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Nai YH, Cheong DLH, Roy S, Kok T, Stephenson MC, Schaefferkoetter J, Totman JJ, Conti M, Eriksson L, Robins EG, Wang Z, Chua WY, Ang BWL, Singha AK, Thamboo TP, Chiong E, Reilhac A. Comparison of quantitative parameters and radiomic features as inputs into machine learning models to predict the Gleason score of prostate cancer lesions. Magn Reson Imaging 2023; 100:64-72. [PMID: 36933775 DOI: 10.1016/j.mri.2023.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 03/01/2023] [Accepted: 03/12/2023] [Indexed: 03/18/2023]
Abstract
INTRODUCTION The classification of prostate cancer (PCa) lesions using Prostate Imaging Reporting and Data System (PI-RADS) suffers from poor inter-reader agreement. This study compared quantitative parameters or radiomic features from multiparametric magnetic resonance imaging (mpMRI) or positron emission tomography (PET), as inputs into machine learning (ML) to predict the Gleason scores (GS) of detected lesions for improved PCa lesion classification. METHODS 20 biopsy-confirmed PCa subjects underwent imaging before radical prostatectomy. A pathologist assigned GS from tumour tissue. Two radiologists and one nuclear medicine physician delineated the lesions on the mpMR and PET images, yielding 45 lesion inputs. Seven quantitative parameters were extracted from the lesions, namely T2-weighted (T2w) image intensity, apparent diffusion coefficient (ADC), transfer constant (KTRANS), efflux rate constant (Kep), and extracellular volume ratio (Ve) from mpMR images, and SUVmean and SUVmax from PET images. Eight radiomic features were selected out of 109 radiomic features from T2w, ADC and PET images. Quantitative parameters or radiomic features, with risk factors of age, prostate-specific antigen (PSA), PSA density and volume, of 45 different lesion inputs were input in different combinations into four ML models - Decision Tree (DT), Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Ensembles model (EM). RESULTS SUVmax yielded the highest accuracy in discriminating detected lesions. Among the 4 ML models, kNN yielded the highest accuracies of 0.929 using either quantitative parameters or radiomic features with risk factors as input. CONCLUSIONS ML models' performance is dependent on the input combinations and risk factors further improve ML classification accuracy.
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Affiliation(s)
- Ying-Hwey Nai
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Dennis Lai Hong Cheong
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Sharmili Roy
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Trina Kok
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Mary C Stephenson
- Centre for Translational MR, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Josh Schaefferkoetter
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Siemens Medical Solutions USA, Inc., Molecular Imaging, Knoxville, TN, USA
| | - John J Totman
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Maurizio Conti
- Siemens Medical Solutions USA, Inc., Molecular Imaging, Knoxville, TN, USA
| | - Lars Eriksson
- Siemens Medical Solutions USA, Inc., Molecular Imaging, Knoxville, TN, USA
| | - Edward G Robins
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Singapore BioImaging Consortium, Agency for Science, Technology and Research (A*Star), Singapore
| | - Ziting Wang
- Department of Urology, National University Hospital, Singapore
| | - Wynne Yuru Chua
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | | | | | | | - Edmund Chiong
- Department of Diagnostic Imaging, National University Hospital, Singapore; Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Anthonin Reilhac
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Advanced Diffusion-Weighted Imaging Sequences for Breast MRI: Comprehensive Comparison of Improved Sequences and Ultra-High B-Values to Identify the Optimal Combination. Diagnostics (Basel) 2023; 13:diagnostics13040607. [PMID: 36832095 PMCID: PMC9955562 DOI: 10.3390/diagnostics13040607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 01/21/2023] [Accepted: 02/04/2023] [Indexed: 02/10/2023] Open
Abstract
This study investigated the image quality and choice of ultra-high b-value of two DWI breast-MRI research applications. The study cohort comprised 40 patients (20 malignant lesions). In addition to s-DWI with two m-b-values (b50 and b800) and three e-b-values (e-b1500, e-b2000, and e-b2500), z-DWI and IR m-b1500 DWI were applied. z-DWI was acquired with the same measured b-values and e-b-values as the standard sequence. For IR m-b1500 DWI, b50 and b1500 were measured, and e-b2000 and e-b2500 were mathematically extrapolated. Three readers used Likert scales to independently analyze all ultra-high b-values (b1500-b2500) for each DWI with regards to scan preference and image quality. ADC values were measured in all 20 lesions. z-DWI was the most preferred (54%), followed by IR m-b1500 DWI (46%). b1500 was significantly preferred over b2000 for z-DWI and IR m-b1500 DWI (p = 0.001 and p = 0.002, respectively). Lesion detection was not significantly different among sequences or b-values (p = 0.174). There were no significant differences in measured ADC values within lesions between s-DWI (ADC: 0.97 [±0.09] × 10-3 mm2/s) and z-DWI (ADC: 0.99 [±0.11] × 10-3 mm2/s; p = 1.000). However, there was a trend toward lower values in IR m-b1500 DWI (ADC: 0.80 [±0.06] × 10-3 mm2/s) than in s-DWI (p = 0.090) and z-DWI (p = 0.110). Overall, image quality was superior and there were fewer image artifacts when using the advanced sequences (z-DWI + IR m-b1500 DWI) compared with s-DWI. Considering scan preferences, we found that the optimal combination was z-DWI with a calculated b1500, especially regarding examination time.
