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Jin L, Yu Z, Gao F, Li M. T2-weighted imaging-based deep-learning method for noninvasive prostate cancer detection and Gleason grade prediction: a multicenter study. Insights Imaging 2024; 15:111. [PMID: 38713377 PMCID: PMC11076444 DOI: 10.1186/s13244-024-01682-z] [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: 03/13/2024] [Accepted: 03/23/2024] [Indexed: 05/08/2024] Open
Abstract
OBJECTIVES To noninvasively detect prostate cancer and predict the Gleason grade using single-modality T2-weighted imaging with a deep-learning approach. METHODS Patients with prostate cancer, confirmed by histopathology, who underwent magnetic resonance imaging examinations at our hospital during September 2015-June 2022 were retrospectively included in an internal dataset. An external dataset from another medical center and a public challenge dataset were used for external validation. A deep-learning approach was designed for prostate cancer detection and Gleason grade prediction. The area under the curve (AUC) was calculated to compare the model performance. RESULTS For prostate cancer detection, the internal datasets comprised data from 195 healthy individuals (age: 57.27 ± 14.45 years) and 302 patients (age: 72.20 ± 8.34 years) diagnosed with prostate cancer. The AUC of our model for prostate cancer detection in the validation set (n = 96, 19.7%) was 0.918. For Gleason grade prediction, datasets comprising data from 283 of 302 patients with prostate cancer were used, with 227 (age: 72.06 ± 7.98 years) and 56 (age: 72.78 ± 9.49 years) patients being used for training and testing, respectively. The external and public challenge datasets comprised data from 48 (age: 72.19 ± 7.81 years) and 91 patients (unavailable information on age), respectively. The AUC of our model for Gleason grade prediction in the training set (n = 227) was 0.902, whereas those of the validation (n = 56), external validation (n = 48), and public challenge validation sets (n = 91) were 0.854, 0.776, and 0.838, respectively. CONCLUSION Through multicenter dataset validation, our proposed deep-learning method could detect prostate cancer and predict the Gleason grade better than human experts. CRITICAL RELEVANCE STATEMENT Precise prostate cancer detection and Gleason grade prediction have great significance for clinical treatment and decision making. KEY POINTS Prostate segmentation is easier to annotate than prostate cancer lesions for radiologists. Our deep-learning method detected prostate cancer and predicted the Gleason grade, outperforming human experts. Non-invasive Gleason grade prediction can reduce the number of unnecessary biopsies.
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Affiliation(s)
- Liang Jin
- Radiology Department, Huashan Hospital, Affiliated with Fudan University, Shanghai, 200040, China
- Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, 200040, China
| | - Zhuo Yu
- School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China
| | - Feng Gao
- Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, 200040, China
| | - Ming Li
- Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, 200040, China.
- Institute of Functional and Molecular Medical Imaging, Shanghai, 200040, China.
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Jansen JFA, Paudyal R, Mazaheri Y, Shukla-Dave A. Editorial: Bridging quantitative imaging and artificial intelligence methods in preclinical and clinical oncology. Front Oncol 2023; 13:1272030. [PMID: 37727204 PMCID: PMC10505749 DOI: 10.3389/fonc.2023.1272030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 08/23/2023] [Indexed: 09/21/2023] Open
Affiliation(s)
- Jacobus FA. Jansen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, Netherlands
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Yousef Mazaheri
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
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Jin J, Zhang L, Leng E, Metzger GJ, Koopmeiners JS. Multi-resolution super learner for voxel-wise classification of prostate cancer using multi-parametric MRI. J Appl Stat 2021; 50:805-826. [PMID: 36819087 PMCID: PMC9930806 DOI: 10.1080/02664763.2021.2017411] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 12/05/2021] [Indexed: 10/19/2022]
Abstract
Multi-parametric MRI (mpMRI) is a critical tool in prostate cancer (PCa) diagnosis and management. To further advance the use of mpMRI in patient care, computer aided diagnostic methods are under continuous development for supporting/supplanting standard radiological interpretation. While voxel-wise PCa classification models are the gold standard, few if any approaches have incorporated the inherent structure of the mpMRI data, such as spatial heterogeneity and between-voxel correlation, into PCa classification. We propose a machine learning-based method to fill in this gap. Our method uses an ensemble learning approach to capture regional heterogeneity in the data, where classifiers are developed at multiple resolutions and combined using the super learner algorithm, and further account for between-voxel correlation through a Gaussian kernel smoother. It allows any type of classifier to be the base learner and can be extended to further classify PCa sub-categories. We introduce the algorithms for binary PCa classification, as well as for classifying the ordinal clinical significance of PCa for which a weighted likelihood approach is implemented to improve the detection of less prevalent cancer categories. The proposed method has shown important advantages over conventional modeling and machine learning approaches in simulations and application to our motivating patient data.
