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Harbi E, Aschner M. Nuclear Medicine Imaging Techniques in Glioblastomas. Neurochem Res 2024; 49:3006-3013. [PMID: 39235579 DOI: 10.1007/s11064-024-04233-6] [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: 07/23/2024] [Revised: 08/21/2024] [Accepted: 08/22/2024] [Indexed: 09/06/2024]
Abstract
Glioblastomas are the most common primary malignant grade 4 tumors of the central nervous system (CNS). The treatment and management of such tumors requires a multidisciplinary approach and nuclear medicine techniques play an important role in this process. Glioblastoma, which recurs despite current treatments and becomes resistant to treatments, is among the tumors with the lowest survival rate, with a survival rate of approximately 8 months. Currently, the standard treatment of glioblastoma is adjuvant chemoradiotherapy after surgical resection. There have been many recent advances in the field of Nuclear Medicine in glioblastoma. PET scans are critical in determining tumor localization, pre-surgical planning, evaluation of post-treatment response and detection of recurrence. Advances in the treatment of glioblastoma and a better understanding of the biological characteristics of the disease have contributed to the development of nuclear medicine techniques. This review, in addition to other studies, is intended as a general imaging summary guide and includes some new expressions discovered in glioblastoma. This review discusses recent advances in nuclear medicine in glioblastoma.
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Affiliation(s)
- Emirhan Harbi
- Department of Molecular Pharmacology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
| | - Michael Aschner
- Department of Molecular Pharmacology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
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Zinsz A, Ahrari S, Becker J, Mortada A, Roch V, Doriat L, Santi M, Blonski M, Taillandier L, Zaragori T, Verger A. Amino-acid PET as a prognostic tool after post Stupp protocol temozolomide therapy in high-grade glioma patients. J Neurooncol 2024; 169:241-245. [PMID: 38842696 PMCID: PMC11341581 DOI: 10.1007/s11060-024-04722-2] [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: 04/24/2024] [Accepted: 05/22/2024] [Indexed: 06/07/2024]
Abstract
PURPOSE This study aimed to evaluate the prognostic performance of amino-acid PET in high-grade gliomas (HGG) patients at the time of temozolomide (TMZ) treatment discontinuation, after the Stupp protocol. METHODS The analysis included consecutive HGG patients with dynamic [18F]FDOPA PET imaging within 3 months of the end of TMZ therapy, post-Stupp protocol. Static and dynamic PET parameters, responses to RANO criteria for MRI and clinical and histo-molecular factors were correlated to progression-free (PFS). RESULTS Thirty-two patients (59.4 [54.0;67.6] years old, 13 (41%) women) were included. Static PET parameters peak tumor-to-background ratio and metabolic tumor volume (respective thresholds of 1.9 and 1.5 mL) showed the best 84% accuracies for predicting PFS at 6 months (p = 0.02). These static PET parameters were also independent predictor of PFS in multivariate analysis (p ≤ 0.05). CONCLUSION In HGG patients having undergone a Stupp protocol, the absence of significant PET uptake after TMZ constitutes a favorable prognostic factor.
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Affiliation(s)
- Adeline Zinsz
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, Université de Lorraine, CHRU Nancy, 54000, Nancy, France
| | - Shamimeh Ahrari
- Université de Lorraine, IADI, INSERM U1254, F-54000, Nancy, France
| | - Jason Becker
- Department of Neuro-Oncology, Université de Lorraine, CHRU Nancy, 54000, Nancy, France
| | - Ali Mortada
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, Université de Lorraine, CHRU Nancy, 54000, Nancy, France
| | - Veronique Roch
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, Université de Lorraine, CHRU Nancy, 54000, Nancy, France
| | - Louis Doriat
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, Université de Lorraine, CHRU Nancy, 54000, Nancy, France
| | - Matthieu Santi
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, Université de Lorraine, CHRU Nancy, 54000, Nancy, France
| | - Marie Blonski
- Department of Neuro-Oncology, Université de Lorraine, CHRU Nancy, 54000, Nancy, France
| | - Luc Taillandier
- Department of Neuro-Oncology, Université de Lorraine, CHRU Nancy, 54000, Nancy, France
| | - Timothée Zaragori
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, Université de Lorraine, CHRU Nancy, 54000, Nancy, France
| | - Antoine Verger
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, Université de Lorraine, CHRU Nancy, 54000, Nancy, France.
- Université de Lorraine, IADI, INSERM U1254, F-54000, Nancy, France.
- Nuclear Medicine Department, CHRU Nancy, Rue du Morvan, 54500, Vandoeuvre Les Nancy, France.
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Zinsz A, Pouget C, Rech F, Taillandier L, Blonski M, Amlal S, Imbert L, Zaragori T, Verger A. The role of [18 F]FDOPA PET as an adjunct to conventional MRI in the diagnosis of aggressive glial lesions. Eur J Nucl Med Mol Imaging 2024; 51:2672-2683. [PMID: 38637354 DOI: 10.1007/s00259-024-06720-y] [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: 11/22/2023] [Accepted: 04/15/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND Amino acid PET is recommended for the initial diagnosis of brain lesions, but its value for identifying aggressive lesions remains to be established. The current study therefore evaluates the added-value of dynamic [18 F]FDOPA PET as an adjunct to conventional MRI for determining the aggressiveness of presumed glial lesions at diagnosis. METHODS Consecutive patients, with a minimal 1 year-follow-up, underwent contrast-enhanced MRI (CE MRI) and dynamic [18 F]FDOPA PET to characterize their suspected glial lesion. Lesions were classified semi-automatically by their CE MRI (MRI-/+), and PET parameters (static tumor-to-background ratio, TBR; dynamic time-to-peak ratio, TTPratio). Diagnostic accuracies of MRI and PET parameters for the differentiation of tumor aggressiveness were evaluated by chi-square test or receiver operating characteristic analyses. Aggressive lesions were either defined as lesions with dismal molecular characteristics based on the WHO 2021 classification of brain tumors or with compatible clinico-radiological profiles. Time-to-treatment failure (TTF) and overall survival (OS) were evaluated. RESULTS Of the 109 patients included, 46 had aggressive lesions (45 confirmed by histo-molecular analyses). CE MRI identified aggressive lesions with an accuracy of 73%. TBRmax (threshold of 3.2), and TTPratio (threshold of 5.4 min) respectively identified aggressive lesions with an accuracy of 83% and 76% and were independent of CE MRI and clinical factors in the multivariate analysis. Among the MRI-lesions, 11/56 (20%) were aggressive and respectively 55% and 50% of these aggressive lesions showed high TBRmax and short TTPratio in PET. High TBRmax and short TTPratio in PET were significantly associated to poorer survivals (p ≤ 0.009). CONCLUSION Dynamic [18 F]FDOPA PET provides a similar diagnostic accuracy as contrast enhancement in MRI to identify the aggressiveness of suspected glial lesions at diagnosis. Both methods, however, are complementary and [18 F]FDOPA PET may be a useful additional tool in equivocal cases.
