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Akkaya H, Dilek O, Özdemir S, Taş ZA, Öztürk İS, Gülek B. Can the Gleason score be predicted in patients with prostate cancer? A dynamic contrast-enhanced MRI, (68)Ga-PSMA PET/CT, PSA, and PSA-density comparison study. Diagn Interv Radiol 2023; 29:647-655. [PMID: 37395389 PMCID: PMC10679545 DOI: 10.4274/dir.2023.232186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 05/22/2023] [Indexed: 07/04/2023]
Abstract
PURPOSE The present study aims to evaluate whether perfusion parameters in prostate magnetic resonance imaging (MRI), (68)Ga-prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT), prostate-specific antigen (PSA), and PSA density can be used to predict the lesion grade in patients with prostate cancer (PCa). METHODS The study included a total of 137 PCa cases in which 12-quadrant transrectal ultrasound-guided prostate biopsy (TRUSBx) was performed, the Gleason score (GS) was determined, and pre-biopsy multiparametric prostate MRI and (68)Ga-PSMA PET/CT examinations were undertaken. The patient population was evaluated in three groups according to the GS: (1) low risk; (2) intermediate risk; (3) high risk. The PSA, PSA density, pre-TRUSBx (68)Ga-PSMA PET/CT maximum standardized uptake value (SUVmax), perfusion MRI parameters [maximum enhancement, maximum relative enhancement, T0 (s), time to peak (s), wash-in rate (s-1), and wash-out rate (s-1)] were retrospectively evaluated. RESULTS There was no significant difference between the three groups in relation to the PSA, PSA density, and (68)Ga-PSMA PET/CT SUVmax (P > 0.05). However, the values of maximum enhancement, maximum relative enhancement (%), T0 (s), time to peak (s), wash-in rate (s-1), and wash-out rate (s-1) significantly differed among the groups. A moderate positive correlation was found among the prostate volume, PSA (r = 0.490), and (68)Ga-PSMA SUVmax (r = 0.322) in the patients. The wash-out rate (s-1) and wash-in rate (s-1) had the best diagnostic test performance (area under the curve: 89.1% and 78.4%, respectively). CONCLUSION No significant correlation was found between the (68)Ga-PSMA PET/CT SUVmax and the GS. The wash-out rate was more successful in estimating the pretreatment GS than the (68)Ga-PSMA PET/CT SUVmax.
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Affiliation(s)
- Hüseyin Akkaya
- Clinic of Radiology, University of Health Sciences Turkey, Adana City Training and Research Hospital, Adana, Turkey
| | - Okan Dilek
- Clinic of Radiology, University of Health Sciences Turkey, Adana City Training and Research Hospital, Adana, Turkey
| | - Selim Özdemir
- Clinic of Radiology, University of Health Sciences Turkey, Adana City Training and Research Hospital, Adana, Turkey
| | - Zeynel Abidin Taş
- Clinic of Pathology, University of Health Sciences Turkey, Adana City Training and Research Hospital, Adana, Turkey
| | - İhsan Sabri Öztürk
- Clinic of Nuclear Medicine, University of Health Sciences Turkey, Adana City Training and Research Hospital, Adana, Turkey
| | - Bozkurt Gülek
- Clinic of Radiology, University of Health Sciences Turkey, Adana City Training and Research Hospital, Adana, Turkey
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Chan TH, Haworth A, Wang A, Osanlouy M, Williams S, Mitchell C, Hofman MS, Hicks RJ, Murphy DG, Reynolds HM. Detecting localised prostate cancer using radiomic features in PSMA PET and multiparametric MRI for biologically targeted radiation therapy. EJNMMI Res 2023; 13:34. [PMID: 37099047 PMCID: PMC10133419 DOI: 10.1186/s13550-023-00984-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 04/17/2023] [Indexed: 04/27/2023] Open
Abstract
BACKGROUND Prostate-Specific Membrane Antigen (PSMA) PET/CT and multiparametric MRI (mpMRI) are well-established modalities for identifying intra-prostatic lesions (IPLs) in localised prostate cancer. This study aimed to investigate the use of PSMA PET/CT and mpMRI for biologically targeted radiation therapy treatment planning by: (1) analysing the relationship between imaging parameters at a voxel-wise level and (2) assessing the performance of radiomic-based machine learning models to predict tumour location and grade. METHODS PSMA PET/CT and mpMRI data from 19 prostate cancer patients were co-registered with whole-mount histopathology using an established registration framework. Apparent Diffusion Coefficient (ADC) maps were computed from DWI and semi-quantitative and quantitative parameters from DCE MRI. Voxel-wise correlation analysis was conducted between mpMRI parameters and PET Standardised Uptake Value (SUV) for all tumour voxels. Classification models were built using radiomic and clinical features to predict IPLs at a voxel level and then classified further into high-grade or low-grade voxels. RESULTS Perfusion parameters from DCE MRI were more highly correlated with PET SUV than ADC or T2w. IPLs were best detected with a Random Forest Classifier using radiomic features from PET and mpMRI rather than either modality alone (sensitivity, specificity and area under the curve of 0.842, 0.804 and 0.890, respectively). The tumour grading model had an overall accuracy ranging from 0.671 to 0.992. CONCLUSIONS Machine learning classifiers using radiomic features from PSMA PET and mpMRI show promise for predicting IPLs and differentiating between high-grade and low-grade disease, which could be used to inform biologically targeted radiation therapy planning.
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Affiliation(s)
- Tsz Him Chan
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Annette Haworth
- Institute of Medical Physics, School of Physics, The University of Sydney, Sydney, NSW, Australia
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- Centre for Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Mahyar Osanlouy
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Scott Williams
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
- Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Catherine Mitchell
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Michael S Hofman
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
- Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Rodney J Hicks
- Department of Medicine, St Vincent's Hospital Medical School, The University of Melbourne, Melbourne, VIC, Australia
| | - Declan G Murphy
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Hayley M Reynolds
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
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Prata F, Anceschi U, Cordelli E, Faiella E, Civitella A, Tuzzolo P, Iannuzzi A, Ragusa A, Esperto F, Prata SM, Sicilia R, Muto G, Grasso RF, Scarpa RM, Soda P, Simone G, Papalia R. Radiomic Machine-Learning Analysis of Multiparametric Magnetic Resonance Imaging in the Diagnosis of Clinically Significant Prostate Cancer: New Combination of Textural and Clinical Features. Curr Oncol 2023; 30:2021-2031. [PMID: 36826118 PMCID: PMC9955797 DOI: 10.3390/curroncol30020157] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND The aim of our study was to develop a radiomic tool for the prediction of clinically significant prostate cancer. METHODS From September 2020 to December 2021, 91 patients who underwent magnetic resonance imaging prostate fusion biopsy at our institution were selected. Prostate cancer aggressiveness was assessed by combining the three orthogonal planes-Llocal binary pattern the 3Dgray level co-occurrence matrix, and other first order statistical features with clinical (semantic) features. The 487 features were used to predict whether the Gleason score was clinically significant (≥7) in the final pathology. A feature selection algorithm was used to determine the most predictive features, and at the end of the process, nine features were chosen through a 10-fold cross validation. RESULTS The feature analysis revealed a detection accuracy of 83.5%, with a clinically significant precision of 84.4% and a clinically significant sensitivity of 91.5%. The resulting area under the curve was 80.4%. CONCLUSIONS Radiomic analysis allowed us to develop a tool that was able to predict a Gleason score of ≥7. This new tool may improve the detection rate of clinically significant prostate cancer and overcome the limitations of the subjective interpretation of magnetic resonance imaging, reducing the number of useless biopsies.
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Affiliation(s)
- Francesco Prata
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
- Correspondence: ; Tel.: +39-39-3437-3027; Fax: +39-062-2541-1995
| | - Umberto Anceschi
- Department of Urology, IRCCS “Regina Elena” National Cancer Institute, 00144 Rome, Italy
| | - Ermanno Cordelli
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Eliodoro Faiella
- Department of Diagnostic and Interventional Radiology, Sant’Anna Hospital, 22042 San Fermo della Battaglia, Italy
| | - Angelo Civitella
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Piergiorgio Tuzzolo
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Andrea Iannuzzi
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Alberto Ragusa
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Francesco Esperto
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Salvatore Mario Prata
- Simple Operating Unit of Lower Urinary Tract Surgery, SS. Trinità Hospital, 03039 Sora, Italy
| | - Rosa Sicilia
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Giovanni Muto
- Department of Urology, Humanitas Gradenigo University, 10153 Turin, Italy
| | - Rosario Francesco Grasso
- Department of Diagnostic and Interventional Radiology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Roberto Mario Scarpa
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Paolo Soda
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Giuseppe Simone
- Department of Urology, IRCCS “Regina Elena” National Cancer Institute, 00144 Rome, Italy
| | - Rocco Papalia
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
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Reynolds HM, Tadimalla S, Wang YF, Montazerolghaem M, Sun Y, Williams S, Mitchell C, Finnegan ME, Murphy DG, Haworth A. Semi-quantitative and quantitative dynamic contrast-enhanced (DCE) MRI parameters as prostate cancer imaging biomarkers for biologically targeted radiation therapy. Cancer Imaging 2022; 22:71. [PMID: 36536464 PMCID: PMC9762110 DOI: 10.1186/s40644-022-00508-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Biologically targeted radiation therapy treatment planning requires voxel-wise characterisation of tumours. Dynamic contrast enhanced (DCE) DCE MRI has shown promise in defining voxel-level biological characteristics. In this study we consider the relative value of qualitative, semi-quantitative and quantitative assessment of DCE MRI compared with diffusion weighted imaging (DWI) and T2-weighted (T2w) imaging to detect prostate cancer at the voxel level. METHODS Seventy prostate cancer patients had multiparametric MRI prior to radical prostatectomy, including T2w, DWI and DCE MRI. Apparent Diffusion Coefficient (ADC) maps were computed from DWI, and semi-quantitative and quantitative parameters computed from DCE MRI. Tumour location and grade were validated with co-registered whole mount histology. Kolmogorov-Smirnov tests were applied to determine whether MRI parameters in tumour and benign voxels were significantly different. Cohen's d was computed to quantify the most promising biomarkers. The Parker and Weinmann Arterial Input Functions (AIF) were compared for their ability to best discriminate between tumour and benign tissue. Classifier models were used to determine whether DCE MRI parameters improved tumour detection versus ADC and T2w alone. RESULTS All MRI parameters had significantly different data distributions in tumour and benign voxels. For low grade tumours, semi-quantitative DCE MRI parameter time-to-peak (TTP) was the most discriminating and outperformed ADC. For high grade tumours, ADC was the most discriminating followed by DCE MRI parameters Ktrans, the initial rate of enhancement (IRE), then TTP. Quantitative parameters utilising the Parker AIF better distinguished tumour and benign voxel values than the Weinmann AIF. Classifier models including DCE parameters versus T2w and ADC alone, gave detection accuracies of 78% versus 58% for low grade tumours and 85% versus 72% for high grade tumours. CONCLUSIONS Incorporating DCE MRI parameters with DWI and T2w gives improved accuracy for tumour detection at a voxel level. DCE MRI parameters should be used to spatially characterise tumour biology for biologically targeted radiation therapy treatment planning.