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Affiliation(s)
- Jin Jin
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Lin Zhang
- Devision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Ethan Leng
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA
| | | | - Joseph S. Koopmeiners
- Devision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
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Salem H, Soria D, Lund JN, Awwad A. A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology. BMC Med Inform Decis Mak 2021; 21:223. [PMID: 34294092 PMCID: PMC8299670 DOI: 10.1186/s12911-021-01585-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 07/08/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Testing a hypothesis for 'factors-outcome effect' is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified. METHODS The PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system. RESULTS The search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible. CONCLUSION ES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research.
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Affiliation(s)
- Hesham Salem
- Urological Department, NIHR Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, Nottingham, NG72UH, UK
- University Hospitals of Derby and Burton NHS Foundation Trust, Royal Derby Hospital, University of Nottingham, Derby, DE22 3DT, UK
| | - Daniele Soria
- School of Computer Science and Engineering, University of Westminster, London, W1W 6UW, UK
| | - Jonathan N Lund
- University Hospitals of Derby and Burton NHS Foundation Trust, Royal Derby Hospital, University of Nottingham, Derby, DE22 3DT, UK
| | - Amir Awwad
- NIHR Nottingham Biomedical Research Centre, Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, NG72UH, UK.
- Department of Medical Imaging, London Health Sciences Centre, University of Hospital, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
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5
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The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10186428] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Artificial Intelligence (AI) is progressively remodeling our daily life. A large amount of information from “big data” now enables machines to perform predictions and improve our healthcare system. AI has the potential to reshape prostate cancer (PCa) management thanks to growing applications in the field. The purpose of this review is to provide a global overview of AI in PCa for urologists, pathologists, radiotherapists, and oncologists to consider future changes in their daily practice. A systematic review was performed, based on PubMed MEDLINE, Google Scholar, and DBLP databases for original studies published in English from January 2009 to January 2019 relevant to PCa, AI, Machine Learning, Artificial Neural Networks, Convolutional Neural Networks, and Natural-Language Processing. Only articles with full text accessible were considered. A total of 1008 articles were reviewed, and 48 articles were included. AI has potential applications in all fields of PCa management: analysis of genetic predispositions, diagnosis in imaging, and pathology to detect PCa or to differentiate between significant and non-significant PCa. AI also applies to PCa treatment, whether surgical intervention or radiotherapy, skills training, or assessment, to improve treatment modalities and outcome prediction. AI in PCa management has the potential to provide a useful role by predicting PCa more accurately, using a multiomic approach and risk-stratifying patients to provide personalized medicine.
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Wildeboer RR, van Sloun RJG, Wijkstra H, Mischi M. Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 189:105316. [PMID: 31951873 DOI: 10.1016/j.cmpb.2020.105316] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 12/09/2019] [Accepted: 01/04/2020] [Indexed: 05/16/2023]
Abstract
Prostate cancer represents today the most typical example of a pathology whose diagnosis requires multiparametric imaging, a strategy where multiple imaging techniques are combined to reach an acceptable diagnostic performance. However, the reviewing, weighing and coupling of multiple images not only places additional burden on the radiologist, it also complicates the reviewing process. Prostate cancer imaging has therefore been an important target for the development of computer-aided diagnostic (CAD) tools. In this survey, we discuss the advances in CAD for prostate cancer over the last decades with special attention to the deep-learning techniques that have been designed in the last few years. Moreover, we elaborate and compare the methods employed to deliver the CAD output to the operator for further medical decision making.