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Affiliation(s)
- Adeline Zinsz
- Department of Nuclear Medicine & Nancyclotep Imaging Platform, CHRU-Nancy, Université de Lorraine, Nancy, F-54000, France
| | - Celso Pouget
- Department of Pathology, CHRU-Nancy, Université de Lorraine, Nancy, CP, France
- INSERM U1256, Université de Lorraine, Nancy, CP, France
| | - Fabien Rech
- Department of Neurosurgery, CHRU-Nancy, Université de Lorraine, Nancy, FR, France
- Centre de Recherche en Automatique de Nancy CRAN UMR 7039, CNRS, Université de Lorraine, Nancy, France
| | - Luc Taillandier
- Centre de Recherche en Automatique de Nancy CRAN UMR 7039, CNRS, Université de Lorraine, Nancy, France
- Department of Neuro-Oncology, CHRU-Nancy, Université de Lorraine, Nancy, LT, MB, France
| | - Marie Blonski
- Centre de Recherche en Automatique de Nancy CRAN UMR 7039, CNRS, Université de Lorraine, Nancy, France
- Department of Neuro-Oncology, CHRU-Nancy, Université de Lorraine, Nancy, LT, MB, France
| | - Samir Amlal
- Department of Neuro-Radiology, CHRU-Nancy, Université de Lorraine, Nancy, SA, France
| | - Laetitia Imbert
- Department of Nuclear Medicine & Nancyclotep Imaging Platform, CHRU-Nancy, Université de Lorraine, Nancy, F-54000, France
- INSERM, IADI, UMR 1254 Université de Lorraine, Nancy, F-54000, France
| | - Timothée Zaragori
- Department of Nuclear Medicine & Nancyclotep Imaging Platform, CHRU-Nancy, Université de Lorraine, Nancy, F-54000, France
- INSERM, IADI, UMR 1254 Université de Lorraine, Nancy, F-54000, France
| | - Antoine Verger
- Department of Nuclear Medicine & Nancyclotep Imaging Platform, CHRU-Nancy, Université de Lorraine, Nancy, F-54000, France.
- INSERM, IADI, UMR 1254 Université de Lorraine, Nancy, F-54000, France.
- Médecine Nucléaire, Hôpital de Brabois, CHRU- Nancy, Allée du Morvan, Vandoeuvre-les-Nancy, 54500, France.
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Wen DY, Chen JM, Tang ZP, Pang JS, Qin Q, Zhang L, He Y, Yang H. Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model. Biomed Eng Online 2024; 23:56. [PMID: 38890695 PMCID: PMC11184715 DOI: 10.1186/s12938-024-01259-3] [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: 02/25/2024] [Accepted: 06/13/2024] [Indexed: 06/20/2024] Open
Abstract
OBJECTIVES This study was designed to explore and validate the value of different machine learning models based on ultrasound image-omics features in the preoperative diagnosis of lymph node metastasis in pancreatic cancer (PC). METHODS This research involved 189 individuals diagnosed with PC confirmed by surgical pathology (training cohort: n = 151; test cohort: n = 38), including 50 cases of lymph node metastasis. Image-omics features were extracted from ultrasound images. After dimensionality reduction and screening, eight machine learning algorithms, including logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), extra trees (ET), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP), were used to establish image-omics models to predict lymph node metastasis in PC. The best omics prediction model was selected through ROC curve analysis. Machine learning models were used to analyze clinical features and determine variables to establish a clinical model. A combined model was constructed by combining ultrasound image-omics and clinical features. Decision curve analysis (DCA) and a nomogram were used to evaluate the clinical application value of the model. RESULTS A total of 1561 image-omics features were extracted from ultrasound images. 15 valuable image-omics features were determined by regularization, dimension reduction, and algorithm selection. In the image-omics model, the LR model showed higher prediction efficiency and robustness, with an area under the ROC curve (AUC) of 0.773 in the training set and an AUC of 0.850 in the test set. The clinical model constructed by the boundary of lesions in ultrasound images and the clinical feature CA199 (AUC = 0.875). The combined model had the best prediction performance, with an AUC of 0.872 in the training set and 0.918 in the test set. The combined model showed better clinical benefit according to DCA, and the nomogram score provided clinical prediction solutions. CONCLUSION The combined model established with clinical features has good diagnostic ability and can be used to predict lymph node metastasis in patients with PC. It is expected to provide an effective noninvasive method for clinical decision-making, thereby improving the diagnosis and treatment of PC.
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Affiliation(s)
- Dong-Yue Wen
- Department of Medical Ultrasonics, Guangxi Zhuang Autonomous Region, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Jia-Min Chen
- Department of Medical Ultrasonics, Guangxi Zhuang Autonomous Region, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Zhi-Ping Tang
- Department of Medical Ultrasonics, Guangxi Zhuang Autonomous Region, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Jin-Shu Pang
- Department of Medical Ultrasonics, Guangxi Zhuang Autonomous Region, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Qiong Qin
- Department of Medical Ultrasonics, Guangxi Zhuang Autonomous Region, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Lu Zhang
- Department of Medical Pathology, Guangxi Zhuang Autonomous Region, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Yun He
- Department of Medical Ultrasonics, Guangxi Zhuang Autonomous Region, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China.
| | - Hong Yang
- Department of Medical Ultrasonics, Guangxi Zhuang Autonomous Region, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China.