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Liang L, Cheng Y, Qi F, Zhang L, Cao D, Cheng G, Hua L. A Comparative Study of Prostate Cancer Detection Rate Between Transperineal COG-TB and Transperineal FUS-TB in Patients with PSA ≤20 ng/mL. J Endourol 2020; 34:1008-1014. [PMID: 32600058 DOI: 10.1089/end.2020.0276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Background: The combination of prebiopsy MRI and transperineal targeted biopsies is being increasingly used to obtain tissues from patients with suspected prostate cancer (PCa). Objective: To investigate the difference in PCa detection rate between transperineal cognitive fusion TB (COG-TB) and transperineal software fusion TB (FUS-TB). Participants: The present study included 163 male patients with suspected PCa who had not undergone prostate biopsy, had a prostate-specific antigen (PSA) level of ≤20 ng/mL, and had been examined by bi-parameter MRI and confirmed to have prostate nodules by prostate imaging reporting and data system version 2 (PI-RADS V2) scores ≥3 (from December 3, 2018 to October 7, 2019). Intervention: Seventy-one patients underwent transperineal COG-TB, and 92 patients underwent transperineal FUS-TB. The detection rate of the first four needles was compared. Results: No significant difference was found in the overall detection rate of PCa between COG-TB and FUS-TB (60.56% vs 51.08%, p = 0.228). This result was consistent even after stratifying by PI-RADS score. There was also no significant difference between COG-TB and FUS-TB in the detection rate of clinically significant PCa (p = 0.641). Moreover, COG-TB and FUS-TB showed no difference in the detection rate of PCa with different Gleason scores. Conclusions: In patients with suspected PCa with PSA ≤20 ng/mL and PI-RADS ≥3, FUS-TB was comparable to COG-TB in the detection rate of PCa.
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Affiliation(s)
- Linghui Liang
- Department of Urology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yifei Cheng
- Department of Urology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Feng Qi
- Department of Urology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Lei Zhang
- Department of Urology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Dongliang Cao
- Department of Urology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Gong Cheng
- Department of Urology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Lixin Hua
- Department of Urology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Li Q, Lu H, Choi J, Gage K, Feuerlein S, Pow-Sang JM, Gillies R, Balagurunathan Y. Radiological semantics discriminate clinically significant grade prostate cancer. Cancer Imaging 2019; 19:81. [PMID: 31796094 PMCID: PMC6889697 DOI: 10.1186/s40644-019-0272-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 11/22/2019] [Indexed: 01/17/2023] Open
Abstract
Background Identification of imaging traits to discriminate clinically significant prostate cancer is challenging due to the multi focal nature of the disease. The difficulty in obtaining a consensus by the Prostate Imaging and Data Systems (PI-RADS) scores coupled with disagreements in interpreting multi-parametric Magnetic Resonance Imaging (mpMRI) has resulted in increased variability in reporting findings and evaluating the utility of this imaging modality in detecting clinically significant prostate cancer. This study assess the ability of radiological traits (semantics) observed on multi-parametric Magnetic Resonance images (mpMRI) to discriminate clinically significant prostate cancer. Methods We obtained multi-parametric MRI studies from 103 prostate cancer patients with 167 targeted biopsies from a single institution. The study was approved by our Institutional Review Board (IRB) for retrospective analysis. The biopsy location had been identified and marked by a clinical radiologist for targeted biopsy based on initial study interpretation. Using the target locations, two study radiologists independently re-evaluated the scans and scored 16 semantic traits on a point scale (up to 5 levels) based on mpMRI images. The semantic traits describe size, shape, and border characteristics of the prostate lesion, as well as presence of disease around lymph nodes (lymphadenopathy). We built a linear classifier model on these semantic traits and related to pathological outcome to identify clinically significant tumors (Gleason Score ≥ 7). The discriminatory ability of the predictors was tested using cross validation method randomly repeated and ensemble values were reported. We then compared the performance of semantic predictors with the PI-RADS predictors. Results We found several semantic features individually discriminated high grade Gleason score (ADC-intensity, Homogeneity, early-enhancement, T2-intensity and extraprostatic extention), these univariate predictors had an average area under the receiver operator characteristics (AUROC) ranging from 0.54 to 0.68. Multivariable semantic predictors with three features (ADC-intensity; T2-intensity, enhancement homogenicity) had an average AUROC of 0.7 [0.43, 0.94]. The PI-RADS based predictor had average AUROC of 0.6 [0.47, 0.75]. Conclusion We find semantics traits are related to pathological findings with relatively higher reproducibility between radiologists. Multivariable predictors formed on these traits shows higher discriminatory ability compared to PI-RADS scores.
