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Eisazadeh R, Shahbazi-Akbari M, Mirshahvalad SA, Pirich C, Beheshti M. Application of Artificial Intelligence in Oncologic Molecular PET-Imaging: A Narrative Review on Beyond [ 18F]F-FDG Tracers Part II. [ 18F]F-FLT, [ 18F]F-FET, [ 11C]C-MET and Other Less-Commonly Used Radiotracers. Semin Nucl Med 2024; 54:293-301. [PMID: 38331629 DOI: 10.1053/j.semnuclmed.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 01/11/2024] [Indexed: 02/10/2024]
Abstract
Following the previous part of the narrative review on artificial intelligence (AI) applications in positron emission tomography (PET) using tracers rather than 18F-fluorodeoxyglucose ([18F]F-FDG), in this part we review the impact of PET-derived radiomics data on the diagnostic performance of other PET radiotracers, 18F-O-(2-fluoroethyl)-L-tyrosine ([18F]F-FET), 18F-Fluorothymidine ([18F]F-FLT) and 11C-Methionine ([11C]C-MET). [18F]F-FET-PET, using an artificial amino acid taken up into upregulated tumoral cells, showed potential in lesion detection and tumor characterization, especially with its ability to reflect glioma heterogeneity. [18F]F-FET-PET-derived textural features appeared to have the potential to reveal considerable information for accurate delineation for guiding biopsy and treatment, differentiate between low-grade and high-grade glioma and related wild-type genotypes, and distinguish pseudoprogression from true progression. In addition, models built using clinical parameters and [18F]F-FET-PET-derived radiomics features showed acceptable results for survival stratification of glioblastoma patients. [18F]F-FLT-PET-based characteristics also showed potential in evaluating glioma patients, correlating with Ki-67 and patient prognosis. AI-based PET-volumetry using this radiotracer as a proliferation marker also revealed promising preliminary results in terms of guide-targeting bone marrow-preserving adaptive radiation therapy. Similar to [18F]F-FET, the other amino acid tracer which reflects cellular proliferation, [11C]C-MET, has also shown acceptable performance in predicting tumor grade, distinguishing brain tumor recurrence from radiation necrosis, and treatment monitoring by PET-derived radiomics models. In addition, PET-derived radiomics features of various radiotracers such as [18F]F-DOPA, [18F]F-FACBC, [18F]F-NaF, [68Ga]Ga-CXCR-4 and [18F]F-FMISO may also provide useful information for tumor characterization and predict of disease outcome. In conclusion, AI using tracers beyond [18F]F-FDG could improve the diagnostic performance of PET-imaging for specific indications and help clinicians in their daily routine by providing features that are often not detectable by the naked eye.
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Affiliation(s)
- 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
| | - Seyed Ali Mirshahvalad
- 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; Joint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, Ontario, Canada
| | - 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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Fuchs T, Kaiser L, Müller D, Papp L, Fischer R, Tran-Gia J. Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data. Nuklearmedizin 2023; 62:389-398. [PMID: 37907246 PMCID: PMC10689089 DOI: 10.1055/a-2187-5701] [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: 09/05/2023] [Accepted: 09/21/2023] [Indexed: 11/02/2023]
Abstract
Nuclear imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) in combination with computed tomography (CT) are established imaging modalities in clinical practice, particularly for oncological problems. Due to a multitude of manufacturers, different measurement protocols, local demographic or clinical workflow variations as well as various available reconstruction and analysis software, very heterogeneous datasets are generated. This review article examines the current state of interoperability and harmonisation of image data and related clinical data in the field of nuclear medicine. Various approaches and standards to improve data compatibility and integration are discussed. These include, for example, structured clinical history, standardisation of image acquisition and reconstruction as well as standardised preparation of image data for evaluation. Approaches to improve data acquisition, storage and analysis will be presented. Furthermore, approaches are presented to prepare the datasets in such a way that they become usable for projects applying artificial intelligence (AI) (machine learning, deep learning, etc.). This review article concludes with an outlook on future developments and trends related to AI in nuclear medicine, including a brief research of commercial solutions.