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Affiliation(s)
- Hayley M Reynolds
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
| | | | - Yu-Feng Wang
- School of Physics, The University of Sydney, Sydney, NSW, Australia
| | | | - Yu Sun
- School of Physics, The University of Sydney, Sydney, NSW, Australia
| | - Scott Williams
- Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Catherine Mitchell
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Mary E Finnegan
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
- Department of Bioengineering, Imperial College London, London, UK
| | - Declan G Murphy
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Annette Haworth
- School of Physics, The University of Sydney, Sydney, NSW, Australia
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Zhao J, Kader A, Mangarova DB, Brangsch J, Brenner W, Hamm B, Makowski MR. Dynamic Contrast-Enhanced MRI of Prostate Lesions of Simultaneous [ 68Ga]Ga-PSMA-11 PET/MRI: Comparison between Intraprostatic Lesions and Correlation between Perfusion Parameters. Cancers (Basel) 2021; 13:1404. [PMID: 33808685 PMCID: PMC8003484 DOI: 10.3390/cancers13061404] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 02/25/2021] [Accepted: 03/16/2021] [Indexed: 11/29/2022] Open
Abstract
We aimed to retrospectively compare the perfusion parameters measured from dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of prostate benign lesions and malignant lesions to determine the relationship between perfusion parameters. DCE-MRI was performed in patients with PCa who underwent simultaneous [68Ga]Ga-prostate-specific membrane antigen (PSMA)-11 positron emission tomography (PET)/MRI. Six perfusion parameters (arrival time (AT), time to peak (TTP), wash-in slope (W-in), wash-out slope (W-out), peak enhancement intensity (PEI), and initial area under the 60-s curve (iAUC)), and a semi-quantitative parameter, standardized uptake values maximum (SUVmax) were calculated by placing regions of interest in the largest area of the lesions. The DCE-MRI parameters between prostate benign and malignant lesions were compared. The DCE-MRI parameters in both the benign and malignant lesions subgroup with SUVmax ≤ 3.0 and SUVmax > 3.0 were compared. The correlation of DCE-MRI parameters was investigated. Malignant lesions demonstrated significantly shorter TTP and higher SUVmax than did benign lesions. In the benign and malignant lesions subgroup, perfusion parameters of lesions with SUVmax ≤ 3.0 show no significant difference to those with SUVmax > 3.0. DCE-MRI perfusion parameters show a close correlation with each other. DCE-MRI parameters reflect the perfusion characteristics of intraprostatic lesions with malignant lesions, demonstrating significantly shorter TTP. There is a moderate to strong correlation between DCE-MRI parameters. Semi-quantitative analysis reflects that malignant lesions show a significantly higher SUVmax than benign lesions.
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Affiliation(s)
- Jing Zhao
- Institute of Radiology and Nuclear Medicine, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany; (A.K.); (D.B.M.); (J.B.); (B.H.); (M.R.M.)
| | - Avan Kader
- Institute of Radiology and Nuclear Medicine, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany; (A.K.); (D.B.M.); (J.B.); (B.H.); (M.R.M.)
- Department of Biology, Chemistry and Pharmacy, Institute of Biology, Freie Universität Berlin, Königin-Luise-Str. 1-3, 14195 Berlin, Germany
| | - Dilyana B. Mangarova
- Institute of Radiology and Nuclear Medicine, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany; (A.K.); (D.B.M.); (J.B.); (B.H.); (M.R.M.)
- Department of Veterinary Medicine, Institute of Veterinary Pathology, Freie Universität Berlin, Robert-von-Ostertag-Str. 15, Building 12, 14163 Berlin, Germany
| | - Julia Brangsch
- Institute of Radiology and Nuclear Medicine, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany; (A.K.); (D.B.M.); (J.B.); (B.H.); (M.R.M.)
| | - Winfried Brenner
- Department of Nuclear Medicine, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353 Berlin, Germany;
| | - Bernd Hamm
- Institute of Radiology and Nuclear Medicine, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany; (A.K.); (D.B.M.); (J.B.); (B.H.); (M.R.M.)
| | - Marcus R. Makowski
- Institute of Radiology and Nuclear Medicine, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany; (A.K.); (D.B.M.); (J.B.); (B.H.); (M.R.M.)
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675 Munich, Germany
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Prostatitis, the Great Mimicker of Prostate Cancer: Can We Differentiate Them Quantitatively With Multiparametric MRI? AJR Am J Roentgenol 2020; 215:1104-1112. [PMID: 32901562 DOI: 10.2214/ajr.20.22843] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVE. The purpose of this study was to investigate the diagnostic performance of semiquantitative and quantitative pharmacokinetic parameters and quantitative apparent diffusion coefficient (ADC) values obtained from prostate multiparametric MRI (mpMRI) to differentiate prostate cancer (PCa) and prostatitis objectively. MATERIALS AND METHODS. We conducted a retrospective review of patients with biopsy-proven PCa or prostatitis who underwent mpMRI study between January 2015 and February 2018. Mean ADC, forward volume transfer constant (Ktrans), reverse volume transfer constant (kep), plasma volume fraction (Vp), extravascular extracellular space volume fraction (Ve), and time to peak (TTP) values were calculated for both lesions and contralateral normal prostate tissue. Signal intensity-time curves were analyzed. Lesion-to-normal prostate tissue ratios of pharmacokinetic parameters were also calculated. The diagnostic accuracy and cutoff points of all parameters were analyzed to differentiate PCa from prostatitis. RESULTS. A total of 138 patients (94 with PCa and 44 with prostatitis) were included in the study. Statistically, ADC, quantitative pharmacokinetic parameters (Ktrans, kep, Ve, and Vp), their lesion-to-normal prostate tissue ratios, and TTP values successfully differentiated PCa and prostatitis. Surprisingly, we found that Ve values were significantly higher in prostatitis lesions. The combination of these parameters had 92.7% overall diagnostic accuracy. ADC, kep, and TTP made up the most successful combination for differential diagnosis. Analysis of the signal intensity-time curves showed mostly type 2 and type 3 enhancement curve patterns for patients with PCa. Type 3 curves were not seen in any prostatitis cases. CONCLUSION. Quantitative analysis of mpMRI differentiates PCa from prostatitis with high sensitivity and specificity, appears to have significant potential, and may improve diagnostic accuracy. In addition, evaluating these parameters does not cause any extra burden to the patients.
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Automated Classification of Blood Loss from Transurethral Resection of the Prostate Surgery Videos Using Deep Learning Technique. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10144908] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Transurethral resection of the prostate (TURP) is a surgical removal of obstructing prostate tissue. The total bleeding area is used to determine the performance of the TURP surgery. Although the traditional method for the detection of bleeding areas provides accurate results, it cannot detect them in time for surgery diagnosis. Moreover, it is easily disturbed to judge bleeding areas for experienced physicians because a red light pattern arising from the surgical cutting loop often appears on the images. Recently, the automatic computer-aided technique and artificial intelligence deep learning are broadly used in medical image recognition, which can effectively extract the desired features to reduce the burden of physicians and increase the accuracy of diagnosis. In this study, we integrated two state-of-the-art deep learning techniques for recognizing and extracting the red light areas arising from the cutting loop in the TURP surgery. First, the ResNet-50 model was used to recognize the red light pattern appearing in the chipped frames of the surgery videos. Then, the proposed Res-Unet model was used to segment the areas with the red light pattern and remove these areas. Finally, the hue, saturation, and value color space were used to classify the four levels of the blood loss under the circumstances of non-red light pattern images. The experiments have shown that the proposed Res-Unet model achieves higher accuracy than other segmentation algorithms in classifying the images with the red and non-red lights, and is able to extract the red light patterns and effectively remove them in the TURP surgery images. The proposed approaches presented here are capable of obtaining the level classifications of blood loss, which are helpful for physicians in diagnosis.