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Frosina G. Advancements in Image-Based Models for High-Grade Gliomas Might Be Accelerated. Cancers (Basel) 2024; 16:1566. [PMID: 38672647 PMCID: PMC11048778 DOI: 10.3390/cancers16081566] [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: 03/05/2024] [Revised: 04/08/2024] [Accepted: 04/18/2024] [Indexed: 04/28/2024] Open
Abstract
The first half of 2022 saw the publication of several major research advances in image-based models and artificial intelligence applications to optimize treatment strategies for high-grade gliomas, the deadliest brain tumors. We review them and discuss the barriers that delay their entry into clinical practice; particularly, the small sample size and the heterogeneity of the study designs and methodologies used. We will also write about the poor and late palliation that patients suffering from high-grade glioma can count on at the end of life, as well as the current legislative instruments, with particular reference to Italy. We suggest measures to accelerate the gradual progress in image-based models and end of life care for patients with high-grade glioma.
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Affiliation(s)
- Guido Frosina
- Mutagenesis & Cancer Prevention Unit, IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genova, Italy
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Ma A, Yan X, Qu Y, Wen H, Zou X, Liu X, Lu M, Mo J, Wen Z. Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas. BMC Med Imaging 2024; 24:85. [PMID: 38600452 PMCID: PMC11005152 DOI: 10.1186/s12880-024-01262-z] [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: 07/10/2023] [Accepted: 03/27/2024] [Indexed: 04/12/2024] Open
Abstract
BACKGROUND 1p/19q co-deletion in low-grade gliomas (LGG, World Health Organization grade II and III) is of great significance in clinical decision making. We aim to use radiomics analysis to predict 1p/19q co-deletion in LGG based on amide proton transfer weighted (APTw), diffusion weighted imaging (DWI), and conventional MRI. METHODS This retrospective study included 90 patients histopathologically diagnosed with LGG. We performed a radiomics analysis by extracting 8454 MRI-based features form APTw, DWI and conventional MR images and applied a least absolute shrinkage and selection operator (LASSO) algorithm to select radiomics signature. A radiomics score (Rad-score) was generated using a linear combination of the values of the selected features weighted for each of the patients. Three neuroradiologists, including one experienced neuroradiologist and two resident physicians, independently evaluated the MR features of LGG and provided predictions on whether the tumor had 1p/19q co-deletion or 1p/19q intact status. A clinical model was then constructed based on the significant variables identified in this analysis. A combined model incorporating both the Rad-score and clinical factors was also constructed. The predictive performance was validated by receiver operating characteristic curve analysis, DeLong analysis and decision curve analysis. P < 0.05 was statistically significant. RESULTS The radiomics model and the combined model both exhibited excellent performance on both the training and test sets, achieving areas under the curve (AUCs) of 0.948 and 0.966, as well as 0.909 and 0.896, respectively. These results surpassed the performance of the clinical model, which achieved AUCs of 0.760 and 0.766 on the training and test sets, respectively. After performing Delong analysis, the clinical model did not significantly differ in predictive performance from three neuroradiologists. In the training set, both the radiomic and combined models performed better than all neuroradiologists. In the test set, the models exhibited higher AUCs than the neuroradiologists, with the radiomics model significantly outperforming resident physicians B and C, but not differing significantly from experienced neuroradiologist. CONCLUSIONS Our results suggest that our algorithm can noninvasively predict the 1p/19q co-deletion status of LGG. The predictive performance of radiomics model was comparable to that of experienced neuroradiologist, significantly outperforming the diagnostic accuracy of resident physicians, thereby offering the potential to facilitate non-invasive 1p/19q co-deletion prediction of LGG.
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Affiliation(s)
- Andong Ma
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu District, 253 Gongye Middle Avenue, Guangzhou, Guangdong, 510282, China
| | - Xinran Yan
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu District, 253 Gongye Middle Avenue, Guangzhou, Guangdong, 510282, China
| | - Yaoming Qu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu District, 253 Gongye Middle Avenue, Guangzhou, Guangdong, 510282, China
| | - Haitao Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu District, 253 Gongye Middle Avenue, Guangzhou, Guangdong, 510282, China
| | - Xia Zou
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu District, 253 Gongye Middle Avenue, Guangzhou, Guangdong, 510282, China
| | - Xinzi Liu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu District, 253 Gongye Middle Avenue, Guangzhou, Guangdong, 510282, China
| | - Mingjun Lu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu District, 253 Gongye Middle Avenue, Guangzhou, Guangdong, 510282, China
| | - Jianhua Mo
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu District, 253 Gongye Middle Avenue, Guangzhou, Guangdong, 510282, China
| | - Zhibo Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu District, 253 Gongye Middle Avenue, Guangzhou, Guangdong, 510282, China.
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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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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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Zhang X, Zhang G, Qiu X, Yin J, Tan W, Yin X, Yang H, Wang H, Zhang Y. Non-invasive decision support for clinical treatment of non-small cell lung cancer using a multiscale radiomics approach. Radiother Oncol 2024; 191:110082. [PMID: 38195018 DOI: 10.1016/j.radonc.2024.110082] [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: 06/22/2023] [Revised: 12/01/2023] [Accepted: 01/02/2024] [Indexed: 01/11/2024]
Abstract
BACKGROUND Selecting therapeutic strategies for cancer patients is typically based on key target-molecule biomarkers that play an important role in cancer onset, progression, and prognosis. Thus, there is a pressing need for novel biomarkers that can be utilized longitudinally to guide treatment selection. METHODS Using data from 508 non-small cell lung cancer (NSCLC) patients across three institutions, we developed and validated a comprehensive predictive biomarker that distinguishes six genotypes and infiltrative immune phenotypes. These features were analyzed to establish the association between radiological phenotypes and tumor genotypes/immune phenotypes and to create a radiological interpretation of molecular features. In addition, we assessed the sensitivity of the models by evaluating their performance at five different voxel intervals, resulting in improved generalizability of the proposed approach. FINDINGS The radiomics model we developed, which integrates clinical factors and multi-regional features, outperformed the conventional model that only uses clinical and intratumoral features. Our combined model showed significant performance for EGFR, KRAS, ALK, TP53, PIK3CA, and ROS1 mutation status with AUCs of 0.866, 0.874, 0.902, 0.850, 0.860, and 0.900, respectively. Additionally, the predictive performance for PD-1/PD-L1 was 0.852. Although the performance of all models decreased to different degrees at five different voxel space resolutions, the performance advantage of the combined model did not change. CONCLUSIONS We validated multiscale radiomic signatures across tumor genotypes and immunophenotypes in a multi-institutional cohort. This imaging-based biomarker offers a non-invasive approach to select patients with NSCLC who are sensitive to targeted therapies or immunotherapy, which is promising for developing personalized treatment strategies during therapy.
