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Seifert R, Gafita A, Solnes LB, Iagaru A. Prostate-specific Membrane Antigen: Interpretation Criteria, Standardized Reporting, and the Use of Machine Learning. PET Clin 2024; 19:363-369. [PMID: 38705743 DOI: 10.1016/j.cpet.2024.03.002] [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] [Indexed: 05/07/2024]
Abstract
Prostate-specific membrane antigen targeting positron emission tomography (PSMA-PET) is routinely used for the staging and restaging of patients with various stages of prostate cancer. For clear communication with referring physicians and to improve inter-reader agreement, the use of standardized reporting templates is mandatory. Increasingly, tumor volume is used by reporting and response assessment frameworks to prognosticate patient outcome or measure response to therapy. However, the quantification of tumor volume is often too time-consuming in routine clinical practice. Machine learning-based tools can facilitate the quantification of tumor volume for improved outcome prognostication.
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Affiliation(s)
- Robert Seifert
- Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern, Switzerland; Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.
| | - Andrei Gafita
- Division of Nuclear Medicine and Molecular Imaging, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Lilja B Solnes
- Division of Nuclear Medicine and Molecular Imaging, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andrei Iagaru
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Stanford University, 300 Pasteur Drive H2200, Stanford 94305, USA
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Trägårdh E, Ulén J, Enqvist O, Edenbrandt L, Larsson M. Improving sensitivity through data augmentation with synthetic lymph node metastases for AI-based analysis of PSMA PET-CT images. Clin Physiol Funct Imaging 2024; 44:332-339. [PMID: 38563413 DOI: 10.1111/cpf.12879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 03/14/2024] [Accepted: 03/18/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND We developed a fully automated artificial intelligence (AI)AI-based-based method for detecting suspected lymph node metastases in prostate-specific membrane antigen (PSMA)(PSMA) positron emission tomography-computed tomography (PET-CT)(PET-CT) images of prostate cancer patients by using data augmentation that adds synthetic lymph node metastases to the images to expand the training set. METHODS Synthetic data were derived from original training images to which new synthetic lymph node metastases were added. Thus, the original training set from a previous study (n = 420) was expanded by one synthetic image for every original image (n = 840), which was used to train an AI model. The performance of the AI model was compared to that of nuclear medicine physicians and a previously developed AI model. The human readers were alternately used as a reference and compared to either another reading or AI model. RESULTS The new AI model had an average sensitivity of 84% for detecting lymph node metastases compared with 78% for human readings. Our previously developed AI method without synthetic data had an average sensitivity of 79%. The number of false positive lesions were slightly higher for the new AI model (average 3.3 instances per patient) compared to human readings and the previous AI model (average 2.8 instances per patient), while the number of false negative lesions was lower. CONCLUSIONS Creating synthetic lymph node metastases, as a form of data augmentation, on [18F]PSMA-1007F]PSMA-1007 PETPET-CT-CT images improved the sensitivity of an AI model for detecting suspected lymph node metastases. However, the number of false positive lesions increased somewhat.