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Affiliation(s)
- Timo Fuchs
- Medical Data Integration Center (MEDIZUKR), University Hospital Regensburg, Regensburg, Germany
- Partner Site Regensburg, Bavarian Center for Cancer Research (BZKF), Regensburg, Germany
| | - Lena Kaiser
- Department of Nuclear Medicine, LMU University Hospital, LMU, Munich, Germany
| | - Dominik Müller
- IT-Infrastructure for Translational Medical Research, University of Augsburg, Augsburg, Germany
- Medical Data Integration Center, University Hospital Augsburg, Augsburg, Germany
| | - Laszlo Papp
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Wien, Austria
| | - Regina Fischer
- Medical Data Integration Center (MEDIZUKR), University Hospital Regensburg, Regensburg, Germany
- Partner Site Regensburg, Bavarian Center for Cancer Research (BZKF), Regensburg, Germany
| | - Johannes Tran-Gia
- Department of Nuclear Medicine, University Hospital Würzburg, Wurzburg, Germany
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Kendrick J, Francis RJ, Hassan GM, Rowshanfarzad P, Ong JS, Jeraj R, Barry N, Hagan T, Ebert MA. Prospective inter- and intra-tracer repeatability analysis of radiomics features in [ 68Ga]Ga-PSMA-11 and [ 18F]F-PSMA-1007 PET scans in metastatic prostate cancer. Br J Radiol 2023; 96:20221178. [PMID: 37751168 PMCID: PMC10646662 DOI: 10.1259/bjr.20221178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 07/14/2023] [Accepted: 08/25/2023] [Indexed: 09/27/2023] Open
Abstract
OBJECTIVE This study aimed to quantify both the intra- and intertracer repeatability of lesion-level radiomics features in [68Ga]Ga-prostate-specific membrane antigen (PSMA)-11 and [18F]F-PSMA-1007 positron emission tomography (PET) scans. METHODS Eighteen patients with metastatic prostate cancer (mPCa) were prospectively recruited for the study and randomised to one of three test-retest groups: (i) intratracer [68Ga]Ga-PSMA-11 PET, (ii) intratracer [18F]F-PSMA-1007 PET or (iii) intertracer between [68Ga]Ga-PSMA-11 and [18F]F-PSMA-1007 PET. Four conventional PET metrics (standardised uptake value (SUV)max, SUVmean, SUVtotal and volume) and 107 radiomics features were extracted from 75 lesions and assessed using the repeatability coefficient (RC) and the ICC. Radiomic feature repeatability was also quantified after the application of 16 filters to the PET image. RESULTS Test-retest scans were taken a median of 5 days apart (range: 2-7 days). SUVmean demonstrated the lowest RC limits of the conventional features, with RCs of 7.9%, 14.2% and 24.7% for the [68Ga]Ga-PSMA-11 PET, [18F]F-PSMA-1007 PET, and intertracer groups, respectively. 69%, 66% and 9% of all radiomics features had good or excellent ICC values (ICC ≥ 0.75) for the same groups. Feature repeatability therefore diminished considerably for the intertracer group relative to intratracer groups. CONCLUSION In this study, robust biomarkers for each tracer group that can be used in subsequent clinical studies were identified. Overall, the repeatability of conventional and radiomic features were found to be substantially lower for the intertracer group relative to both intratracer groups, suggesting that assessing patient response quantitatively should be done using the same radiotracer where possible. ADVANCES IN KNOWLEDGE Intertracer biomarker repeatability limits are significantly larger than intratracer limits.
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Affiliation(s)
- Jake Kendrick
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, Australia
| | | | - Ghulam Mubashar Hassan
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, Australia
| | - Pejman Rowshanfarzad
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, Australia
| | - Jeremy S.L. Ong
- Department of Nuclear Medicine, Fiona Stanley Hospital, Murdoch, Australia
| | | | - Nathaniel Barry
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, Australia
| | - Tammy Hagan
- Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Perth, Australia
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Zounek AJ, Albert NL, Holzgreve A, Unterrainer M, Brosch-Lenz J, Lindner S, Bollenbacher A, Boening G, Rupprecht R, Brendel M, von Baumgarten L, Tonn JC, Bartenstein P, Ziegler S, Kaiser L. Feasibility of radiomic feature harmonization for pooling of [ 18F]FET or [ 18F]GE-180 PET images of gliomas. Z Med Phys 2023; 33:91-102. [PMID: 36710156 PMCID: PMC10068577 DOI: 10.1016/j.zemedi.2022.12.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/22/2022] [Accepted: 12/22/2022] [Indexed: 01/29/2023]
Abstract
INTRODUCTION Large datasets are required to ensure reliable non-invasive glioma assessment with radiomics-based machine learning methods. This can often only be achieved by pooling images from different centers. Moreover, trained models should perform with high accuracy when applied to data from different centers. In this study, the impact of reconstruction settings and segmentation methods on radiomic features derived from amino acid and TSPO PET images of glioma patients was examined. Additionally, the ability to model and thus reduce feature differences was investigated. METHODS [18F]FET and [18F]GE-180 PET data were acquired from 19 glioma patients. For each acquisition, 10 reconstruction settings and 9 segmentation methods were included to emulate multicentric data. Statistical robustness measures were calculated before and after ComBat harmonization. Differences between features due to setting variations were assessed using Friedman test, coefficient of variation (CV) and inter-rater reliability measures, including intraclass and Spearman's rank correlation coefficients and Fleiss' Kappa. RESULTS According to Friedman analyses, most features (>60%) showed significant differences. Yet, CV and inter-rater reliability measures indicated higher robustness. ComBat resulted in almost complete harmonization (>87%) according to Friedman test and little to no improvement according to CV and inter-rater reliability measures. [18F]GE-180 features were more sensitive to reconstruction settings than [18F]FET features. CONCLUSIONS According to Friedman test, feature distributions could be successfully aligned using ComBat. However, depending on settings, changes in patient ranks were observed for some features and could not be eliminated by harmonization. Thus, for clinical utilization it is recommended to exclude affected features.
