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Huang H, Liu Z, Ma Y, Shao Y, Yang Z, Duan D, Zhao Y, Wen S, Tian J, Liu Y, Wang Z, Yue D, Wang Y. Based on PI-RADS v2.1 combining PHI and ADC values to guide prostate biopsy in patients with PSA 4-20 ng/mL. Prostate 2024; 84:376-388. [PMID: 38116741 DOI: 10.1002/pros.24658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 11/05/2023] [Accepted: 12/05/2023] [Indexed: 12/21/2023]
Abstract
PURPOSE The study aimed to investigate the diagnostic accuracy of prostate health index (PHI) and apparent diffusion coefficient (ADC) values in predicting prostate cancer (PCa) and construct a nomogram for the prediction of PCa and clinically significant PCa (CSPCa) in Prostate Imaging-Reporting and Data System (PI-RADS) three lesions cohort. METHODS This study prospectively enrolled 301 patients who underwent multiparametric magnetic resonance (mpMRI) and were scheduled for prostate biopsy. The receiver operating characteristic curve (ROC) was performed to estimate the diagnostic accuracy of each predictor. Univariable and multivariable logistic regression analysis was conducted to ascertain hidden risk factors and constructed nomograms in PI-RADS three lesions cohort. RESULTS In the whole cohort, the area under the ROC curve (AUC) of PHI is relatively high, which is 0.779. As radiographic parameters, the AUC of PI-RADS and ADC values was 0.702 and 0.756, respectively. The utilization of PHI and ADC values either individually or in combination significantly improved the diagnostic accuracy of the basic model. In PI-RADS three lesions cohort, the AUC for PCa was 0.817 in the training cohort and 0.904 in the validation cohort. The AUC for CSPCa was 0.856 in the training cohort and 0.871 in the validation cohort. When applying the nomogram for predicting PCa, 50.0% of biopsies could be saved, supplemented by 6.9% of CSPCa being missed. CONCLUSION PHI and ADC values can be used as predictors of CSPCa. The nomogram included PHI, ADC values and other clinical predictors demonstrated an enhanced capability in detecting PCa and CSPCa within PI-RADS three lesions cohort.
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Affiliation(s)
- Hua Huang
- Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Zihao Liu
- Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Yuan Ma
- Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Yuan Shao
- Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Zhen Yang
- Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Dengyi Duan
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
- Department of Radiology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Yang Zhao
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
- Department of Radiology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Simeng Wen
- Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Jing Tian
- Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Yang Liu
- Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Zeyuan Wang
- Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Dan Yue
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
- School of Medical Laboratory, Tianjin Medical University, Tianjin, China
| | - Yong Wang
- Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
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Santos FDS, Verma N, Watte G, Marchiori E, Mohammed TLH, Medeiros TM, Hochhegger B. Diffusion-weighted magnetic resonance imaging for differentiating between benign and malignant thoracic lymph nodes: a meta-analysis. Radiol Bras 2021; 54:225-231. [PMID: 34393288 PMCID: PMC8354191 DOI: 10.1590/0100-3984.2020.0084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 07/29/2020] [Indexed: 11/21/2022] Open
Abstract
Objective To establish the diagnostic performance of diffusion-weighted magnetic resonance imaging (DWI) in discriminating malignant from non-malignant thoracic lymph nodes. Materials and Methods This was a meta-analysis involving systematic searches of the MEDLINE, EMBASE, and Web of Science databases up through April 2020. Studies reporting thoracic DWI and lymph node evaluation were included. The pooled sensitivity, specificity, diagnostic odds ratio, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) were calculated. Results We evaluated six studies, involving a collective total of 356 mediastinal lymph nodes in 214 patients. Thoracic DWI had a pooled sensitivity and specificity of 92% (95% confidence interval [95% CI]: 71-98%) and 93% (95% CI: 79-98%), respectively. The positive and negative likelihood ratios were 13.2 (95% CI: 4.0-43.8) and 0.09 (95% CI: 0.02-0.36), respectively. The diagnostic odds ratio was 149 (95% CI: 18-1,243), and the AUC was 0.97 (95% CI: 0.95-0.98). Conclusion DWI is a reproducible technique and has demonstrated high accuracy for differentiating between malignant and benign states in thoracic lymph nodes.