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Affiliation(s)
- Xingping Zhang
- School of Medical Information Engineering, Gannan Medical University, 341000, Ganzhou, China; Cyberspace Institute of Advanced Technology, Guangzhou University, 510006 Guangzhou, China; Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia; Department of New Networks, Peng Cheng Laboratory, 518000, Shenzhen, China
| | - Guijuan Zhang
- Department of Respiratory and Critical Care, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China
| | - Jiao Yin
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, 110189, Shenyang, China
| | - Xiaoxia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006 Guangzhou, China
| | - Hong Yang
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006 Guangzhou, China
| | - Hua Wang
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia.
| | - Yanchun Zhang
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia; School of Computer Science and Technology, Zhejiang Normal University, 321000, Jinhua, China; Department of New Networks, Peng Cheng Laboratory, 518000, Shenzhen, China.
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10
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Albert NL, Galldiks N, Ellingson BM, van den Bent MJ, Chang SM, Cicone F, de Groot J, Koh ES, Law I, Le Rhun E, Mair MJ, Minniti G, Rudà R, Scott AM, Short SC, Smits M, Suchorska B, Tolboom N, Traub-Weidinger T, Tonn JC, Verger A, Weller M, Wen PY, Preusser M. PET-based response assessment criteria for diffuse gliomas (PET RANO 1.0): a report of the RANO group. Lancet Oncol 2024; 25:e29-e41. [PMID: 38181810 DOI: 10.1016/s1470-2045(23)00525-9] [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: 09/18/2023] [Revised: 10/03/2023] [Accepted: 10/06/2023] [Indexed: 01/07/2024]
Abstract
Response Assessment in Neuro-Oncology (RANO) response criteria have been established and were updated in 2023 for MRI-based response evaluation of diffuse gliomas in clinical trials. In addition, PET-based imaging with amino acid tracers is increasingly considered for disease monitoring in both clinical practice and clinical trials. So far, a standardised framework defining timepoints for baseline and follow-up investigations and response evaluation criteria for PET imaging of diffuse gliomas has not been established. Therefore, in this Policy Review, we propose a set of criteria for response assessment based on amino acid PET imaging in clinical trials enrolling participants with diffuse gliomas as defined in the 2021 WHO classification of tumours of the central nervous system. These proposed PET RANO criteria provide a conceptual framework that facilitates the structured implementation of PET imaging into clinical research and, ultimately, clinical routine. To this end, the PET RANO 1.0 criteria are intended to encourage specific investigations of amino acid PET imaging of gliomas.
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Affiliation(s)
- Nathalie L Albert
- Department of Nuclear Medicine, LMU Hospital, LMU Munich, Munich, Germany
| | - Norbert Galldiks
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Juelich, Germany; Center for Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne, and Duesseldorf, Cologne, Germany
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | | | - Susan M Chang
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Francesco Cicone
- Nuclear Medicine Unit, Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - John de Groot
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Eng-Siew Koh
- Department of Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, NSW, Australia; South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - Ian Law
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, Copenhagen, Denmark
| | - Emilie Le Rhun
- Department of Neurosurgery, University Hospital Zurich, Zurich, Switzerland; Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | - Maximilian J Mair
- Division of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria
| | - Giuseppe Minniti
- Department of Radiological Sciences, Oncology and Anatomical Pathology, Sapienza University of Rome, Policlinico Umberto I, Rome, Italy; IRCCS Neuromed, Pozzilli IS, Italy
| | - Roberta Rudà
- Division of Neuro-Oncology, Department of Neuroscience, University of Turin and City of Health and Science of Turin, Turin, Italy
| | - Andrew M Scott
- Department of Molecular Imaging and Therapy, Austin Health and University of Melbourne, Melbourne, VIC, Australia; Olivia Newton-John Cancer Research Institute and School of Cancer Medicine, La Trobe University, Melbourne, VIC, Australia
| | - Susan C Short
- Leeds Institute of Medical Research at St James's, The University of Leeds, Leeds, UK
| | - Marion Smits
- Department of Radiology & Nuclear Medicine, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, Netherlands; Brain Tumour Centre, Erasmus MC Cancer Institute, Rotterdam, Netherlands; Medical Delta, Delft, Netherlands
| | - Bogdana Suchorska
- Department of Neurosurgery, Heidelberg University Hospital, Ruprecht-Karls-University Heidelberg, Heidelberg, Germany
| | - Nelleke Tolboom
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Utrecht, Netherlands
| | - Tatjana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | | | - Antoine Verger
- Department of Nuclear Medicine & Nancyclotep Imaging Platform, CHRU Nancy and IADI INSERM UMR 1254, Universitè de Lorraine, Nancy, France
| | - Michael Weller
- Department of Neurology, University Hospital Zurich, Zurich, Switzerland; Department of Neurology, University of Zurich, Zurich, Switzerland
| | - Patrick Y Wen
- Center For Neuro-Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Matthias Preusser
- Division of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
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11
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Karlberg A, Pedersen LK, Vindstad BE, Skjulsvik AJ, Johansen H, Solheim O, Skogen K, Kvistad KA, Bogsrud TV, Myrmel KS, Giskeødegård GF, Ingebrigtsen T, Berntsen EM, Eikenes L. Diagnostic accuracy of anti-3-[ 18F]-FACBC PET/MRI in gliomas. Eur J Nucl Med Mol Imaging 2024; 51:496-509. [PMID: 37776502 PMCID: PMC10774221 DOI: 10.1007/s00259-023-06437-4] [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: 06/22/2023] [Accepted: 09/06/2023] [Indexed: 10/02/2023]
Abstract