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Affiliation(s)
- Rogier R Wildeboer
- Lab of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, De Zaale, 5600 MB, Eindhoven, the Netherlands.
| | - Ruud J G van Sloun
- Lab of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, De Zaale, 5600 MB, Eindhoven, the Netherlands.
| | - Hessel Wijkstra
- Lab of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, De Zaale, 5600 MB, Eindhoven, the Netherlands; Department of Urology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, the Netherlands
| | - Massimo Mischi
- Lab of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, De Zaale, 5600 MB, Eindhoven, the Netherlands
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Su R, Xu G, Xiang L, Ding S, Wu R. A Novel Scoring System for Prediction of Prostate Cancer Based on Shear Wave Elastography and Clinical Parameters. Urology 2018; 121:112-117. [PMID: 30171925 DOI: 10.1016/j.urology.2018.08.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2018] [Revised: 07/31/2018] [Accepted: 08/21/2018] [Indexed: 11/30/2022]
Abstract
OBJECTIVE To develop a novel scoring system for the prediction of prostate cancer (PCa). METHODS We assessed 127 patients who underwent a prostate biopsy. Prior to biopsy, we performed shear wave elastography (SWE), transrectal ultrasound, digital rectal exam, total prostatic specific antigen, PSA density (PSAD), and free PSA/total PSA ratio (F/T). We developed an 11-point scoring system based on SWE and these clinical parameters. RESULTS PCa was diagnosed in 51 (40.2%) of 127 patients and 192 (25.2%) of 762 sextants on initial biopsy. ROC curve analyses showed that the cutoff value (COV) for SWE was 40.8 kpa at the sextant level. The AUC of score system based on the SWE and clinical parameters (0.911) was significantly different from scoring systems based on SWE alone (0.842) or clinical parameters alone (0.868). For this 11-point scoring system, the optimal COV, Youden index, sensitivity, specificity, PPV, NPV, and AUC were 3 points, 0.66, 76.5% 89.5%, 82.98%, 85.00%, and 0.911, respectively. There were 68 negative biopsy results in patients with 0-3 points, and the detection rate of PCa was 100% in patients with scores exceeding 6 points. CONCLUSION This 11-point scoring system based on SWE and clinical parameters has the good diagnostic performance for predicting PCa. It may be useful in selecting patients for biopsy, substantially reducing the number of unnecessary biopsies while ensuring that few cancers are missed.
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Affiliation(s)
- Rui Su
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China; Department of Urology, Ningbo First Hospital, the Affiliated Hospital of Ningbo University, Ningbo, China
| | - Guang Xu
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China; Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China
| | - Lihua Xiang
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China; Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China
| | - Shisi Ding
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China; Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China
| | - Rong Wu
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China; Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China.
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8
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Jin J, Zhang L, Leng E, Metzger GJ, Koopmeiners JS. Detection of prostate cancer with multiparametric MRI utilizing the anatomic structure of the prostate. Stat Med 2018; 37:3214-3229. [PMID: 29923345 DOI: 10.1002/sim.7810] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Revised: 03/05/2018] [Accepted: 04/05/2018] [Indexed: 01/02/2023]
Abstract
Multiparametric magnetic resonance imaging (mpMRI), which combines traditional anatomic and newer quantitative MRI methods, has been shown to result in improved voxel-wise classification of prostate cancer as compared with any single MRI parameter. While these results are promising, substantial heterogeneity in the mpMRI parameter values and voxel-wise prostate cancer risk has been observed both between and within regions of the prostate. This suggests that classification of prostate cancer can potentially be improved by incorporating structural information into the classifier. In this paper, we propose a novel voxel-wise classifier of prostate cancer that accounts for the anatomic structure of the prostate by Bayesian hierarchical modeling, which can be combined with post hoc spatial Gaussian kernel smoothing to account for residual spatial correlation. Our proposed classifier results in significantly improved area under the ROC curve (0.822 vs 0.729, P < .001) and sensitivity corresponding to 90% specificity (0.599 vs 0.429, P < .001), compared with a baseline model that does not account for the anatomic structure of the prostate. Furthermore, the classifier can also be applied on voxels with missing mpMRI parameters, resulting in similar performance, which is an important practical consideration that cannot be easily accommodated using regression-based classifiers. In addition, our classifier achieved high computational efficiency with a closed-form solution for the posterior predictive cancer probability.
