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Stamatelatou A, Bertinetto CG, Jansen JJ, Postma G, Selnaes KM, Bathen TF, Heerschap A, Scheenen TWJ. A multivariate curve resolution analysis of multicenter proton spectroscopic imaging of the prostate for cancer localization and assessment of aggressiveness. NMR Biomed 2024; 37:e5062. [PMID: 37920145 DOI: 10.1002/nbm.5062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/21/2023] [Accepted: 09/25/2023] [Indexed: 11/04/2023]
Abstract
In this study, we investigated the potential of the multivariate curve resolution alternating least squares (MCR-ALS) algorithm for analyzing three-dimensional (3D) 1 H-MRSI data of the prostate in prostate cancer (PCa) patients. MCR-ALS generates relative intensities of components representing spectral profiles derived from a large training set of patients, providing an interpretable model. Our objectives were to classify magnetic resonance (MR) spectra, differentiating tumor lesions from benign tissue, and to assess PCa aggressiveness. We included multicenter 3D 1 H-MRSI data from 106 PCa patients across eight centers. The patient cohort was divided into a training set (N = 63) and an independent test set (N = 43). Singular value decomposition determined that MR spectra were optimally represented by five components. The profiles of these components were extracted from the training set by MCR-ALS and assigned to specific tissue types. Using these components, MCR-ALS was applied to the test set for a quantitative analysis to discriminate tumor lesions from benign tissue and to assess tumor aggressiveness. Relative intensity maps of the components were reconstructed and compared with histopathology reports. The quantitative analysis demonstrated a significant separation between tumor and benign voxels (t-test, p < 0.001). This result was achieved including voxels with low-quality MR spectra. A receiver operating characteristic analysis of the relative intensity of the tumor component revealed that low- and high-risk tumor lesions could be distinguished with an area under the curve of 0.88. Maps of this component properly identified the extent of tumor lesions. Our study demonstrated that MCR-ALS analysis of 1 H-MRSI of the prostate can reliably identify tumor lesions and assess their aggressiveness. It handled multicenter data with minimal preprocessing and without using prior knowledge or quality control. These findings indicate that MCR-ALS can serve as an automated tool to assess the presence, extent, and aggressiveness of tumor lesions in the prostate, enhancing diagnostic capabilities and treatment planning of PCa patients.
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Affiliation(s)
- Angeliki Stamatelatou
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Jeroen J Jansen
- Department of Analytical Chemistry & Chemometrics, Radboud University, Nijmegen, The Netherlands
| | - Geert Postma
- Department of Analytical Chemistry & Chemometrics, Radboud University, Nijmegen, The Netherlands
| | - Kirsten Margrete Selnaes
- Department of Circulation and Medical Imaging, Norwegian University of Technology and Science, Trondheim, Norway
| | - Tone F Bathen
- Department of Circulation and Medical Imaging, Norwegian University of Technology and Science, Trondheim, Norway
- Department of radiology and nuclear medicine, St. Olavs Hospital - Trondheim University Hospital, Trondheim, Norway
| | - Arend Heerschap
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Tom W J Scheenen
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
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Li X, Li C, Chen M. Patients With "Gray Zone" PSA Levels: Application of Prostate MRI and MRS in the Diagnosis of Prostate Cancer. J Magn Reson Imaging 2023; 57:992-1010. [PMID: 36326563 DOI: 10.1002/jmri.28505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022] Open
Abstract
Improving the detection rates of prostate cancer (PCa) and avoiding unnecessary prostate biopsies in men with prostate-specific antigen (PSA) levels within the gray zone require urgent attention. In this context, rapid advances in MR technology in recent years may offer a promising possibility. A systematic review to evaluate the applications of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) in detecting PCa and clinically significant PCa (csPCa) in men with PSA levels within the gray zone. The study type is defined as systematic review. In July 2022, out of 229 studies identified by the database search and from other sources, 23 articles related to the selected topic of interest were included in this review. No field strength or sequence restrictions. The data including the study population, study characteristics, as well as basic MRI characteristics, from the final studies included in this review, were extracted independently by two reviewers. The major results of the original study were summarized and no additional statistical analysis was performed. Among the 23 studies