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Affiliation(s)
- Elin Trägårdh
- Department of Translational Medicine and Wallenberg Centre for Molecular Medicine, Lund University, Malmö, Sweden
- Department of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, Malmö, Sweden
| | | | - Olof Enqvist
- Eigenvision AB, Malmö, Sweden
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Lars Edenbrandt
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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Talyshinskii A, Hameed BMZ, Ravinder PP, Naik N, Randhawa P, Shah M, Rai BP, Tokas T, Somani BK. Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management. Cancers (Basel) 2024; 16:1809. [PMID: 38791888 PMCID: PMC11119252 DOI: 10.3390/cancers16101809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND The aim was to analyze the current state of deep learning (DL)-based prostate cancer (PCa) diagnosis with a focus on magnetic resonance (MR) prostate reconstruction; PCa detection/stratification/reconstruction; positron emission tomography/computed tomography (PET/CT); androgen deprivation therapy (ADT); prostate biopsy; associated challenges and their clinical implications. METHODS A search of the PubMed database was conducted based on the inclusion and exclusion criteria for the use of DL methods within the abovementioned areas. RESULTS A total of 784 articles were found, of which, 64 were included. Reconstruction of the prostate, the detection and stratification of prostate cancer, the reconstruction of prostate cancer, and diagnosis on PET/CT, ADT, and biopsy were analyzed in 21, 22, 6, 7, 2, and 6 studies, respectively. Among studies describing DL use for MR-based purposes, datasets with magnetic field power of 3 T, 1.5 T, and 3/1.5 T were used in 18/19/5, 0/1/0, and 3/2/1 studies, respectively, of 6/7 studies analyzing DL for PET/CT diagnosis which used data from a single institution. Among the radiotracers, [68Ga]Ga-PSMA-11, [18F]DCFPyl, and [18F]PSMA-1007 were used in 5, 1, and 1 study, respectively. Only two studies that analyzed DL in the context of DT met the inclusion criteria. Both were performed with a single-institution dataset with only manual labeling of training data. Three studies, each analyzing DL for prostate biopsy, were performed with single- and multi-institutional datasets. TeUS, TRUS, and MRI were used as input modalities in two, three, and one study, respectively. CONCLUSION DL models in prostate cancer diagnosis show promise but are not yet ready for clinical use due to variability in methods, labels, and evaluation criteria. Conducting additional research while acknowledging all the limitations outlined is crucial for reinforcing the utility and effectiveness of DL-based models in clinical settings.
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Affiliation(s)
- Ali Talyshinskii
- Department of Urology and Andrology, Astana Medical University, Astana 010000, Kazakhstan;
| | | | - Prajwal P. Ravinder
- Department of Urology, Kasturba Medical College, Mangaluru, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Nithesh Naik
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Princy Randhawa
- Department of Mechatronics, Manipal University Jaipur, Jaipur 303007, India;
| | - Milap Shah
- Department of Urology, Aarogyam Hospital, Ahmedabad 380014, India;
| | - Bhavan Prasad Rai
- Department of Urology, Freeman Hospital, Newcastle upon Tyne NE7 7DN, UK;
| | - Theodoros Tokas
- Department of Urology, Medical School, University General Hospital of Heraklion, University of Crete, 14122 Heraklion, Greece;
| | - Bhaskar K. Somani
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India;
- Department of Urology, University Hospital Southampton NHS Trust, Southampton SO16 6YD, UK
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Zamani-Siahkali N, Mirshahvalad SA, Farbod A, Divband G, Pirich C, Veit-Haibach P, Cook G, Beheshti M. SPECT/CT, PET/CT, and PET/MRI for Response Assessment of Bone Metastases. Semin Nucl Med 2024; 54:356-370. [PMID: 38172001 DOI: 10.1053/j.semnuclmed.2023.11.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 11/14/2023] [Indexed: 01/05/2024]
Abstract
Recent developments in hybrid SPECT/CT systems and the use of cadmium-zinc-telluride (CZT) detectors have improved the diagnostic accuracy of bone scintigraphy. These advancements have paved the way for novel quantitative approaches to accurate and reproducible treatment monitoring of bone metastases. PET/CT imaging using [18F]F-FDG and [18F]F-NaF have shown promising clinical utility in bone metastases assessment and monitoring response to therapy and prediction of treatment response in a broad range of malignancies. Additionally, specific tumor-targeting tracers like [99mTc]Tc-PSMA, [68Ga]Ga-PSMA, or [11C]C- or [18F]F-Choline revealed high diagnostic performance for early assessment and prognostication of bone metastases, particularly in prostate cancer. PET/MRI appears highly accurate imaging modality, but has associated limitations notably, limited availability, more complex logistics and high installation costs. Advances in artificial intelligence (Al) seem to improve the accuracy of imaging modalities and provide an assistant role in the evaluation of treatment response of bone metastases.