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Affiliation(s)
- Jin Jin
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Lin Zhang
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Ethan Leng
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Gregory J Metzger
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Joseph S Koopmeiners
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
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Wang Z, Liu C, Cheng D, Wang L, Yang X, Cheng KT. Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:1127-1139. [PMID: 29727276 DOI: 10.1109/tmi.2017.2789181] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Automated methods for detecting clinically significant (CS) prostate cancer (PCa) in multi-parameter magnetic resonance images (mp-MRI) are of high demand. Existing methods typically employ several separate steps, each of which is optimized individually without considering the error tolerance of other steps. As a result, they could either involve unnecessary computational cost or suffer from errors accumulated over steps. In this paper, we present an automated CS PCa detection system, where all steps are optimized jointly in an end-to-end trainable deep neural network. The proposed neural network consists of concatenated subnets: 1) a novel tissue deformation network (TDN) for automated prostate detection and multimodal registration and 2) a dual-path convolutional neural network (CNN) for CS PCa detection. Three types of loss functions, i.e., classification loss, inconsistency loss, and overlap loss, are employed for optimizing all parameters of the proposed TDN and CNN. In the training phase, the two nets mutually affect each other and effectively guide registration and extraction of representative CS PCa-relevant features to achieve results with sufficient accuracy. The entire network is trained in a weakly supervised manner by providing only image-level annotations (i.e., presence/absence of PCa) without exact priors of lesions' locations. Compared with most existing systems which require supervised labels, e.g., manual delineation of PCa lesions, it is much more convenient for clinical usage. Comprehensive evaluation based on fivefold cross validation using 360 patient data demonstrates that our system achieves a high accuracy for CS PCa detection, i.e., a sensitivity of 0.6374 and 0.8978 at 0.1 and 1 false positives per normal/benign patient.
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Abstract
Multi-parametric magnetic resonance imaging (mp-MRI) has an increasingly important role in the diagnosis of prostate cancer. Due to the large amount of data and variations in mp-MRI, tumor detection can be affected by multiple factors, such as the observer's clinical experience, image quality, and appearance of the lesions. In order to improve the quantitative assessment of the disease and reduce the reporting time, various computer-aided diagnosis (CAD) systems have been designed to help radiologists identify lesions. This manuscript presents an overview of the literature regarding prostate CAD using mp-MRI, while focusing on the studies of the most recent five years. Current prostate CAD technologies and their utilization are discussed in this review.
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Automated Prostate Gland Segmentation Based on an Unsupervised Fuzzy C-Means Clustering Technique Using Multispectral T1w and T2w MR Imaging. INFORMATION 2017. [DOI: 10.3390/info8020049] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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12
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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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13
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Liu L, Tian Z, Zhang Z, Fei B. Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications. Acad Radiol 2016; 23:1024-46. [PMID: 27133005 PMCID: PMC5355004 DOI: 10.1016/j.acra.2016.03.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2015] [Revised: 03/18/2016] [Accepted: 03/21/2016] [Indexed: 01/10/2023]
Abstract
One in six men will develop prostate cancer in his lifetime. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs. Recently, imaging of prostate cancer has greatly advanced since the introduction of multiparametric magnetic resonance imaging (mp-MRI). Mp-MRI consists of T2-weighted sequences combined with functional sequences including dynamic contrast-enhanced MRI, diffusion-weighted MRI, and magnetic resonance spectroscopy imaging. Because of the big data and variations in imaging sequences, detection can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. To improve quantitative assessment of the disease, various computer-aided detection systems have been designed to help radiologists in their clinical practice. This review paper presents an overview of literatures on computer-aided detection of prostate cancer with mp-MRI, which include the technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.