PURPOSE The primary aim was to evaluate whether anti-3-[18F]FACBC PET combined with conventional MRI correlated better with histomolecular diagnosis (reference standard) than MRI alone in glioma diagnostics. The ability of anti-3-[18F]FACBC to differentiate between molecular and histopathological entities in gliomas was also evaluated. METHODS In this prospective study, patients with suspected primary or recurrent gliomas were recruited from two sites in Norway and examined with PET/MRI prior to surgery. Anti-3-[18F]FACBC uptake (TBRpeak) was compared to histomolecular features in 36 patients. PET results were then added to clinical MRI readings (performed by two neuroradiologists, blinded for histomolecular results and PET data) to assess the predicted tumor characteristics with and without PET. RESULTS Histomolecular analyses revealed two CNS WHO grade 1, nine grade 2, eight grade 3, and 17 grade 4 gliomas. All tumors were visible on MRI FLAIR. The sensitivity of contrast-enhanced MRI and anti-3-[18F]FACBC PET was 61% (95%CI [45, 77]) and 72% (95%CI [58, 87]), respectively, in the detection of gliomas. Median TBRpeak was 7.1 (range: 1.4-19.2) for PET positive tumors. All CNS WHO grade 1 pilocytic astrocytomas/gangliogliomas, grade 3 oligodendrogliomas, and grade 4 glioblastomas/astrocytomas were PET positive, while 25% of grade 2-3 astrocytomas and 56% of grade 2-3 oligodendrogliomas were PET positive. Generally, TBRpeak increased with malignancy grade for diffuse gliomas. A significant difference in PET uptake between CNS WHO grade 2 and 4 gliomas (p < 0.001) and between grade 3 and 4 gliomas (p = 0.002) was observed. Diffuse IDH wildtype gliomas had significantly higher TBRpeak compared to IDH1/2 mutated gliomas (p < 0.001). Adding anti-3-[18F]FACBC PET to MRI improved the accuracy of predicted glioma grades, types, and IDH status, and yielded 13.9 and 16.7 percentage point improvement in the overall diagnoses for both readers, respectively. CONCLUSION Anti-3-[18F]FACBC PET demonstrated high uptake in the majority of gliomas, especially in IDH wildtype gliomas, and improved the accuracy of preoperatively predicted glioma diagnoses. CLINICAL TRIAL REGISTRATION ClinicalTrials.gov ID: NCT04111588, URL: https://clinicaltrials.gov/study/NCT04111588.
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Affiliation(s)
- Anna Karlberg
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Prinsesse Kristinas gate 3, N-7030, Trondheim, Norway.
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.
| | | | - Benedikte Emilie Vindstad
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Anne Jarstein Skjulsvik
- Department of Pathology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine, Faculty of Medical and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Håkon Johansen
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Prinsesse Kristinas gate 3, N-7030, Trondheim, Norway
| | - Ole Solheim
- Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
| | - Karoline Skogen
- Department of Radiology and Nuclear Medicine, Oslo University Hospitals, Oslo, Norway
| | - Kjell Arne Kvistad
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Prinsesse Kristinas gate 3, N-7030, Trondheim, Norway
| | - Trond Velde Bogsrud
- PET-Centre, University Hospital of North Norway, Tromsø, Norway
- Department of Nuclear Medicine and PET-Centre, Aarhus University Hospital, Aarhus, Denmark
| | | | - Guro F Giskeødegård
- Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tor Ingebrigtsen
- Department of Neurosurgery, University Hospital of North Norway, Tromsø, Norway
- Department of Clinical Medicine, Faculty of Health Sciences, UiT the Arctic University of Norway, Tromsø, Norway
| | - Erik Magnus Berntsen
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Prinsesse Kristinas gate 3, N-7030, Trondheim, Norway
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Live Eikenes
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
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12
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Xie X, Shen C, Zhang X, Wu G, Yang B, Qi Z, Tang Q, Wang Y, Ding H, Shi Z, Yu J. Rapid intraoperative multi-molecular diagnosis of glioma with ultrasound radio frequency signals and deep learning. EBioMedicine 2023; 98:104899. [PMID: 38041959 PMCID: PMC10711390 DOI: 10.1016/j.ebiom.2023.104899] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/16/2023] [Accepted: 11/17/2023] [Indexed: 12/04/2023] Open
Abstract
BACKGROUND Molecular diagnosis is crucial for biomarker-assisted glioma resection and management. However, some limitations of current molecular diagnostic techniques prevent their widespread use intraoperatively. With the unique advantages of ultrasound, this study developed a rapid intraoperative molecular diagnostic method based on ultrasound radio-frequency signals. METHODS We built a brain tumor ultrasound bank with 169 cases enrolled since July 2020, of which 43483 RF signal patches from 67 cases with a pathological diagnosis of glioma were a retrospective cohort for model training and validation. IDH1 and TERT promoter (TERTp) mutations and 1p/19q co-deletion were detected by next-generation sequencing. We designed a spatial-temporal integration model (STIM) to diagnose the three molecular biomarkers, thus establishing a rapid intraoperative molecular diagnostic system for glioma, and further analysed its consistency with the fifth edition of the WHO Classification of Tumors of the Central Nervous System (WHO CNS5). We tested STIM in 16-case prospective cohorts, which contained a total of 10384 RF signal patches. Two other RF-based classical models were used for comparison. Further, we included 20 cases additional prospective data for robustness test (ClinicalTrials.govNCT05656053). FINDINGS In the retrospective cohort, STIM achieved a mean accuracy and AUC of 0.9190 and 0.9650 (95% CI, 0.94-0.99) respectively for the three molecular biomarkers, with a total time of 3 s and a 96% match to WHO CNS5. In the prospective cohort, the diagnostic accuracy of STIM is 0.85 ± 0.04 (mean ± SD) for IDH1, 0.84 ± 0.05 for TERTp, and 0.88 ± 0.04 for 1p/19q. The AUC is 0.89 ± 0.02 (95% CI, 0.84-0.94) for IDH1, 0.80 ± 0.04 (95% CI, 0.71-0.89) for TERTp, and 0.85 ± 0.06 (95% CI, 0.73-0.98) for 1p/19q. Compared to the second best available method based on RF signal, the diagnostic accuracy of STIM is improved by 16.70% and the AUC is improved by 19.23% on average. INTERPRETATION STIM is a rapid, cost-effective, and easy-to-manipulate AI method to perform real-time intraoperative molecular diagnosis. In the future, it may help neurosurgeons designate personalized surgical plans and predict survival outcomes. FUNDING A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.