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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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Liu B, Cheng J, Guo D, He X, Luo Y, Zeng Y, Li C. Prediction of prostate cancer aggressiveness with a combination of radiomics and machine learning-based analysis of dynamic contrast-enhanced MRI. Clin Radiol 2019; 74:896.e1-896.e8. [PMID: 31495546 DOI: 10.1016/j.crad.2019.07.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Accepted: 07/16/2019] [Indexed: 12/13/2022]
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Lu Y, Peng W, Song J, Chen T, Wang X, Hou Z, Yan Z, Koh TS. On the potential use of dynamic contrast-enhanced (DCE) MRI parameters as radiomic features of cervical cancer. Med Phys 2019; 46:5098-5109. [PMID: 31523829 DOI: 10.1002/mp.13821] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 07/30/2019] [Accepted: 09/05/2019] [Indexed: 12/26/2022] Open
Abstract
PURPOSE To evaluate whether the analysis of high-temporal resolution DCE-MRI by various tracer kinetic models could yield useful radiomic features in discriminating cervix carcinoma and normal cervix tissue. METHODS Forty-three patients (median age 51 yr; range 26-78 yr) diagnosed with cervical cancer based on postoperative pathology were enrolled in this study with informed consent. DCE-MRI data with temporal resolution of 2 s were acquired and analyzed using the Tofts (TOFTS), extended Tofts (EXTOFTS), conventional two-compartment (CC), adiabatic tissue homogeneity (ATH), and distributed parameter (DP) models. Ability of all kinetic parameters in distinguishing tumor from normal tissue was assessed using Mann-Whitney U test and receiver operating characteristic (ROC) curves. Repeatability of parameter estimates due to sampling of arterial input functions (AIFs) was also studied using intraclass correlation (ICC) analysis. RESULTS Fractional extravascular, extracellular volume (Ve) of all models were significantly smaller in cervix carcinoma than normal cervix tissue, and were associated with large values of area under ROC curve (AUC 0.884-0.961). Capillary permeability PS derived from the ATH, CC, and DP models also yielded large AUC values (0.730, 0.860, and 0.797). Transfer constant Ktrans derived from TOFTS and EXTOFTS models yielded smaller AUC (0.587 and 0.701). Repeatability of parameters derived from all models was robust to AIF sampling, with ICC coefficients typically larger than 0.80. CONCLUSIONS With the use of high-temporal resolution DCE-MRI, all tracer kinetic models could reflect pathophysiological differences between cervix carcinoma and normal tissue (with significant differences in Ve and PS) and potentially yield radiomic features with diagnostic value.
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Affiliation(s)
- Yi Lu
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Wenwen Peng
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Jiao Song
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Tao Chen
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Xue Wang
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Zujun Hou
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Zhihan Yan
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Tong San Koh
- Department of Oncologic Imaging, National Cancer Centre, 247969, Singapore, Singapore
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Wang Y, Wang D, Geng N, Wang Y, Yin Y, Jin Y. Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.01.015] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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12
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Olsson LE, Johansson M, Zackrisson B, Blomqvist LK. Basic concepts and applications of functional magnetic resonance imaging for radiotherapy of prostate cancer. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2019; 9:50-57. [PMID: 33458425 PMCID: PMC7807726 DOI: 10.1016/j.phro.2019.02.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Revised: 12/27/2018] [Accepted: 02/08/2019] [Indexed: 12/30/2022]
Abstract
Recently, the interest to integrate magnetic resonance imaging (MRI) in radiotherapy for prostate cancer has increased considerably. MRI can contribute in all steps of the radiotherapy workflow from diagnosis, staging, and target definition to treatment follow-up. Of particular interest is the ability of MRI to provide a wide range of functional measures. The complexity of MRI as an imaging modality combined with the growing interest of the application to prostate cancer radiotherapy, emphasize the need for dedicated education within the radiation oncology community. In this context, an overview of the most common as well as a few upcoming functional MR imaging techniques is presented: the basic methodology and measurement is described, the link between the functional measures and the underlying biology is established, and finally relevant applications of functional MRI useful for prostate cancer radiotherapy are given.
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Affiliation(s)
- Lars E Olsson
- Department of Medical Radiation Physics, Translational Medicine, Lund University, Sweden.,Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Sweden
| | | | | | - Lennart K Blomqvist
- Department of Radiology, Molecular Medicine and Surgery, Karolinska University, Sweden
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13
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Sun Y, Reynolds HM, Parameswaran B, Wraith D, Finnegan ME, Williams S, Haworth A. Multiparametric MRI and radiomics in prostate cancer: a review. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2019; 42:3-25. [PMID: 30762223 DOI: 10.1007/s13246-019-00730-z] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 01/22/2019] [Indexed: 12/30/2022]
Abstract
Multiparametric MRI (mpMRI) is an imaging modality that combines anatomical MR imaging with one or more functional MRI sequences. It has become a versatile tool for detecting and characterising prostate cancer (PCa). The traditional role of mpMRI was confined to PCa staging, but due to the advanced imaging techniques, its role has expanded to various stages in clinical practises including tumour detection, disease monitor during active surveillance and sequential imaging for patient follow-up. Meanwhile, with the growing speed of data generation and the increasing volume of imaging data, it is highly demanded to apply computerised methods to process mpMRI data and extract useful information. Hence quantitative analysis for imaging data using radiomics has become an emerging paradigm. The application of radiomics approaches in prostate cancer has not only enabled automatic localisation of the disease but also provided a non-invasive solution to assess tumour biology (e.g. aggressiveness and the presence of hypoxia). This article reviews mpMRI and its expanding role in PCa detection, staging and patient management. Following that, an overview of prostate radiomics will be provided, with a special focus on its current applications as well as its future directions.
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Affiliation(s)
- Yu Sun
- University of Sydney, Sydney, Australia. .,Peter MacCallum Cancer Centre, Melbourne, Australia.
| | | | | | - Darren Wraith
- Queensland University of Technology, Brisbane, Australia
| | - Mary E Finnegan
- Imperial College Healthcare NHS Trust, London, UK.,Imperial College London, London, UK
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14
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Mazzetti S, Giannini V, Russo F, Regge D. Computer-aided diagnosis of prostate cancer using multi-parametric MRI: comparison between PUN and Tofts models. Phys Med Biol 2018; 63:095004. [PMID: 29570456 DOI: 10.1088/1361-6560/aab956] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Computer-aided diagnosis (CAD) systems are increasingly being used in clinical settings to report multi-parametric magnetic resonance imaging (mp-MRI) of the prostate. Usually, CAD systems automatically highlight cancer-suspicious regions to the radiologist, reducing reader variability and interpretation errors. Nevertheless, implementing this software requires the selection of which mp-MRI parameters can best discriminate between malignant and non-malignant regions. To exploit functional information, some parameters are derived from dynamic contrast-enhanced (DCE) acquisitions. In particular, much CAD software employs pharmacokinetic features, such as K trans and k ep, derived from the Tofts model, to estimate a likelihood map of malignancy. However, non-pharmacokinetic models can be also used to describe DCE-MRI curves, without any requirement for prior knowledge or measurement of the arterial input function, which could potentially lead to large errors in parameter estimation. In this work, we implemented an empirical function derived from the phenomenological universalities (PUN) class to fit DCE-MRI. The parameters of the PUN model are used in combination with T2-weighted and diffusion-weighted acquisitions to feed a support vector machine classifier to produce a voxel-wise malignancy likelihood map of the prostate. The results were all compared to those for a CAD system based on Tofts pharmacokinetic features to describe DCE-MRI curves, using different quality aspects of image segmentation, while also evaluating the number and size of false positive (FP) candidate regions. This study included 61 patients with 70 biopsy-proven prostate cancers (PCa). The metrics used to evaluate segmentation quality between the two CAD systems were not statistically different, although the PUN-based CAD reported a lower number of FP, with reduced size compared to the Tofts-based CAD. In conclusion, the CAD software based on PUN parameters is a feasible means with which to detect PCa, without affecting segmentation quality, and hence it could be successfully applied in clinical settings, improving the automated diagnosis process and reducing computational complexity.
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Affiliation(s)
- S Mazzetti
- Department of Surgical Sciences, University of Torino, 10124 Turin, Italy. Department of Radiology, Candiolo Cancer Institute-FPO, IRCCS, 10060 Candiolo, Turin, Italy
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15
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Variability induced by the MR imager in dynamic contrast-enhanced imaging of the prostate. Diagn Interv Imaging 2018; 99:255-264. [DOI: 10.1016/j.diii.2017.12.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2017] [Revised: 12/03/2017] [Accepted: 12/07/2017] [Indexed: 12/22/2022]
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16
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Zhang YD, Wang J, Wu CJ, Bao ML, Li H, Wang XN, Tao J, Shi HB. An imaging-based approach predicts clinical outcomes in prostate cancer through a novel support vector machine classification. Oncotarget 2018; 7:78140-78151. [PMID: 27542201 PMCID: PMC5363650 DOI: 10.18632/oncotarget.11293] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Accepted: 08/11/2016] [Indexed: 11/29/2022] Open
Abstract
Preoperatively predict the probability of Prostate cancer (PCa) biochemical recurrence (BCR) is of definite clinical relevance. The purpose of this study was to develop an imaging-based approach in the prediction of 3-years BCR through a novel support vector machine (SVM) classification. We collected clinicopathologic and MR imaging datasets in 205 patients pathologically confirmed PCa after radical prostatectomy. Univariable and multivariable analyses were used to assess the association between MR findings and 3-years BCR, and modeled the imaging variables and follow-up data to predict 3-year PCa BCR using SVM analysis. The performance of SVM was compared with conventional Logistic regression (LR) and D'Amico risk stratification scheme by area under the receiver operating characteristic curve (Az) analysis. We found that SVM had significantly higher Az (0.959 vs. 0.886; p = 0.007), sensitivity (93.3% vs. 83.3%; p = 0.025), specificity (91.7% vs. 77.2%; p = 0.009) and accuracy (92.2% vs. 79.0%; p = 0.006) than LR analysis. Performance of popularized D'Amico scheme was effectively improved by adding MRI-derived variables (Az: 0.970 vs. 0.859, p < 0.001; sensitivity: 91.7% vs. 86.7%, p = 0.031; specificity: 94.5% vs. 78.6%, p = 0.001; and accuracy: 93.7% vs. 81.0%, p = 0.007). Additionally, beside pathological Gleason score (hazard ratio [HR] = 1.560, p = 0.008), surgical-T3b (HR = 4.525, p < 0.001) and positive surgical margin (HR = 1.314, p = 0.007), apparent diffusion coefficient (HR = 0.149, p = 0.035) was the only independent imaging predictor of time to PSA failure. Therefore, We concluded that imaging-based approach using SVM was superior to LR analysis in predicting PCa outcome. Adding MR variables improved the performance of D'Amico scheme.