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Affiliation(s)
- Francisco de Souza Santos
- Graduate Program in Internal Medicine and Health Sciences, Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
| | - Nupur Verma
- Department of Radiology, University of Florida (UF), Gainesville, FL, USA
| | - Guilherme Watte
- Graduate Program in Internal Medicine and Health Sciences, Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
| | - Edson Marchiori
- Department of Radiology, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil
| | | | - Tássia Machado Medeiros
- Graduate Program in Internal Medicine and Health Sciences, Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
| | - Bruno Hochhegger
- Graduate Program in Internal Medicine and Health Sciences, Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
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Singh D, Kumar V, Das CJ, Singh A, Mehndiratta A. Segmentation of prostate zones using probabilistic atlas-based method with diffusion-weighted MR images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 196:105572. [PMID: 32544780 DOI: 10.1016/j.cmpb.2020.105572] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 05/10/2020] [Accepted: 05/24/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurate segmentation of prostate and its zones constitute an essential preprocessing step for computer-aided diagnosis and detection system for prostate cancer (PCa) using diffusion-weighted imaging (DWI). However, low signal-to-noise ratio and high variability of prostate anatomic structures are challenging for its segmentation using DWI. We propose a semi-automated framework that segments the prostate gland and its zones simultaneously using DWI. METHODS In this paper, the Chan-Vese active contour model along with morphological opening operation was used for segmentation of prostate gland. Then segmentation of prostate zones into peripheral zone (PZ) and transition zone (TZ) was carried out using in-house developed probabilistic atlas with partial volume (PV) correction algorithm. The study cohort included MRI dataset of 18 patients (n = 18) as our dataset and methodology were also independently evaluated using 15 MRI scans (n = 15) of QIN-PROSTATE-Repeatability dataset. The atlas for zones of prostate gland was constructed using dataset of twelve patients of our patient cohort. Three-fold cross-validation was performed with 10 repetitions, thus total 30 instances of training and testing were performed on our dataset followed by independent testing on the QIN-PROSTATE-Repeatability dataset. Dice similarity coefficient (DSC), Jaccard coefficient (JC), and accuracy were used for quantitative assessment of the segmentation results with respect to boundaries delineated manually by an expert radiologist. A paired t-test was performed to evaluate the improvement in zonal segmentation performance with the proposed PV correction algorithm. RESULTS For our dataset, the proposed segmentation methodology produced improved segmentation with DSC of 90.76 ± 3.68%, JC of 83.00 ± 5.78%, and accuracy of 99.42 ± 0.36% for the prostate gland, DSC of 77.73 ± 2.76%, JC of 64.46 ± 3.43%, and accuracy of 82.47 ± 2.22% for the PZ, and DSC of 86.05 ± 1.50%, JC of 75.80 ± 2.10%, and accuracy of 91.67 ± 1.56% for the TZ. The segmentation performance for QIN-PROSTATE-Repeatability dataset was, DSC of 85.50 ± 4.43%, JC of 75.00 ± 6.34%, and accuracy of 81.52 ± 5.55% for prostate gland, DSC of 74.40 ± 1.79%, JC of 59.53 ± 8.70%, and accuracy of 80.91 ± 5.16% for PZ, and DSC of 85.80 ± 5.55%, JC of 74.87 ± 7.90%, and accuracy of 90.59 ± 3.74% for TZ. With the implementation of the PV correction algorithm, statistically significant (p<0.05) improvements were observed in all the metrics (DSC, JC, and accuracy) for both prostate zones, PZ and TZ segmentation. CONCLUSIONS The proposed segmentation methodology is stable, accurate, and easy to implement for segmentation of prostate gland and its zones (PZ and TZ). The atlas-based segmentation framework with PV correction algorithm can be incorporated into a computer-aided diagnostic system for PCa localization and treatment planning.
