the diagnosis of prostate cancer in our study.

Conclusions:

Significant correlation between lesion score on PI-RADS v2 with the local stage and S.PSA levels was seen, thus signifying the importance of mp-MRI in detecting clinically significant prostate cancer. Diffusion-weighted and T2W sequences were the primary diagnostic sequence for the prostate cancer with no additional role of dynamic contrast enhanced sequences.

Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy

Rationale: To reduce upgrading and downgrading between needle biopsy (NB) and radical prostatectomy (RP) by predicting patient-level Gleason grade groups (GGs) of RP to avoid over- and under-treatment. Methods: In this study, we retrospectively enrolled 575 patients from two medical institutions. All patients received prebiopsy magnetic resonance (MR) examinations, and pathological evaluations of NB and RP were available. A total of 12,708 slices of original male pelvic MR images (T2-weighted sequences with fat suppression, T2WI-FS) containing 5405 slices of prostate tissue, and 2,753 tumor annotations (only T2WI-FS were annotated using RP pathological sections as ground truth) were analyzed for the prediction of patient-level RP GGs. We present a prostate cancer (PCa) framework, PCa-GGNet, that mimics radiologist behavior based on deep reinforcement learning (DRL). We developed and validated it using a multi-center format. Results: Accuracy (ACC) of our model outweighed NB results (0.815 [95% confidence interval (CI): 0.773-0.857] vs. 0.437 [95% CI: 0.335-0.539]). The PCa-GGNet scored higher (kappa value: 0.761) than NB (kappa value: 0.289). Our model significantly reduced the upgrading rate by 27.9% (P < 0.001) and downgrading rate by 6.4% (P = 0.029). Conclusions: DRL using MRI can be applied to the prediction of patient-level RP GGs to reduce upgrading and downgrading from biopsy, potentially improving the clinical benefits of prostate cancer oncologic controls.

Prospective evaluation of using multiparametric magnetic resonance imaging in cognitive fusion prostate biopsy compared to the standard systematic 12-core biopsy in the detection of prostate cancer

Purpose:

There is mounting evidence to suggest that multiparametric magnetic resonance imaging (mpMRI)-guided biopsy is better than systematic biopsy for the diagnosis of prostate cancer (PCa). Cognitive fusion biopsy (CFB) involves targeted biopsies of areas of suspicious lesions noted on the mpMRI by transrectal ultrasound (TRUS) operator. This study was undertaken to determine the accuracy of mpMRI of the prostate with Prostate Imaging–Reporting and Data System (PI-RADS) version 2 in detecting PCa. We also compare the cancer detection rates between systematic 12-core TRUS biopsy and CFB.

Materials and Methods:

Sixty-nine men underwent mpMRI of the prostate followed by TRUS biopsy. In addition to 12-core biopsy, CFB was performed on abnormal lesions detected on MRI.

Results:

Abnormal lesions were identified in 98.6% of the patients, and 59.4% had the highest PI-RADS score of 3 or more. With the use of PI-RADS 3 as cutoff, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of MRI for the detection of PCa were 91.7%, 57.8%, 53.7%, and 92.8%, respectively. With the use of PI-RADS 4 as cutoff, the sensitivity, specificity, PPV, and NPV of mpMRI were 66.7%, 91.1%, 80%, and 83.7%, respectively. Systematic biopsy detected more PCa compared to CFB (29% vs. 26.1%), but CFB detected more significant (Gleason grade ≥7) PCa (17.4% vs. 14.5%) (P < 0.01). CFB cores have a higher PCa detection rate as compared to systematic cores (P < 0.01).

Conclusions:

mpMRI has a good predictive ability for PCa. CFB is superior to systematic biopsy in the detection of the significant PCa.

Prediction of prostate cancer aggressiveness using 18F-Fluciclovine (FACBC) PET and multisequence multiparametric MRI

The aim of this prospective single-institution clinical trial (NCT02002455) was to evaluate the potential of advanced post-processing methods for 18F-Fluciclovine PET and multisequence multiparametric MRI in the prediction of prostate cancer (PCa) aggressiveness, defined by Gleason Grade Group (GGG). 21 patients with PCa underwent PET/CT, PET/MRI and MRI before prostatectomy. DWI was post-processed using kurtosis (ADCk, K), mono- (ADCm), and biexponential functions (f, Dp, Df) while Logan plots were used to calculate volume of distribution (VT). In total, 16 unique PET (VT, SUV) and MRI derived quantitative parameters were evaluated. Univariate and multivariate analysis were carried out to estimate the potential of the quantitative parameters and their combinations to predict GGG 1 vs >1, using logistic regression with a nested leave-pair out cross validation (LPOCV) scheme and recursive feature elimination technique applied for feature selection. The second order rotating frame imaging (RAFF), monoexponential and kurtosis derived parameters had LPOCV AUC in the range of 0.72 to 0.92 while the corresponding value for VT was 0.85. The best performance for GGG prediction was achieved by K parameter of kurtosis function followed by quantitative parameters based on DWI, RAFF and 18F-FACBC PET. No major improvement was achieved using parameter combinations with or without feature selection. Addition of 18F-FACBC PET derived parameters (VT, SUV) to DWI and RAFF derived parameters did not improve LPOCV AUC.

Surface-enhanced Raman spectroscopy of preoperative serum samples predicts Gleason grade group upgrade in biopsy Gleason grade group 1 prostate cancer

PURPOSE

To predict Gleason grade group (GG) upgrade in biopsy Gleason grade group 1 (GG1) prostate cancer (CaP) patients using surface-enhanced Raman spectroscopy (SERS).

MATERIALS AND METHODS

Preoperative serum samples of patients with biopsy GG1 and subsequent radical prostatectomy were analyzed using SERS. The role of clinical variables and distinctive SERS spectra in the prediction of GG upgrade were evaluated. Principal component analysis and linear discriminant analysis (PCA-LDA) were used to manage spectral data and develop diagnostic algorithms.

RESULTS

A total of 342 preoperative serum SERS spectra from 114 patients were obtained. SERS detected a higher level of circulating free nucleic acid bases and a lower level of lipids in patients with GG upgrade to GG3 and higher, presenting as SERS spectral peaks of 728 cm^-1 and 1,655 cm^-1, respectively. Both spectral peaks were independent predictors of GG upgrade and their addition to clinical predictors of PSA and positive core percent significantly improved predictive power of the logistic regression model with area under curve improved from 0.65 to 0.80 (P = 0.0045). Meanwhile, PCA-LDA diagnostic model based on serum SERS spectra showed a high accuracy of 91.2% in predicted groups and remained stable with a sensitivity, specificity, and accuracy of 65%, 97.3%, 86.0%, respectively when validated by leave-one-out cross-validation method.

CONCLUSIONS

By analyzing preoperative serum samples, SERS combined with PCA-LDA model could be a promising tool for prediction of Gleason GG upgrade in biopsy GG1 CaP and assist in treatment decision-making in clinical practice.

