Prostate Core

Clare Tempany Kemal Tuncali Fiona Fennessy Junichi Tokuda Andrey Fedorov
Clare M. Tempany, MD
Core Lead
Kemal Tuncali, MD
Fiona Fennessy, MD, PhD
Project Lead
Junichi Tokuda, PhD
Project Lead
Andriy Fedorov, PhD
Project Lead

There are two complex issues that drive the clinical need to change current paradigms for prostate cancer (PCa): The inability to predict aggressiveness of a given cancer, which in turn leads to over treatment, and the increasing evidence that disease progression in men with seemingly low-risk PCa is due to inadequate biopsy sampling. Recent trends indicate that the treatment of patients with localized PCa is shifting more and more towards either active surveillance or focal therapy. Technical solutions to address these challenges, and their validation in clinic, are lacking. We are working to address these challenges by integrating innovative MR image acquisition and analysis with the MR targeted biopsy platform we developed in the previous cycle. We are developing a diagnostic biomedical imaging platform to detect, characterize and diagnose prostate cancer and will provide new opportunities to understand the aggressiveness and heterogeneity of prostate cancer and ultimately allow for development and testing of new predictive markers in focal therapy. Our projects are:

Platform for validating novel imaging biomarkers with molecular and routine pathology.  We are developing methods of assessment of tumor heterogeneity by supplementing mpMRI with new hypoxia and multi b value MR imaging and add molecular profiling to the pathology options for core biopsy tissue, thus provide a unique platform for imaging, biopsy and both routine and molecular pathology. We will correlate genomic diversity with MR imaging parameters. We will propose novel motion compensation techniques combined with hypoxia imaging that will be applied jointly with the multi-b-value diffusion weighted imaging (DWI) for improved characterization of PCa. These novel-imaging approaches will be validated in biopsy and cryotherapy patient cohorts. (Contact: Clare M. Tempany, Fiona Fennessy)

Platform for focal cryoablation of PCa with accurate temperature mapping and motion compensation. Our goal is to develop and evaluate thermometry methods to monitor MR-guided focal cryoablation for localized prostate cancer, by internal ice ball thermometry using a “voxelwise thermal history” method. We will develop and test a new method for tracking the prostate gland motion, using active MR tracking coils embedded in a urethral warming catheter, investigate Ultrashort TE (UTE) MRI to monitor the internal thermal dosimetry within the ice-ball, and develop and evaluate software for voxel-wise thermal history tracking during all stages of the procedure. (Contact: Junichi Tokuda)

Informatics solution in support of targeted prostate biopsy and focal therapy for localized prostate cancer. This aim will have three tasks: 1) develop software tools to support structured PCa reporting and image registration for biopsy and focal therapy applications; 2) investigate and implement improved practices for structured data collection and provenance, prepare and disseminate curated validation datasets to facilitate validation of the role of mpMRI in cancer characterization and the evaluation of image registration accuracy/reliability; 3) investigate methods for non-rigid registration to enable recovery of prostate gland deformation for treatment response assessment and propose and apply methodologies for statistical assessment of the reliability of the registration tools. All three projects are interconnected, and leverage unique resources provided by this Center. In addition to developing novel technologies, we are creating a platform for collecting validation imaging datasets annotated with the analysis results, molecular and pathology markers, to build a unique resource for investigating the role of imaging and development of novel image analysis tools for prostate cancer. (Contact: Andriy Fedorov)

Software and Documentation

3D Slicer, a comprehensive open source platform for medical image analysis, contains several modules that have been contributed by us for Image-Guided Prostate Interventions. These include:



These presentations have been selected as tutorials for readers interested in learning about the clinical science and technology of the Prostate Core.