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Affiliation(s)
- Xuan Xie
- School of Information Science and Technology, Fudan University, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China
| | - Chao Shen
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China
| | - Xiandi Zhang
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China
| | - Guoqing Wu
- School of Information Science and Technology, Fudan University, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China
| | - Bojie Yang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China
| | - Zengxin Qi
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China
| | - Qisheng Tang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China
| | - Yuanyuan Wang
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Hong Ding
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China.
| | - Zhifeng Shi
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China.
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China.
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13
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Chilaca-Rosas MF, Contreras-Aguilar MT, Garcia-Lezama M, Salazar-Calderon DR, Vargas-Del-Angel RG, Moreno-Jimenez S, Piña-Sanchez P, Trejo-Rosales RR, Delgado-Martinez FA, Roldan-Valadez E. Identification of Radiomic Signatures in Brain MRI Sequences T1 and T2 That Differentiate Tumor Regions of Midline Gliomas with H3.3K27M Mutation. Diagnostics (Basel) 2023; 13:2669. [PMID: 37627927 PMCID: PMC10453217 DOI: 10.3390/diagnostics13162669] [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: 06/22/2023] [Revised: 08/02/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND Radiomics refers to the acquisition of traces of quantitative features that are usually non-perceptible to human vision and are obtained from different imaging techniques and subsequently transformed into high-dimensional data. Diffuse midline gliomas (DMG) represent approximately 20% of pediatric CNS tumors, with a median survival of less than one year after diagnosis. We aimed to identify which radiomics can discriminate DMG tumor regions (viable tumor and peritumoral edema) from equivalent midline normal tissue (EMNT) in patients with the positive H3.F3K27M mutation, which is associated with a worse prognosis. PATIENTS AND METHODS This was a retrospective study. From a database of 126 DMG patients (children, adolescents, and young adults), only 12 had H3.3K27M mutation and available brain magnetic resonance DICOM file. The MRI T1 post-gadolinium and T2 sequences were uploaded to LIFEx software to post-process and extract radiomic features. Statistical analysis included normal distribution tests and the Mann-Whitney U test performed using IBM SPSS® (Version 27.0.0.1, International Business Machines Corp., Armonk, NY, USA), considering a significant statistical p-value ≤ 0.05. RESULTS EMNT vs. Tumor: From the T1 sequence 10 radiomics were identified, and 14 radiomics from the T2 sequence, but only one radiomic identified viable tumors in both sequences (p < 0.05) (DISCRETIZED_Q1). Peritumoral edema vs. EMNT: From the T1 sequence, five radiomics were identified, and four radiomics from the T2 sequence. However, four radiomics could discriminate peritumoral edema in both sequences (p < 0.05) (CONVENTIONAL_Kurtosis, CONVENTIONAL_ExcessKurtosis, DISCRETIZED_Kurtosis, and DISCRETIZED_ExcessKurtosis). There were no radiomics useful for distinguishing tumor tissue from peritumoral edema in both sequences. CONCLUSIONS Less than 5% of the radiomic characteristics identified tumor regions of medical-clinical interest in T1 and T2 sequences of conventional magnetic resonance imaging. The first-order and second-order radiomic features suggest support to investigators and clinicians for careful evaluation for diagnosis, patient classification, and multimodality cancer treatment planning.
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Affiliation(s)
- Maria-Fatima Chilaca-Rosas
- Radiotherapy Department, Hospital de Oncología, Centro Medico Nacional Siglo XXI, Instituto Mexicano Del Seguro Social, Mexico City 06720, Mexico; (M.-F.C.-R.); (D.-R.S.-C.)
| | - Manuel-Tadeo Contreras-Aguilar
- Radiotherapy Department, Hospital de Oncología, Centro Medico Nacional Siglo XXI, Instituto Mexicano Del Seguro Social, Mexico City 06720, Mexico; (M.-F.C.-R.); (D.-R.S.-C.)
| | - Melissa Garcia-Lezama
- Directorate of Research, Hospital General de Mexico Dr Eduardo Liceaga, Mexico City 06720, Mexico;
| | - David-Rafael Salazar-Calderon
- Radiotherapy Department, Hospital de Oncología, Centro Medico Nacional Siglo XXI, Instituto Mexicano Del Seguro Social, Mexico City 06720, Mexico; (M.-F.C.-R.); (D.-R.S.-C.)
| | | | - Sergio Moreno-Jimenez
- Neurological Center, Neurosurgery Department of National Institute of Neurology and Neurosurgery, Mexico City 14269, Mexico;
- Neurological Center, Neurosurgery Department of American British Cowdray Medical Center, Mexico City 01120, Mexico
| | - Patricia Piña-Sanchez
- Oncology Diagnostic, Unidad de Investigacion Medica en Enfermedades Oncologicas U.I.M.E.O, Hospital de Oncología, Centro Medico Nacional Siglo XXI, Instituto Mexicano Del Seguro Social, Mexico City 06720, Mexico;
| | - Raul-Rogelio Trejo-Rosales
- Medical Oncology, Hospital de Oncología, Centro Medico Nacional Siglo XXI, Instituto Mexicano Del Seguro Social, Mexico City 06720, Mexico;
| | - Felipe-Alfredo Delgado-Martinez
- Magnetic Resonance Service, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City 06720, Mexico;
| | - Ernesto Roldan-Valadez
- Directorate of Research, Hospital General de Mexico Dr Eduardo Liceaga, Mexico City 06720, Mexico;
- Department of Radiology, I.M. Sechenov First Moscow State Medical University (Sechenov University), 119992 Moscow, Russia
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14
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Hajri R, Nicod-Lalonde M, Hottinger AF, Prior JO, Dunet V. Prediction of Glioma Grade and IDH Status Using 18F-FET PET/CT Dynamic and Multiparametric Texture Analysis. Diagnostics (Basel) 2023; 13:2604. [PMID: 37568967 PMCID: PMC10417545 DOI: 10.3390/diagnostics13152604] [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: 06/21/2023] [Revised: 08/01/2023] [Accepted: 08/03/2023] [Indexed: 08/13/2023] Open
Abstract
Mutations in isocitrate dehydrogenase (IDH) represent an independent predictor of better survival in patients with gliomas. We aimed to assess grade and IDH mutation status in patients with untreated gliomas, by evaluating the respective value of 18F-FET PET/CT via dynamic and texture analyses. A total of 73 patients (male: 48, median age: 47) who underwent an 18F-FET PET/CT for initial glioma evaluation were retrospectively included. IDH status was available in 61 patients (20 patients with WHO grade 2 gliomas, 41 with grade 3-4 gliomas). Time-activity curve type and 20 parameters obtained from static analysis using LIFEx© v6.30 software were recorded. Respective performance was assessed using receiver operating characteristic curve analysis and stepwise multivariate regression analysis adjusted for patients' age and sex. The time-activity curve type and texture parameters derived from the static parameters showed satisfactory-to-good performance in predicting glioma grade and IDH status. Both time-activity curve type (stepwise OR: 101.6 (95% CI: 5.76-1791), p = 0.002) and NGLDM coarseness (stepwise OR: 2.08 × 1043 (95% CI: 2.76 × 1012-1.57 × 1074), p = 0.006) were independent predictors of glioma grade. No independent predictor of IDH status was found. Dynamic and texture analyses of 18F-FET PET/CT have limited predictive value for IDH status when adjusted for confounding factors. However, they both help predict glioma grade.