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Affiliation(s)
- Yu-Dong Zhang
- Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Jing Wang
- Center for Medical Device Evaluation, CFDA, Beijing, China
| | - Chen-Jiang Wu
- Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Mei-Ling Bao
- Department of Pathology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Hai Li
- Department of Pathology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Xiao-Ning Wang
- Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Jun Tao
- Department of Urology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Hai-Bin Shi
- Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
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17
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Multiparametric magnetic resonance imaging for transition zone prostate cancer: essential findings, limitations, and future directions. Abdom Radiol (NY) 2017; 42:2732-2744. [PMID: 28702787 DOI: 10.1007/s00261-017-1184-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
OBJECTIVE Review the multiparametric MRI (mpMRI) findings of transition zone (TZ) prostate cancer (PCa) using T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI and to integrate mpMRI findings with clinical history, laboratory values, and histopathology. CONCLUSION TZ prostate tumors are challenging to detect clinically and at MRI. mpMRI using the combination of sequences has the potential to improve accuracy of TZ cancer detection and staging.
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18
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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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Wang J, Wu CJ, Bao ML, Zhang J, Wang XN, Zhang YD. Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer. Eur Radiol 2017; 27:4082-4090. [PMID: 28374077 DOI: 10.1007/s00330-017-4800-5] [Citation(s) in RCA: 156] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Accepted: 03/13/2017] [Indexed: 12/22/2022]
Abstract
OBJECTIVE To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa). METHODS This IRB-approved study included 54 patients with PCa undergoing multi-parametric (mp) MRI before prostatectomy. Imaging analysis was performed on 54 tumours, 47 normal peripheral (PZ) and 48 normal transitional (TZ) zone based on histological-radiological correlation. Mp-MRI was scored via PI-RADS, and quantified by measuring radiomic features. Predictive model was developed using a novel support vector machine trained with: (i) radiomics, (ii) PI-RADS scores, (iii) radiomics and PI-RADS scores. Paired comparison was made via ROC analysis. RESULTS For PCa versus normal TZ, the model trained with radiomics had a significantly higher area under the ROC curve (Az) (0.955 [95% CI 0.923-0.976]) than PI-RADS (Az: 0.878 [0.834-0.914], p < 0.001). The Az between them was insignificant for PCa versus PZ (0.972 [0.945-0.988] vs. 0.940 [0.905-0.965], p = 0.097). When radiomics was added, performance of PI-RADS was significantly improved for PCa versus PZ (Az: 0.983 [0.960-0.995]) and PCa versus TZ (Az: 0.968 [0.940-0.985]). CONCLUSION Machine learning analysis of MR radiomics can help improve the performance of PI-RADS in clinically relevant PCa. KEY POINTS • Machine-based analysis of MR radiomics outperformed in TZ cancer against PI-RADS. • Adding MR radiomics significantly improved the performance of PI-RADS. • DKI-derived Dapp and Kapp were two strong markers for the diagnosis of PCa.
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Affiliation(s)
- Jing Wang
- Center for Medical Device Evaluation, CFDA, Beijing, China, 100044
| | - Chen-Jiang Wu
- Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, 300, Guangzhou Road, Nanjing, Jiangsu Province, China, 210009
| | - Mei-Ling Bao
- Department of Pathology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China, 210009
| | - Jing Zhang
- Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, 300, Guangzhou Road, Nanjing, Jiangsu Province, China, 210009
| | - Xiao-Ning Wang
- Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, 300, Guangzhou Road, Nanjing, Jiangsu Province, China, 210009
| | - Yu-Dong Zhang
- Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, 300, Guangzhou Road, Nanjing, Jiangsu Province, China, 210009.
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20
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Dikaios N, Atkinson D, Tudisca C, Purpura P, Forster M, Ahmed H, Beale T, Emberton M, Punwani S. A comparison of Bayesian and non-linear regression methods for robust estimation of pharmacokinetics in DCE-MRI and how it affects cancer diagnosis. Comput Med Imaging Graph 2017; 56:1-10. [PMID: 28192761 DOI: 10.1016/j.compmedimag.2017.01.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Revised: 11/16/2016] [Accepted: 01/26/2017] [Indexed: 11/23/2022]
Abstract
The aim of this work is to compare Bayesian Inference for nonlinear models with commonly used traditional non-linear regression (NR) algorithms for estimating tracer kinetics in Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI). The algorithms are compared in terms of accuracy, and reproducibility under different initialization settings. Further it is investigated how a more robust estimation of tracer kinetics affects cancer diagnosis. The derived tracer kinetics from the Bayesian algorithm were validated against traditional NR algorithms (i.e. Levenberg-Marquardt, simplex) in terms of accuracy on a digital DCE phantom and in terms of goodness-of-fit (Kolmogorov-Smirnov test) on ROI-based concentration time courses from two different patient cohorts. The first cohort consisted of 76 men, 20 of whom had significant peripheral zone prostate cancer (any cancer-core-length (CCL) with Gleason>3+3 or any-grade with CCL>=4mm) following transperineal template prostate mapping biopsy. The second cohort consisted of 9 healthy volunteers and 24 patients with head and neck squamous cell carcinoma. The diagnostic ability of the derived tracer kinetics was assessed with receiver operating characteristic area under curve (ROC AUC) analysis. The Bayesian algorithm accurately recovered the ground-truth tracer kinetics for the digital DCE phantom consistently improving the Structural Similarity Index (SSIM) across the 50 different initializations compared to NR. For optimized initialization, Bayesian did not improve significantly the fitting accuracy on both patient cohorts, and it only significantly improved the ve ROC AUC on the HN population from ROC AUC=0.56 for the simplex to ROC AUC=0.76. For both cohorts, the values and the diagnostic ability of tracer kinetic parameters estimated with the Bayesian algorithm weren't affected by their initialization. To conclude, the Bayesian algorithm led to a more accurate and reproducible quantification of tracer kinetic parameters in DCE-MRI, improving their ROC-AUC and decreasing their dependence on initialization settings.
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Affiliation(s)
- Nikolaos Dikaios
- Centre for Vision, Speech and Signal Processing, University of Surrey, United Kingdom.
| | - David Atkinson
- Centre for Medical Imaging, University College London, United Kingdom
| | - Chiara Tudisca
- Centre for Medical Imaging, University College London, United Kingdom
| | - Pierpaolo Purpura
- Centre for Medical Imaging, University College London, United Kingdom
| | - Martin Forster
- Department of Head and Neck Oncology, University College London Hospital, United Kingdom; Cancer Institute, University College London, United Kingdom
| | - Hashim Ahmed
- Department of Urology, University College London, London NW1 2 PG, United Kingdom
| | - Timothy Beale
- Department of Head and Neck Oncology, University College London Hospital, United Kingdom
| | - Mark Emberton
- Department of Urology, University College London, London NW1 2 PG, United Kingdom
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, United Kingdom; Department of Head and Neck Oncology, University College London Hospital, United Kingdom
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21
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Sun Y, Reynolds H, Wraith D, Williams S, Finnegan ME, Mitchell C, Murphy D, Ebert MA, Haworth A. Predicting prostate tumour location from multiparametric MRI using Gaussian kernel support vector machines: a preliminary study. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2017; 40:39-49. [PMID: 28120144 DOI: 10.1007/s13246-016-0515-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Accepted: 12/12/2016] [Indexed: 01/08/2023]
Abstract
The performance of a support vector machine (SVM) algorithm was investigated to predict prostate tumour location using multi-parametric MRI (mpMRI) data. The purpose was to obtain information of prostate tumour location for the implementation of bio-focused radiotherapy. In vivo mpMRI data were collected from 16 patients prior to radical prostatectomy. Sequences included T2-weighted imaging, diffusion-weighted imaging and dynamic contrast enhanced imaging. In vivo mpMRI was registered with 'ground truth' histology, using ex vivo MRI as an intermediate registration step to improve accuracy. Prostate contours were delineated by a radiation oncologist and tumours were annotated on histology by a pathologist. Five patients with minimal imaging artefacts were selected for this study. A Gaussian kernel SVM was trained and tested on different patient data subsets. Parameters were optimised using leave-oneout cross validation. Signal intensities of mpMRI were used as features and histology annotations as true labels. Prediction accuracy, as well as area under the curve (AUC) of the receiver operating characteristics (ROC) curve, were used to assess performance. Results demonstrated the prediction accuracy ranged from 70.4 to 87.1% and AUC of ROC ranged from 0.81 to 0.94. Additional investigations showed the apparent diffusion coefficient map from diffusion weighted imaging was the most important imaging modality for predicting tumour location. Future work will incorporate additional patient data into the framework to increase the sensitivity and specificity of the model, and will be extended to incorporate predictions of biological characteristics of the tumour which will be used in bio-focused radiotherapy optimisation.
