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Kaiser L, Quach S, Zounek AJ, Wiestler B, Zatcepin A, Holzgreve A, Bollenbacher A, Bartos LM, Ruf VC, Böning G, Thon N, Herms J, Riemenschneider MJ, Stöcklein S, Brendel M, Rupprecht R, Tonn JC, Bartenstein P, von Baumgarten L, Ziegler S, Albert NL. Enhancing predictability of IDH mutation status in glioma patients at initial diagnosis: a comparative analysis of radiomics from MRI, [ 18F]FET PET, and TSPO PET. Eur J Nucl Med Mol Imaging 2024; 51:2371-2381. [PMID: 38396261 PMCID: PMC11178656 DOI: 10.1007/s00259-024-06654-5] [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: 10/13/2023] [Accepted: 02/10/2024] [Indexed: 02/25/2024]
Abstract
PURPOSE According to the World Health Organization classification for tumors of the central nervous system, mutation status of the isocitrate dehydrogenase (IDH) genes has become a major diagnostic discriminator for gliomas. Therefore, imaging-based prediction of IDH mutation status is of high interest for individual patient management. We compared and evaluated the diagnostic value of radiomics derived from dual positron emission tomography (PET) and magnetic resonance imaging (MRI) data to predict the IDH mutation status non-invasively. METHODS Eighty-seven glioma patients at initial diagnosis who underwent PET targeting the translocator protein (TSPO) using [18F]GE-180, dynamic amino acid PET using [18F]FET, and T1-/T2-weighted MRI scans were examined. In addition to calculating tumor-to-background ratio (TBR) images for all modalities, parametric images quantifying dynamic [18F]FET PET information were generated. Radiomic features were extracted from TBR and parametric images. The area under the receiver operating characteristic curve (AUC) was employed to assess the performance of logistic regression (LR) classifiers. To report robust estimates, nested cross-validation with five folds and 50 repeats was applied. RESULTS TBRGE-180 features extracted from TSPO-positive volumes had the highest predictive power among TBR images (AUC 0.88, with age as co-factor 0.94). Dynamic [18F]FET PET reached a similarly high performance (0.94, with age 0.96). The highest LR coefficients in multimodal analyses included TBRGE-180 features, parameters from kinetic and early static [18F]FET PET images, age, and the features from TBRT2 images such as the kurtosis (0.97). CONCLUSION The findings suggest that incorporating TBRGE-180 features along with kinetic information from dynamic [18F]FET PET, kurtosis from TBRT2, and age can yield very high predictability of IDH mutation status, thus potentially improving early patient management.
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Affiliation(s)
- Lena Kaiser
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
| | - S Quach
- Department of Neurosurgery, University Hospital, LMU Munich, 81377, Munich, Germany
| | - A J Zounek
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - B Wiestler
- Department of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
- Bavarian Cancer Research Center (BZKF), 91054, Erlangen, Germany
| | - A Zatcepin
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), 81377, Munich, Germany
| | - A Holzgreve
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - A Bollenbacher
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - L M Bartos
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - V C Ruf
- Center for Neuropathology and Prion Research, Faculty of Medicine, LMU Munich, Munich, Germany
| | - G Böning
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - N Thon
- Department of Neurosurgery, University Hospital, LMU Munich, 81377, Munich, Germany
| | - J Herms
- Center for Neuropathology and Prion Research, Faculty of Medicine, LMU Munich, Munich, Germany
| | - M J Riemenschneider
- Department of Neuropathology, University Hospital Regensburg, 93053, Regensburg, Germany
- Bavarian Cancer Research Center (BZKF), 91054, Erlangen, Germany
| | - S Stöcklein
- Department of Radiology, University Hospital, LMU Munich, 81377, Munich, Germany
| | - M Brendel
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), 81377, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), 81377, Munich, Germany
| | - R Rupprecht
- Department of Psychiatry and Psychotherapy, University of Regensburg, 93053, Regensburg, Germany
| | - J C Tonn
- Department of Neurosurgery, University Hospital, LMU Munich, 81377, Munich, Germany
- Bavarian Cancer Research Center (BZKF), 91054, Erlangen, Germany
| | - P Bartenstein
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - L von Baumgarten
- Department of Neurosurgery, 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
| | - S Ziegler
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - N L Albert
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 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
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Ekmekcioglu O, Albert NL, Heinrich K, Tolboom N, Van Weehaeghe D, Traub-Weidinger T, Atay LO, Garibotto V, Morbelli S. Neurological Disorders and Women's Health: Contribution of Molecular Neuroimaging Techniques. Semin Nucl Med 2024; 54:237-246. [PMID: 38365546 DOI: 10.1053/j.semnuclmed.2024.01.010] [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: 10/28/2023] [Revised: 01/26/2024] [Accepted: 01/30/2024] [Indexed: 02/18/2024]
Abstract
Sex differences in brain physiology and the mechanisms of drug action have been extensively reported. These biological variances, from structure to hormonal and genetic aspects, can profoundly influence healthy functioning and disease mechanisms and might have implications for treatment and drug development. Molecular neuroimaging techniques may help to disclose sex's impact on brain functioning, as well as the neuropathological changes underpinning several diseases. This narrative review summarizes recent lines of evidence based on PET and SPECT imaging, highlighting sex differences in normal conditions and various neurological disorders.