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Affiliation(s)
- Nazanin Zamani-Siahkali
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Ali Mirshahvalad
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, Toronto, Canada
| | - Abolfazl Farbod
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Christian Pirich
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Patrick Veit-Haibach
- Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, Toronto, Canada
| | - Gary Cook
- Cancer Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Mohsen Beheshti
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria.
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Liu J, Cundy TP, Woon DTS, Lawrentschuk N. A Systematic Review on Artificial Intelligence Evaluating Metastatic Prostatic Cancer and Lymph Nodes on PSMA PET Scans. Cancers (Basel) 2024; 16:486. [PMID: 38339239 PMCID: PMC10854940 DOI: 10.3390/cancers16030486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
Early detection of metastatic prostate cancer (mPCa) is crucial. Whilst the prostate-specific membrane antigen (PSMA) PET scan has high diagnostic accuracy, it suffers from inter-reader variability, and the time-consuming reporting process. This systematic review was registered on PROSPERO (ID CRD42023456044) and aims to evaluate AI's ability to enhance reporting, diagnostics, and predictive capabilities for mPCa on PSMA PET scans. Inclusion criteria covered studies using AI to evaluate mPCa on PSMA PET, excluding non-PSMA tracers. A search was conducted on Medline, Embase, and Scopus from inception to July 2023. After screening 249 studies, 11 remained eligible for inclusion. Due to the heterogeneity of studies, meta-analysis was precluded. The prediction model risk of bias assessment tool (PROBAST) indicated a low overall risk of bias in ten studies, though only one incorporated clinical parameters (such as age, and Gleason score). AI demonstrated a high accuracy (98%) in identifying lymph node involvement and metastatic disease, albeit with sensitivity variation (62-97%). Advantages included distinguishing bone lesions, estimating tumour burden, predicting treatment response, and automating tasks accurately. In conclusion, AI showcases promising capabilities in enhancing the diagnostic potential of PSMA PET scans for mPCa, addressing current limitations in efficiency and variability.
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Affiliation(s)
- Jianliang Liu
- E.J. Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne, VIC 3005, Australia; (J.L.)
- Department of Urology, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC 3052, Australia
- Department of Surgery, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Thomas P. Cundy
- Discipline of Surgery, University of Adelaide, Adelaide, SA 5005, Australia
| | - Dixon T. S. Woon
- E.J. Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne, VIC 3005, Australia; (J.L.)
- Department of Surgery, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Nathan Lawrentschuk
- E.J. Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne, VIC 3005, Australia; (J.L.)
- Department of Urology, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC 3052, Australia
- Department of Surgery, University of Melbourne, Melbourne, VIC 3052, Australia
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Yazdani E, Geramifar P, Karamzade-Ziarati N, Sadeghi M, Amini P, Rahmim A. Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens. Diagnostics (Basel) 2024; 14:181. [PMID: 38248059 PMCID: PMC10814892 DOI: 10.3390/diagnostics14020181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 01/11/2024] [Accepted: 01/12/2024] [Indexed: 01/23/2024] Open
Abstract
Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.
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Affiliation(s)
- Elmira Yazdani
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran 14496-14535, Iran;
- Finetech in Medicine Research Center, Iran University of Medical Sciences, Tehran 14496-14535, Iran
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran 14117-13135, Iran; (P.G.); (N.K.-Z.)
| | - Najme Karamzade-Ziarati
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran 14117-13135, Iran; (P.G.); (N.K.-Z.)