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Affiliation(s)
- Lizhi Liu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1841 Clifton Road NE, Atlanta, GA 30329; Center of Medical Imaging and Image-guided Therapy, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, China
| | - Zhiqiang Tian
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1841 Clifton Road NE, Atlanta, GA 30329
| | - Zhenfeng Zhang
- Center of Medical Imaging and Image-guided Therapy, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, China
| | - Baowei Fei
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1841 Clifton Road NE, Atlanta, GA 30329; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, 1841 Clifton Road NE, Atlanta, Georgia 30329; Winship Cancer Institute of Emory University, 1841 Clifton Road NE, Atlanta, Georgia 30329.
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Metzger GJ, Kalavagunta C, Spilseth B, Bolan PJ, Li X, Hutter D, Nam JW, Johnson AD, Henriksen JC, Moench L, Konety B, Warlick CA, Schmechel SC, Koopmeiners JS. Detection of Prostate Cancer: Quantitative Multiparametric MR Imaging Models Developed Using Registered Correlative Histopathology. Radiology 2016; 279:805-16. [PMID: 26761720 DOI: 10.1148/radiol.2015151089] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To develop multiparametric magnetic resonance (MR) imaging models to generate a quantitative, user-independent, voxel-wise composite biomarker score (CBS) for detection of prostate cancer by using coregistered correlative histopathologic results, and to compare performance of CBS-based detection with that of single quantitative MR imaging parameters. Materials and Methods Institutional review board approval and informed consent were obtained. Patients with a diagnosis of prostate cancer underwent multiparametric MR imaging before surgery for treatment. All MR imaging voxels in the prostate were classified as cancer or noncancer on the basis of coregistered histopathologic data. Predictive models were developed by using more than one quantitative MR imaging parameter to generate CBS maps. Model development and evaluation of quantitative MR imaging parameters and CBS were performed separately for the peripheral zone and the whole gland. Model accuracy was evaluated by using the area under the receiver operating characteristic curve (AUC), and confidence intervals were calculated with the bootstrap procedure. The improvement in classification accuracy was evaluated by comparing the AUC for the multiparametric model and the single best-performing quantitative MR imaging parameter at the individual level and in aggregate. Results Quantitative T2, apparent diffusion coefficient (ADC), volume transfer constant (K(trans)), reflux rate constant (kep), and area under the gadolinium concentration curve at 90 seconds (AUGC90) were significantly different between cancer and noncancer voxels (P < .001), with ADC showing the best accuracy (peripheral zone AUC, 0.82; whole gland AUC, 0.74). Four-parameter models demonstrated the best performance in both the peripheral zone (AUC, 0.85; P = .010 vs ADC alone) and whole gland (AUC, 0.77; P = .043 vs ADC alone). Individual-level analysis showed statistically significant improvement in AUC in 82% (23 of 28) and 71% (24 of 34) of patients with peripheral-zone and whole-gland models, respectively, compared with ADC alone. Model-based CBS maps for cancer detection showed improved visualization of cancer location and extent. Conclusion Quantitative multiparametric MR imaging models developed by using coregistered correlative histopathologic data yielded a voxel-wise CBS that outperformed single quantitative MR imaging parameters for detection of prostate cancer, especially when the models were assessed at the individual level. (©) RSNA, 2016 Online supplemental material is available for this article.