Full Publication List

In NIH/NLM database and in our Abstracts Database

Select Recent Publications

Taghipour M, Ziaei A, Alessandrino F, Hassanzadeh E, Harisinghani M, Vangel M, Tempany CM, Fennessy FM. Investigating the Role of DCE-MRI, over T2 and DWI, in accurate PI-RADS v2 Assessment of Clinically Significant Peripheral Zone Prostate Lesions as Defined at Radical Prostatectomy. Abdom Radiol (NY). 2018.Abstract
PURPOSE: PI-RADS v2 dictates that dynamic contrast-enhanced (DCE) imaging be used to further classify peripheral zone (PZ) cases that receive a diffusion-weighted imaging equivocal score of three (DWI3), a positive DCE resulting in an increase in overall assessment score to a four, indicative of clinically significant prostate cancer (csPCa). However, the accuracy of DCE in predicting csPCa in DWI3 PZ cases is unknown. This study sought to determine the frequency with which DCE changes the PI-RADS v2 DWI3 assessment category, and to determine the overall accuracy of DCE-MRI in equivocal PZ DWI3 lesions. MATERIALS AND METHODS: This is a retrospective study of patients with pathologically proven PCa who underwent prostate mpMRI at 3T and subsequent radical prostatectomy. PI-RADS v2 assessment categories were determined by a radiologist, aware of a diagnosis of PCa, but blinded to final pathology. csPCa was defined as a Gleason score ≥ 7 or extra prostatic extension at pathology review. Performance characteristics and diagnostic accuracy of DCE in assigning a csPCa assessment in PZ lesions were calculated. RESULTS: A total of 271 men with mean age of 59 ± 6 years mean PSA 6.7 ng/mL were included. csPCa was found in 212/271 (78.2%) cases at pathology, 209 of which were localized in the PZ. DCE was necessary to further classify (45/209) of patients who received a score of DWI3. DCE was positive in 29/45 cases, increasing the final PI-RADS v2 assessment category to a category 4, with 16/45 having a negative DCE. When compared with final pathology, DCE was correct in increasing the assessment category in 68.9% ± 7% (31/45) of DWI3 cases. CONCLUSION: DCE increases the accuracy of detection of csPCa in the majority of PZ lesions that receive an equivocal PI-RADS v2 assessment category using DWI.
deSouza NM, Tempany CM. A Risk-based Approach to Identifying Oligometastatic Disease on Imaging. Int J Cancer. 2018.Abstract
Recognition of <3 metastases in <2 organs, particularly in cancers with a known predisposition to oligometastatic disease (OMD) (colorectal, prostate, renal, sarcoma and lung), offers the opportunity to focally treat the lesions identified and confers a survival advantage. The reliability with which OMD is identified depends on the sensitivity of the imaging technique used for detection and may be predicted from phenotypic and genetic factors of the primary tumour, which determine metastatic risk. Whole-body or organ-specific imaging to identify oligometastases requires optimization to achieve maximal sensitivity. Metastatic lesions at multiple locations may require a variety of imaging modalities for best visualisation because the optimal image contrast is determined by tumour biology. Newer imaging techniques used for this purpose require validation. Additionally, rationalisation of imaging strategies is needed, particularly with regard to timing of imaging and follow-up studies. This article reviews the current evidence for the use of imaging for recognising OMD and proposes a risk-based roadmap for identifying patients with true OMD, or at risk of metastatic disease likely to be OM.
Moreira P, Patel N, Wartenberg M, Li G, Tuncali K, Heffter T, Burdette EC, Iordachita I, Fischer GS, Hata N, et al. Evaluation of Robot-assisted MRI-guided Prostate Biopsy: Needle Path Analysis during Clinical Trials. Phys Med Biol. 2018;63 (20) :20NT02.Abstract
PURPOSE: While the interaction between a needle and the surrounding tissue is known to cause a significant targeting error in prostate biopsy leading to false-negative results, few studies have demonstrated how it impacts in the actual procedure. We performed a pilot study on robot-assisted MRI-guided prostate biopsy with an emphasis on the in-depth analysis of the needle-tissue interaction in-vivo. Methods: The data were acquired during in-bore transperineal prostate biopsies in patients using a 4 degrees-of-freedom (DoF) MRI-compatible robot. The anatomical structures in the pelvic area and the needle path were reconstructed from MR images, and quantitatively analyzed. We analyzed each structure individually and also proposed a mathematical model to investigate the influence of those structures in the targeting error using