Prostate Research Publications

Clare MC Tempany-Afdhal. 2/2021. “Focal Treatment of Prostate Cancer: MRI Helps Guide the Way Forward.” Editorial. Radiology, Pp. 204333.
Fiona M Fennessy, Andriy Fedorov, Mark G Vangel, Robert V. Mulkern, Maria Tretiakova, Rosina T Lis, Clare Tempany, and Mary-Ellen Taplin. 10/2020. “Multiparametric MRI as a Biomarker of Response to Neoadjuvant Therapy for Localized Prostate Cancer-A Pilot Study.” Acad Radiol, 27, 10, Pp. 1432-9.Abstract
RATIONALE AND OBJECTIVES: To explore a role for multiparametric MRI (mpMRI) as a biomarker of response to neoadjuvant androgen deprivation therapy (ADT) for prostate cancer (PCa). MATERIALS AND METHODS: This prospective study was approved by the institutional review board and was HIPAA compliant. Eight patients with localized PCa had a baseline mpMRI, repeated after 6-months of ADT, followed by prostatectomy. mpMRI indices were extracted from tumor and normal regions of interest (TROI/NROI). Residual cancer burden (RCB) was measured on mpMRI and on the prostatectomy specimen. Paired t-tests compared TROI/NROI mpMRI indices and pre/post-treatment TROI mpMRI indices. Spearman's rank tested for correlations between MRI/pathology-based RCB, and between pathological RCB and mpMRI indices. RESULTS: At baseline, TROI apparent diffusion coefficient (ADC) was lower and dynamic contrast enhanced (DCE) metrics were higher, compared to NROI (ADC: 806 ± 137 × 10 vs. 1277 ± 213 × 10 mm/sec, p = 0.0005; K: 0.346 ± 0.16 vs. 0.144 ± 0.06 min, p = 0.002; AUC: 0.213 ± 0.08 vs. 0.11 ± 0.03, p = 0.002). Post-treatment, there was no change in TROI ADC, but a decrease in TROI K (0.346 ± 0.16 to 0.188 ± 0.08 min; p = 0.02) and AUC (0.213 ± 0.08 to 0.13 ± 0.06; p = 0.02). Tumor volume decreased with ADT. There was no difference between mpMRI-based and pathology-based RCB, which positively correlated (⍴ = 0.74-0.81, p < 0.05). Pathology-based RCB positively correlated with post-treatment DCE metrics (⍴ = 0.76-0.70, p < 0.05) and negatively with ADC (⍴ = -0.79, p = 0.03). CONCLUSION: Given the heterogeneity of PCa, an individualized approach to ADT may maximize potential benefit. This pilot study suggests that mpMRI may serve as a biomarker of ADT response and as a surrogate for RCB at prostatectomy.
Pedro Moreira, Kemal Tuncali, Clare M Tempany, and Junichi Tokuda. 8/2020. “The Impact of Placement Errors on the Tumor Coverage in MRI-Guided Focal Cryoablation of Prostate Cancer.” Acad Radiol, S1076, Pp. 6332(20)30429-3.Abstract
RATIONALE AND OBJECTIVES: There have been multiple investigations defining and reporting the effectiveness of focal cryoablation as a treatment option for organ-confined prostate cancer. However, the impact of cryo-needle/probe placement accuracy within the tumor and gland has not been extensively studied. We analyzed how variations in the placement of the cryo-needles, specifically errors leading to incomplete ablation, may affect prostate cancer's resulting cryoablation. MATERIALS AND METHODS: We performed a study based on isothermal models using Monte Carlo simulations to analyze the impact of needle placement errors on tumor coverage and the probability of positive ablation margin. We modeled the placement error as a Gaussian noise on the cryo-needle position. The analysis used retrospective MRI data of 15 patients with biopsy-proven, unifocal, and MRI visible prostate cancer to calculate the impact of placement error on the volume of the tumor encompassed by the -40°C and -20°C isotherms using one to four cryo-needles. RESULTS: When the standard deviation of the placement error reached 3 mm, the tumor coverage was still above 97% with the -20°C isotherm, and above 81% with the -40°C isotherm using two cryo-needles or more. The probability of positive margin was significantly lower considering the -20°C isotherm (0.04 for three needles) than using the -40°C isotherm (0.66 for three needles). CONCLUSION: The results indicated that accurate cryo-needle placement is essential for the success of focal cryoablation of prostate cancer. The analysis shows that an admissible targeting error depends on the lethal temperature considered and the number of cryo-needles used.