The prognostic impact of downgrading and upgrading from biopsy to radical prostatectomy among men with Gleason score 7 prostate cancer

BACKGROUND

Recently, a new prostate cancer (PC) grading system was introduced, where Gleason score (GS) 7 was divided into 3 + 4 = 7 and 4 + 3 = 7 due to the different prognoses associated with each tumor type. However, whether downgrading or upgrading from needle biopsy (NB) to radical prostatectomy (RP) affects oncologic outcomes is currently unknown. Herein, we investigated the prognostic impact of downgrading and upgrading from NB to RP among men with GS 7 PC.

METHODS

We retrospectively reviewed the medical records of 3003 patients with localized PC who underwent RP between 2005 and 2014. We included 692 patients with GS 7 PC on both NB and RP specimens. We analyzed the data using Kaplan-Meier methods and Cox proportional hazard models.

RESULTS

Of the 692 patients enrolled in this study, 389 (56.2%) and 303 (43.8%) patients had RP GS 3 + 4 = 7 and RP GS 4 + 3 = 7 PC, respectively. On the basis of NB and RP GS, 264 (38.1%), 125 (18.1%), 142 (20.5%), and 161 (23.3%) patients were classified as 3 + 4/3 + 4, 4 + 3/3 + 4, 3 + 4/4 + 3, and 4 + 3/4 + 3, respectively. Kaplan-Meier curves showed significant differences in biochemical recurrence (BCR)-free survival across the groups (P < .001). In the multivariate analyses, these groups were significantly associated with BCR (4 + 3/3 + 4: hazard ratio [HR], 1.675; 3 + 4/4 + 3: HR, 1.908; and 4 + 3/4 + 3: HR, 2.699).

CONCLUSIONS

Downgrading and upgrading from NB to RP was an independent predictor of BCR in men with GS 7 PC, which could be due to the amount of Gleason pattern 4.

Diagnostic evaluation of diffusion kurtosis imaging for prostate cancer: Detection in a biopsy population

PURPOSE

To prospectively assess the feasibility of diffusional kurtosis (DK) imaging for distinguishing prostate cancer(PCa) from benign prostate hyperplasia (BPH) in comparison with standard diffusion-weighted (DW) imaging, as well as low-from high-grade malignant regions.

MATERIALS AND METHODS

147 consecutive patients with suspected PCa underwent multi-parametric 1.5-TMR. Diffusion kurtosis imaging was acquired with with 5 b values (0,600,800,1600,and 2400sec/mm2).Region of interest (ROI)-based measurements were performed on ADC, D, and K map by two radiologists. Data were analyzed by using mixed-model analysis of variance and receiver operating characteristic curves. Correlations among the three parameters (ADC,D and K) in all patients, and correlations between three parameters with the tumor Gleason score (GS) in PCa group were analyzed using Pearson's correlation coefficient in peripheral zone(PZ) and transiton zone(TZ).

RESULTS

58 patients were proved with PCa (9 GS 3 + 3[PZ/TZ = 4/5], 49 GS ≥ 7 [PZ/TZ = 26/23]), and 89 patients were with BPH. ADC,D and K were able to distinguish benignance from tumor tissue both in PZ and TZ(P＜0.01), but performed poorly in neither differentiating low-(GS 3 + 3) from high-grade (GS≥3 + 4) disease, nor GS(3 + 4) from GS(4 + 3).There was a weak correlation between the GS and ADC, D (PZ:ADC r=-0.113, D r=-0.139; TZ:ADC r=-0.104,D r=-0.103), while a moderate correlation between the GS and K(PZ:K r = 0.492; TZ:K r = 0.433, P＜0.01).K had significantly greater area under the curve for differentiating PCa from BHP than ADC both in PZ and TZ.

CONCLUSION

DK model may add value in PCa detection and diagnosis, but none can differentiate low-from high-grade PCas (including GS=3+4 from GS=4+3).

Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization

Purpose

To develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason score (GS) using radiomics and texture features of T₂-weighted imaging (T₂w), diffusion weighted imaging (DWI) acquired using high b values, and T₂-mapping (T₂).

Methods

T₂w, DWI (12 b values, 0–2000 s/mm²), and T₂ data sets of 62 patients with histologically confirmed PCa were acquired at 3T using surface array coils. The DWI data sets were post-processed using monoexponential and kurtosis models, while T₂w was standardized to a common scale. Local statistics and 8 different radiomics/texture descriptors were utilized at different configurations to extract a total of 7105 unique per-tumor features. Regularized logistic regression with implicit feature selection and leave pair out cross validation was used to discriminate tumors with 3+3 vs >3+3 GS.

Results

In total, 100 PCa lesions were analysed, of those 20 and 80 had GS of 3+3 and >3+3, respectively. The best model performance was obtained by selecting the top 1% features of T₂w, ADC_m and K with ROC AUC of 0.88 (95% CI of 0.82–0.95). Features from T₂ mapping provided little added value. The most useful texture features were based on the gray-level co-occurrence matrix, Gabor transform, and Zernike moments.

Conclusion

Texture feature analysis of DWI, post-processed using monoexponential and kurtosis models, and T₂w demonstrated good classification performance for GS of PCa. In multisequence setting, the optimal radiomics based texture extraction methods and parameters differed between different image types.

Impact of PI-RADS v2 on indication of prostate biopsy

OBJECTIVES

To identify the group of patients who could safely avoid prostate biopsy based on the findings of multiparametric prostate resonance imaging (MRmp), parameterized with PI-RADS v2, using prostate biopsy as reference test and to assess the sensitivity and specificity of mpMR in identifying clinically significant prostate cancer using prostate biopsy as a reference test.

PATIENTS AND METHODS

Three hundred and forty two patients with suspected prostate cancer were evaluated with mpMR and prostate biopsy. Agreement between imaging findings and histopathological findings was assessed using the Kappa index. The accuracy of mpMR in relation to biopsy was assessed by calculations of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

RESULTS

A total of 342 biopsies were performed. In 201 (61.4%), mpMR had a negative result for cancer, which was confirmed on biopsy in 182 (53%) of the cases, 17 (4.9%) presented non-clinically significant cancer and only 2 (0.5%) clinically significant cancer. 131 (38.3%) patients had a positive biopsy. Clinically significant cancer corresponded to 83 (34.2%), of which 81 (97.5%) had a positive result in mpMR. Considering only the clinically significant cancers the mpMR had a sensitivity of 97.6%, specificity of 76.8%, PPV 57.4% and VPN of 99%.

CONCLUSIONS

mpMR is a useful tool to safely identify which patients at risk for prostate cancer need to undergo biopsy and has high sensitivity and specificity in identifying clinically significant prostate cancer.