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Affiliation(s)
- Rami Hajri
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland;
| | - Marie Nicod-Lalonde
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland; (M.N.-L.); (J.O.P.)
| | - Andreas F. Hottinger
- Department of Neurology, Lausanne University Hospital, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland;
- Lukas Lundin & Family Brain Tumor Research Center, Lausanne University Hospital, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland
| | - John O. Prior
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland; (M.N.-L.); (J.O.P.)
| | - Vincent Dunet
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland;
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15
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Peira E, Sensi F, Rei L, Gianeri R, Tortora D, Fiz F, Piccardo A, Bottoni G, Morana G, Chincarini A. Towards an Automated Approach to the Semi-Quantification of [ 18F]F-DOPA PET in Pediatric-Type Diffuse Gliomas. J Clin Med 2023; 12:jcm12082765. [PMID: 37109101 PMCID: PMC10142802 DOI: 10.3390/jcm12082765] [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: 12/31/2022] [Revised: 03/28/2023] [Accepted: 04/04/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND This study aims to evaluate the use of a computer-aided, semi-quantification approach to [18F]F-DOPA positron emission tomography (PET) in pediatric-type diffuse gliomas (PDGs) to calculate the tumor-to-background ratio. METHODS A total of 18 pediatric patients with PDGs underwent magnetic resonance imaging and [18F]F-DOPA PET, which were analyzed using both manual and automated procedures. The former provided a tumor-to-normal-tissue ratio (TN) and tumor-to-striatal-tissue ratio (TS), while the latter provided analogous scores (tn, ts). We tested the correlation, consistency, and ability to stratify grading and survival between these methods. RESULTS High Pearson correlation coefficients resulted between the ratios calculated with the two approaches: ρ = 0.93 (p < 10-4) and ρ = 0.814 (p < 10-4). The analysis of the residuals suggested that tn and ts were more consistent than TN and TS. Similarly to TN and TS, the automatically computed scores showed significant differences between low- and high-grade gliomas (p ≤ 10-4, t-test) and the overall survival was significantly shorter in patients with higher values when compared to those with lower ones (p < 10-3, log-rank test). CONCLUSIONS This study suggested that the proposed computer-aided approach could yield similar results to the manual procedure in terms of diagnostic and prognostic information.
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Affiliation(s)
- Enrico Peira
- Istituto Nazionale di Fisica Nucleare (INFN), 16146 Genoa, Italy
| | - Francesco Sensi
- Istituto Nazionale di Fisica Nucleare (INFN), 16146 Genoa, Italy
| | - Luca Rei
- Istituto Nazionale di Fisica Nucleare (INFN), 16146 Genoa, Italy
| | - Ruben Gianeri
- Istituto Nazionale di Fisica Nucleare (INFN), 16146 Genoa, Italy
| | - Domenico Tortora
- Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy
| | - Francesco Fiz
- S.C. di Medicina Nucleare, E.O. Ospedali Galliera, 16128 Genoa, Italy
| | - Arnoldo Piccardo
- S.C. di Medicina Nucleare, E.O. Ospedali Galliera, 16128 Genoa, Italy
| | - Gianluca Bottoni
- S.C. di Medicina Nucleare, E.O. Ospedali Galliera, 16128 Genoa, Italy
| | - Giovanni Morana
- Department of Neurosciences, University of Turin, 10124 Turin, Italy
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16
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Differentiating high-grade glioma progression from treatment-related changes with dynamic [ 18F]FDOPA PET: a multicentric study. Eur Radiol 2023; 33:2548-2560. [PMID: 36367578 DOI: 10.1007/s00330-022-09221-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 09/09/2022] [Accepted: 10/05/2022] [Indexed: 11/13/2022]
Abstract
OBJECTIVES Diagnostic accuracy of amino-acid PET for distinguishing progression from treatment-related changes (TRC) is currently based on single-center non-homogeneous glioma populations. Our study assesses the diagnostic value of static and dynamic [18F]FDOPA PET acquisitions to differentiate between high-grade glioma (HGG) recurrence and TRC in a large cohort sourced from two independent nuclear medicine centers. METHODS We retrospectively identified 106 patients with suspected glioma recurrences (WHO GIII, n = 38; GIV, n = 68; IDH-mutant, n = 35, IDH-wildtype, n = 71). Patients underwent dynamic [18F]FDOPA PET/CT (n = 83) or PET/MRI (n = 23), and static tumor-to-background ratios (TBRs), metabolic tumor volumes and dynamic parameters (time to peak and slope) were determined. The final diagnosis was either defined by histopathology or a clinical-radiological follow-up at 6 months. Optimal [18F]FDOPA PET parameter cut-offs were obtained by receiver operating characteristic analysis. Predictive factors and clinical parameters were assessed using univariate and multivariate Cox regression survival analyses. RESULTS Surgery or the clinical-radiological 6-month follow-up identified 71 progressions and 35 treatment-related changes. TBRmean, with a threshold of 1.8, best-differentiated glioma recurrence/progression from post-treatment changes in the whole population (sensitivity 82%, specificity 71%, p < 0.0001) whereas curve slope was only significantly different in IDH-mutant HGGs (n = 25). In survival analyses, MTV was a clinical independent predictor of progression-free and overall survival on the multivariate analysis (p ≤ 0.01). A curve slope > -0.12/h was an independent predictor for longer PFS in IDH-mutant HGGs CONCLUSION: Our multicentric study confirms the high accuracy of [18F]FDOPA PET to differentiate recurrent malignant gliomas from TRC and emphasizes the diagnostic and prognostic value of dynamic acquisitions for IDH-mutant HGGs. KEY POINTS • The diagnostic accuracy of dynamic amino-acid PET, for distinguishing progression from treatment-related changes, is currently based on single-center non-homogeneous glioma populations. • This multicentric study confirms the high accuracy of static [18F]FDOPA PET images for differentiating progression from treatment-related changes in a homogeneous population of high-grade gliomas and highlights the diagnostic and prognostic value of dynamic acquisitions for IDH-mutant high-grade gliomas. • Dynamic acquisitions should be performed in IDH-mutant glioma patients to provide valuable information for the differential diagnosis of recurrence and treatment-related changes.