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Affiliation(s)
- Yu Sun
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia. .,Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
| | - Hayley Reynolds
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia.,Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Darren Wraith
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | - Scott Williams
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia.,Division of Radiation Oncology and Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Mary E Finnegan
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK.,Department of Bioengineering, Imperial College London, London, UK
| | - Catherine Mitchell
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Declan Murphy
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Martin A Ebert
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, WA, Australia.,School of Physics, University of Western Australia, Perth, WA, Australia
| | - Annette Haworth
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia.,Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
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22
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Gao G, Wang C, Zhang X, Hu J, Yang X, Wang H, Zhang J, Wang X. Quantitative analysis of diffusion-weighted magnetic resonance images: differentiation between prostate cancer and normal tissue based on a computer-aided diagnosis system. SCIENCE CHINA-LIFE SCIENCES 2017; 60:37-43. [DOI: 10.1007/s11427-016-0389-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 09/28/2016] [Indexed: 12/24/2022]
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23
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Borofsky S, Haji-Momenian S, Shah S, Taffel M. Multiparametric MRI of the prostate gland: technical aspects. Future Oncol 2016; 12:2445-2462. [DOI: 10.2217/fon-2016-0218] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
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24
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Differentiating Transition Zone Cancers From Benign Prostatic Hyperplasia by Quantitative Multiparametric Magnetic Resonance Imaging. J Comput Assist Tomogr 2016; 40:218-24. [PMID: 26760185 DOI: 10.1097/rct.0000000000000353] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
OBJECTIVE The aim of this study was to evaluate the value of quantitative diffusion and perfusion parameters to aid in discriminating between transition zone carcinomas and benign prostatic hyperplasia (BPH). MATERIALS AND METHODS Twenty-four transition zone cancers and BPH nodules were contoured on T2-weighted magnetic resonance imaging (MRI), apparent diffusion coefficient (ADC) maps, and raw dynamic contrast-enhanced (DCE) MRI. Benign prostatic hyperplasia nodules were then stratified into 2 groups based on the presence or absence of a capsule. Apparent diffusion coefficient values, per-voxel Ktrans, kep, vp, and ve were all compared across all groups. RESULTS Average ADCs (×10 mm/s) were 1019.22, 1338.11, and 1272.46 for cancer, encapsulated BPH, and nonencapsulated BPH, respectively. Both subgroups of BPH were found to be significantly different than that of cancer (P < 0.05). No individual DCE-MRI parameter was significantly different between cancer and either BPH group. The area under the curve for ADC alone was 0.83, and no individual DCE imaging parameter improved the area under the curve of ADC. CONCLUSIONS Apparent diffusion coefficient may play a role in distinguishing TZ cancers from non-encapsulated BPH nodules that closely resemble cancer.
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25
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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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26
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Rampun A, Chen Z, Malcolm P, Tiddeman B, Zwiggelaar R. Computer-aided diagnosis: detection and localization of prostate cancer within the peripheral zone. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2016; 32:e02745. [PMID: 26313267 DOI: 10.1002/cnm.2745] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2014] [Revised: 05/21/2015] [Accepted: 08/24/2015] [Indexed: 06/04/2023]
Abstract
We propose a methodology for prostate cancer detection and localization within the peripheral zone based on combining multiple segmentation techniques. We extract four image features using Gaussian and median filters. Subsequently, we use each image feature separately to generate binary segmentations. Finally, we take the intersection of all four binary segmentations, incorporating a model of the peripheral zone, and perform erosion to remove small false-positive regions. The initial evaluation of this method is based on 275 MRI images from 37 patients, and 86% of the slices were classified correctly with 87% and 86% sensitivity and specificity achieved, respectively. This paper makes two contributions: firstly, a novel computer-aided diagnosis approach, which is based on combining multiple segmentation techniques using only a small number of simple image features, and secondly, the development of the proposed method and its application in prostate cancer detection and localization using a single MRI modality with the results comparable with the state-of-the-art multimodality and advanced computer vision methods in the literature. Copyright © 2015 John Wiley & Sons, Ltd.
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Affiliation(s)
- Andrik Rampun
- Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK
| | - Zhili Chen
- Information and Control Engineering Faculty, Shenyang Jianzhu University, Liaoning, 110168, China
| | - Paul Malcolm
- Department of Radiology, Norfolk Norwich University Hospital, Norwich, NR4 7UY, UK
| | - Bernie Tiddeman
- Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK
| | - Reyer Zwiggelaar
- Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK
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27
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Duda D, Kretowski M, Mathieu R, de Crevoisier R, Bezy-Wendling J. Multi-sequence texture analysis in classification of in vivo MR images of the prostate. Biocybern Biomed Eng 2016. [DOI: 10.1016/j.bbe.2016.05.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Khalifa F, Soliman A, El-Baz A, Abou El-Ghar M, El-Diasty T, Gimel'farb G, Ouseph R, Dwyer AC. Models and methods for analyzing DCE-MRI: a review. Med Phys 2015; 41:124301. [PMID: 25471985 DOI: 10.1118/1.4898202] [Citation(s) in RCA: 199] [Impact Index Per Article: 22.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
PURPOSE To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches. METHODS DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases. RESULTS Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors. CONCLUSIONS Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.
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Affiliation(s)
- Fahmi Khalifa
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292 and Electronics and Communication Engineering Department, Mansoura University, Mansoura 35516, Egypt
| | - Ahmed Soliman
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292
| | - Ayman El-Baz
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292
| | - Mohamed Abou El-Ghar
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | - Tarek El-Diasty
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | - Georgy Gimel'farb
- Department of Computer Science, University of Auckland, Auckland 1142, New Zealand
| | - Rosemary Ouseph
- Kidney Transplantation-Kidney Disease Center, University of Louisville, Louisville, Kentucky 40202
| | - Amy C Dwyer
- Kidney Transplantation-Kidney Disease Center, University of Louisville, Louisville, Kentucky 40202
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Dikaios N, Alkalbani J, Abd-Alazeez M, Sidhu HS, Kirkham A, Ahmed HU, Emberton M, Freeman A, Halligan S, Taylor S, Atkinson D, Punwani S. Zone-specific logistic regression models improve classification of prostate cancer on multi-parametric MRI. Eur Radiol 2015; 25:2727-37. [PMID: 25680730 DOI: 10.1007/s00330-015-3636-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 12/18/2014] [Accepted: 01/21/2015] [Indexed: 11/26/2022]
Abstract
OBJECTIVES To assess the interchangeability of zone-specific (peripheral-zone (PZ) and transition-zone (TZ)) multiparametric-MRI (mp-MRI) logistic-regression (LR) models for classification of prostate cancer. METHODS Two hundred and thirty-one patients (70 TZ training-cohort; 76 PZ training-cohort; 85 TZ temporal validation-cohort) underwent mp-MRI and transperineal-template-prostate-mapping biopsy. PZ and TZ uni/multi-variate mp-MRI LR-models for classification of significant cancer (any cancer-core-length (CCL) with Gleason > 3 + 3 or any grade with CCL ≥ 4 mm) were derived from the respective cohorts and validated within the same zone by leave-one-out analysis. Inter-zonal performance was tested by applying TZ models to the PZ training-cohort and vice-versa. Classification performance of TZ models for TZ cancer was further assessed in the TZ validation-cohort. ROC area-under-curve (ROC-AUC) analysis was used to compare models. RESULTS The univariate parameters with the best classification performance were the normalised T2 signal (T2nSI) within the TZ (ROC-AUC = 0.77) and normalized early contrast-enhanced T1 signal (DCE-nSI) within the PZ (ROC-AUC = 0.79). Performance was not significantly improved by bi-variate/tri-variate modelling. PZ models that contained DCE-nSI performed poorly in classification of TZ cancer. The TZ model based solely on maximum-enhancement poorly classified PZ cancer. CONCLUSION LR-models dependent on DCE-MRI parameters alone are not interchangable between prostatic zones; however, models based exclusively on T2 and/or ADC are more robust for inter-zonal application. KEY POINTS • The ADC and T2-nSI of benign/cancer PZ are higher than benign/cancer TZ. • DCE parameters are significantly different between benign PZ and TZ, but not between cancerous PZ and TZ. • Diagnostic models containing contrast enhancement parameters have reduced performance when applied across zones.
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Affiliation(s)
- Nikolaos Dikaios
- Centre for Medical Imaging, University College London, Level 3 East, 250 Euston Road, London, NW1 2PG, UK
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Cho E, Chung DJ, Yeo DM, Sohn D, Son Y, Kim T, Hahn ST. Optimal cut-off value of perfusion parameters for diagnosing prostate cancer and for assessing aggressiveness associated with Gleason score. Clin Imaging 2015; 39:834-40. [PMID: 26001659 DOI: 10.1016/j.clinimag.2015.04.020] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2014] [Revised: 04/16/2015] [Accepted: 04/28/2015] [Indexed: 11/16/2022]
Abstract
To determine cut-off value of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters for differentiation of prostate malignant from benign and cancer with high-grade Gleason score (GS) (GS>7) from low-grade GS (GS≤7), 35 patients (24 malignant and 11 benign) who underwent DCE-MRI were included. Difference between malignant and benign was statistically significant for all magnetic resonance parameters except Ve. The cut-off values were K(trans)=0.184min(-1), Kep=0.695min(-1), iAUC=4.219mmol/l/min, and ADC=1340.5mm(2)/s. A significant difference in mean values of K(trans) and Kep between cancer with high-grade GS and low-grade GS was also observed. K(trans) and Kep showed a significant correlation with GS.