Clinical Significance of Multiparametric Magnetic Resonance Imaging as a Preoperative Predictor of Oncologic Outcome in Very Low-Risk Prostate Cancer

Currently, multiparametric magnetic resonance imaging (mpMRI) is not an indication for patients with very low-risk prostate cancer. In this study, we aimed to evaluate the usefulness of mpMRI as a diagnostic tool in these patients. We retrospectively analyzed the clinical and pathological data of individuals with very low-risk prostate cancer, according to the NCCN guidelines, who underwent mpMRI before radical prostatectomy at our institution between 2010 and 2016. Patients who did not undergo pre-evaluation with mpMRI were excluded. We analyzed the factors associated with biochemical recurrence (BCR) using Cox regression model, logistic regression analysis, and Kaplan–Meier curve. Of 253 very low-risk prostate cancer patients, we observed 26 (10.3%) with BCR during the follow-up period in this study. The median follow-up from radical prostatectomy was 53 months (IQR 33–74). The multivariate Cox regression analyses demonstrated that the only factor associated with BCR in very low-risk patients was increase in the pathologic Gleason score (GS) (HR: 2.185, p-value 0.048). In addition, multivariate logistic analyses identified prostate specific antigen (PSA) (OR: 1.353, p-value 0.010), PSA density (OR: 1.160, p-value 0.013), and suspicious lesion on mpMRI (OR: 1.995, p-value 0.019) as the independent preoperative predictors associated with the pathologic GS upgrade. In our study, the pathologic GS upgrade after radical prostatectomy in very low-risk prostate cancer patients demonstrated a negative impact on BCR and mpMRI is a good prognostic tool to predict the pathologic GS upgrade. We believe that the implementation of mpMRI would be beneficial to determine the treatment strategy for these patients.

Predictive Factors and Oncologic Outcome of Downgrade to Pathologic Gleason Score 6⁻7 after Radical Prostatectomy in Patients with Biopsy Gleason Score 8⁻10

Gleason score (GS) 8⁻10 is associated with adverse outcomes in prostate cancer (PCa). However, biopsy GS (bGS) may be upgraded or downgraded post-radical prostatectomy (RP). We aimed to investigate predictive factors and oncologic outcomes of downgrade to pathologic GS (pGS) 6⁻7 after RP in PCa patients with bGSs 8⁻10. We retrospectively reviewed clinical data of patients with bGS ≥ 8 undergoing RP. pGS downgrade was defined as a pGS ≤ 7 from bGS ≥ 8 post-RP. Univariate and multivariate cox regression analysis, logistic regression analysis, and Kaplan⁻Meier curves were used to analyze pGS downgrade and biochemical recurrence (BCR). Of 860 patients, 623 and 237 had bGS 8 and bGS ≥ 9, respectively. Post-RP, 332 patients were downgraded to pGS ≤ 7; of these, 284 and 48 had bGS 8 and bGS ≥ 9, respectively. Prostate-specific antigen (PSA) levels; clinical stage; and adverse pathologic features such as extracapsular extension, seminal vesicle invasion and positive surgical margin were significantly different between patients with pGS ≤ 7 and pGS ≥ 8. Furthermore, bGS 8 (odds ratio (OR): 0.349, p < 0.001), PSA level < 10 ng/mL (OR: 0.634, p = 0.004), and ≤cT3a (OR: 0.400, p < 0.001) were identified as significant predictors of pGS downgrade. pGS downgrade was a significant positive predictor of BCR following RP in patients with high bGS (vs. pGS 8, hazard radio (HR): 1.699, p < 0.001; vs. pGS ≥ 9, HR: 1.765, p < 0.001). In addition, the 5-year BCR-free survival rate in patients with pGS downgrade significantly differed from that in patients with bGS 8 and ≥ 9 (52.9% vs. 40.7%, p < 0.001). Among patients with bGS ≥ 8, those with bGS 8, PSA level < 10 ng/mL, and ≤cT3a may achieve pGS downgrade after RP. These patients may have fewer adverse pathologic features and show a favorable prognosis; thus we suggest that active treatment is needed in these patients. In addition, patients with high-grade bGS should be managed aggressively, even if they show pGS downgrade.

Building a high-resolution T2-weighted MR-based probabilistic model of tumor occurrence in the prostate

PURPOSE

We present a method for generating a T2 MR-based probabilistic model of tumor occurrence in the prostate to guide the selection of anatomical sites for targeted biopsies and serve as a diagnostic tool to aid radiological evaluation of prostate cancer.

MATERIALS AND METHODS

In our study, the prostate and any radiological findings within were segmented retrospectively on 3D T2-weighted MR images of 266 subjects who underwent radical prostatectomy. Subsequent histopathological analysis determined both the ground truth and the Gleason grade of the tumors. A randomly chosen subset of 19 subjects was used to generate a multi-subject-derived prostate template. Subsequently, a cascading registration algorithm involving both affine and non-rigid B-spline transforms was used to register the prostate of every subject to the template. Corresponding transformation of radiological findings yielded a population-based probabilistic model of tumor occurrence. The quality of our probabilistic model building approach was statistically evaluated by measuring the proportion of correct placements of tumors in the prostate template, i.e., the number of tumors that maintained their anatomical location within the prostate after their transformation into the prostate template space.

RESULTS

Probabilistic model built with tumors deemed clinically significant demonstrated a heterogeneous distribution of tumors, with higher likelihood of tumor occurrence at the mid-gland anterior transition zone and the base-to-mid-gland posterior peripheral zones. Of 250 MR lesions analyzed, 248 maintained their original anatomical location with respect to the prostate zones after transformation to the prostate.

CONCLUSION

We present a robust method for generating a probabilistic model of tumor occurrence in the prostate that could aid clinical decision making, such as selection of anatomical sites for MR-guided prostate biopsies.

A comparison of prostate tumor targeting strategies using magnetic resonance imaging-targeted, transrectal ultrasound-guided fusion biopsy

PURPOSE

Magnetic resonance imaging (MRI)-targeted, three-dimensional (3D) transrectal ultrasound (TRUS)-guided prostate biopsy aims to reduce the 21-47% false-negative rate of clinical two-dimensional (2D) TRUS-guided systematic biopsy, but continues to yield false-negative results. This may be improved via needle target optimization, accounting for guidance system errors and image registration errors. As an initial step toward the goal of optimized prostate biopsy targeting, we investigated how needle delivery error impacts tumor sampling probability for two targeting strategies.

METHODS

We obtained MRI and 3D TRUS images from 49 patients. A radiologist and radiology resident assessed these MR images and contoured 81 suspicious regions, yielding tumor surfaces that were registered to 3D TRUS. The biopsy system's root-mean-squared needle delivery error (RMSE) and systematic error were modeled using an isotropic 3D Gaussian distribution. We investigated two different prostate tumor-targeting strategies using (a) the tumor's centroid and (b) a ring in the lateral-elevational plane. For each simulation, targets were spaced at equal arc lengths on a ring with radius equal to the systematic error magnitude. A total of 1000 biopsy simulations were conducted for each tumor, with RMSE and systematic error magnitudes ranging from 1 to 6 mm. The difference in median tumor sampling probability and probability of obtaining a 50% core involvement was determined for ring vs centroid targeting.