Junichi Tokuda, Qun Wang, Kemal Tuncali, Ravi T Seethamraju, Clare M Tempany, and Ehud J Schmidt. 5/2020. “Temperature-Sensitive Frozen-Tissue Imaging for Cryoablation Monitoring Using STIR-UTE MRI.” Invest Radiol, 55, 5, Pp. 310-7.Abstract
PURPOSE: The aim of this study was to develop a method to delineate the lethally frozen-tissue region (temperature less than -40°C) arising from interventional cryoablation procedures using a short tau inversion-recovery ultrashort echo-time (STIR-UTE) magnetic resonance (MR) imaging sequence. This method could serve as an intraprocedural validation of the completion of tumor ablation, reducing the number of local recurrences after cryoablation procedures. MATERIALS AND METHODS: The method relies on the short T1 and T2* relaxation times of frozen soft tissue. Pointwise Encoding Time with Radial Acquisition, a 3-dimensional UTE sequence with TE = 70 microseconds, was optimized with STIR to null tissues with a T1 of approximately 271 milliseconds, the threshold T1. Because the T1 relaxation time of frozen tissue in the temperature range of -40°C < temperature < -8°C is shorter than the threshold T1 at the 3-tesla magnetic field, tissues in this range should appear hyperintense. The sequence was evaluated in ex vivo frozen tissue, where image intensity and actual tissue temperatures, measured by thermocouples, were correlated. Thereafter, the sequence was evaluated clinically in 12 MR-guided prostate cancer cryoablations, where MR-compatible cryoprobes were used to destroy cancerous tissue and preserve surrounding normal tissue. RESULTS: The ex vivo experiment using a bovine muscle demonstrated that STIR-UTE images showed regions approximately between -40°C and -8°C as hyperintense, with tissues at lower and higher temperatures appearing dark, making it possible to identify the region likely to be above the lethal temperature inside the frozen tissue. In the clinical cases, the STIR-UTE images showed a dark volume centered on the cryoprobe shaft, Vinner, where the temperature is likely below -40°C, surrounded by a doughnut-shaped hyperintense volume, where the temperature is likely between -40°C and -8°C. The hyperintense region was itself surrounded by a dark volume, where the temperature is likely above -8°C, permitting calculation of Vouter. The STIR-UTE frozen-tissue volumes, Vinner and Vouter, appeared significantly smaller than signal voids on turbo spin echo images (P < 1.0 × 10), which are currently used to quantify the frozen-tissue volume ("the iceball"). The ratios of the Vinner and Vouter volumes to the iceball were 0.92 ± 0.08 and 0.29 ± 0.07, respectively. In a single postablation follow-up case, a strong correlation was seen between Vinner and the necrotic volume. CONCLUSIONS: Short tau inversion-recovery ultrashort echo-time MR imaging successfully delineated the area approximately between -40°C and -8°C isotherms in the frozen tissue, demonstrating its potential to monitor the lethal ablation volume during MR-guided cryoablation.
Aida Steiner, Gabriela Alban, Teresa Cheng, Tina Kapur, Camden Bay, Pierre-Yves McLaughlin, Martin King, Clare Tempany, and Larissa J Lee. 4/2020. “Vaginal Recurrence of Endometrial Cancer: MRI Characteristics and Correlation With Patient Outcome After Salvage Radiation Therapy.” Abdom Radiol (NY), 45, 4, Pp. 1122-31.Abstract
PURPOSE: To evaluate MRI characteristics in vaginal recurrence of endometrial cancer (EC) including tumor volume shrinkage during salvage radiotherapy, and to identify imaging features associated with survival. METHODS: Patients with vaginal recurrence of EC treated with external beam radiotherapy (EBRT) followed by brachytherapy (BT), and with available pelvic MRI at two time points: baseline and/or before BT were retrospectively identified from 2004 to 2017. MRI features including recurrence location and tissue characteristics on T2- and T1-weighted images were evaluated at baseline only. Tumor volumes were measured both at baseline and pre-BT. Survival rates and associations were evaluated by Cox regression and Fisher's exact test, respectively. RESULTS: Sixty-two patients with 36 baseline and 50 pre-BT pelvic MRIs were included (24/62 with both MRIs). Vaginal recurrence of EC was most commonly located in the vaginal apex (27/36, 75%). Tumors with a post-contrast enhancing peripheral rim or low T2 signal rim at baseline showed longer recurrence-free survival (RFS) (HR 0.2, 95% CI 0.1-0.9, P < 0.05 adjusted for histology; HR 0.2, 95% CI 0.1-0.8, P < 0.05, respectively). The median tumor shrinkage at pre-BT was 69% (range 1-99%). Neither absolute tumor volumes nor volume regression at pre-BT were associated with RFS. Lymphovascular space invasion (LVSI) at hysterectomy and adjuvant RT were associated with recurrence involving the distal vagina (both P < 0.05). CONCLUSION: Vaginal recurrences with rim enhancement at baseline MRI predicted improved RFS, while tumor volume shrinkage at pre-BT did not. Distal vaginal recurrence was more common in patients with LVSI and adjuvant RT at EC diagnosis.