included in this review, 17 focused on the applications of MRS and MRI for the prebiopsy diagnosis of PCa. Nine of these 17 articles used Prostate Imaging Reporting and Data System (PI-RADS) score to interpret MRI results, thereby confirming the practicality of the PI-RADS score in predicting PCa and csPCa. The remaining six articles evaluated the applications of MRI and MRS in guiding prostate biopsy. Although there was a variation in the biopsy modalities used in these studies, both MRI- and MRS-guided prostate biopsies were observed to improve the detection rates of PCa and csPCa in patients with PSA levels within the gray zone. MRS and MRI showed good performance in the detection of PCa and csPCa before biopsy. In addition, MRS- or MRI-guided prostate-targeted biopsies were able to improve the detection rates of PCa and csPCa. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Xue Li
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Chunmei Li
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Min Chen
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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Stamatelatou A, Scheenen TWJ, Heerschap A. Developments in proton MR spectroscopic imaging of prostate cancer. MAGMA 2022; 35:645-665. [PMID: 35445307 PMCID: PMC9363347 DOI: 10.1007/s10334-022-01011-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/04/2022] [Accepted: 03/22/2022] [Indexed: 10/25/2022]
Abstract
In this paper, we review the developments of 1H-MR spectroscopic imaging (MRSI) methods designed to investigate prostate cancer, covering key aspects such as specific hardware, dedicated pulse sequences for data acquisition and data processing and quantification techniques. Emphasis is given to recent advancements in MRSI methodologies, as well as future developments, which can lead to overcome difficulties associated with commonly employed MRSI approaches applied in clinical routine. This includes the replacement of standard PRESS sequences for volume selection, which we identified as inadequate for clinical applications, by sLASER sequences and implementation of 1H MRSI without water signal suppression. These may enable a new evaluation of the complementary role and significance of MRSI in prostate cancer management.
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Affiliation(s)
- Angeliki Stamatelatou
- Department of Medical Imaging (766), Radboud University Medical Center Nijmegen, Geert Grooteplein 10, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.
| | - Tom W J Scheenen
- Department of Medical Imaging (766), Radboud University Medical Center Nijmegen, Geert Grooteplein 10, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Arend Heerschap
- Department of Medical Imaging (766), Radboud University Medical Center Nijmegen, Geert Grooteplein 10, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands
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Bottani S, Burgos N, Maire A, Wild A, Ströer S, Dormont D, Colliot O. Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse. Med Image Anal 2021; 75:102219. [PMID: 34773767 DOI: 10.1016/j.media.2021.102219] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 08/12/2021] [Accepted: 08/26/2021] [Indexed: 10/20/2022]
Abstract
Many studies on machine learning (ML) for computer-aided diagnosis have so far been mostly restricted to high-quality research data. Clinical data warehouses, gathering routine examinations from hospitals, offer great promises for training and validation of ML models in a realistic setting. However, the use of such clinical data warehouses requires quality control (QC) tools. Visual QC by experts is time-consuming and does not scale to large datasets. In this paper, we propose a convolutional neural network (CNN) for the automatic QC of 3D T1-weighted brain MRI for a large heterogeneous clinical data warehouse. To that purpose, we used the data warehouse of the hospitals of the Greater Paris area (Assistance Publique-Hôpitaux de Paris [AP-HP]). Specifically, the objectives were: 1) to identify images which are not proper T1-weighted brain MRIs; 2) to identify acquisitions for which gadolinium was injected; 3) to rate the overall image quality. We used 5000 images for training and validation and a separate set of 500 images for testing. In order to train/validate the CNN, the data were annotated by two trained raters according to a visual QC protocol that we specifically designed for application in the setting of a data warehouse. For objectives 1 and 2, our approach achieved excellent accuracy (balanced accuracy and F1-score >90%), similar to the human raters. For objective 3, the performance was good but substantially lower than that of human raters. Nevertheless, the automatic approach accurately identified (balanced accuracy and F1-score >80%) low quality images, which would typically need to be excluded. Overall, our approach shall be useful for exploiting hospital data warehouses in medical image computing.