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Affiliation(s)
- Adrian Jun Zounek
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Nathalie Lisa Albert
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Bavarian Cancer Research Center (BZKF), 91054 Erlangen, Germany.
| | - Adrien Holzgreve
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Marcus Unterrainer
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany; Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Julia Brosch-Lenz
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Simon Lindner
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Andreas Bollenbacher
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Guido Boening
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Rainer Rupprecht
- Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany.
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), 81377 Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany.
| | - Louisa von Baumgarten
- Bavarian Cancer Research Center (BZKF), 91054 Erlangen, Germany; Department of Neurosurgery, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Joerg-Christian Tonn
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
| | - Sibylle Ziegler
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Lena Kaiser
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany.
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Danilov G, Kalayeva D, Vikhrova N, Konakova T, Zagorodnova A, Popova A, Postnov A, Shugay S, Pronin I. Radiomics in Determining Tumor-to-Normal Brain SUV Ratio Based on 11C-Methionine PET/CT in Glioblastoma. Sovrem Tekhnologii Med 2023; 15:5-11. [PMID: 37388754 PMCID: PMC10306961 DOI: 10.17691/stm2023.15.1.01] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Indexed: 12/10/2023] Open
Abstract
UNLABELLED Modern methodology of PET/CT quantitative analysis in patients with glioblastomas is not strictly standardized in clinic settings and does not exclude the influence of the human factor. Methods of radiomics may facilitate unification, and improve objectivity and efficiency of the medical image analysis. The aim of the study is to evaluate the potential of radiomics in the analysis of PET/CT glioblastoma images identifying the relationship between the radiomic features and the 11С-methionine tumor-to-normal brain uptake ratio (TNR) determined by an expert in routine. MATERIALS AND METHODS PET/CT data (2018-2020) from 40 patients (average age was 55±12 years; 77.5% were males) with a histologically confirmed diagnosis of "glioblastoma" were included in the analysis. TNR was calculated as a ratio of the standardized uptake value of 11C-methionine measured in the tumor and intact tissue. Calculation of radiomic features for each PET was performed in the specified volumetric region of interest, capturing the tumor with the surrounding tissues. The relationship between TNR and the radiomic features was determined using the linear regression model. Predictors were included in the model following correlation analysis and LASSO regularization. The experiment with machine learning was repeated 300 times, splitting the training (70%) and test (30%) subsets randomly. The model quality metrics and predictor significance obtained in 300 tests were summarized. RESULTS Of 412 PET/CT radiomic parameters significantly correlated with TNR (p<0.05), the regularization procedure left no more than 30 in each model (the median number of predictors was 9 [7; 13]). The experiment has demonstrated a non-random linear correlation (the Spearman correlation coefficient was 0.58 [0.43; 0.74]) between TNR and separate radiomic features, primarily fractal dimensions, characterizing the geometrical properties of the image. CONCLUSION Radiomics enabled an objective determination of PET/CT image texture features reflecting the biological activity of glioblastomas. Despite the existing limitations in the application, the first results provide a good perspective of these methods in neurooncology.
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Affiliation(s)
- G.V. Danilov
- Scientific Board Secretary, Head of the Laboratory of Biomedical Informatics and Artificial Intelligence; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - D.B. Kalayeva
- Medical Physicist; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia; PhD Student; National Research Nuclear University MEPhI, 31 Kashirskoe Shosse, Moscow, 115409, Russia
| | - N.B. Vikhrova
- Radiologist; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - T.A. Konakova
- PhD Student; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - A.I. Zagorodnova
- Student; Pirogov Russian National Research Medical University, 1 Ostrovitianova St., Moscow, 117997, Russia
| | - A.A. Popova
- Student; Pirogov Russian National Research Medical University, 1 Ostrovitianova St., Moscow, 117997, Russia
| | - A.A. Postnov
- Researcher; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia; Associate Professor; National Research Nuclear University MEPhI, 31 Kashirskoe Shosse, Moscow, 115409, Russia; Researcher; Lebedev Physical Institute of the Russian Academy of Sciences, 53 Leninskiy Prospect, Moscow, 119991, Russia; Head of the Project; Research Institute of Technical Physics and Automation, 46 Varshavskoye Shosse, Moscow, 115230, Russia
| | - S.V. Shugay
- Professor, Academician of the Russian Academy of Sciences, Deputy Director for Research, Head of the Unit for X-ray and Radioisotopic Methods of Diagnosis; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - I.N. Pronin
- Pathologist; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
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