Christian Herz, Kyle MacNeil, Peter A Behringer, Junichi Tokuda, Alireza Mehrtash, Parvin Mousavi, Ron Kikinis, Fiona M Fennessy, Clare M Tempany, Kemal Tuncali, and Andriy Fedorov. 2/2020. “Open Source Platform for Transperineal In-Bore MRI-Guided Targeted Prostate Biopsy.” IEEE Trans Biomed Eng, 67, 2, Pp. 565-76.Abstract
OBJECTIVE: Accurate biopsy sampling of the suspected lesions is critical for the diagnosis and clinical management of prostate cancer. Transperineal in-bore MRI-guided prostate biopsy (tpMRgBx) is a targeted biopsy technique that was shown to be safe, efficient, and accurate. Our goal was to develop an open source software platform to support evaluation, refinement, and translation of this biopsy approach. METHODS: We developed SliceTracker, a 3D Slicer extension to support tpMRgBx. We followed modular design of the implementation to enable customization of the interface and interchange of image segmentation and registration components to assess their effect on the processing time, precision, and accuracy of the biopsy needle placement. The platform and supporting documentation were developed to enable the use of software by an operator with minimal technical training to facilitate translation. Retrospective evaluation studied registration accuracy, effect of the prostate segmentation approach, and re-identification time of biopsy targets. Prospective evaluation focused on the total procedure time and biopsy targeting error (BTE). RESULTS: Evaluation utilized data from 73 retrospective and ten prospective tpMRgBx cases. Mean landmark registration error for retrospective evaluation was 1.88 ± 2.63 mm, and was not sensitive to the approach used for prostate gland segmentation. Prospectively, we observed target re-identification time of 4.60 ± 2.40 min and BTE of 2.40 ± 0.98 mm. CONCLUSION: SliceTracker is modular and extensible open source platform for supporting image processing aspects of the tpMRgBx procedure. It has been successfully utilized to support clinical research procedures at our site.
Ananya Panda, Verena C Obmann, Wei-Ching Lo, Seunghee Margevicius, Yun Jiang, Mark Schluchter, Indravadan J Patel, Dean Nakamoto, Chaitra Badve, Mark A Griswold, Irina Jaeger, Lee E Ponsky, and Vikas Gulani. 9/2019. “MR Fingerprinting and ADC Mapping for Characterization of Lesions in the Transition Zone of the Prostate Gland.” Radiology, 292, 3, Pp. 685-94.Abstract
BackgroundPreliminary studies have shown that MR fingerprinting-based relaxometry combined with apparent diffusion coefficient (ADC) mapping can be used to differentiate normal peripheral zone from prostate cancer and prostatitis. The utility of relaxometry and ADC mapping for the transition zone (TZ) is unknown.PurposeTo evaluate the utility of MR fingerprinting combined with ADC mapping for characterizing TZ lesions.Materials and MethodsTZ lesions that were suspicious for cancer in men who underwent MRI with T2-weighted imaging and ADC mapping ( values, 50-1400 sec/mm), MR fingerprinting with steady-state free precession, and targeted biopsy (60 in-gantry and 15 cognitive targeting) between September 2014 and August 2018 in a single university hospital were retrospectively analyzed. Two radiologists blinded to Prostate Imaging Reporting and Data System (PI-RADS) scores and pathologic diagnosis drew regions of interest on cancer-suspicious lesions and contralateral visually normal TZs (NTZs) on MR fingerprinting and ADC maps. Linear mixed models compared two-reader means of T1, T2, and ADC. Generalized estimating equations logistic regression analysis was used to evaluate both MR fingerprinting and ADC in differentiating NTZ, cancers and noncancers, clinically significant (Gleason score ≥ 7) cancers from clinically insignificant lesions (noncancers and Gleason 6 cancers), and characterizing PI-RADS version 2 category 3 lesions.ResultsIn 67 men (mean age, 66 years ± 8 [standard deviation]) with 75 lesions, targeted biopsy revealed 37 cancers (six PI-RADS category 3 cancers and 31 PI-RADS category 4 or 5 cancers) and 38 noncancers (31 PI-RADS category 3 lesions and seven PI-RADS category 4 or 5 lesions). The T1, T2, and ADC of NTZ (1800 msec ± 150, 65 msec ± 22, and [1.13 ± 0.19] × 10 mm/sec, respectively) were higher than those in cancers (1450 msec ± 110, 36 msec ± 11, and [0.57 ± 0.13] × 10 mm/sec, respectively; < .001 for all). The T1, T2, and ADC in cancers were lower than those in noncancers (1620 msec ± 120, 47 msec ± 16, and [0.82 ± 0.13] × 10 mm/sec, respectively; = .001 for T1 and ADC and = .03 for T2). The area under the receiver operating characteristic curve (AUC) for T1 plus ADC was 0.94 for separation. T1 and ADC in clinically significant cancers (1440 msec ± 140 and [0.58 ± 0.14] × 10 mm/sec, respectively) were lower than those in clinically insignificant lesions (1580 msec ± 120 and [0.75 ± 0.17] × 10 mm/sec, respectively; = .001 for all). The AUC for T1 plus ADC was 0.81 for separation. Within PI-RADS category 3 lesions, T1 and ADC of cancers (1430 msec ± 220 and [0.60 ± 0.17] × 10 mm/sec, respectively) were lower than those of noncancers (1630 msec ± 120 and [0.81 ± 0.13] × 10 mm/sec, respectively; = .006 for T1 and = .004 for ADC). The AUC for T1 was 0.79 for differentiating category 3 lesions.ConclusionMR fingerprinting-based relaxometry combined with apparent diffusion coefficient mapping may improve transition zone lesion characterization.© RSNA, 2019
Elizabeth C Randall, Giorgia Zadra, Paolo Chetta, Begona GC Lopez, Sudeepa Syamala, Sankha S Basu, Jeffrey N Agar, Massimo Loda, Clare M Tempany, Fiona M Fennessy, and Nathalie YR Agar. 5/2019. “Molecular Characterization of Prostate Cancer with Associated Gleason Score using Mass Spectrometry Imaging.” Mol Cancer Res, 17, 5, Pp. 1155-65.Abstract
Diagnosis of prostate cancer is based on histological evaluation of tumor architecture using a system known as the 'Gleason score'. This diagnostic paradigm, while the standard of care, is time-consuming, shows intra-observer variability and provides no information about the altered metabolic pathways, which result in altered tissue architecture. Characterization of the molecular composition of prostate cancer and how it changes with respect to the Gleason score (GS) could enable a more objective and faster diagnosis. It may also aid in our understanding of disease onset and progression. In this work, we present mass spectrometry imaging for identification and mapping of lipids and metabolites in prostate tissue from patients with known prostate cancer with GS from 6 to 9. A gradient of changes in the intensity of various lipids was observed, which correlated with increasing GS. Interestingly, these changes were identified in both regions of high tumor cell density, and in regions of tissue that appeared histologically benign, possibly suggestive of pre-cancerous metabolomic changes. A total of 31 lipids, including several phosphatidylcholines, phosphatidic acids, phosphatidylserines, phosphatidylinositols and cardiolipins were detected with higher intensity in GS (4+3) compared with GS (3+4), suggesting they may be markers of prostate cancer aggression. Results obtained through mass spectrometry imaging studies were subsequently correlated with a fast, ambient mass spectrometry method for potential use as a clinical tool to support image-guided prostate biopsy. Implications: In this study we suggest that metabolomic differences between prostate cancers with different Gleason scores can be detected by mass spectrometry imaging.