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Affiliation(s)
- Simona Bottani
- Inria, Aramis project-team, Paris, 75013, France; Sorbonne Université, Paris, 75013, France; Institut du Cerveau - Paris Brain Institute-ICM, Paris, 75013, France; Inserm, Paris, 75013, France; CNRS, Paris, 75013, France; AP-HP, Hôpital de la Pitié Salpêtrière, Paris, 75013, France
| | - Ninon Burgos
- Sorbonne Université, Paris, 75013, France; Institut du Cerveau - Paris Brain Institute-ICM, Paris, 75013, France; Inserm, Paris, 75013, France; CNRS, Paris, 75013, France; AP-HP, Hôpital de la Pitié Salpêtrière, Paris, 75013, France; Inria, Aramis project-team, Paris, 75013, France
| | | | - Adam Wild
- Sorbonne Université, Paris, 75013, France; Institut du Cerveau - Paris Brain Institute-ICM, Paris, 75013, France; Inserm, Paris, 75013, France; CNRS, Paris, 75013, France; AP-HP, Hôpital de la Pitié Salpêtrière, Paris, 75013, France; Inria, Aramis project-team, Paris, 75013, France
| | - Sebastian Ströer
- AP-HP, Hôpital de la Pitié Salpêtrière, Department of Neuroradiology, Paris, 75013, France
| | - Didier Dormont
- Sorbonne Université, Paris, 75013, France; Institut du Cerveau - Paris Brain Institute-ICM, Paris, 75013, France; Inserm, Paris, 75013, France; CNRS, Paris, 75013, France; AP-HP, Hôpital de la Pitié Salpêtrière, Paris, 75013, France; Inria, Aramis project-team, Paris, 75013, France; AP-HP, Hôpital de la Pitié Salpêtrière, Department of Neuroradiology, Paris, 75013, France
| | - Olivier Colliot
- Sorbonne Université, Paris, 75013, France; Institut du Cerveau - Paris Brain Institute-ICM, Paris, 75013, France; Inserm, Paris, 75013, France; CNRS, Paris, 75013, France; AP-HP, Hôpital de la Pitié Salpêtrière, Paris, 75013, France; Inria, Aramis project-team, Paris, 75013, France.
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Gholizadeh N, Pundavela J, Nagarajan R, Dona A, Quadrelli S, Biswas T, Greer PB, Ramadan S. Nuclear magnetic resonance spectroscopy of human body fluids and in vivo magnetic resonance spectroscopy: Potential role in the diagnosis and management of prostate cancer. Urol Oncol 2020; 38:150-173. [PMID: 31937423 DOI: 10.1016/j.urolonc.2019.10.019] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 09/22/2019] [Accepted: 10/31/2019] [Indexed: 01/17/2023]
Abstract
Prostate cancer is the most common solid organ cancer in men, and the second most common cause of male cancer-related mortality. It has few effective therapies, and is difficult to diagnose accurately. Prostate-specific antigen (PSA), which is currently the most effective diagnostic tool available, cannot reliably discriminate between different pathologies, and in fact only around 30% of patients found to have elevated levels of PSA are subsequently confirmed to actually have prostate cancer. As such, there is a desperate need for more reliable diagnostic tools that will allow the early detection of prostate cancer so that the appropriate interventions can be applied. Nuclear magnetic resonance (NMR) spectroscopy and magnetic resonance spectroscopy (MRS) are 2 high throughput, noninvasive analytical procedures that have the potential to enable differentiation of prostate cancer from other pathologies using metabolomics, by focusing specifically on certain metabolites which are associated with the development of prostate cancer cells and its progression. The value that this type of approach has for the early detection, diagnosis, prognosis, and personalized treatment of prostate cancer is becoming increasingly apparent. Recent years have seen many promising developments in the fields of NMR spectroscopy and MRS, with improvements having been made to hardware as well as to techniques associated with the acquisition, processing, and analysis of related data. This review focuses firstly on proton NMR spectroscopy of blood serum, urine, and expressed prostatic secretions in vitro, and then on 1- and 2-dimensional proton MRS of the prostate in vivo. Major advances in these fields and methodological principles of data collection, acquisition, processing, and analysis are described along with some discussion of related challenges, before prospects that proton MRS has for future improvements to the clinical management of prostate cancer are considered.
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Affiliation(s)
- Neda Gholizadeh
- School of Health Sciences, Faculty of Health and Medicine, University of Newcastle, Newcastle, NSW, Australia
| | - Jay Pundavela
- Experimental Hematology and Cancer Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Rajakumar Nagarajan
- Human Magnetic Resonance Center, Institute for Applied Life Sciences, University of Massachusetts Amherst, MA, USA
| | - Anthony Dona
- Kolling Institute of Medical Research, Royal North Shore Hospital, University of Sydney, St Leonards, NSW, Australia
| | - Scott Quadrelli
- School of Health Sciences, Faculty of Health and Medicine, University of Newcastle, Newcastle, NSW, Australia; Radiology Department, Princess Alexandra Hospital, Brisbane, QLD, Australia
| | - Tapan Biswas
- Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata, India
| | - Peter B Greer
- School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, NSW, Australia; Radiation Oncology, Calvary Mater Newcastle, Newcastle, NSW, Australia
| | - Saadallah Ramadan
- School of Health Sciences, Faculty of Health and Medicine, University of Newcastle, Newcastle, NSW, Australia; Imaging Centre, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia.
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