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Affiliation(s)
- Gregory J Metzger
- From the Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN 55455 (G.J.M., C.K., B.S., P.J.B., X.L., D.H., J.W.N.), Department of Laboratory Medicine and Pathology (A.D.J., J.C.H., L.M., S.C.S.), Department of Urologic Surgery, Institute of Prostate and Urologic Cancers (B.K., C.A.W.), and Division of Biostatistics (J.S.K.)
| | - Chaitanya Kalavagunta
- From the Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN 55455 (G.J.M., C.K., B.S., P.J.B., X.L., D.H., J.W.N.), Department of Laboratory Medicine and Pathology (A.D.J., J.C.H., L.M., S.C.S.), Department of Urologic Surgery, Institute of Prostate and Urologic Cancers (B.K., C.A.W.), and Division of Biostatistics (J.S.K.)
| | - Benjamin Spilseth
- From the Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN 55455 (G.J.M., C.K., B.S., P.J.B., X.L., D.H., J.W.N.), Department of Laboratory Medicine and Pathology (A.D.J., J.C.H., L.M., S.C.S.), Department of Urologic Surgery, Institute of Prostate and Urologic Cancers (B.K., C.A.W.), and Division of Biostatistics (J.S.K.)
| | - Patrick J Bolan
- From the Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN 55455 (G.J.M., C.K., B.S., P.J.B., X.L., D.H., J.W.N.), Department of Laboratory Medicine and Pathology (A.D.J., J.C.H., L.M., S.C.S.), Department of Urologic Surgery, Institute of Prostate and Urologic Cancers (B.K., C.A.W.), and Division of Biostatistics (J.S.K.)
| | - Xiufeng Li
- From the Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN 55455 (G.J.M., C.K., B.S., P.J.B., X.L., D.H., J.W.N.), Department of Laboratory Medicine and Pathology (A.D.J., J.C.H., L.M., S.C.S.), Department of Urologic Surgery, Institute of Prostate and Urologic Cancers (B.K., C.A.W.), and Division of Biostatistics (J.S.K.)
| | - Diane Hutter
- From the Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN 55455 (G.J.M., C.K., B.S., P.J.B., X.L., D.H., J.W.N.), Department of Laboratory Medicine and Pathology (A.D.J., J.C.H., L.M., S.C.S.), Department of Urologic Surgery, Institute of Prostate and Urologic Cancers (B.K., C.A.W.), and Division of Biostatistics (J.S.K.)
| | - Jung W Nam
- From the Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN 55455 (G.J.M., C.K., B.S., P.J.B., X.L., D.H., J.W.N.), Department of Laboratory Medicine and Pathology (A.D.J., J.C.H., L.M., S.C.S.), Department of Urologic Surgery, Institute of Prostate and Urologic Cancers (B.K., C.A.W.), and Division of Biostatistics (J.S.K.)
| | - Andrew D Johnson
- From the Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN 55455 (G.J.M., C.K., B.S., P.J.B., X.L., D.H., J.W.N.), Department of Laboratory Medicine and Pathology (A.D.J., J.C.H., L.M., S.C.S.), Department of Urologic Surgery, Institute of Prostate and Urologic Cancers (B.K., C.A.W.), and Division of Biostatistics (J.S.K.)
| | - Jonathan C Henriksen
- From the Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN 55455 (G.J.M., C.K., B.S., P.J.B., X.L., D.H., J.W.N.), Department of Laboratory Medicine and Pathology (A.D.J., J.C.H., L.M., S.C.S.), Department of Urologic Surgery, Institute of Prostate and Urologic Cancers (B.K., C.A.W.), and Division of Biostatistics (J.S.K.)
| | - Laura Moench
- From the Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN 55455 (G.J.M., C.K., B.S., P.J.B., X.L., D.H., J.W.N.), Department of Laboratory Medicine and Pathology (A.D.J., J.C.H., L.M., S.C.S.), Department of Urologic Surgery, Institute of Prostate and Urologic Cancers (B.K., C.A.W.), and Division of Biostatistics (J.S.K.)
| | - Badrinath Konety
- From the Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN 55455 (G.J.M., C.K., B.S., P.J.B., X.L., D.H., J.W.N.), Department of Laboratory Medicine and Pathology (A.D.J., J.C.H., L.M., S.C.S.), Department of Urologic Surgery, Institute of Prostate and Urologic Cancers (B.K., C.A.W.), and Division of Biostatistics (J.S.K.)