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Affiliation(s)
- Eunkyung Cho
- Department of Radiology, The Catholic University of Korea, Yeouido St. Mary's Hospital, 62, Yeouido-dong, Yeoungdeungpo-gu, Seoul, Korea, 150-713.
| | - Dong Jin Chung
- Department of Radiology, The Catholic University of Korea, Yeouido St. Mary's Hospital, 62, Yeouido-dong, Yeoungdeungpo-gu, Seoul, Korea, 150-713.
| | - Dong Myung Yeo
- Department of Radiology, The Catholic University of Korea, Yeouido St. Mary's Hospital, 62, Yeouido-dong, Yeoungdeungpo-gu, Seoul, Korea, 150-713.
| | - Dongwan Sohn
- Department of Urology, The Catholic University of Korea, Yeouido St. Mary's Hospital, 62, Yeouido-dong, Yeoungdeungpo-gu, Seoul, Korea, 150-713.
| | - Yohan Son
- Imaging and Therapy Systems Division, SIEMENS Healthcare Korea, Poongsan Building 6th Floor, 23, Chungjeong-ro, Seodaemun-gu, Seoul, Korea, 120-714.
| | - Taejung Kim
- Department of Pathology, The Catholic University of Korea, Yeouido St. Mary's Hospital, 62, Yeouido-dong, Yeoungdeungpo-gu, Seoul, Korea, 150-713.
| | - Sung-Tae Hahn
- Department of Radiology, The Catholic University of Korea, Yeouido St. Mary's Hospital, 62, Yeouido-dong, Yeoungdeungpo-gu, Seoul, Korea, 150-713.
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Ginsburg SB, Viswanath SE, Bloch BN, Rofsky NM, Genega EM, Lenkinski RE, Madabhushi A. Novel PCA-VIP scheme for ranking MRI protocols and identifying computer-extracted MRI measurements associated with central gland and peripheral zone prostate tumors. J Magn Reson Imaging 2015; 41:1383-93. [PMID: 24943647 PMCID: PMC8176951 DOI: 10.1002/jmri.24676] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2014] [Accepted: 06/02/2014] [Indexed: 12/23/2022] Open
Abstract
PURPOSE To identify computer-extracted features for central gland and peripheral zone prostate cancer localization on multiparametric magnetic resonance imaging (MRI). MATERIALS AND METHODS Preoperative T2-weighted (T2w), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI were acquired from 23 men with confirmed prostate cancer. Following radical prostatectomy, the cancer extent was delineated by a pathologist on ex vivo histology and mapped to MRI by nonlinear registration of histology and corresponding MRI slices. In all, 244 computer-extracted features were extracted from MRI, and principal component analysis (PCA) was employed to reduce the data dimensionality so that a generalizable classifier could be constructed. A novel variable importance on projection (VIP) measure for PCA (PCA-VIP) was leveraged to identify computer-extracted MRI features that discriminate between cancer and normal prostate, and these features were used to construct classifiers for cancer localization. RESULTS Classifiers using features selected by PCA-VIP yielded an area under the curve (AUC) of 0.79 and 0.85 for peripheral zone and central gland tumors, respectively. For tumor localization in the central gland, T2w, DCE, and DWI MRI features contributed 71.6%, 18.1%, and 10.2%, respectively; for peripheral zone tumors T2w, DCE, and DWI MRI contributed 29.6%, 21.7%, and 48.7%, respectively. CONCLUSION PCA-VIP identified relatively stable subsets of MRI features that performed well in localizing prostate cancer on MRI.
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Affiliation(s)
- Shoshana B. Ginsburg
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Satish E. Viswanath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - B. Nicolas Bloch
- Department of Radiology, Boston University School of Medicine, Boston, MA, USA
| | - Neil M. Rofsky
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Elizabeth M. Genega
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Robert E. Lenkinski
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
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Lemaître G, Martí R, Freixenet J, Vilanova JC, Walker PM, Meriaudeau F. Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review. Comput Biol Med 2015; 60:8-31. [PMID: 25747341 DOI: 10.1016/j.compbiomed.2015.02.009] [Citation(s) in RCA: 121] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 02/11/2015] [Accepted: 02/12/2015] [Indexed: 12/30/2022]
Abstract
Prostate cancer is the second most diagnosed cancer of men all over the world. In the last few decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed to improve diagnosis. In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systems have been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field of research for the last 10 years. This survey aims to provide a comprehensive review of the state-of-the-art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aided system. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to the research community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey.
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Affiliation(s)
- Guillaume Lemaître
- LE2I-UMR CNRS 6306, Université de Bourgogne, 12 rue de la Fonderie, 71200 Le Creusot, France; ViCOROB, Universitat de Girona, Campus Montilivi, Edifici P4, 17071 Girona, Spain.
| | - Robert Martí
- ViCOROB, Universitat de Girona, Campus Montilivi, Edifici P4, 17071 Girona, Spain.
| | - Jordi Freixenet
- ViCOROB, Universitat de Girona, Campus Montilivi, Edifici P4, 17071 Girona, Spain.
| | - Joan C Vilanova
- Department of Magnetic Resonance, Clínica Girona, Lorenzana 36, 17002 Girona, Spain
| | - Paul M Walker
- LE2I-UMR CNRS 6306, Université de Bourgogne, Avenue Alain Savary, 21000 Dijon, France.
| | - Fabrice Meriaudeau
- LE2I-UMR CNRS 6306, Université de Bourgogne, 12 rue de la Fonderie, 71200 Le Creusot, France.
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Dikaios N, Alkalbani J, Sidhu HS, Fujiwara T, Abd-Alazeez M, Kirkham A, Allen C, Ahmed H, Emberton M, Freeman A, Halligan S, Taylor S, Atkinson D, Punwani S. Logistic regression model for diagnosis of transition zone prostate cancer on multi-parametric MRI. Eur Radiol 2015; 25:523-32. [PMID: 25226842 PMCID: PMC4291517 DOI: 10.1007/s00330-014-3386-4] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2014] [Accepted: 08/05/2014] [Indexed: 12/29/2022]
Abstract
OBJECTIVES We aimed to develop logistic regression (LR) models for classifying prostate cancer within the transition zone on multi-parametric magnetic resonance imaging (mp-MRI). METHODS One hundred and fifty-five patients (training cohort, 70 patients; temporal validation cohort, 85 patients) underwent mp-MRI and transperineal-template-prostate-mapping (TPM) biopsy. Positive cores were classified by cancer definitions: (1) any-cancer; (2) definition-1 [≥Gleason 4 + 3 or ≥ 6 mm cancer core length (CCL)] [high risk significant]; and (3) definition-2 (≥Gleason 3 + 4 or ≥ 4 mm CCL) cancer [intermediate-high risk significant]. For each, logistic-regression mp-MRI models were derived from the training cohort and validated internally and with the temporal cohort. Sensitivity/specificity and the area under the receiver operating characteristic (ROC-AUC) curve were calculated. LR model performance was compared to radiologists' performance. RESULTS Twenty-eight of 70 patients from the training cohort, and 25/85 patients from the temporal validation cohort had significant cancer on TPM. The ROC-AUC of the LR model for classification of cancer was 0.73/0.67 at internal/temporal validation. The radiologist A/B ROC-AUC was 0.65/0.74 (temporal cohort). For patients scored by radiologists as Prostate Imaging Reporting and Data System (Pi-RADS) score 3, sensitivity/specificity of radiologist A 'best guess' and LR model was 0.14/0.54 and 0.71/0.61, respectively; and radiologist B 'best guess' and LR model was 0.40/0.34 and 0.50/0.76, respectively. CONCLUSIONS LR models can improve classification of Pi-RADS score 3 lesions similar to experienced radiologists. KEY POINTS • MRI helps find prostate cancer in the anterior of the gland • Logistic regression models based on mp-MRI can classify prostate cancer • Computers can help confirm cancer in areas doctors are uncertain about.
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Affiliation(s)
- Nikolaos Dikaios
- Centre for Medical Imaging, University College London, 3rd Floor East Wing, 250 Euston Road, London, UK NW1 2PG
- Departments of Radiology, University College London Hospital, 235 Euston Road, London, UK NW1 2BU
| | - Jokha Alkalbani
- Centre for Medical Imaging, University College London, 3rd Floor East Wing, 250 Euston Road, London, UK NW1 2PG
| | - Harbir Singh Sidhu
- Centre for Medical Imaging, University College London, 3rd Floor East Wing, 250 Euston Road, London, UK NW1 2PG
| | - Taiki Fujiwara
- Centre for Medical Imaging, University College London, 3rd Floor East Wing, 250 Euston Road, London, UK NW1 2PG
| | - Mohamed Abd-Alazeez
- Research Department of Urology, University College London, London, UK NW1 2PG
| | - Alex Kirkham
- Departments of Radiology, University College London Hospital, 235 Euston Road, London, UK NW1 2BU
| | - Clare Allen
- Departments of Radiology, University College London Hospital, 235 Euston Road, London, UK NW1 2BU
| | - Hashim Ahmed
- Research Department of Urology, University College London, London, UK NW1 2PG
| | - Mark Emberton
- Research Department of Urology, University College London, London, UK NW1 2PG
| | - Alex Freeman
- Department of Histopathology, University College London Hospital, London, UK NW1 2PG
| | - Steve Halligan
- Centre for Medical Imaging, University College London, 3rd Floor East Wing, 250 Euston Road, London, UK NW1 2PG
- Departments of Radiology, University College London Hospital, 235 Euston Road, London, UK NW1 2BU
| | - Stuart Taylor
- Centre for Medical Imaging, University College London, 3rd Floor East Wing, 250 Euston Road, London, UK NW1 2PG
- Departments of Radiology, University College London Hospital, 235 Euston Road, London, UK NW1 2BU
| | - David Atkinson
- Centre for Medical Imaging, University College London, 3rd Floor East Wing, 250 Euston Road, London, UK NW1 2PG
- Departments of Radiology, University College London Hospital, 235 Euston Road, London, UK NW1 2BU
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, 3rd Floor East Wing, 250 Euston Road, London, UK NW1 2PG
- Departments of Radiology, University College London Hospital, 235 Euston Road, London, UK NW1 2BU
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Final Gleason score prediction using discriminant analysis and support vector machine based on preoperative multiparametric MR imaging of prostate cancer at 3T. BIOMED RESEARCH INTERNATIONAL 2014; 2014:690787. [PMID: 25544944 PMCID: PMC4269213 DOI: 10.1155/2014/690787] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2014] [Revised: 09/10/2014] [Accepted: 09/12/2014] [Indexed: 12/11/2022]
Abstract
Objective. This study aimed at evaluating linear discriminant analysis (LDA) and support vector machine (SVM) classifiers for estimating final Gleason score preoperatively using multiparametric magnetic resonance imaging (mp-MRI) and clinical parameters. Materials and Methods. Thirty-three patients who underwent mp-MRI on a 3T clinical MR scanner and radical prostatectomy were enrolled in this study. The input features for classifiers were age, the presence of a palpable prostate abnormality, prostate specific antigen (PSA) level, index lesion size, and Likert scales of T2 weighted MRI (T2w-MRI), diffusion weighted MRI (DW-MRI), and dynamic contrast enhanced MRI (DCE-MRI) estimated by an experienced radiologist. SVM based recursive feature elimination (SVM-RFE) was used for eliminating features. Principal component analysis (PCA) was applied for data uncorrelation. Results. Using a standard PCA before final Gleason score classification resulted in mean sensitivities of 51.19% and 64.37% and mean specificities of 72.71% and 39.90% for LDA and SVM, respectively. Using a Gaussian kernel PCA resulted in mean sensitivities of 86.51% and 87.88% and mean specificities of 63.99% and 56.83% for LDA and SVM, respectively. Conclusion. SVM classifier resulted in a slightly higher sensitivity but a lower specificity than LDA method for final Gleason score prediction for prostate cancer for this limited patient population.