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Affiliation(s)
- Ozgul Ekmekcioglu
- Department of Nuclear Medicine, University of Health Sciences, Sisli Hamidiye Etfal Education and Research Hospital, Istanbul, Turkey.
| | - Nathalie L Albert
- Department of Nuclear Medicine, LMU University Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Kathrin Heinrich
- Department of Medicine III, LMU University Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Nelleke Tolboom
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Tatiana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | | | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, University Hospitals of Geneva, Faculty of Medicine, University of Geneva, CIBM Center for Biomedical Imaging, Geneva, Switzerland
| | - Silvia Morbelli
- Nuclear Medicine Unit, AOU Città Della Salute e Della Scienza di Torino, University of Turin, Turin, Italy
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Ahrari S, Zaragori T, Zinsz A, Oster J, Imbert L, Verger A. Application of PET imaging delta radiomics for predicting progression-free survival in rare high-grade glioma. Sci Rep 2024; 14:3256. [PMID: 38332004 PMCID: PMC10853227 DOI: 10.1038/s41598-024-53693-x] [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: 08/16/2023] [Accepted: 02/03/2024] [Indexed: 02/10/2024] Open
Abstract
This study assesses the feasibility of using a sample-efficient model to investigate radiomics changes over time for predicting progression-free survival in rare diseases. Eighteen high-grade glioma patients underwent two L-3,4-dihydroxy-6-[18F]-fluoro-phenylalanine positron emission tomography (PET) dynamic scans: the first during treatment and the second at temozolomide chemotherapy discontinuation. Radiomics features from static/dynamic parametric images, alongside conventional features, were extracted. After excluding highly correlated features, 16 different models were trained by combining various feature selection methods and time-to-event survival algorithms. Performance was assessed using cross-validation. To evaluate model robustness, an additional dataset including 35 patients with a single PET scan at therapy discontinuation was used. Model performance was compared with a strategy extracting informative features from the set of 35 patients and applying them to the 18 patients with 2 PET scans. Delta-absolute radiomics achieved the highest performance when the pipeline was directly applied to the 18-patient subset (support vector machine (SVM) and recursive feature elimination (RFE): C-index = 0.783 [0.744-0.818]). This result remained consistent when transferring informative features from 35 patients (SVM + RFE: C-index = 0.751 [0.716-0.784], p = 0.06). In addition, it significantly outperformed delta-absolute conventional (C-index = 0.584 [0.548-0.620], p < 0.001) and single-time-point radiomics features (C-index = 0.546 [0.512-0.580], p < 0.001), highlighting the considerable potential of delta radiomics in rare cancer cohorts.
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Affiliation(s)
- Shamimeh Ahrari
- Imagerie Adaptative Diagnostique et Interventionnelle, Institut National de la Santé et de la Recherche Médicale U1254, Université de Lorraine, 54000, Nancy, France
- Nancyclotep Imaging Platform, Université de Lorraine, 54000, Nancy, France
| | - Timothée Zaragori
- Imagerie Adaptative Diagnostique et Interventionnelle, Institut National de la Santé et de la Recherche Médicale U1254, Université de Lorraine, 54000, Nancy, France
- Nancyclotep Imaging Platform, Université de Lorraine, 54000, Nancy, France
| | - Adeline Zinsz
- Department of Nuclear Medicine, Centre Hospitalier Régional Universitaire de Nancy, 54000, Nancy, France
| | - Julien Oster
- Imagerie Adaptative Diagnostique et Interventionnelle, Institut National de la Santé et de la Recherche Médicale U1254, Université de Lorraine, 54000, Nancy, France
| | - Laetitia Imbert
- Imagerie Adaptative Diagnostique et Interventionnelle, Institut National de la Santé et de la Recherche Médicale U1254, Université de Lorraine, 54000, Nancy, France
- Nancyclotep Imaging Platform, Université de Lorraine, 54000, Nancy, France
- Department of Nuclear Medicine, Centre Hospitalier Régional Universitaire de Nancy, 54000, Nancy, France
| | - Antoine Verger
- Imagerie Adaptative Diagnostique et Interventionnelle, Institut National de la Santé et de la Recherche Médicale U1254, Université de Lorraine, 54000, Nancy, France.
- Nancyclotep Imaging Platform, Université de Lorraine, 54000, Nancy, France.
- Department of Nuclear Medicine, Centre Hospitalier Régional Universitaire de Nancy, 54000, Nancy, France.
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