the mixed-model regression. Results: The median targeting error in 188 insertions (27 patients) was 6.3mm. Both the individual anatomical structure analysis and the mixed-model analysis showed that the deviation resulted from the contact between the needle and the skin as the main source of error. On contrary, needle bending inside the tissue (expressed as needle curvature) did not vary among insertions with targeting errors above and below the average. The analysis indicated that insertions crossing the bulbospongiosus presented a targeting error lower than the average. The mixed-model analysis demonstrated that the distance between the needle guide and the patient skin, the deviation at the entry point, and the path length inside the pelvic diaphragm had a statistically significant contribution to the targeting error (p<0.05). Conclusions: Our results indicate that the errors associated with the elastic contact between the needle and the skin were more prominent than the needle bending along the insertion. Our findings will help to improve the preoperative planning of transperineal prostate biopsies.
van Beek EJR, Kuhl C, Anzai Y, Desmond P, Ehman RL, Gong Q, Gold G, Gulani V, Hall-Craggs M, Leiner T, et al. Value of MRI in Medicine: More Than Just Another Test?. J Magn Reson Imaging. 2018.Abstract
There is increasing scrutiny from healthcare organizations towards the utility and associated costs of imaging. MRI has traditionally been used as a high-end modality, and although shown extremely important for many types of clinical scenarios, it has been suggested as too expensive by some. This editorial will try and explain how value should be addressed and gives some insights and practical examples of how value of MRI can be increased. It requires a global effort to increase accessibility, value for money, and impact on patient management. We hope this editorial sheds some light and gives some indications of where the field may wish to address some of its research to proactively demonstrate the value of MRI. LEVEL OF EVIDENCE: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018.
Alessandrino F, Taghipour M, Hassanzadeh E, Ziaei A, Vangel M, Fedorov A, Tempany CM, Fennessy FM. Predictive Role of PI-RADSv2 and ADC Parameters in Differentiating Gleason Pattern 3 + 4 and 4 + 3 Prostate Cancer. Abdom Radiol (NY). 2018.Abstract
PURPOSE: To compare the predictive roles of qualitative (PI-RADSv2) and quantitative assessment (ADC metrics), in differentiating Gleason pattern (GP) 3 + 4 from the more aggressive GP 4 + 3 prostate cancer (PCa) using radical prostatectomy (RP) specimen as the reference standard. METHODS: We retrospectively identified treatment-naïve peripheral (PZ) and transitional zone (TZ) Gleason Score 7 PCa patients who underwent multiparametric 3T prostate MRI (DWI with b value of 0,1400 and where unavailable, 0,500) and subsequent RP from 2011 to 2015. For each lesion identified on MRI, a PI-RADSv2 score was assigned by a radiologist blinded to pathology data. A PI-RADSv2 score ≤ 3 was defined as "low risk," a PI-RADSv2 score ≥ 4 as "high risk" for clinically significant PCa. Mean tumor ADC (ADC), ADC of adjacent normal tissue (ADC), and ADC (ADC/ADC) were calculated. Stepwise regression analysis using tumor location, ADC and ADC, b value, low vs. high PI-RADSv2 score was performed to differentiate GP 3 + 4 from 4 + 3. RESULTS: 119 out of 645 cases initially identified met eligibility requirements. 76 lesions were GP 3 + 4, 43 were 4 + 3. ADC was significantly different between the two GP groups (p = 0.001). PI-RADSv2 score ("low" vs. "high") was not significantly different between the two GP groups (p = 0.17). Regression analysis selected ADC (p = 0.03) and ADC (p = 0.0007) as best predictors to differentiate GP 4 + 3 from 3 + 4. Estimated sensitivity, specificity, and accuracy of the predictive model in differentiating GP 4 + 3 from 3 + 4 were 37, 82, and 66%, respectively. CONCLUSIONS: ADC metrics could differentiate GP 3 + 4 from 4 + 3 PCa with high specificity and moderate accuracy while PI-RADSv2, did not differentiate between these patterns.
King MT, Nguyen PL, Boldbaatar N, Tempany CM, Cormack RA, Beard CJ, Hurwitz MD, Suh WW, D'Amico AV, Orio PF. Long-Term Outcomes of Partial Prostate Treatment with Magnetic Resonance Imaging-Guided Brachytherapy for Patients with Favorable-Risk Prostate Cancer. Cancer. 2018;124 (17) :3528-35.Abstract