| | - Mahdi Sadeghi
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran 14496-14535, Iran;
- Finetech in Medicine Research Center, Iran University of Medical Sciences, Tehran 14496-14535, Iran
| | - Payam Amini
- Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran 14496-14535, Iran;
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC V5Z 1L3, Canada
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Lindgren Belal S, Frantz S, Minarik D, Enqvist O, Wikström E, Edenbrandt L, Trägårdh E. Applications of Artificial Intelligence in PSMA PET/CT for Prostate Cancer Imaging. Semin Nucl Med 2024; 54:141-149. [PMID: 37357026 DOI: 10.1053/j.semnuclmed.2023.06.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/12/2023] [Indexed: 06/27/2023]
Abstract
Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) has emerged as an important imaging technique for prostate cancer. The use of PSMA PET/CT is rapidly increasing, while the number of nuclear medicine physicians and radiologists to interpret these scans is limited. Additionally, there is variability in interpretation among readers. Artificial intelligence techniques, including traditional machine learning and deep learning algorithms, are being used to address these challenges and provide additional insights from the images. The aim of this scoping review was to summarize the available research on the development and applications of AI in PSMA PET/CT for prostate cancer imaging. A systematic literature search was performed in PubMed, Embase and Cinahl according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 26 publications were included in the synthesis. The included studies focus on different aspects of artificial intelligence in PSMA PET/CT, including detection of primary tumor, local recurrence and metastatic lesions, lesion classification, tumor quantification and prediction/prognostication. Several studies show similar performances of artificial intelligence algorithms compared to human interpretation. Few artificial intelligence tools are approved for use in clinical practice. Major limitations include the lack of external validation and prospective design. Demonstrating the clinical impact and utility of artificial intelligence tools is crucial for their adoption in healthcare settings. To take the next step towards a clinically valuable artificial intelligence tool that provides quantitative data, independent validation studies are needed across institutions and equipment to ensure robustness.
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Affiliation(s)
- Sarah Lindgren Belal
- Department of Translational Medicine and Wallenberg Centre for Molecular Medicine, Lund University, Malmö, Sweden; Department of Surgery, Skåne University Hospital, Malmö, Sweden
| | - Sophia Frantz
- Department of Translational Medicine and Wallenberg Centre for Molecular Medicine, Lund University, Malmö, Sweden; Department of Health Technology Assessment South, Skåne University Hospital, Lund, Sweden
| | - David Minarik
- Department of Translational Medicine and Wallenberg Centre for Molecular Medicine, Lund University, Malmö, Sweden; Department of Radiation Physics, Skåne University Hospital, Malmö, Sweden
| | - Olof Enqvist
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden; Department of Clinical Physiology and Nuclear Medicine, Malmö Sweden
| | - Erik Wikström
- Department of Health Technology Assessment South, Skåne University Hospital, Lund, Sweden
| | - Lars Edenbrandt
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Elin Trägårdh
- Department of Translational Medicine and Wallenberg Centre for Molecular Medicine, Lund University, Malmö, Sweden; Department of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, Malmö, Sweden.