RESULTS

Our simulation results indicate that ring targeting outperformed centroid targeting in situations where systematic error exceeds RMSE. In these instances, we observed statistically significant differences showing 1-32% improvement in sampling probability due to ring targeting. Likewise, we observed statistically significant differences showing 1-39% improvement in 50% core involvement probability due to ring targeting.

CONCLUSIONS

Our results suggest that the optimal targeting scheme for prostate biopsy depends on the relative levels of systematic and random errors in the system. Where systematic error dominates, a ring-targeting scheme may yield improved probability of tumor sampling. The findings presented in this paper may be used to aid in target selection strategies for clinicians performing targeted prostate biopsies on any MRI targeted, 3D TRUS-guided biopsy system and could support earlier diagnosis of prostate cancer while it remains localized to the gland and curable.

The values of neutrophil-lymphocyte ratio and/or prostate-specific antigen in discriminating real Gleason score ≥ 7 prostate cancer from group of biopsy-based Gleason score ≤ 6

BACKGROUND

The discrepant concordance between biopsy and radical prostatectomy (RP) specimen are well reported. To validate the clinical usefulness of neutrophil-lymphocyte ratio (NLR) in discriminating real GS ≥ 7 PCa from biopsy-based GS ≤ 6 PCa in comparison with serum total prostate-specific antigen (tPSA) and value of their combination.

METHODS

One hundred one patients who underwent physical examinations incidentally found elevated tPSA and subsequently received biopsy with a conclusion of GS ≤ 6 and RP with an interval of 4-6 weeks after biopsy were enrolled. NLR and tPSA were obtained within 15 days prior to biopsy. Logistic regression model was applied appropriately; McNemar tests and AUC model were performed to evaluate differences among tPSA, NLR and their combination and corresponding diagnostic power respectively.

RESULTS

The pathological results from RP specimen comprised 61 patients with GS ≤ 6 and 100 patients with GS ≥ 7. Higher tPSA and NLR were significantly associated with patients with actual GS ≥ 7 (All P < 0.05) concurrently. Multivariate logistic regression indicated that tPSA (OR = 1.088, 95% C.I. = 1.029-1.151, P = 0.003) and NLR (OR = 1.807, 95% C.I. = 1.021-3.200, P = 0.042) could be independent predictors for GS groupings. Under cutoff value of 14.09 ng/ml for tPSA and 2.25 for NLR, the sensitivity, specificity and accuracy were 60.0%, 80.3% and 67.7% for tPSA, 42%, 88.5% and 59.6% for NLR, and 71.0%, 75.4% and 72.7% for combination of tPSA and NLR (tPSA + NLR) respectively. The sensitivity of tPSA + NLR was significantly higher in comparison with tPSA (P = 0.001) and NLR (P < 0.001). Except for sensitivity, no significant difference was found between tPSA and NLR in specificity (P = 0.227) and accuracy (P = 0.132). tPSA got the largest AUC with 0.732 (p < 0.001, 95% C.I.: 0.651-0.813).

CONCLUSIONS

Serum tPSA and NLR were significantly elevated among GS ≥ 7 PCa concurrently. The combination of tPSA and NLR might have additional benefit to biopsy on discriminating real GS ≥ 7 Pca from biopsy-based GS ≤ 6 PCa. More stratification models and prospectively multicenter studies are necessary.

Surface-enhanced Raman spectroscopy of serum accurately detects prostate cancer in patients with prostate-specific antigen levels of 4-10 ng/mL

The surface-enhanced Raman spectroscopy (SERS) of blood serum was investigated to differentiate between prostate cancer (PCa) and benign prostatic hyperplasia (BPH) in males with a prostate-specific antigen level of 4-10 ng/mL, so as to reduce unnecessary biopsies. A total of 240 SERS spectra from blood serum were acquired from 40 PCa subjects and 40 BPH subjects who had all received prostate biopsies and were given a pathological diagnosis. Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA) diagnostic algorithms, were used to analyze the spectra data of serum from patients in control (CTR), PCa and BPH groups; results offered a sensitivity of 97.5%, a specificity of 100.0%, a precision of 100.0% and an accuracy of 99.2% for CTR; a sensitivity of 90.0%, a specificity of 97.5%, a precision of 94.7% and an accuracy of 98.3% for BPH; a sensitivity of 95.0%, a specificity of 93.8%, a precision of 88.4% and an accuracy of 94.2% for PCa. Similarly, this technique can significantly differentiate low- and high-risk PCa with an accuracy of 92.3%, a specificity of 95% and a sensitivity of 89.5%. The results suggest that analyzing blood serum using SERS combined with PCA-LDA diagnostic algorithms is a promising clinical tool for PCa diagnosis and assessment.

Current Status of Prostate-Specific Membrane Antigen Targeting in Nuclear Medicine: Clinical Translation of Chelator Containing Prostate-Specific Membrane Antigen Ligands Into Diagnostics and Therapy for Prostate Cancer

The prostate-specific membrane antigen (PSMA) is expressed by approximately 90% of prostate carcinomas. The expression correlates with unfavorable prognostic factors, such as a high Gleason score, infiltrative growth, metastasis, and hormone-independence. The high specificity, especially in the undifferentiated stage, makes it an excellent target for diagnosis and therapy. Therefore, antibodies and small molecule inhibitors have been developed for imaging and therapy. In 2011 PSMA-11, a ligand that consists of the Glu-urea-motif and the chelator HBED-CC, which can be exclusively radiolabeled with (68)Ga for PET imaging, presented the clinical breakthrough for prostate cancer diagnostics. In two large diagnostic studies (n = 319 and n = 248) PET/CT with PSMA-11 successfully localized the recurrent tumor in approximately 90% of patients with biochemical relapse. Integrating PSMA-PET/CT into the planning phase of radiotherapy, the treatment concept is changed in 30%-50% of the patients. The combination of the Glu-urea-motif with DOTA, which can be labeled with several diagnostic and therapeutic radionuclides, opened new avenues for therapeutic usage of the small-molecule PSMA ligands. In the beginning of 2016, there are four confirmative reports (n = 19, n = 24, n = 30, and n = 56) from four different centers reporting a PSA response in approximately 70% of patients treated with (177)Lu-labeled PSMA ligands. In conclusion, the data available up to now indicate a widespread use of PSMA ligands for diagnostic applications with respect to staging, detection of recurrence, or metastases in patients with rising tumor markers and for therapy in case of failure of guideline-compliant treatment.