BACKGROUND: Partial prostate treatment has emerged as a potential method for treating patients with favorable-risk prostate cancer while minimizing toxicity. The authors previously demonstrated poor rates of biochemical disease control for patients with National Comprehensive Cancer Network (NCCN) intermediate-risk disease using partial gland treatment with brachytherapy. The objective of the current study was to estimate the rates of distant metastasis and prostate cancer-specific mortality (PCSM) for this cohort. METHODS: Between 1997 and 2007, a total of 354 men with clinical T1c disease, a prostate-specific antigen (PSA) level < 15 ng/mL, and Gleason grade ≤3 + 4 prostate cancer underwent partial prostate treatment with brachytherapy to the peripheral zone under 0.5-Tesla magnetic resonance guidance. The cumulative incidences of metastasis and PCSM for the NCCN very low-risk, low-risk, and intermediate-risk groups were estimated. Fine and Gray competing risk regression was used to evaluate clinical factors associated with time to metastasis. RESULTS: A total of 22 patients developed metastases at a median of 11.0 years (interquartile range, 6.9-13.9 years). The 12-year metastasis rates for patients with very low-risk, low-risk, and intermediate-risk disease were 0.8% (95% confidence interval [95% CI], 0.1%-4.4%), 8.7% (95% CI, 3.4%-17.2%), and 15.7% (95% CI, 5.7%-30.2%), respectively, and the 12-year PCSM estimates were 1.6% (95% CI, 0.1%-7.6%), 1.4% (95% CI, 0.1%-6.8%), and 8.2% (95% CI, 1.9%-20.7%), respectively. On multivariate analysis, NCCN risk category (low risk: hazard ratio, 6.34 [95% CI, 1.18-34.06; P = .03] and intermediate risk: hazard ratio, 6.98 [95% CI, 1.23-39.73; P = .03]) was found to be significantly associated with the time to metastasis. CONCLUSIONS: Partial prostate treatment with brachytherapy may be associated with higher rates of distant metastasis and PCSM for patients with intermediate-risk disease after long-term follow-up. Treatment of less than the full gland may not be appropriate for this cohort. Cancer 2018. © 2018 American Cancer Society.
Langkilde F, Kobus T, Fedorov A, Dunne R, Tempany C, Mulkern RV, Maier SE. Evaluation of Fitting Models for Prostate Tissue Characterization using Extended-range b-factor Diffusion-weighted Imaging. Magn Reson Med. 2018;79 (4) :2346-58.Abstract
PURPOSE: To compare the fitting and tissue discrimination performance of biexponential, kurtosis, stretched exponential, and gamma distribution models for high b-factor diffusion-weighted images in prostate cancer. METHODS: Diffusion-weighted images with 15 b-factors ranging from b = 0 to 3500 s/mmwere obtained in 62 prostate cancer patients. Pixel-wise signal decay fits for each model were evaluated with the Akaike Information Criterion (AIC). Parameter values for each model were determined within normal prostate and the index lesion. Their potential to differentiate normal from cancerous tissue was investigated through receiver operating characteristic analysis and comparison with Gleason score. RESULTS: The biexponential slow diffusion fraction f, the apparent kurtosis diffusion coefficient ADC, and the excess kurtosis factor K differ significantly among normal peripheral zone (PZ), normal transition zone (TZ), tumor PZ, and tumor TZ. Biexponential and gamma distribution models result in the lowest AIC, indicating a superior fit. Maximum areas under the curve (AUCs) of all models ranged from 0.93 to 0.96 for the PZ and from 0.95 to 0.97 for the TZ. Similar AUCs also result from the apparent diffusion coefficient (ADC) of a monoexponential fit to a b-factor sub-range up to 1250 s/mm. For kurtosis and stretched exponential models, single parameters yield the highest AUCs, whereas for the biexponential and gamma distribution models, linear combinations of parameters produce the highest AUCs. Parameters with high AUC show a trend in differentiating low from high Gleason score, whereas parameters with low AUC show no such ability. CONCLUSION: All models, including a monoexponential fit to a lower-b sub-range, achieve similar AUCs for discrimination of normal and cancer tissue. The biexponential model, which is favored statistically, also appears to provide insight into disease-related microstructural changes. Magn Reson Med 79:2346-2358, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
Hassanzadeh E, Alessandrino F, Olubiyi OI, Glazer DI, Mulkern RV, Fedorov A, Tempany CM, Fennessy FM. Comparison of Quantitative Apparent Diffusion Coefficient Parameters with Prostate Imaging Reporting and Data System V2 Assessment for Detection of Clinically Significant Peripheral Zone Prostate Cancer. Abdom Radiol (NY). 2018;43 (5) :1237-44.Abstract