Wenya Linda Bi, Ahmed Hosny, Matthew B Schabath, Maryellen L Giger, Nicolai J Birkbak, Alireza Mehrtash, Tavis Allison, Omar Arnaout, Christopher Abbosh, Ian F Dunn, Raymond H Mak, Rulla M Tamimi, Clare M Tempany, Charles Swanton, Udo Hoffmann, Lawrence H Schwartz, Robert J Gillies, Raymond Y Huang, and Hugo JWL Aerts. 3/2019. “Artificial Intelligence in Cancer Imaging: Clinical Challenges and Applications.” CA Cancer J Clin, 69, 2, Pp. 127-57.Abstract
Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.
Martin T King, Paul L Nguyen, Ninjin Boldbaatar, David D Yang, Vinayak Muralidhar, Clare M Tempany, Robert A Cormack, Mark D Hurwitz, Warren W Suh, Mark M Pomerantz, Anthony V D'Amico, and Peter F Orio. 3/2019. “Evaluating the Influence of Prostate-specific Antigen Kinetics on Metastasis in Men with PSA Recurrence after Partial Gland Therapy.” Brachytherapy, 18, 2, Pp. 198-203.Abstract
PURPOSE: Although current Delphi Consensus guidelines do not recommend a specific definition of biochemical recurrence after partial gland therapy, these guidelines acknowledge that serial prostate-specific antigen (PSA) tests remain the best marker for monitoring disease after treatment. The purpose of this study was to determine whether PSA velocity at failure per the Phoenix (nadir + 2 ng/mL) definition is associated with metastasis and prostate cancer-specific mortality (PCSM) in a cohort of patients who experienced PSA failure after partial gland therapy. METHODS: Between 1997 and 2007, 285 patients with favorable risk prostate cancer underwent partial prostate brachytherapy to the peripheral zone. PSA velocity was calculated for 94 patients who experienced PSA failure per the Phoenix (nadir + 2) definition. Fine and Gray competing risks regression was performed to determine whether PSA velocity and other clinical factors were associated with metastasis and PCSM. RESULTS: The median time to PSA failure was 4.2 years (interquartile range: 2.2, 7.9), and the median followup time after PSA failure was 6.5 years (3.5-9.7). Seventeen patients developed metastases, and five experienced PCSM. On multivariate analysis, PSA velocity ≥3.0 ng/mL/year (adjusted hazard ratio 5.97; [2.57, 13.90]; p < 0.001) and PSA nadir (adjusted hazard ratio 0.39; [0.24, 0.64]; p < 0.001) were significantly associated with metastasis. PSA velocity ≥3.0 ng/mL/year was also associated with PCSM (HR 15.3; [1.8, 128.0]; p = 0.012) on univariate analysis. CONCLUSIONS: Rapid PSA velocity at PSA failure after partial gland treatment may be prognostic for long-term outcomes.
Wei Huang, Yiyi Chen, Andriy Fedorov, Xia Li, Guido H Jajamovich, Dariya I Malyarenko, Madhava P Aryal, Peter S LaViolette, Matthew J Oborski, Finbarr O'Sullivan, Richard G Abramson, Kourosh Jafari-Khouzani, Aneela Afzal, Alina Tudorica, Brendan Moloney, Sandeep N Gupta, Cecilia Besa, Jayashree Kalpathy-Cramer, James M Mountz, Charles M Laymon, Mark Muzi, Paul E Kinahan, Kathleen Schmainda, Yue Cao, Thomas L Chenevert, Bachir Taouli, Thomas E Yankeelov, Fiona Fennessy, and Xin Li. 3/2019. “The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II.” Tomography, 5, 1, Pp. 99-109.Abstract
This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate K (volume transfer rate constant), v (extravascular, extracellular volume fraction), k (efflux rate constant), and τ (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for K, v, k, and τ, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in K and v (wCV = 0.50 and 0.10, respectively), but had smaller effects on k and τ (wCV = 0.39 and 0.22, respectively). k is less sensitive to AIF variation than K, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τ parameter may have advantages over the conventional PK parameters in a longitudinal study.