Apparent diffusion coefficient values are superior to transrectal ultrasound-guided prostate biopsy for the assessment of prostate cancer aggressiveness

Background Few studies have focused on comparing the utility of diffusion-weighted imaging (DWI) and transrectal ultrasound (TRUS)-guided biopsy in predicting prostate cancer aggressiveness. Whether apparent diffusion coefficient (ADC) values can provide more information than TRUS-guided biopsy should be confirmed. Purpose To retrospectively assess the utility of ADC values in predicting prostate cancer aggressiveness, compared to the TRUS-guided prostate biopsy Gleason score (GS). Material and Methods The DW images of 54 patients with biopsy-proven prostate cancer were obtained using 1.5-T magnetic resonance (MR). The mean ADC values of cancerous areas and biopsy GS were correlated with prostatectomy GS and D'Amico clinical risk scores, respectively. Meanwhile, the utility of ADC values in identifying high-grade prostate cancer (with Gleason 4 and/or 5 components in prostatectomy) in patients with a biopsy GS ≤ 3 + 3 = 6 was also evaluated. Results A significant negative correlation was found between mean ADC values of cancerous areas and the prostatectomy GS ( P < 0.001) and D'Amico clinical risk scores ( P < 0.001). No significant correlation was found between biopsy GS and prostatectomy GS ( P = 0.140) and D'Amico clinical risk scores ( P = 0.342). Patients harboring Gleason 4 and/or 5 components in prostatectomy had significantly lower ADC values than those harboring no Gleason 4 and/or 5 components ( P = 0.004). Conclusion The ADC values of cancerous areas in the prostate are a better indicator than the biopsy GS in predicting prostate cancer aggressiveness. Moreover, the use of ADC values can help identify the presence of high-grade tumor in patients with a Gleason score ≤ 3 + 3 = 6 during biopsy.

The Impact of Downgrading from Biopsy Gleason 7 to Prostatectomy Gleason 6 on Biochemical Recurrence and Prostate Cancer Specific Mortality

PURPOSE

Gleason score is one of the most important prognostic indicators for prostate cancer. Downgrading from biopsy Gleason score 7 to radical prostatectomy Gleason score 6 occurs commonly and yet to our knowledge the impact on survival outcomes is unknown. We examined biochemical recurrence and prostate cancer specific mortality risk in a large cohort evaluated by a single group of expert urological pathologists.

MATERIALS AND METHODS

Of 23,918 men who underwent radical prostatectomy at our institution between 1984 and 2014, 10,236 with biopsy and radical prostatectomy Gleason score 6 or 7 without upgrading were included in analysis. The cohort was divided into 3 groups, including group 1-biopsy and radical prostatectomy Gleason score 6 in 6,923 patients (67.6%), group 2-Gleason score 7 downgraded to radical prostatectomy Gleason score 6 in 648 (6.3%) and group 3-biopsy and radical prostatectomy Gleason score 7 in 2,665 (26.0%). Biochemical recurrence and prostate cancer specific mortality risks were compared using Cox regression and competing risk analyses adjusting for clinicopathological variables.

RESULTS

At a median followup of 5 years (range 1 to 29), 992 men experienced biochemical recurrence and 95 had died of prostate cancer. Biochemical recurrence-free survival in downgraded cases (group 2) was better than in group 3 cases, which had Gleason score 7 on biopsy and radical prostatectomy (p <0.001), but worse than group 1 cases, which had Gleason score 6 on biopsy and radical prostatectomy (p <0.001). Downgrading was independently associated with biochemical recurrence (adjusted HR 1.87, p <0.0001) but not with prostate cancer specific mortality (adjusted HR 1.65, p = 0.636).

CONCLUSIONS

Downgrading from biopsy Gleason score 7 to radical prostatectomy Gleason score 6 was an independent predictor of biochemical recurrence but not prostate cancer specific mortality, likely due to the presence of minor amounts of Gleason pattern 4.

Prostate Cancer: Epigenetic Alterations, Risk Factors, and Therapy

Prostate cancer (PCa) is the most prevalent urological cancer that affects aging men in South Africa, and mechanisms underlying prostate tumorigenesis remain elusive. Research advancements in the field of PCa and epigenetics have allowed for the identification of specific alterations that occur beyond genetics but are still critically important in the pathogenesis of tumorigenesis. Anomalous epigenetic changes associated with PCa include histone modifications, DNA methylation, and noncoding miRNA. These mechanisms regulate and silence hundreds of target genes including some which are key components of cellular signalling pathways that, when perturbed, promote tumorigenesis. Elucidation of mechanisms underlying epigenetic alterations and the manner in which these mechanisms interact in regulating gene transcription in PCa are an unmet necessity that may lead to novel chemotherapeutic approaches. This will, therefore, aid in developing combination therapies that will target multiple epigenetic pathways, which can be used in conjunction with the current conventional PCa treatment.

Correlation between apparent diffusion coefficient value on diffusion-weighted MR imaging and Gleason score in prostate cancer

OBJECTIVES

To investigate whether diffusion-weighted imaging (DWI) apparent diffusion coefficient (ADC) correlates with prostate cancer aggressiveness and further to compare the diagnostic performance of ADC and normalized ADC (nADC: normalized to non-tumor tissue).

PATIENTS AND METHODS

Thirty pre-treatment patients (mean age, 69years; range: 59-78years) with prostate cancer underwent magnetic resonance imaging (MRI) examination, including DWI with three b values: 50, 400, and 800s/mm². Both ADC and nADC were correlated with the Gleason score obtained through transrectal ultrasound-guided biopsy.

RESULTS

The tumor minimum ADC (ADC_min: the lowest ADC value within tumor) had an inverse correlation with the Gleason score (r=-0.43, P<0.05), and it was lower in patients with Gleason score 3+4 than in those with Gleason score 3+3 (0.54±0.11×10³mm²/s vs. 0.64±0.12×10^-3mm²/s, P<0.05). Both the nADC_min and nADC_mean correlated with the Gleason score (r=-0.52 and r=-0.55, P<0.01; respectively), and they were lower in patients with Gleason score 3+4 than those with Gleason score 3+3 (P<0.01; respectively). Receiver operating characteristic (ROC) analysis showed that the area under the ROC curve was 0.765, 0.818, or 0.833 for the ADC_min, nADC_min, or nADC_mean; respectively, in differentiating between Gleason score 3+4 and 3+3 tumors.

CONCLUSION

Tumor ADC_min, nADC_min, and nADC_mean are useful markers to predict the aggressiveness of prostate cancer.

Prediction of pathological stage based on clinical stage, serum prostate-specific antigen, and biopsy Gleason score: Partin Tables in the contemporary era

OBJECTIVE

To update the Partin Tables for prediction of pathological stage in the contemporary setting and examine trends in patients treated with radical prostatectomy (RP) over the past three decades.

PATIENTS AND METHODS

From January 2010 to October 2015, 4459 men meeting inclusion criteria underwent RP and pelvic lymphadenectomy for histologically confirmed prostate cancer at the Johns Hopkins Hospital. Preoperative clinical stage, serum prostate-specific antigen (PSA) level, and biopsy Gleason score (i.e. prognostic Grade Group) were used in a polychotomous logistic regression model to predict the probability of pathological outcomes categorised as: organ-confined (OC), extraprostatic extension (EPE), seminal vesicle involvement (SV+), or lymph node involvement (LN+). Preoperative characteristics and pathological findings in men treated with RP since 1983 were collected and clinical-pathological trends were described.