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Affiliation(s)
- Qian Li
- Department of Radiology, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Department of Cancer Physiology, H.Lee.Moffitt Cancer Center, Tampa, FL, USA
| | - Hong Lu
- Department of Radiology, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Department of Cancer Physiology, H.Lee.Moffitt Cancer Center, Tampa, FL, USA
| | - Jung Choi
- Department of Radiology, H.Lee.Moffitt Cancer Center, Tampa, FL, USA
| | - Kenneth Gage
- Department of Radiology, H.Lee.Moffitt Cancer Center, Tampa, FL, USA
| | | | - Julio M Pow-Sang
- Department of GenitoUrology, H.Lee.Moffitt Cancer Center, Tampa, FL, USA
| | - Robert Gillies
- Department of Cancer Physiology, H.Lee.Moffitt Cancer Center, Tampa, FL, USA.,Department of Radiology, H.Lee.Moffitt Cancer Center, Tampa, FL, USA
| | - Yoganand Balagurunathan
- Department of Radiology, H.Lee.Moffitt Cancer Center, Tampa, FL, USA. .,Department of GenitoUrology, H.Lee.Moffitt Cancer Center, Tampa, FL, USA. .,Quantitative Sciences, Department of Biostatistics and Bioinformatics, H.Lee.Moffitt Cancer, Tampa, FL, 33612, USA.
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Diagnostic efficacy of multiple MRI parameters in differentiating benign vs. malignant thyroid nodules. BMC Med Imaging 2018; 18:50. [PMID: 30509198 PMCID: PMC6278127 DOI: 10.1186/s12880-018-0294-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Accepted: 11/21/2018] [Indexed: 12/22/2022] Open
Abstract
Background Diffusion weighted imaging (DWI) has a good diagnostic value for malignant thyroid nodules, but the published protocols suffer from flaws and focus on the apparent diffusion coefficient (ADC). This study investigated the diagnostic performance of multiple MRI parameters in differentiating malignant from benign thyroid nodules. Methods This was a retrospective study of 181 consecutive patients (148 benign and 111 malignant nodules, confirmed by pathological results). The patients underwent conventional MRI, DWI, and dynamic contrast-enhanced MRI before surgery. The chi-square test and the Student t test were used to compare the conventional features and ADC value between malignant and benign groups. Multivariate logistic regression was used to identify the independent predictors and to construct a model. Receiver operator characteristic (ROC) curve analysis was used to assess the diagnostic performance of the independent variables and model. Results Tumor diameter, ADC value, cystic degeneration, pseudocapsule sign, high signal cystic area on T1-weighted imaging, ring sign in the delayed phase, and irregular shape showed significant differences between two groups (all P < 0.05). The multivariable analysis revealed that ADC value (OR = 694.006, P < 0.001), irregular shape (OR = 32.798, P < 0.001), ring sign in the delayed phase (OR = 20.381, P = 0.004), and cystic degeneration (OR = 8.468, P = 0.016) were independent predictors. Among them, ADC performed the best in discriminating benign from malignant nodules, with an area under the curve (AUC) of 0.95, 0.90 sensitivity, and 0.91 specificity. When the independent factors were combined, the diagnostic performance was improved with an AUC of 0.99, 0.97 sensitivity, and 0.95 specificity. Conclusions ADC value could discriminate between benign and malignant thyroid nodules with a good performance. Subjective features such as the ring sign, irregular shape, and cystic degeneration associated with malignant thyroid nodules could provide complementary information for differentiation.