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Affiliation(s)
- Dharmesh Singh
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Virendra Kumar
- Department of NMR, All India Institute of Medical Sciences, New Delhi, India
| | - Chandan J Das
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India
| | - Anup Singh
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India; Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India
| | - Amit Mehndiratta
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India; Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India.
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Gündoğdu E, Emekli E, Kebapçı M. Evaluation of relationships between the final Gleason score, PI-RADS v2 score, ADC value, PSA level, and tumor diameter in patients that underwent radical prostatectomy due to prostate cancer. Radiol Med 2020; 125:827-837. [PMID: 32266690 DOI: 10.1007/s11547-020-01183-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2019] [Accepted: 03/23/2020] [Indexed: 12/13/2022]
Abstract
INTRODUCTION This study aimed to investigate the relationship between the serum PSA level, Gleason score (GS), PI-RADS v2 score, tumor ADCmin value, and the largest tumor diameter in patients that underwent radical prostatectomy (RP) due to prostate cancer (PCa) and to comparatively evaluate the variables of these parameters in clinically significant and insignificant PCa groups. MATERIALS AND METHODS The mpMRI examinations of the patients who underwent RP due to PCa were retrospectively evaluated. According to the final GS, the lesions were divided into two groups as clinically significant (GS ≥ 7) and insignificant (GS ≤ 6). The PSA value, tumor ADCmin value, tumor diameter, and PI-RADS score were compared between the clinically significant and nonsignificant PCa groups using Student's t-test. The correlations between the serum PSA level, GS, PI-RADS v2 score, tumor ADCmin value, and tumor diameter were evaluated separately (Pearson's correlation analysis was used for peripheral gland tumors, and Spearman's correlation analysis for central gland tumors). A ROC analysis was undertaken to evaluate the efficacy of the tumor ADCmin, diameter and PSA values in differentiating clinically significant and nonsignificant tumors. RESULTS In both central and peripheral gland tumors, there was a correlation between the PSA level, tumor diameter, PI-RADS score, ADCmin value, and GS at various levels (poor, moderate, and high). In central gland tumors, there was no significant difference between the two groups in terms of the PSA value and PI-RADS scores (p > 0.05), but the ADCmin value and diameter of the tumor significantly differed (p < 0.05). For peripheral gland tumors, significant differences were observed in all parameters (p < 0.05). The cut-off values for the peripheral and central gland tumors are as follows: lesion diameter, 13.5 mm and 19 mm; tumor ADCmin, 0.709 × 10-3 mm2/s and 0.874 × 10-3 mm2/s; and PSA level, 8.47 ng/ml and 11.10 ng/ml, respectively. CONCLUSION The current PI-RADS v2 scoring system can be inadequate in distinguishing clinically significant and insignificant groups in central gland tumors. A separate cut-off value of the tumor diameter should be determined for central and peripheral gland tumors. Tumor ADCmin values can be used as a predictive parameter. The PSA cut-off value should be kept lower in peripheral gland tumors.
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Affiliation(s)
- Elif Gündoğdu
- Department of Radiology, Faculty of Medicine, Eskişehir Osmangazi University, Meşelik Yerleşkesi, 26480, Eskişehir, Turkey.