RESULTS

The median (range) age at surgery was 60 (34-77) years and the median (range) PSA level was 4.9 (0.1-125.0) ng/mL. The observed probabilities of pathological outcomes were: OC disease in 74%, EPE in 20%, SV+ in 4%, and LN+ in 2%. The probability of EPE increased substantially when biopsy Gleason score increased from 6 (Grade Group 1, GG1) to 3 + 4 (GG2), with smaller increases for higher grades. The probability of LN+ was substantially higher for biopsy Gleason score 9-10 (GG5) as compared to lower Gleason scores. Area under the receiver operating characteristic curves for binary logistic models predicting EPE, SV+, and LN+ vs OC were 0.724, 0.856, and 0.918, respectively. The proportion of men treated with biopsy Gleason score ≤6 cancer (GG1) was 47%, representing a substantial decrease from 63% in the previous cohort and 77% in 2000-2005. The proportion of men with OC cancer has remained similar during that time, equalling 73-74% overall. The proportions of men with SV+ (4.1% from 3.4%) and LN+ (2.3% from 1.4%) increased relative to the preceding era for the first time since the Partin Tables were introduced in 1993.

CONCLUSIONS

The Partin Tables remain a straightforward and accurate approach for projecting pathological outcomes based on readily available clinical data. Acknowledging these data are derived from a tertiary care referral centre, the proportion of men with OC disease has remained stable since 2000, despite a substantial decline in the proportion of men with biopsy Gleason score 6 (GG1). This is consistent with the notion that many men with Gleason score 6 (GG1) disease were over treated in previous eras.

In vivo prostate cancer detection and grading using restriction spectrum imaging-MRI

BACKGROUND

Magnetic resonance imaging (MRI) is emerging as a robust, noninvasive method for detecting and characterizing prostate cancer (PCa), but limitations remain in its ability to distinguish cancerous from non-cancerous tissue. We evaluated the performance of a novel MRI technique, restriction spectrum imaging (RSI-MRI), to quantitatively detect and grade PCa compared with current standard-of-care MRI.

METHODS

In a retrospective evaluation of 33 patients with biopsy-proven PCa who underwent RSI-MRI and standard MRI before radical prostatectomy, receiver-operating characteristic (ROC) curves were performed for RSI-MRI and each quantitative MRI term, with area under the ROC curve (AUC) used to compare each term's ability to differentiate between PCa and normal prostate. Spearman rank-order correlations were performed to assess each term's ability to predict PCa grade in the radical prostatectomy specimens.

RESULTS

RSI-MRI demonstrated superior differentiation of PCa from normal tissue, with AUC of 0.94 and 0.85 for RSI-MRI and conventional diffusion MRI, respectively (P=0.04). RSI-MRI also demonstrated superior performance in predicting PCa aggressiveness, with Spearman rank-order correlation coefficients of 0.53 (P=0.002) and -0.42 (P=0.01) for RSI-MRI and conventional diffusion MRI, respectively, with tumor grade.

CONCLUSIONS

RSI-MRI significantly improves upon current noninvasive PCa imaging and may potentially enhance its diagnosis and characterization.

MR-TRUS Fusion Biopsy

The leading application of multiparametric magnetic resonance imaging (mpMRI) of the prostate is for lesion detection with the intention of tissue sampling (biopsy). Although direct in-bore magnetic resonance (MR)-guided biopsy allows for confirmation of the biopsy site, this can be expensive, time-consuming, and most importantly limited in availability. MR-transrectal ultrasound (MR-TRUS) image fusion targeted biopsy (TBx) allows for lesions identified on MRI to be targeted with the ease, efficiency, and availability of ultrasound.The learning objectives are optimized mpMRI protocol and reporting for image fusion targeted biopsy; methods of TRUS TBx; performance and limitations of MR-TRUS TBx; future improvements and applications.

Restriction spectrum imaging improves MRI-based prostate cancer detection

PURPOSE

To compare the diagnostic performance of restriction spectrum imaging (RSI), with that of conventional multi-parametric (MP) magnetic resonance imaging (MRI) for prostate cancer (PCa) detection in a blinded reader-based format.

METHODS

Three readers independently evaluated 100 patients (67 with proven PCa) who underwent MP-MRI and RSI within 6 months of systematic biopsy (N = 67; 23 with targeting performed) or prostatectomy (N = 33). Imaging was performed at 3 Tesla using a phased-array coil. Readers used a five-point scale estimating the likelihood of PCa present in each prostate sextant. Evaluation was performed in two separate sessions, first using conventional MP-MRI alone then immediately with MP-MRI and RSI in the same session. Four weeks later, another scoring session used RSI and T2-weighted imaging (T2WI) without conventional diffusion-weighted or dynamic contrast-enhanced imaging. Reader interpretations were then compared to prostatectomy data or biopsy results. Receiver operating characteristic curves were performed, with area under the curve (AUC) used to compare across groups.

RESULTS

MP-MRI with RSI achieved higher AUCs compared to MP-MRI alone for identifying high-grade (Gleason score greater than or equal to 4 + 3=7) PCa (0.78 vs. 0.70 at the sextant level; P < 0.001 and 0.85 vs. 0.79 at the hemigland level; P = 0.04). RSI and T2WI alone achieved AUCs similar to MP-MRI for high-grade PCa (0.71 vs. 0.70 at the sextant level). With hemigland analysis, high-grade disease results were similar when comparing RSI + T2WI with MP-MRI, although with greater AUCs compared to the sextant analysis (0.80 vs. 0.79).

CONCLUSION

Including RSI with MP-MRI improves PCa detection compared to MP-MRI alone, and RSI with T2WI achieves similar PCa detection as MP-MRI.

MR-targeted TRUS prostate biopsy using local reference augmentation: initial experience

Purpose

To evaluate MR-targeted TRUS prostate biopsy using a novel local reference augmentation method.

Patients and methods

Tracker-based MR–TRUS fusion was applied using local reference augmentation. In contrast to conventional whole gland fusion, local reference augmentation focuses the highest registration accuracy to the region surrounding the lesion to be biopsied. Pre-acquired multi-parametric MR images (mpMRI) were evaluated using PIRADS classification. T2-weighted MR images were imported on an ultrasound machine to allow for MR–TRUS fusion. Biopsies were targeted to the most suspicious lesion area identified on mpMRI. Each target was biopsied 1–5 times. For each biopsied lesion the diameter, PIRADS and Gleason scores, visibility during fusion, and representativeness were recorded.