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Zhang T, Yu JM, Wang YQ, Yin DD, Fang LJ. WHO grade I meningioma subtypes: MRI features and pathological analysis. Life Sci 2018; 213:50-56. [DOI: 10.1016/j.lfs.2018.08.061] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 08/17/2018] [Accepted: 08/25/2018] [Indexed: 11/27/2022]
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Abstract
A successful paradigm shift toward personalized management strategies for patients with prostate cancer (PCa) is heavily dependent on the availability of noninvasive diagnostic tools capable of accurately establishing the true extent of disease at the time of diagnosis and estimating the risk of subsequent disease progression and related mortality. Although there is still considerable scope for improvement in its diagnostic, predictive, and prognostic capabilities, multiparametric prostate magnetic resonance imaging (MRI) is currently regarded as the imaging modality of choice for local staging of PCa. A negative MRI, that is, the absence of any MRI-visible intraprostatic lesion, has a high negative predictive value for the presence of clinically significant PCa and can substantiate the consideration of active surveillance as a preferred initial management approach. MRI-derived quantitative and semi-quantitative parameters can be utilized to noninvasively characterize MRI-visible prostate lesions and identify those patients who are most likely to benefit from radical treatment, and differentiate them from patients with benign or indolent prostate pathology that may also be visible on MRI. This literature review summarizes current strategies how MRI can be used to determine a tailored management strategy for an individual patient.
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Weiss J, Martirosian P, Taron J, Othman AE, Kuestner T, Erb M, Bedke J, Bamberg F, Nikolaou K, Notohamiprodjo M. Feasibility of accelerated simultaneous multislice diffusion-weighted MRI of the prostate. J Magn Reson Imaging 2017; 46:1507-1515. [DOI: 10.1002/jmri.25665] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 01/25/2017] [Indexed: 01/22/2023] Open
Affiliation(s)
- Jakob Weiss
- Department of Diagnostic and Interventional Radiology; Eberhard Karls University Tuebingen; Germany
| | - Petros Martirosian
- Section on Experimental Radiology; Eberhard Karls University Tuebingen; Germany
| | - Jana Taron
- Department of Diagnostic and Interventional Radiology; Eberhard Karls University Tuebingen; Germany
| | - Ahmed E. Othman
- Department of Diagnostic and Interventional Radiology; Eberhard Karls University Tuebingen; Germany
| | - Thomas Kuestner
- Institute of Signal Processing and System Theory; University of Stuttgart; Germany
| | - Michael Erb
- Department of Biomedical Magnetic Resonance; Eberhard Karls University Tuebingen; Germany
| | - Jens Bedke
- Department of Urology; Eberhard Karls University Tuebingen; Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology; Eberhard Karls University Tuebingen; Germany
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology; Eberhard Karls University Tuebingen; Germany
| | - Mike Notohamiprodjo
- Department of Diagnostic and Interventional Radiology; Eberhard Karls University Tuebingen; Germany
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Trigui R, Mitéran J, Walker P, Sellami L, Ben Hamida A. Automatic classification and localization of prostate cancer using multi-parametric MRI/MRS. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.07.015] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Gilani N, Malcolm P, Johnson G. A model describing diffusion in prostate cancer. Magn Reson Med 2016; 78:316-326. [PMID: 27439379 DOI: 10.1002/mrm.26340] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2016] [Revised: 06/08/2016] [Accepted: 06/20/2016] [Indexed: 12/15/2022]
Abstract
PURPOSE Quantitative diffusion MRI has frequently been studied as a means of grading prostate cancer. Interpretation of results is complicated by the nature of prostate tissue, which consists of four distinct compartments: vascular, ductal lumen, epithelium, and stroma. Current diffusion measurements are an ill-defined weighted average of these compartments. In this study, prostate diffusion is analyzed in terms of a model that takes explicit account of tissue compartmentalization, exchange effects, and the non-Gaussian behavior of tissue diffusion. METHOD The model assumes that exchange between the cellular (ie, stromal plus epithelial) and the vascular and ductal compartments is slow. Ductal and cellular diffusion characteristics are estimated by Monte Carlo simulation and a two-compartment exchange model, respectively. Vascular pseudodiffusion is represented by an additional signal at b = 0. Most model parameters are obtained either from published data or by comparing model predictions with the published results from 41 studies. Model prediction error is estimated using 10-fold cross-validation. RESULTS Agreement between model predictions and published results is good. The model satisfactorily explains the variability of ADC estimates found in the literature. CONCLUSION A reliable model that predicts the diffusion behavior of benign and cancerous prostate tissue of different Gleason scores has been developed. Magn Reson Med 78:316-326, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Nima Gilani