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Mirshahvalad SA, Eisazadeh R, Shahbazi-Akbari M, Pirich C, Beheshti M. Application of Artificial Intelligence in Oncologic Molecular PET-Imaging: A Narrative Review on Beyond [ 18F]F-FDG Tracers - Part I. PSMA, Choline, and DOTA Radiotracers. Semin Nucl Med 2024; 54:171-180. [PMID: 37752032 DOI: 10.1053/j.semnuclmed.2023.08.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 08/29/2023] [Indexed: 09/28/2023]
Abstract
Artificial intelligence (AI) has evolved significantly in the past few decades. This thriving trend has also been seen in medicine in recent years, particularly in the field of imaging. Machine learning (ML), deep learning (DL), and their methods (eg, SVM, CNN), as well as radiomics, are the terminologies that have been introduced to this field and, to some extent, become familiar to the expert clinicians. PET is one of the modalities that has been enhanced via these state-of-the-art algorithms. This robust imaging technique further merged with anatomical modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), to provide reliable hybrid modalities, PET/CT and PET/MRI. Applying AI-based algorithms on the different components (PET, CT, and MRI) has resulted in promising results, maximizing the value of PET imaging. However, [18F]F-FDG, the most commonly utilized tracer in molecular imaging, has been mainly in the spotlight. Thus, we aimed to look into the less discussed tracers in this review, moving beyond [18F]F-FDG. The novel non-[18F]F-FDG agents also showed to be valuable in various clinical tasks, including lesion detection and tumor characterization, accurate delineation, and prognostic impact. Regarding prostate patients, PSMA-based models were highly accurate in determining tumoral lesions' location and delineating them, particularly within the prostate gland. However, they also could assess whole-body images to detect extra-prostatic lesions in a patient automatically. Considering the prognostic value of prostate-specific membrane antigen (PSMA) PET using AI, it could predict response to treatment and patient survival, which are crucial in patient management. Choline imaging, another non-[18F]F-FDG tracer, similarly showed acceptable results that may be of benefit in the clinic, though the current evidence is significantly more limited than PSMA. Lastly, different subtypes of DOTA ligands were found to be valuable. They could diagnose tumoral lesions in challenging sites and even predict histopathology grade, being a highly advantageous noninvasive tool. In conclusion, the current limited investigations have shown promising results, leading us to a bright future for AI in molecular imaging beyond [18F]F-FDG.
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Affiliation(s)
- Seyed Ali Mirshahvalad
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Canada
| | - Roya Eisazadeh
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Malihe Shahbazi-Akbari
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Research Center for Nuclear Medicine, Department of Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Christian Pirich
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Mohsen Beheshti
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria.
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Faiella E, Vaccarino F, Ragone R, D’Amone G, Cirimele V, Piccolo CL, Vertulli D, Grasso RF, Zobel BB, Santucci D. Can Machine Learning Models Detect and Predict Lymph Node Involvement in Prostate Cancer? A Comprehensive Systematic Review. J Clin Med 2023; 12:7032. [PMID: 38002646 PMCID: PMC10672480 DOI: 10.3390/jcm12227032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/02/2023] [Accepted: 11/04/2023] [Indexed: 11/26/2023] Open
Abstract
(1) Background: Recently, Artificial Intelligence (AI)-based models have been investigated for lymph node involvement (LNI) detection and prediction in Prostate cancer (PCa) patients, in order to reduce surgical risks and improve patient outcomes. This review aims to gather and analyze the few studies available in the literature to examine their initial findings. (2) Methods: Two reviewers conducted independently a search of MEDLINE databases, identifying articles exploring AI's role in PCa LNI. Sixteen studies were selected, and their methodological quality was appraised using the Radiomics Quality Score. (3) Results: AI models in Magnetic Resonance Imaging (MRI)-based studies exhibited comparable LNI prediction accuracy to standard nomograms. Computed Tomography (CT)-based and Positron Emission Tomography (PET)-CT models demonstrated high diagnostic and prognostic results. (4) Conclusions: AI models showed promising results in LN metastasis prediction and detection in PCa patients. Limitations of the reviewed studies encompass retrospective design, non-standardization, manual segmentation, and limited studies and participants. Further research is crucial to enhance AI tools' effectiveness in this area.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Domiziana Santucci
- Radiology Department, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Roma, Italy; (E.F.); (F.V.); (R.R.); (G.D.); (V.C.); (C.L.P.); (D.V.); (R.F.G.); (B.B.Z.)