Results

Included were 23 consecutive patients with 25 MR suspicious lesions, of which 11 patients had a previous negative TRUS-guided biopsy and 12 were biopsy naïve. The cancer detection rate was 64 % (Gleason score ≥6). Biopsy was negative (i.e., no Gleason score) in seven patients confirmed by follow-up in all of them (up to 18 months). After MR–TRUS fusion, 88 % of the lesions could be visualized on TRUS. The cancer detection rate increases with increasing lesion size, being 73 % for lesions larger than 10 mm.

Conclusion

Tracker-based MR–TRUS fusion biopsy with local reference augmentation is feasible, especially for lesions with an MR maximum diameter of at least 10 mm or PIRADS 5 lesions. If this is not the case, we recommend in-bore MR-guided biopsy.

Biomechanical modeling constrained surface-based image registration for prostate MR guided TRUS biopsy

PURPOSE

Adding magnetic resonance (MR)-derived information to standard transrectal ultrasound (TRUS) images for guiding prostate biopsy is of substantial clinical interest. A tumor visible on MR images can be projected on ultrasound (US) by using MR-US registration. A common approach is to use surface-based registration. The authors hypothesize that biomechanical modeling will better control deformation inside the prostate than a regular nonrigid surface-based registration method. The authors developed a novel method by extending a nonrigid surface-based registration algorithm with biomechanical finite element (FE) modeling to better predict internal deformations of the prostate.

METHODS

Data were collected from ten patients and the MR and TRUS images were rigidly registered to anatomically align prostate orientations. The prostate was manually segmented in both images and corresponding surface meshes were generated. Next, a tetrahedral volume mesh was generated from the MR image. Prostate deformations due to the TRUS probe were simulated using the surface displacements as the boundary condition. A three-dimensional thin-plate spline deformation field was calculated by registering the mesh vertices. The target registration errors (TREs) of 35 reference landmarks determined by surface and volume mesh registrations were compared.

RESULTS

The median TRE of a surface-based registration with biomechanical regularization was 2.76 (0.81-7.96) mm. This was significantly different than the median TRE of 3.47 (1.05-7.80) mm for regular surface-based registration without biomechanical regularization.

CONCLUSIONS

Biomechanical FE modeling has the potential to improve the accuracy of multimodal prostate registration when comparing it to a regular nonrigid surface-based registration algorithm and can help to improve the effectiveness of MR guided TRUS biopsy procedures.

Computer-Aided Prostate Cancer Detection Using Ultrasound RF Time Series: In Vivo Feasibility Study

This paper presents the results of a computer-aided intervention solution to demonstrate the application of RF time series for characterization of prostate cancer, in vivo.

METHODS

We pre-process RF time series features extracted from 14 patients using hierarchical clustering to remove possible outliers. Then, we demonstrate that the mean central frequency and wavelet features extracted from a group of patients can be used to build a nonlinear classifier which can be applied successfully to differentiate between cancerous and normal tissue regions of an unseen patient.

RESULTS

In a cross-validation strategy, we show an average area under receiver operating characteristic curve (AUC) of 0.93 and classification accuracy of 80%. To validate our results, we present a detailed ultrasound to histology registration framework.

CONCLUSION

Ultrasound RF time series results in differentiation of cancerous and normal tissue with high AUC.

Preoperative low serum testosterone is associated with high-grade prostate cancer and an increased Gleason score upgrading

BACKGROUND

To compare histological feature of prostate cancer (PCa) according androgenic status in patients who underwent radical prostatectomy (RP).

METHODS

Between March 2007 and September 2013, we prospectively analysed 937 patients who were referred to our centre for RP. Clinical, pathological and biological data have been prospectively collected. Preoperative total testosterone (TT) and bioavailable testosterone (BT) serum determinations were carried out. The threshold for low serum testosterone was set at TT<3 ng/ml. Preoperative PSA value was registered. Gleason score (GS) and predominant Gleason pattern were determined in prostate biopsies and in prostate tissue specimens, crosschecked by two uro-pathologists.

RESULTS

Nine hundred and thirty-seven consecutive patients were included. In all, 14.9% patients had low TT in the population. An exact match between biopsy and prostate specimens in GS grading was observed for 50.6% patients (n=474). Also, 40.9% of all patients were upgraded (n=383): 45.3% (n=63) in low serum testosterone patients and 40.1% (n=320) in normal serum testosterone patients. For prostate specimens, the proportion of patients with predominant Gleason pattern 4 was higher in patients with low TT compared with normal TT (41.7% vs 29.1%, P=0.0029). In all, 20.1% were upgraded from predominant Gleason pattern 3 on biopsies specimen to predominant Gleason 4 pattern on the prostate specimen in patients with low TT, whereas 11.6% were upgraded for normal TT patients (P=0.002).

CONCLUSIONS

Low serum testosterone is an independent risk factor for predominant Gleason pattern 4 on prostate specimen after RP and for upgrading from low- to high-grade cancer between prostate needle biopsies and RP specimen. This observation should be taken into account in localised PCa management, especially for active surveillance or when a nerve-sparing approach is considered.

MRI-guided biopsies and minimally invasive therapy for prostate cancer

Recent advances in multiparametric magnetic resonance imaging (mp-MRI) have led to a paradigm shift in the diagnosis and management of prostate cancer (PCa). Its sensitivity in detecting clinically significant cancer and the ability to localize the tumor within the prostate gland has opened up discussion on targeted diagnosis and therapy in PCa. Use of mp-MRI in conjunction with prostate-specific antigen followed by targeted biopsy allows for a better diagnostic pathway than transrectal ultrasound (TRUS) biopsy and improves the diagnosis of PCa. Improved detection of PCa by mp-MRI has also opened up opportunities for focal therapy within the organ while reducing the incidence of side-effects associated with the radical treatment methods for PCa. This review discusses the evidence and techniques for in-bore MRI-guided prostate biopsy and provides an update on the status of MRI-guided targeted focal therapy in PCa.

Relaxation along fictitious field, diffusion-weighted imaging, and T2 mapping of prostate cancer: Prediction of cancer aggressiveness

PURPOSE

To evaluate the performance of relaxation along a fictitious field (RAFF) relaxation time (TRAFF ), diffusion-weighted imaging (DWI)-derived parameters, and T2 relaxation time values for prostate cancer (PCa) detection and characterization.

METHODS

Fifty patients underwent 3T MR examination using surface array coils before prostatectomy. DWI was performed using 14 and 12 b values in the ranges of 0-500 s/mm(2) and 0-2000 s/mm(2) , respectively. Repeated MR examination was performed in 16 patients. TRAFF , DWI-derived parameters (monoexponential, kurtosis, biexponential models), and T2 values were measured and averaged over regions of interest placed in PCa and normal tissue. Repeatability of TRAFF and DWI-derived parameters were assessed by coefficient of repeatability and intraclass correlation coefficient ICC(3,1). Areas under the receiver operating characteristic curve (AUCs) for PCa detection and Gleason score classification were estimated. The parameters were correlated with Gleason score groups using Spearman correlation coefficient (ρ).