PURPOSE: To compare diagnostic performance of PI-RADSv2 with ADC parameters to identify clinically significant prostate cancer (csPC) and to determine the impact of csPC definitions on diagnostic performance of ADC and PI-RADSv2. METHODS: We retrospectively identified treatment-naïve pathology-proven peripheral zone PC patients who underwent 3T prostate MRI, using high b-value diffusion-weighted imaging from 2011 to 2015. Using 3D slicer, areas of suspected tumor (T) and normal tissue (N) on ADC (b = 0, 1400) were outlined volumetrically. Mean ADCT, mean ADCN, ADCratio (ADCT/ADCN) were calculated. PI-RADSv2 was assigned. Three csPC definitions were used: (A) Gleason score (GS) ≥ 4 + 3; (B) GS ≥ 3 + 4; (C) MRI-based tumor volume >0.5 cc. Performances of ADC parameters and PI-RADSv2 in identifying csPC were measured using nonparametric comparison of receiver operating characteristic curves using the area under the curve (AUC). RESULTS: Eighty five cases met eligibility requirements. Diagnostic performances (AUC) in identifying csPC using three definitions were: (A) ADCT (0.83) was higher than PI-RADSv2 (0.65, p = 0.006); (B) ADCT (0.86) was higher than ADCratio (0.68, p < 0.001), and PI-RADSv2 (0.70, p = 0.04); (C) PI-RADSv2 (0.73) performed better than ADCratio (0.56, p = 0.02). ADCT performance was higher when csPC was defined by A or B versus C (p = 0.038 and p = 0.01, respectively). ADCratio performed better when csPC was defined by A versus C (p = 0.01). PI-RADSv2 performance was not affected by csPC definition. CONCLUSIONS: When csPC was defined by GS, ADC parameters provided better csPC discrimination than PI-RADSv2, with ADCT providing best result. When csPC was defined by MRI-calculated volume, PI-RADSv2 provided better discrimination than ADCratio. csPC definition did not affect PI-RADSv2 diagnostic performance.
Verma S, Choyke PL, Eberhardt SC, Oto A, Tempany CM, Turkbey B, Rosenkrantz AB. The Current State of MR Imaging-targeted Biopsy Techniques for Detection of Prostate Cancer. Radiology. 2017;285 (2) :343-56.Abstract
Systematic transrectal ultrasonography (US)-guided biopsy is the standard approach for histopathologic diagnosis of prostate cancer. However, this technique has multiple limitations because of its inability to accurately visualize and target prostate lesions. Multiparametric magnetic resonance (MR) imaging of the prostate is more reliably able to localize significant prostate cancer. Targeted prostate biopsy by using MR imaging may thus help to reduce false-negative results and improve risk assessment. Several commercial devices are now available for targeted prostate biopsy, including in-gantry MR imaging-targeted biopsy and real-time transrectal US-MR imaging fusion biopsy systems. This article reviews the current status of MR imaging-targeted biopsy platforms, including technical considerations, as well as advantages and challenges of each technique.
Glazer DI, Mayo-Smith WW, Sainani NI, Sadow CA, Vangel MG, Tempany CM, Dunne RM. Interreader Agreement of Prostate Imaging Reporting and Data System Version 2 Using an In-Bore MRI-Guided Prostate Biopsy Cohort: A Single Institution's Initial Experience. AJR Am J Roentgenol. 2017;209 (3) :W145-51.Abstract
OBJECTIVE: The purpose of this study is to determine the interobserver agreement of the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) for diagnosing prostate cancer using in-bore MRI-guided prostate biopsy as the reference standard. MATERIALS AND METHODS: Fifty-nine patients underwent in-bore MRI-guided prostate biopsy between January 21, 2010, and August 21, 2013, and underwent diagnostic multiparametric MRI 6 months or less before biopsy. A single index lesion per patient was selected after retrospective review of MR images. Three fellowship-trained abdominal radiologists (with 1-11 years' experience) blinded to clinical information interpreted all studies according to PI-RADSv2. Interobserver agreement was assessed using Cohen kappa statistics. RESULTS: Thirty-eight lesions were in the peripheral zone and 21 were in the transition zone. Cancer was diagnosed in 26 patients (44%). Overall PI-RADS scores were higher for all biopsy-positive lesions (mean ± SD, 3.9 ± 1.1) than for biopsy-negative lesions (3.1 ± 1.0; p < 0.0001) and for clinically significant lesions (4.2 ± 1.0) than for clinically insignificant lesions (3.1 ± 1.0; p < 0.0001). Overall suspicion score interobserver agreement was moderate (κ = 0.45). There was moderate interobserver agreement among overall PI-RADS scores in the peripheral zone (κ = 0.46) and fair agreement in the transition zone (κ = 0.36). CONCLUSION: PI-RADSv2 scores were higher in the biopsy-positive group. PI-RADSv2 showed moderate interobserver agreement among abdominal radiologists with no prior experience using the scoring system.