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Rich JM, Bhardwaj LN, Shah A, Gangal K, Rapaka MS, Oberai AA, Fields BKK, Matcuk GR, Duddalwar VA. Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis. FRONTIERS IN RADIOLOGY 2023; 3:1241651. [PMID: 37614529 PMCID: PMC10442705 DOI: 10.3389/fradi.2023.1241651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 07/28/2023] [Indexed: 08/25/2023]
Abstract
Introduction Image segmentation is an important process for quantifying characteristics of malignant bone lesions, but this task is challenging and laborious for radiologists. Deep learning has shown promise in automating image segmentation in radiology, including for malignant bone lesions. The purpose of this review is to investigate deep learning-based image segmentation methods for malignant bone lesions on Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron-Emission Tomography/CT (PET/CT). Method The literature search of deep learning-based image segmentation of malignant bony lesions on CT and MRI was conducted in PubMed, Embase, Web of Science, and Scopus electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 41 original articles published between February 2017 and March 2023 were included in the review. Results The majority of papers studied MRI, followed by CT, PET/CT, and PET/MRI. There was relatively even distribution of papers studying primary vs. secondary malignancies, as well as utilizing 3-dimensional vs. 2-dimensional data. Many papers utilize custom built models as a modification or variation of U-Net. The most common metric for evaluation was the dice similarity coefficient (DSC). Most models achieved a DSC above 0.6, with medians for all imaging modalities between 0.85-0.9. Discussion Deep learning methods show promising ability to segment malignant osseous lesions on CT, MRI, and PET/CT. Some strategies which are commonly applied to help improve performance include data augmentation, utilization of large public datasets, preprocessing including denoising and cropping, and U-Net architecture modification. Future directions include overcoming dataset and annotation homogeneity and generalizing for clinical applicability.
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Affiliation(s)
- Joseph M. Rich
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Lokesh N. Bhardwaj
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Aman Shah
- Department of Applied Biostatistics and Epidemiology, University of Southern California, Los Angeles, CA, United States
| | - Krish Gangal
- Bridge UnderGrad Science Summer Research Program, Irvington High School, Fremont, CA, United States
| | - Mohitha S. Rapaka
- Department of Biology, University of Texas at Austin, Austin, TX, United States
| | - Assad A. Oberai
- Department of Aerospace and Mechanical Engineering Department, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Brandon K. K. Fields
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - George R. Matcuk
- Department of Radiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Vinay A. Duddalwar
- Department of Radiology, Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States
- Department of Radiology, USC Radiomics Laboratory, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
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Gålne A, Enqvist O, Sundlöv A, Valind K, Minarik D, Trägårdh E. AI-based quantification of whole-body tumour burden on somatostatin receptor PET/CT. Eur J Hybrid Imaging 2023; 7:14. [PMID: 37544941 PMCID: PMC10404578 DOI: 10.1186/s41824-023-00172-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 06/28/2023] [Indexed: 08/08/2023] Open
Abstract
BACKGROUND Segmenting the whole-body somatostatin receptor-expressing tumour volume (SRETVwb) on positron emission tomography/computed tomography (PET/CT) images is highly time-consuming but has shown value as an independent prognostic factor for survival. An automatic method to measure SRETVwb could improve disease status assessment and provide a tool for prognostication. This study aimed to develop an artificial intelligence (AI)-based method to detect and quantify SRETVwb and total lesion somatostatin receptor expression (TLSREwb) from [68Ga]Ga-DOTA-TOC/TATE PET/CT images. METHODS A UNet3D convolutional neural network (CNN) was used to train an AI model with [68Ga]Ga-DOTA-TOC/TATE PET/CT images, where all tumours were manually segmented with a semi-automatic method. The training set consisted of 148 patients, of which 108 had PET-positive tumours. The test group consisted of 30 patients, of which 25 had PET-positive tumours. Two physicians segmented tumours in the test group for comparison with the AI model. RESULTS There were good correlations between the segmented SRETVwb and TLSREwb by the AI model and the physicians, with Spearman rank correlation coefficients of r = 0.78 and r = 0.73, respectively, for SRETVwb and r = 0.83 and r = 0.81, respectively, for TLSREwb. The sensitivity on a lesion detection level was 80% and 79%, and the positive predictive value was 83% and 84% when comparing the AI model with the two physicians. CONCLUSION It was possible to develop an AI model to segment SRETVwb and TLSREwb with high performance. A fully automated method makes quantification of tumour burden achievable and has the potential to be more widely used when assessing PET/CT images.