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Sharma S. Imaging and intervention in prostate cancer: Current perspectives and future trends. Indian J Radiol Imaging 2014; 24:139-48. [PMID: 25024523 PMCID: PMC4094966 DOI: 10.4103/0971-3026.134399] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Prostate cancer is the commonest malignancy in men that causes significant morbidity and mortality worldwide. Screening by digital rectal examination (DRE) and serum prostate-specific antigen (PSA) is used despite its limitations. Gray-scale transrectal ultrasound (TRUS), used to guide multiple random prostatic biopsies, misses up to 20% cancers and frequently underestimates the grade of malignancy. Increasing the number of biopsy cores marginally increases the yield. Evolving techniques of real-time ultrasound elastography (RTE) and contrast-enhanced ultrasound (CEUS) are being investigated to better detect and improve the yield by allowing “targeted” biopsies. Last decade has witnessed rapid developments in magnetic resonance imaging (MRI) for improved management of prostate cancer. In addition to the anatomical information, it is capable of providing functional information through diffusion-weighted imaging (DWI), magnetic resonance spectroscopy (MRS), and dynamic contrast-enhanced (DCE) MRI. Multi-parametric MRI has the potential to exclude a significant cancer in majority of cases. Inclusion of MRI before prostatic biopsy can reduce the invasiveness of the procedure by limiting the number of cores needed to make a diagnosis and support watchful waiting in others. It is made possible by targeted biopsies as opposed to random. With the availability of minimally invasive therapeutic modalities like high-intensity focused ultrasound (HIFU) and interstitial laser therapy, detecting early cancer is even more relevant today. [18F]--fluorodeoxyglucose positron emission tomography/computed tomography (18FDG PET/CT) has no role in the initial evaluation of prostate cancer. Choline PET has been recently found to be more useful. Fluoride-PET has a higher sensitivity and resolution than a conventional radionuclide bone scan in detecting skeletal metastases.
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Affiliation(s)
- Sanjay Sharma
- Department of Radiodiagnosis, All Institute of Medical Sciences, New Delhi, India
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Rosenkrantz AB, Geppert C, Grimm R, Block TK, Glielmi C, Feng L, Otazo R, Ream JM, Romolo MM, Taneja SS, Sodickson DK, Chandarana H. Dynamic contrast-enhanced MRI of the prostate with high spatiotemporal resolution using compressed sensing, parallel imaging, and continuous golden-angle radial sampling: preliminary experience. J Magn Reson Imaging 2014; 41:1365-73. [PMID: 24833417 DOI: 10.1002/jmri.24661] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2014] [Revised: 04/24/2014] [Accepted: 04/28/2014] [Indexed: 01/06/2023] Open
Abstract
PURPOSE To demonstrate dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of the prostate with both high spatial and temporal resolution via a combination of golden-angle radial k-space sampling, compressed sensing, and parallel-imaging reconstruction (GRASP), and to compare image quality and lesion depiction between GRASP and conventional DCE in prostate cancer patients. MATERIALS AND METHODS Twenty prostate cancer patients underwent two 3T prostate MRI examinations on separate dates, one using standard DCE (spatial resolution 3.0 × 1.9 × 1.9 mm, temporal resolution 5.5 sec) and the other using GRASP (spatial resolution 3.0 × 1.1 × 1.1 mm, temporal resolution 2.3 sec). Two radiologists assessed measures of image quality and dominant lesion size. The experienced reader recorded differences in contrast arrival times between the dominant lesion and benign prostate. RESULTS Compared with standard DCE, GRASP demonstrated significantly better clarity of the capsule, peripheral/transition zone boundary, urethra, and periprostatic vessels; image sharpness; and lesion conspicuity for both readers (P < 0.001-0.020). GRASP showed improved interreader correlation for lesion size (GRASP: r = 0.691-0.824, standard: r = 0.495-0.542). In 8/20 cases, only GRASP showed earlier contrast arrival in tumor than benign; in no case did only standard DCE show earlier contrast arrival in tumor. CONCLUSION High spatiotemporal resolution prostate DCE is possible with GRASP, which has the potential to improve image quality and lesion depiction as compared with standard DCE.
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Dankerl P, Cavallaro A, Dietzel M, Tsymbal A, Kramer M, Seifert S, Uder M, Hammon M. Clinical evaluation of semi-automatic landmark-based lesion tracking software for CT-scans. Cancer Imaging 2014; 14:6. [PMID: 25609496 PMCID: PMC4212533 DOI: 10.1186/1470-7330-14-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2013] [Accepted: 01/09/2014] [Indexed: 11/10/2022] Open
Abstract
Background To evaluate a semi-automatic landmark-based lesion tracking software enabling navigation between RECIST lesions in baseline and follow-up CT-scans. Methods The software automatically detects 44 stable anatomical landmarks in each thoraco/abdominal/pelvic CT-scan, sets up a patient specific coordinate-system and cross-links the coordinate-systems of consecutive CT-scans. Accuracy of the software was evaluated on 96 RECIST lesions (target- and non-target lesions) in baseline and follow-up CT-scans of 32 oncologic patients (64 CT-scans). Patients had to present at least one thoracic, one abdominal and one pelvic RECIST lesion. Three radiologists determined the deviation between lesions’ centre and the software’s navigation result in consensus. Results The initial mean runtime of the system to synchronize baseline and follow-up examinations was 19.4 ± 1.2 seconds, with subsequent navigation to corresponding RECIST lesions facilitating in real-time. Mean vector length of the deviations between lesions’ centre and the semi-automatic navigation result was 10.2 ± 5.1 mm without a substantial systematic error in any direction. Mean deviation in the cranio-caudal dimension was 5.4 ± 4.0 mm, in the lateral dimension 5.2 ± 3.9 mm and in the ventro-dorsal dimension 5.3 ± 4.0 mm. Conclusion The investigated software accurately and reliably navigates between lesions in consecutive CT-scans in real-time, potentially accelerating and facilitating cancer staging.
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Duda D, Kretowski M, Mathieu R, de Crevoisier R, Bezy-Wendling J. Multi-Image Texture Analysis in Classification of Prostatic Tissues from MRI. Preliminary Results. ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2014. [DOI: 10.1007/978-3-319-06593-9_13] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Puech P, Sufana-Iancu A, Renard B, Lemaitre L. Prostate MRI: can we do without DCE sequences in 2013? Diagn Interv Imaging 2013; 94:1299-311. [PMID: 24211261 DOI: 10.1016/j.diii.2013.09.010] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Multiparametric MRI (mp-MRI) of the prostate currently provides stable and reproducible performances. The usefulness of dynamic contrast-enhanced (DCE) sequences is currently challenged, as they sometimes only confirm what has already been observed on diffusion-weighted imaging (DWI) and require the additional purchase of a contrast agent. Eliminating these sequences may help accelerate the use of MRI in addition to, or in lieu of, prostate biopsies in selected patients. However, many studies show that these sequences can detect lesions invisible on T2-weighted and diffusion-weighted images, better assess cancer extension and aggressiveness, and finally help detecting recurrence after treatment. We present the various applications of dynamic MRI and discuss the possible consequences of its omission from the current protocol.
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Affiliation(s)
- P Puech
- Department of Uroradiology, Lille Hospital, 1, rue Michel-Polonovski, 59037 Lille cedex, France; University of Lille Nord de France, Lille 59800, France; Inserm U703, 59120 Loos, France.
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Kirkham APS, Haslam P, Keanie JY, McCafferty I, Padhani AR, Punwani S, Richenberg J, Rottenberg G, Sohaib A, Thompson P, Turnbull LW, Kurban L, Sahdev A, Clements R, Carey BM, Allen C. Prostate MRI: who, when, and how? Report from a UK consensus meeting. Clin Radiol 2013; 68:1016-23. [PMID: 23827086 DOI: 10.1016/j.crad.2013.03.030] [Citation(s) in RCA: 71] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Revised: 03/16/2013] [Accepted: 03/20/2013] [Indexed: 10/26/2022]
Abstract
The current pathway for men suspected of having prostate cancer [transrectal biopsy, followed in some cases by magnetic resonance imaging (MRI) for staging] results in over-diagnosis of insignificant tumours, and systematically misses disease in the anterior prostate. Multiparametric MRI has the potential to change this pathway, and if performed before biopsy, might enable the exclusion of significant disease in some men without biopsy, targeted biopsy in others, and improvements in the performance of active surveillance. For the potential benefits to be realized, the setting of standards is vital. This article summarizes the outcome of a meeting of UK radiologists, at which a consensus was achieved on (1) the indications for MRI, (2) the conduct of the scan, (3) a method and template for reporting, and (4) minimum standards for radiologists.