- Norwich Medical School, University of East Anglia, Norwich, United Kingdom
| | - Paul Malcolm
- Norfolk and Norwich University Hospital, Norwich, United Kingdom
| | - Glyn Johnson
- Norwich Medical School, University of East Anglia, Norwich, United Kingdom
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Salami SS, Ben-Levi E, Yaskiv O, Turkbey B, Villani R, Rastinehad AR. Risk stratification of prostate cancer utilizing apparent diffusion coefficient value and lesion volume on multiparametric MRI. J Magn Reson Imaging 2016; 45:610-616. [PMID: 27405584 DOI: 10.1002/jmri.25363] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2016] [Accepted: 06/15/2016] [Indexed: 11/08/2022] Open
Abstract
PURPOSE To evaluate the performance of apparent diffusion coefficient (ADC) and lesion volume in potentially risk-stratifying patients with prostate cancer (PCa). MATERIALS AND METHODS Men with elevated prostate-specific antigen or abnormal digital rectal exam underwent a 3T multiparametric magnetic resonance imaging (mpMRI) with endorectal coil. ADC maps were calculated using b values of 0, 500, 1000, and 1500; additional images were obtained with b value of 2000. We prospectively enrolled 312 men with lesions suspicious for cancer (suspicion score 2-5) on mpMRI. MRI/ultrasound fusion-guided prostate biopsies were performed. Mean ADC of suspicious lesions were correlated against lesion volume, Gleason and D'Amico risk. RESULTS The cancer detection rate of fusion biopsy per lesion was 45.6% (206/452). Cancerous lesions were larger (median volume: 0.40 vs. 0.30 cm3 ; P = 0.016). The median ADC (×10-6 mm2 /sec) for lesions negative and positive for PCa were 984.5 and 666.5, respectively (P < 0.0001). The AUC of ADC in predicting PCa was 0.79. Larger lesions were associated with higher risk PCa (Gleason and D'Amico) and lower ADC (all P < 0.0001). CONCLUSION The mean ADC of suspicious lesions on mpMRI was inversely correlated, while lesion volume had a direct correlation with PCa detection. Future follow-up studies are needed to assess longitudinal cancer risks of suspicious mpMRI lesions. LEVEL OF EVIDENCE 2 J. Magn. Reson. Imaging 2017;45:610-616.
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Affiliation(s)
- Simpa S Salami
- Department of Urology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Eran Ben-Levi
- Department of Radiology, Hofstra North Shore-LIJ School of Medicine, New Hyde Park, New York, USA
| | - Oksana Yaskiv
- Department of Pathology, Hofstra North Shore-LIJ School of Medicine, New Hyde Park, New York, USA
| | - Baris Turkbey
- Molecular Imaging Program, National Institutes of Health, Bethesda, Maryland, USA
| | - Robert Villani
- Department of Radiology, Hofstra North Shore-LIJ School of Medicine, New Hyde Park, New York, USA
| | - Ardeshir R Rastinehad
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Katelaris NC, Bolton DM, Weerakoon M, Toner L, Katelaris PM, Lawrentschuk N. Current role of multiparametric magnetic resonance imaging in the management of prostate cancer. Korean J Urol 2015; 56:337-45. [PMID: 25964833 PMCID: PMC4426504 DOI: 10.4111/kju.2015.56.5.337] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2015] [Accepted: 04/06/2015] [Indexed: 01/04/2023] Open
Abstract
The purpose of this review was to evaluate the current role of multiparametric magnetic resonance imaging (mp-MRI) in the management of prostate cancer (PC). The diagnosis of PC remains controversial owing to overdetection of indolent disease, which leads to overtreatment and subsequent patient harm. mp-MRI has the potential to equilibrate the imbalance between detection and treatment. The limitation of the data for analysis with this new technology is problematic, however. This issue has been compounded by a paradigm shift in clinical practice aimed at utilizing this modality, which has been rolled out in an ad hoc fashion often with commercial motivation. Despite a growing body of literature, pertinent clinical questions remain. For example, can mp-MRI be calibrated to reliably detect biologically significant disease? As with any new technology, objective evaluation of the clinical applications of mp-MRI is essential. The focus of this review was on the evaluation of mp-MRI of the prostate with respect to clinical utility.
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Affiliation(s)
| | - Damien Michael Bolton
- Department of Surgery, Austin Hospital, University of Melbourne, Melbourne, VIC, Australia
| | - Mahesha Weerakoon
- Department of Surgery, Austin Hospital, University of Melbourne, Melbourne, VIC, Australia
| | - Liam Toner
- Department of Surgery, Austin Hospital, University of Melbourne, Melbourne, VIC, Australia
| | | | - Nathan Lawrentschuk
- Department of Surgery, Austin Hospital, University of Melbourne, Melbourne, VIC, Australia. ; Olivia Newton-John Cancer Research Institute, Austin Hospital, Heidelberg, VIC, Australia. ; Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
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