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Affiliation(s)
- Anni Gålne
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden.
- Department of Translational Medicine, Faculty of Medicine, Lund University, Malmö, Sweden.
- WCMM Wallenberg Centre for Molecular Medicine, Lund, Sweden.
| | - Olof Enqvist
- Eigenvision AB, Malmö, Sweden
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Anna Sundlöv
- Department of Clinical Sciences, Oncology and Pathology, Lund University, Lund, Sweden
| | - Kristian Valind
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
- Department of Translational Medicine, Faculty of Medicine, Lund University, Malmö, Sweden
- WCMM Wallenberg Centre for Molecular Medicine, Lund, Sweden
| | - David Minarik
- Department of Translational Medicine, Faculty of Medicine, Lund University, Malmö, Sweden
- WCMM Wallenberg Centre for Molecular Medicine, Lund, Sweden
- Radiation Physics, Skåne University Hospital, Malmö, Sweden
| | - Elin Trägårdh
- Department of Translational Medicine, Faculty of Medicine, Lund University, Malmö, Sweden
- WCMM Wallenberg Centre for Molecular Medicine, Lund, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Malmö, Sweden
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Lindgren Belal S, Larsson M, Holm J, Buch-Olsen KM, Sörensen J, Bjartell A, Edenbrandt L, Trägårdh E. Automated quantification of PET/CT skeletal tumor burden in prostate cancer using artificial intelligence: The PET index. Eur J Nucl Med Mol Imaging 2023; 50:1510-1520. [PMID: 36650356 PMCID: PMC10027829 DOI: 10.1007/s00259-023-06108-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 01/05/2023] [Indexed: 01/19/2023]
Abstract
PURPOSE Consistent assessment of bone metastases is crucial for patient management and clinical trials in prostate cancer (PCa). We aimed to develop a fully automated convolutional neural network (CNN)-based model for calculating PET/CT skeletal tumor burden in patients with PCa. METHODS A total of 168 patients from three centers were divided into training, validation, and test groups. Manual annotations of skeletal lesions in [18F]fluoride PET/CT scans were used to train a CNN. The AI model was evaluated in 26 patients and compared to segmentations by physicians and to a SUV 15 threshold. PET index representing the percentage of skeletal volume taken up by lesions was estimated. RESULTS There was no case in which all readers agreed on prevalence of lesions that the AI model failed to detect. PET index by the AI model correlated moderately strong to physician PET index (mean r = 0.69). Threshold PET index correlated fairly with physician PET index (mean r = 0.49). The sensitivity for lesion detection was 65-76% for AI, 68-91% for physicians, and 44-51% for threshold depending on which physician was considered reference. CONCLUSION It was possible to develop an AI-based model for automated assessment of PET/CT skeletal tumor burden. The model's performance was superior to using a threshold and provides fully automated calculation of whole-body skeletal tumor burden. It could be further developed to apply to different radiotracers. Objective scan evaluation is a first step toward developing a PET/CT imaging biomarker for PCa skeletal metastases.
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Affiliation(s)
- Sarah Lindgren Belal
- Division of Nuclear Medicine, Department of Translational Medicine, Lund University, Malmö, Sweden.
- Department of Surgery, Skåne University Hospital, Malmö, Sweden.
- Wallenberg Center for Molecular Medicine, Lund University, Malmö, Sweden.
| | | | - Jorun Holm
- Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | | | - Jens Sörensen
- Division of Radiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Anders Bjartell
- Division of Urological Cancer, Department of Translational Medicine, Lund University, Malmö, Sweden
| | - Lars Edenbrandt
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Elin Trägårdh
- Division of Nuclear Medicine, Department of Translational Medicine, Lund University, Malmö, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Malmö, Sweden
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