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Affiliation(s)
- A P S Kirkham
- Department of Imaging, University College Hospital, London, UK.
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Chesnais A, Niaf E, Bratan F, Mège-Lechevallier F, Roche S, Rabilloud M, Colombel M, Rouvière O. Differentiation of transitional zone prostate cancer from benign hyperplasia nodules: Evaluation of discriminant criteria at multiparametric MRI. Clin Radiol 2013; 68:e323-30. [DOI: 10.1016/j.crad.2013.01.018] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2012] [Revised: 01/12/2013] [Accepted: 01/23/2013] [Indexed: 10/27/2022]
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Hammon M, Dankerl P, Tsymbal A, Wels M, Kelm M, May M, Suehling M, Uder M, Cavallaro A. Automatic detection of lytic and blastic thoracolumbar spine metastases on computed tomography. Eur Radiol 2013; 23:1862-70. [PMID: 23397381 PMCID: PMC3674341 DOI: 10.1007/s00330-013-2774-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2012] [Revised: 12/06/2012] [Accepted: 12/19/2012] [Indexed: 11/29/2022]
Abstract
Objective To evaluate a computer-aided detection (CADe) system for lytic and blastic spinal metastases on computed tomography (CT). Methods We retrospectively evaluated the CADe system on 20 consecutive patients with 42 lytic and on 30 consecutive patients with 172 blastic metastases. The CADe system was trained using CT images of 114 subjects with 102 lytic and 308 blastic spinal metastases. Lesions were annotated by experienced radiologists. Detected benign lesions were considered false-positive findings. Detector sensitivity and the number of false-positive findings were calculated as the criteria for detector performance, and free-response receiver operating characteristic (FROC) analysis was conducted. Detailed analysis of false-positive and false-negative findings was performed. Results Algorithm runtime is 3 ± 0.5 min per patient. The system achieves a sensitivity of 83 % at 3.5 false positives per patient on average for blastic metastases and a sensitivity of 88 % at 3.7 false positives for lytic metastases. False positives appeared predominantly in the area of degenerative changes in the case of the blastic metastasis detector and in osteoporotic areas in the case of the lytic metastasis detector. Conclusion The CADe system reliably detects thoracolumbar spine metastases in real time. An additional study is planned to evaluate how the bone lesion CADe system improves radiologists’ accuracy and efficiency in a clinical setting. Key Points • Computer-aided detection (CADe) of bone metastases has been developed for spinal CT. • The CADe system exhibits high sensitivity with a tolerable false-positive rate. • Analysis of false-positive detection may further improve the system. • CADe may reduce the number of missed spinal metastases at CT interpretation.
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Affiliation(s)
- Matthias Hammon
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 1, 91054 Erlangen, Germany.
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Peng Y, Jiang Y, Yang C, Brown JB, Antic T, Sethi I, Schmid-Tannwald C, Giger ML, Eggener SE, Oto A. Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score--a computer-aided diagnosis development study. Radiology 2013; 267:787-96. [PMID: 23392430 DOI: 10.1148/radiol.13121454] [Citation(s) in RCA: 208] [Impact Index Per Article: 18.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
PURPOSE To evaluate the potential utility of a number of parameters obtained at T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced multiparametric magnetic resonance (MR) imaging for computer-aided diagnosis (CAD) of prostate cancer and assessment of cancer aggressiveness. MATERIALS AND METHODS In this institutional review board-approved HIPAA-compliant study, multiparametric MR images were acquired with an endorectal coil in 48 patients with prostate cancer (median age, 62.5 years; age range, 44-73 years) who subsequently underwent prostatectomy. A radiologist and a pathologist identified 104 regions of interest (ROIs) (61 cancer ROIs, 43 normal ROIs) based on correlation of histologic and MR findings. The 10th percentile and average apparent diffusion coefficient (ADC) values, T2-weighted signal intensity histogram skewness, and Tofts K(trans) were analyzed, both individually and combined, via linear discriminant analysis, with receiver operating characteristic curve analysis with area under the curve (AUC) as figure of merit, to distinguish cancer foci from normal foci. Spearman rank-order correlation (ρ) was calculated between cancer foci Gleason score (GS) and image features. RESULTS AUC (maximum likelihood estimate ± standard error) values in the differentiation of prostate cancer from normal foci of 10th percentile ADC, average ADC, T2-weighted skewness, and K(trans) were 0.92 ± 0.03, 0.89 ± 0.03, 0.86 ± 0.04, and 0.69 ± 0.04, respectively. The combination of 10th percentile ADC, average ADC, and T2-weighted skewness yielded an AUC value for the same task of 0.95 ± 0.02. GS correlated moderately with 10th percentile ADC (ρ = -0.34, P = .008), average ADC (ρ = -0.30, P = .02), and K(trans) (ρ = 0.38, P = .004). CONCLUSION The combination of 10th percentile ADC, average ADC, and T2-weighted skewness with CAD is promising in the differentiation of prostate cancer from normal tissue. ADC image features and K(trans) moderately correlate with GS.
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Affiliation(s)
- Yahui Peng
- Departments of Radiology, Pathology, and Surgery, Section of Urology, University of Chicago, 5841 S Maryland Ave, MC2026, Chicago, IL 60637, USA.
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Talab SS, Preston MA, Elmi A, Tabatabaei S. Prostate cancer imaging: what the urologist wants to know. Radiol Clin North Am 2013; 50:1015-41. [PMID: 23122036 DOI: 10.1016/j.rcl.2012.08.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
No consensus exists at present regarding the use of imaging for the evaluation of prostate cancer. Ultrasonography is mainly used for biopsy guidance and magnetic resonance imaging is the mainstay in evaluating the extent of local tumor. Computed tomography and radionuclide bone scanning are mainly reserved for assessment of advanced disease. Positron emission tomography is gaining acceptance in the evaluation of treatment response and recurrence. The combination of anatomic, functional, and metabolic imaging modalities has promise to improve treatment. This article reviews current imaging techniques and touches on the evolving technologies being used for detection and follow-up of prostate cancer.
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Affiliation(s)
- Saman Shafaat Talab
- Department of Urology, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, USA
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Mazzetti S, Gliozzi AS, Bracco C, Russo F, Regge D, Stasi M. Comparison between PUN and Tofts models in the quantification of dynamic contrast-enhanced MR imaging. Phys Med Biol 2012. [PMID: 23202297 DOI: 10.1088/0031-9155/57/24/8443] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Dynamic contrast-enhanced study in magnetic resonance imaging (DCE-MRI) is an important tool in oncology to visualize tissues vascularization and to define tumour aggressiveness on the basis of an altered perfusion and permeability. Pharmacokinetic models are generally used to extract hemodynamic parameters, providing a quantitative description of the contrast uptake and wash-out. Empirical functions can also be used to fit experimental data without the need of any assumption about tumour physiology, as in pharmacokinetic models, increasing their diagnostic utility, in particular when automatic diagnosis systems are implemented on the basis of an MRI multi-parametric approach. Phenomenological universalities (PUN) represent a novel tool for experimental research and offer a simple and systematic method to represent a set of data independent of the application field. DCE-MRI acquisitions can thus be advantageously evaluated by the extended PUN class, providing a convenient diagnostic tool to analyse functional studies, adding a new set of features for the classification of malignant and benign lesions in computer aided detection systems. In this work the Tofts pharmacokinetic model and the class EU1 generated by the PUN description were compared in the study of DCE-MRI of the prostate, evaluating complexity of model implementation, goodness of fitting results, classification performances and computational cost. The mean R² obtained with the EU1 and Tofts model were equal to 0.96 and 0.90, respectively, and the classification performances achieved by the EU1 model and the Tofts implementation discriminated malignant from benign tissues with an area under the receiver operating characteristic curve equal to 0.92 and 0.91, respectively. Furthermore, the EU1 model has a simpler functional form which reduces implementation complexity and computational time, requiring 6 min to complete a patient elaboration process, instead of 8 min needed for the Tofts model analysis.
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Affiliation(s)
- S Mazzetti
- Institute for Cancer Research and Treatment, Strada Provinciale 142, km 3.95, 10060 Candiolo, Torino, Italy.
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Valentini AL, Gui B, Cina A, Pinto F, Totaro A, Pierconti F, Bassi PF, Bonomo L. T2-weighted hypointense lesions within prostate gland: Differential diagnosis using wash-in rate parameter on the basis of dynamic contrast-enhanced magnetic resonance imaging—Hystopatology correlations. Eur J Radiol 2012; 81:3090-5. [DOI: 10.1016/j.ejrad.2012.05.019] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2012] [Revised: 05/15/2012] [Accepted: 05/16/2012] [Indexed: 01/09/2023]
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Perfusion MDCT of Prostate Cancer: Correlation of Perfusion CT Parameters and Immunohistochemical Markers of Angiogenesis. AJR Am J Roentgenol 2012; 199:1042-8. [DOI: 10.2214/ajr.11.8267] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Koh TS, Shi W, Thng CH, Kwek JW, Bisdas S, Khoo JBK. Interpretation and applicability of empirical tissue enhancement metrics in dynamic contrast-enhanced MRI based on a multiple pathway model. Phys Med Biol 2012; 57:N279-94. [DOI: 10.1088/0031-9155/57/15/n279] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Role of pelvic phased array magnetic resonance imaging in staging of prostate cancer specifically in patients diagnosed with clinically locally advanced tumours by digital rectal examination. World J Urol 2011; 31:881-6. [DOI: 10.1007/s00345-011-0811-z] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2011] [Accepted: 12/08/2011] [Indexed: 01/18/2023] Open
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