Francesco Alessandrino, Mehdi Taghipour, Elmira Hassanzadeh, Alireza Ziaei, Mark Vangel, Andriy Fedorov, Clare M Tempany, and Fiona M Fennessy. 1/2019. “Predictive Role of PI-RADSv2 and ADC Parameters in Differentiating Gleason Pattern 3 + 4 and 4 + 3 Prostate Cancer.” Abdom Radiol (NY), 44, 1, Pp. 279-85.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.
Pelin Aksit Ciris, Jr-yuan George Chiou, Daniel I Glazer, Tzu-Cheng Chao, Clare M Tempany-Afdhal, Bruno Madore, and Stephan E. Maier. 2019. “Accelerated Segmented Diffusion-Weighted Prostate Imaging for Higher Resolution, Higher Geometric Fidelity, and Multi-b Perfusion Estimation.” Invest Radiol, 54, 4, Pp. 238-46.Abstract
PURPOSE: The aim of this study was to improve the geometric fidelity and spatial resolution of multi-b diffusion-weighted magnetic resonance imaging of the prostate. MATERIALS AND METHODS: An accelerated segmented diffusion imaging sequence was developed and evaluated in 25 patients undergoing multiparametric magnetic resonance imaging examinations of the prostate. A reduced field of view was acquired using an endorectal coil. The number of sampled diffusion weightings, or b-factors, was increased to allow estimation of tissue perfusion based on the intravoxel incoherent motion (IVIM) model. Apparent diffusion coefficients measured with the proposed segmented method were compared with those obtained with conventional single-shot echo-planar imaging (EPI). RESULTS: Compared with single-shot EPI, the segmented method resulted in faster acquisition with 2-fold improvement in spatial resolution and a greater than 3-fold improvement in geometric fidelity. Apparent diffusion coefficient values measured with the novel sequence demonstrated excellent agreement with those obtained from the conventional scan (R = 0.91 for bmax = 500 s/mm and R = 0.89 for bmax = 1400 s/mm). The IVIM perfusion fraction was 4.0% ± 2.7% for normal peripheral zone, 6.6% ± 3.6% for normal transition zone, and 4.4% ± 2.9% for suspected tumor lesions. CONCLUSIONS: The proposed accelerated segmented prostate diffusion imaging sequence achieved improvements in both spatial resolution and geometric fidelity, along with concurrent quantification of IVIM perfusion.
Mehdi Taghipour, Alireza Ziaei, Francesco Alessandrino, Elmira Hassanzadeh, Mukesh Harisinghani, Mark Vangel, Clare M Tempany, and Fiona M Fennessy. 2019. “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), 44, 4, Pp. 1520-7.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.
Nandita M deSouza and Clare M Tempany. 2019. “A Risk-based Approach to Identifying Oligometastatic Disease on Imaging.” Int J Cancer, 144, 3, Pp. 422-30.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.
Sharon Peled, Mark Vangel, Ron Kikinis, Clare M Tempany, Fiona M Fennessy, and Andrey Fedorov. 2019. “Selection of Fitting Model and Arterial Input Function for Repeatability in Dynamic Contrast-Enhanced Prostate MRI.” Acad Radiol, 26, 9, Pp. e241-e251.Abstract
RATIONALE AND OBJECTIVES: Analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging is notable for the variability of calculated parameters. The purpose of this study was to evaluate the level of measurement variability and error/variability due to modeling in DCE magnetic resonance imaging parameters. MATERIALS AND METHODS: Two prostate DCE scans were performed on 11 treatment-naïve patients with suspected or confirmed prostate peripheral zone cancer within an interval of less than two weeks. Tumor-suspicious and normal-appearing regions of interest (ROI) in the prostate peripheral zone were segmented. Different Tofts-Kety based models and different arterial input functions, with and without bolus arrival time (BAT) correction, were used to extract pharmacokinetic parameters. The percent repeatability coefficient (%RC) of fitted model parameters K, v, and k was calculated. Paired t-tests comparing parameters in tumor-suspicious ROIs and in normal-appearing tissue evaluated each parameter's sensitivity to pathology. RESULTS: Although goodness-of-fit criteria favored the four-parameter extended Tofts-Kety model with the BAT correction included, the simplest two-parameter Tofts-Kety model overall yielded the best repeatability scores. The best %RC in the tumor-suspicious ROI was 63% for k, 28% for v and 83% for K . The best p values for discrimination between tissues were p <10 for k and K, and p = 0.11 for v. Addition of the BAT correction to the models did not improve repeatability. CONCLUSION: The parameter k, using an arterial input functions directly measured from blood signals, was more repeatable than K. Both K and k values were highly discriminatory between healthy and diseased tissues in all cases. The parameter v had high repeatability but could not distinguish the two tissue types.