RESULTS

ICC(3,1) values for TRAFF were in the range of 0.82-0.92. TRAFF values had higher AUC values for Gleason score classification compared with DWI-derived parameters and T2 . The RAFF method demonstrated the highest ρ value (-0.65).

CONCLUSION

In a quantitative region of interest-based analysis, RAFF outperformed DWI ("low" and "high" b values) and T2 mapping in the characterization of PCa.

Apparent diffusion coefficient value and ratio as noninvasive potential biomarkers to predict prostate cancer grading: comparison with prostate biopsy and radical prostatectomy specimen

OBJECTIVE. The purpose of this study is to test the association between diffusion-weighted MRI and prostate cancer Gleason score at both biopsy and final pathologic analysis after radical prostatectomy. SUBJECTS AND METHODS. Patients with prostate cancer (n = 72) underwent diffusion-weighted MRI (b values, 0, 800, and 1600 s/mm(2)) with an endorectal coil. Apparent diffusion coefficient (ADC) and ADC ratio were obtained in normal and pathologic tissue and were correlated with transrectal ultrasound-guided biopsy (n = 72) and histopathologic (n = 39) Gleason scores using the ANOVA test. ADC accuracy was estimated using ROC curves. RESULTS. Lesions suspicious for prostate cancer were detected in 65 patients. The mean ADC was 1.47 and 0.87 × 10(-3) mm(2)/s for normal and pathologic tissue, respectively (p < 0.001). When we divided the population into four groups (normal tissue and biopsy Gleason scores of 6, 7, and 8-10), then the mean ADC value was 1.47, 0.96, 0.80, and 0.78 × 10(-3) mm(2)/s, respectively (p < 0.001). The ADC ratio decreased along with an increase in biopsy Gleason score (66.9%, 56.7%, and 51.5% for Gleason scores of 6, 7 and 8-10, respectively) (ANOVA, p = 0.003) and pathologic Gleason score (ANOVA, p < 0.001). ROC curves had an AUC of 0.94 and 0.86 for ADC and ADC ratio, respectively (p = 0.012 and 0.042, respectively). CONCLUSION. Decreasing ADC values may represent a strong risk factor of harboring a poorly differentiated prostate cancer, independently of biopsy characteristics.

Rotating frame relaxation imaging of prostate cancer: Repeatability, cancer detection, and Gleason score prediction

PURPOSE

To investigate relaxation along a fictitious field (RAFF) and continuous wave (cw) T1ρ imaging of prostate cancer (PCa) in the terms of repeatability, PCa detection, and characterization.

METHODS

Thirty-six patients (PSA 11.6 ± 7.6 ng/mL, mean ± standard deviation) with histologically confirmed PCa underwent two repeated 3T MR examinations using surface array coils before prostatectomy. Relaxation along fictitious field, cw T1ρ, and T2 relaxation times (TRAFF, T1ρcw, T2) were measured and averaged over regions of interest placed in PCa, normal peripheral zone (PZ), and normal central gland (CG) positioned using whole-mount prostatectomy sections and anatomical T2-weighted images. Receiver operating characteristic curve analysis with area under the curve (AUC) was calculated to distinguish PCa from PZ/CG and PCa with Gleason score (GS) of 3+3 from GS of 3+4/≥ 3+4.

RESULTS

TRAFF and T1ρcw relaxation times were repeatable with coefficients of repeatability as a percentage of median value in the range of 7.8-23.2%. AUC (mean, 95% confidence interval) in the differentiation of PCa with GS of 3+3 from PCa with CS of ≥ 3+4 were 0.88 (0.72-0.99), 0.69 (0.46-0.90), and 0.68 (0.45-0.88), for TRAFF, T1ρcw, and T2, respectively.

CONCLUSION

In quantitative region of interest based analysis, TRAFF outperformed T1ρcw and T2 in PCa detection and characterization.

Magnetic resonance imaging-targeted, 3D transrectal ultrasound-guided fusion biopsy for prostate cancer: Quantifying the impact of needle delivery error on diagnosis

PURPOSE

Magnetic resonance imaging (MRI)-targeted, 3D transrectal ultrasound (TRUS)-guided "fusion" prostate biopsy intends to reduce the ∼23% false negative rate of clinical two-dimensional TRUS-guided sextant biopsy. Although it has been reported to double the positive yield, MRI-targeted biopsies continue to yield false negatives. Therefore, the authors propose to investigate how biopsy system needle delivery error affects the probability of sampling each tumor, by accounting for uncertainties due to guidance system error, image registration error, and irregular tumor shapes.

METHODS

T2-weighted, dynamic contrast-enhanced T1-weighted, and diffusion-weighted prostate MRI and 3D TRUS images were obtained from 49 patients. A radiologist and radiology resident contoured 81 suspicious regions, yielding 3D tumor surfaces that were registered to the 3D TRUS images using an iterative closest point prostate surface-based method to yield 3D binary images of the suspicious regions in the TRUS context. The probabilityP of obtaining a sample of tumor tissue in one biopsy core was calculated by integrating a 3D Gaussian distribution over each suspicious region domain. Next, the authors performed an exhaustive search to determine the maximum root mean squared error (RMSE, in mm) of a biopsy system that gives P ≥ 95% for each tumor sample, and then repeated this procedure for equal-volume spheres corresponding to each tumor sample. Finally, the authors investigated the effect of probe-axis-direction error on measured tumor burden by studying the relationship between the error and estimated percentage of core involvement.

RESULTS

Given a 3.5 mm RMSE for contemporary fusion biopsy systems,P ≥ 95% for 21 out of 81 tumors. The authors determined that for a biopsy system with 3.5 mm RMSE, one cannot expect to sample tumors of approximately 1 cm(3) or smaller with 95% probability with only one biopsy core. The predicted maximum RMSE giving P ≥ 95% for each tumor was consistently greater when using spherical tumor shapes as opposed to no shape assumption. However, an assumption of spherical tumor shape for RMSE = 3.5 mm led to a mean overestimation of tumor sampling probabilities of 3%, implying that assuming spherical tumor shape may be reasonable for many prostate tumors. The authors also determined that a biopsy system would need to have a RMS needle delivery error of no more than 1.6 mm in order to sample 95% of tumors with one core. The authors' experiments also indicated that the effect of axial-direction error on the measured tumor burden was mitigated by the 18 mm core length at 3.5 mm RMSE.

CONCLUSIONS

For biopsy systems with RMSE ≥ 3.5 mm, more than one biopsy core must be taken from the majority of tumors to achieveP ≥ 95%. These observations support the authors' perspective that some tumors of clinically significant sizes may require more than one biopsy attempt in order to be sampled during the first biopsy session. This motivates the authors' ongoing development of an approach to optimize biopsy plans with the aim of achieving a desired probability of obtaining a sample from each tumor, while minimizing the number of biopsies. Optimized planning of within-tumor targets for MRI-3D TRUS fusion biopsy could support earlier diagnosis of prostate cancer while it remains localized to the gland and curable.