| | - Emre Emekli
- Department of Radiology, Faculty of Medicine, Eskişehir Osmangazi University, Meşelik Yerleşkesi, 26480, Eskişehir, Turkey
| | - Mahmut Kebapçı
- Department of Radiology, Faculty of Medicine, Eskişehir Osmangazi University, Meşelik Yerleşkesi, 26480, Eskişehir, Turkey
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Effects of the addition of quantitative apparent diffusion coefficient data on the diagnostic performance of the PI-RADS v2 scoring system to detect clinically significant prostate cancer. World J Urol 2019; 38:981-991. [PMID: 31175458 DOI: 10.1007/s00345-019-02827-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 05/27/2019] [Indexed: 12/24/2022] Open
Abstract
PURPOSE To evaluate the impact of the addition of quantitative apparent diffusion coefficient (ADC) data into the diagnostic performance of the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) scoring system to predict clinically significant prostate cancer (CSPCa). METHODS We retrospectively included 91 consecutive patients who underwent prostate multiparametric magnetic resonance imaging (mp-MRI) and histopathological evaluation. Mp-MRI images were reported by the PI-RADSv2 scoring system and patients were divided into groups considering the likelihood of CSPCa. ADC value and ratio were obtained. Findings were correlated with histopathological data. RESULTS CSPCa was found in 41.8% of cases (n = 38). PI-RADSv2 score 3-5 yielded a sensitivity of 97.4% (95% confidence intervals 86.5-99.5), a specificity of 50.9% (37.9-63.9), and AUC of 0.74 (0.67-0.81) to predict CSPCa. ADC value < 750 µm2/s and an ADC ratio < 0.62 were the most accurate thresholds for differentiation of CSPCa, with AUC of 0.81 and 0.76, respectively. Combined PI-RADSv2 score 3-5 and ADC value < 750 µm2/s yielded a specificity of 84.9 (72.9-92.2), sensitivity of 70.3 (54.2-82.5), and AUC of 0.77 (0.68-0.86). Combined PI-RADSv2 score 3-5 and ADC ratio < 0.62 yielded a specificity of 86.5 (74.7-93.3), sensitivity of was 64.9 (48.8-78.2), and AUC of 0.75 (0.66-0.84). CONCLUSION Quantitative ADC data might not be beneficial to be used routinely in mp-MR imaging as criteria to detect clinically significant lesions due to the reduced sensitivity. Instead, when prostate lesions present a PI-RADSv2 score ≥ 3, additional quantitative ADC criteria can be helpful to increase the PI-RADS score specificity.
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Pagoto A, Tripepi M, Stefania R, Lanzardo S, Livio Longo D, Garello F, Porpiglia F, Manfredi M, Aime S, Terreno E. An efficient MRI agent targeting extracellular markers in prostate adenocarcinoma. Magn Reson Med 2018; 81:1935-1946. [DOI: 10.1002/mrm.27494] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 07/23/2018] [Accepted: 07/23/2018] [Indexed: 01/10/2023]
Affiliation(s)
- Amerigo Pagoto
- Molecular and Preclinical Imaging Centers, Department of Molecular Biotechnology and Health Sciences University of Torino Torino Italy
| | - Martina Tripepi
- Molecular and Preclinical Imaging Centers, Department of Molecular Biotechnology and Health Sciences University of Torino Torino Italy
| | - Rachele Stefania
- Molecular and Preclinical Imaging Centers, Department of Molecular Biotechnology and Health Sciences University of Torino Torino Italy
| | - Stefania Lanzardo
- Molecular and Preclinical Imaging Centers, Department of Molecular Biotechnology and Health Sciences University of Torino Torino Italy
| | - Dario Livio Longo
- Molecular and Preclinical Imaging Centers, Department of Molecular Biotechnology and Health Sciences University of Torino Torino Italy
| | - Francesca Garello
- Molecular and Preclinical Imaging Centers, Department of Molecular Biotechnology and Health Sciences University of Torino Torino Italy
| | - Francesco Porpiglia
- Division of Urology University of Torino, San Luigi Gonzaga Hospital Orbassano, Torino Italy
| | - Matteo Manfredi
- Division of Urology University of Torino, San Luigi Gonzaga Hospital Orbassano, Torino Italy
| | - Silvio Aime
- Molecular and Preclinical Imaging Centers, Department of Molecular Biotechnology and Health Sciences University of Torino Torino Italy
- IBB‐CNR Sede Secondaria c/o MBC Torino Italy
| | - Enzo Terreno
- Molecular and Preclinical Imaging Centers, Department of Molecular Biotechnology and Health Sciences University of Torino Torino Italy