Edwin JR van Beek, Christiane Kuhl, Yoshimi Anzai, Patricia Desmond, Richard L Ehman, Qiyong Gong, Garry Gold, Vikas Gulani, Margaret Hall-Craggs, Tim Leiner, Tschoyoson CC Lim, James G Pipe, Scott Reeder, Caroline Reinhold, Marion Smits, Daniel K Sodickson, Clare Tempany, Alberto H Vargas, and Meiyun Wang. 2019. “Value of MRI in Medicine: More Than Just Another Test?” J Magn Reson Imaging, 49, 7, Pp. e14-e25.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.
Beek JMRI 2018
Alireza Mehrtash, Mohsen Ghafoorian, Guillaume Pernelle, Alireza Ziaei, Friso G Heslinga, Kemal Tuncali, Andriy Fedorov, Ron Kikinis, Clare M Tempany, William M Wells, Purang Abolmaesumi, and Tina Kapur. 2019. “Automatic Needle Segmentation and Localization in MRI with 3D Convolutional Neural Networks: Application to MRI-targeted Prostate Biopsy.” IEEE Trans Med Imaging., 38, 4, Pp. 1026-36.Abstract
Image-guidance improves tissue sampling during biopsy by allowing the physician to visualize the tip and trajectory of the biopsy needle relative to the target in MRI, CT, ultrasound, or other relevant imagery. This paper reports a system for fast automatic needle tip and trajectory localization and visualization in MRI that has been developed and tested in the context of an active clinical research program in prostate biopsy. To the best of our knowledge, this is the first reported system for this clinical application, and also the first reported system that leverages deep neural networks for segmentation and localization of needles in MRI across biomedical applications. Needle tip and trajectory were annotated on 583 T2-weighted intra-procedural MRI scans acquired after needle insertion for 71 patients who underwent transperenial MRI-targeted biopsy procedure at our institution. The images were divided into two independent training-validation and test sets at the patient level. A deep 3-dimensional fully convolutional neural network model was developed, trained and deployed on these samples. The accuracy of the proposed method, as tested on previously unseen data, was 2.80 mm average in needle tip detection, and 0.98° in needle trajectory angle. An observer study was designed in which independent annotations by a second observer, blinded to the original observer, were compared to the output of the proposed method. The resultant error was comparable to the measured inter-observer concordance, reinforcing the clinical acceptability of the proposed method. The proposed system has the potential for deployment in clinical routine.
Andriy Fedorov, Michael Schwier, David Clunie, Christian Herz, Steve Pieper, Ron Kikinis, Clare Tempany, and Fiona Fennessy. 2018. “An Annotated Test-retest Collection of Prostate Multiparametric MRI.” Sci Data, 5, Pp. 180281.Abstract
Multiparametric Magnetic Resonance Imaging (mpMRI) is widely used for characterizing prostate cancer. Standard of care use of mpMRI in clinic relies on visual interpretation of the images by an expert. mpMRI is also increasingly used as a quantitative imaging biomarker of the disease. Little is known about repeatability of such quantitative measurements, and no test-retest datasets have been available publicly to support investigation of the technical characteristics of the MRI-based quantification in the prostate. Here we present an mpMRI dataset consisting of baseline and repeat prostate MRI exams for 15 subjects, manually annotated to define regions corresponding to lesions and anatomical structures, and accompanied by region-based measurements. This dataset aims to support further investigation of the repeatability of mpMRI-derived quantitative prostate measurements, study of the robustness and reliability of the automated analysis approaches, and to support development and validation of new image analysis techniques. The manuscript can also serve as an example of the use of DICOM for standardized encoding of the image annotation and quantification results.
Elmira Hassanzadeh, Francesco Alessandrino, Olutayo I Olubiyi, Daniel I Glazer, Robert V. Mulkern, Andriy Fedorov, Clare M Tempany, and Fiona M Fennessy. 2018. “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), 43, 5, Pp. 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.