| | - Christopher A Warlick
- From the Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN 55455 (G.J.M., C.K., B.S., P.J.B., X.L., D.H., J.W.N.), Department of Laboratory Medicine and Pathology (A.D.J., J.C.H., L.M., S.C.S.), Department of Urologic Surgery, Institute of Prostate and Urologic Cancers (B.K., C.A.W.), and Division of Biostatistics (J.S.K.)
| | - Stephen C Schmechel
- From the Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN 55455 (G.J.M., C.K., B.S., P.J.B., X.L., D.H., J.W.N.), Department of Laboratory Medicine and Pathology (A.D.J., J.C.H., L.M., S.C.S.), Department of Urologic Surgery, Institute of Prostate and Urologic Cancers (B.K., C.A.W.), and Division of Biostatistics (J.S.K.)
| | - Joseph S Koopmeiners
- From the Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN 55455 (G.J.M., C.K., B.S., P.J.B., X.L., D.H., J.W.N.), Department of Laboratory Medicine and Pathology (A.D.J., J.C.H., L.M., S.C.S.), Department of Urologic Surgery, Institute of Prostate and Urologic Cancers (B.K., C.A.W.), and Division of Biostatistics (J.S.K.)
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15
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Zakian KL, Hatfield W, Aras O, Cao K, Yakar D, Goldman DA, Moskowitz CS, Shukla-Dave A, Tehrani YM, Fine S, Eastham J, Hricak H. Prostate MRSI predicts outcome in radical prostatectomy patients. Magn Reson Imaging 2016; 34:674-81. [PMID: 26821278 DOI: 10.1016/j.mri.2016.01.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Revised: 01/21/2016] [Accepted: 01/22/2016] [Indexed: 01/08/2023]
Abstract
BACKGROUND New non-invasive methods are needed for sub-stratifying high-risk prostate cancer patients. Magnetic resonance spectroscopic imaging (MRSI) maps metabolites in prostate cancer, providing information on tumor aggressiveness and volume. PURPOSE To investigate the correlation between MRSI and treatment failure (TF) after radical prostatectomy (RP). METHODS Two-hundred sixty-two patients who underwent endorectal MRI/MRSI followed by RP at our institution from 2003 to 2007 were studied. MRI stage, number of voxels in the MRSI index lesion (NILV), number of high-grade voxels (NHGV), and number of voxels containing undetectable polyamines (NUPV) were derived. Clinical outcome was followed until August, 2014. Treatment failure was defined as 1) biochemical recurrence (BCR), 2) persistently detectable PSA after RP, or 3) adjuvant therapy initiated in the absence of BCR. MRI/MRSI features and clinical parameters were compared to TF by univariate Cox Proportional Hazards Regression. After backward selection, each MRSI parameter was included in a separate regression model adjusted for NCCN-based clinical risk score (CRS), number of biopsy cores positive (NPC), and MRI stage. RESULTS In univariate analysis, all clinical variables were associated with TF in addition to MRI stage, NILV, NHGV, and NUPV. In multivariate analysis, NILV, NHGV, and NUPV were also significant risk factors for TF (p=0.016, p=0.002, p=0.006, respectively). The association between the number of tumor voxels with undetectable polyamines and the probability of treatment failure has not been previously reported. The number of MRSI cancer voxels correlated with extracapsular extension (ECE) (p<0.0001). CONCLUSIONS MRSI was associated with post-radical prostatectomy treatment failure in models adjusted for the number of positive biopsy cores and clinical risk score. This is the first report that in radical prostatectomy patients MRSI has an association with treatment failure independent of the number of positive biopsy cores. MRSI may help the clinician determine whether patients with high risk disease who undergo RP are candidates for specialized additional treatment.
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Affiliation(s)
- Kristen L Zakian
- Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, NY, NY, 10065, USA.
| | | | - Omer Aras
- MSKCC, 1275 York Avenue, NY, NY, 10065, USA.
| | - Kun Cao
- MSKCC, 1275 York Avenue, NY, NY, 10065, USA.
| | - Derya Yakar
- MSKCC, 1275 York Avenue, NY, NY, 10065, USA.
| | | | | | | | | | - Samson Fine
- MSKCC, 1275 York Avenue, NY, NY, 10065, USA.
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