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Gaur S, Harmon S, Rosenblum L, Greer MD, Mehralivand S, Coskun M, Merino MJ, Wood BJ, Shih JH, Pinto PA, Choyke PL, Turkbey B. Can Apparent Diffusion Coefficient Values Assist PI-RADS Version 2 DWI Scoring? A Correlation Study Using the PI-RADSv2 and International Society of Urological Pathology Systems. AJR Am J Roentgenol 2018; 211:W33-W41. [PMID: 29733695 PMCID: PMC7984719 DOI: 10.2214/ajr.17.18702] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
OBJECTIVE The purposes of this study were to assess correlation of apparent diffusion coefficient (ADC) and normalized ADC (ratio of tumor to nontumor tissue) with the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) and updated International Society of Urological Pathology (ISUP) categories and to determine how to optimally use ADC metrics for objective assistance in categorizing lesions within PI-RADSv2 guidelines. MATERIALS AND METHODS In this retrospective study, 100 patients (median age, 62 years; range, 44-75 years; prostate-specific antigen level, 7.18 ng/mL; range, 1.70-84.56 ng/mL) underwent 3-T multiparametric MRI of the prostate with an endorectal coil. Mean ADC was extracted from ROIs based on subsequent prostatectomy specimens. Histopathologic analysis revealed 172 lesions (113 peripheral, 59 transition zone). Two radiologists blinded to histopathologic outcome assigned PI-RADSv2 categories. Kendall tau was used to correlate ADC metrics with PI-RADSv2 and ISUP categories. ROC curves were used to assess the utility of ADC metrics in differentiating each reader's PI-RADSv2 DWI category 4 or 5 assessment in the whole prostate and by zone. RESULTS ADC metrics negatively correlated with ISUP category in the whole prostate (ADC, τ = -0.21, p = 0.0002; normalized ADC, τ = -0.21, p = 0.0001). Moderate negative correlation was found in expert PI-RADSv2 DWI categories (ADC, τ = -0.34; normalized ADC, τ = -0.31; each p < 0.0001) maintained across zones. In the whole prostate, AUCs of ADC and normalized ADC were 87% and 82% for predicting expert PI-RADSv2 DWI category 4 or 5. A derived optimal cutoff ADC less than 1061 and normalized ADC less than 0.65 achieved positive predictive values of 83% and 84% for correct classification of PI-RADSv2 DWI category 4 or 5 by an expert reader. Consistent relations and predictive values were found by an independent novice reader. CONCLUSION ADC and normalized ADC inversely correlate with PI-RADSv2 and ISUP categories and can serve as quantitative metrics to assist with assigning PI-RADSv2 DWI category 4 or 5.
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Affiliation(s)
- Sonia Gaur
- 1 Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, Rm B3B85, Bethesda, MD 20814
| | - Stephanie Harmon
- 2 Clinical Research Directorate, Clinical Monitoring Research Program, Leidos Biomedical Research, National Cancer Institute, Frederick, MD
| | - Lauren Rosenblum
- 1 Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, Rm B3B85, Bethesda, MD 20814
| | - Matthew D Greer
- 1 Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, Rm B3B85, Bethesda, MD 20814
| | - Sherif Mehralivand
- 3 Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Mehmet Coskun
- 4 İzmir Katip Çelebi University, Atatürk Training and Research Hospital, Izmir, Turkey
| | - Maria J Merino
- 1 Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, Rm B3B85, Bethesda, MD 20814
| | - Bradford J Wood
- 5 Center for Interventional Oncology, Clinical Center, National Institutes of Health, Bethesda, MD
| | - Joanna H Shih
- 6 Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, MD
| | - Peter A Pinto
- 3 Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Peter L Choyke
- 1 Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, Rm B3B85, Bethesda, MD 20814
| | - Baris Turkbey
- 1 Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, Rm B3B85, Bethesda, MD 20814
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Incorporating imaging into personalized medicine for the detection of prostate cancer. Pharmacol Res 2016; 114:163-165. [DOI: 10.1016/j.phrs.2016.10.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Revised: 09/26/2016] [Accepted: 10/20/2016] [Indexed: 11/21/2022]
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