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Horowitz T, Tabouret E, Graillon T, Salgues B, Chinot O, Verger A, Guedj E. Contribution of nuclear medicine to the diagnosis and management of primary brain tumours. Rev Neurol (Paris) 2023; 179:394-404. [PMID: 36934021 DOI: 10.1016/j.neurol.2023.03.002] [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: 02/18/2023] [Revised: 02/27/2023] [Accepted: 03/01/2023] [Indexed: 03/18/2023]
Abstract
Positron emission tomography (PET) is a powerful tool that can help physicians manage primary brain tumours at diagnosis and follow-up. In this context, PET imaging is used with three main types of radiotracers: 18F-FDG, amino acid radiotracers, and 68Ga conjugated to somatostatin receptor ligands (SSTRs). At initial diagnosis, 18F-FDG helps to characterize primary central nervous system (PCNS) lymphomas and high-grade gliomas, amino acid radiotracers are indicated for gliomas, and SSTR PET ligands are indicated for meningiomas. Such radiotracers provide information on tumour grade or type, assist in directing biopsies and help with treatment planning. During follow-up, in the presence of symptoms and/or MRI modifications, the differential diagnosis between tumour recurrence and post-therapeutic changes, in particular radiation necrosis, may be challenging, and there is strong interest in using PET to evaluate therapeutic toxicity. PET may also contribute to identifying specific complications, such as postradiation therapy encephalopathy, encephalitis associated with PCNS lymphoma, and stroke-like migraine after radiation therapy (SMART) syndrome associated with glioma recurrence and temporal epilepsy, originally illustrated in this review. This review summarizes the main contribution of PET to the diagnosis, management, and follow-up of brain tumours, specifically gliomas, meningiomas, and primary central nervous system lymphomas.
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Affiliation(s)
- T Horowitz
- CNRS, CERIMED, nuclear medicine department, Centrale Marseille, Institut Fresnel, Timone hospital, Aix-Marseille university, AP-HM, Marseille, France
| | - E Tabouret
- Neuro-oncology department, Timone hospital, AP-HM, Marseille, France; Team 8 GlioME, CNRS 7051, Inst. neurophysiopathol, Aix-Marseille university, Marseille, France
| | - T Graillon
- Inserm, MMG, neurosurgery department, Timone hospital, Aix-Marseille university, AP-HM, Marseille, France
| | - B Salgues
- CNRS, CERIMED, nuclear medicine department, Centrale Marseille, Institut Fresnel, Timone hospital, Aix-Marseille university, AP-HM, Marseille, France
| | - O Chinot
- Neuro-oncology department, Timone hospital, AP-HM, Marseille, France
| | - A Verger
- IADI, Inserm, UMR 1254, department of nuclear medicine & nancyclotep imaging platform, université de Lorraine, CHRU-Nancy, Nancy, France
| | - E Guedj
- CNRS, CERIMED, nuclear medicine department, Centrale Marseille, Institut Fresnel, Timone hospital, Aix-Marseille university, AP-HM, Marseille, France.
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Dai J, Wang H, Xu Y, Chen X, Tian R. Clinical application of AI-based PET images in oncological patients. Semin Cancer Biol 2023; 91:124-142. [PMID: 36906112 DOI: 10.1016/j.semcancer.2023.03.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 02/28/2023] [Accepted: 03/07/2023] [Indexed: 03/11/2023]
Abstract
Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insufficient image quality, the lack of a convincing evaluation tool and intra- and interobserver variation in human work are well-known limitations of nuclear medicine imaging and restrict its clinical application. Artificial intelligence (AI) has gained increasing interest in the field of medical imaging due to its powerful information collection and interpretation ability. The combination of AI and PET imaging potentially provides great assistance to physicians managing patients. Radiomics, an important branch of AI applied in medical imaging, can extract hundreds of abstract mathematical features of images for further analysis. In this review, an overview of the applications of AI in PET imaging is provided, focusing on image enhancement, tumor detection, response and prognosis prediction and correlation analyses with pathology or specific gene mutations in several types of tumors. Our aim is to describe recent clinical applications of AI-based PET imaging in malignant diseases and to focus on the description of possible future developments.
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Affiliation(s)
- Jiaona Dai
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Hui Wang
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yuchao Xu
- School of Nuclear Science and Technology, University of South China, Hengyang City 421001, China
| | - Xiyang Chen
- Division of Vascular Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China.
| | - Rong Tian
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China.
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Ahrari S, Zaragori T, Bros M, Oster J, Imbert L, Verger A. Implementing the Point Spread Function Deconvolution for Better Molecular Characterization of Newly Diagnosed Gliomas: A Dynamic 18F-FDOPA PET Radiomics Study. Cancers (Basel) 2022; 14:cancers14235765. [PMID: 36497245 PMCID: PMC9738921 DOI: 10.3390/cancers14235765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 11/10/2022] [Accepted: 11/17/2022] [Indexed: 11/25/2022] Open
Abstract
Purpose: This study aims to investigate the effects of applying the point spread function deconvolution (PSFd) to the radiomics analysis of dynamic L-3,4-dihydroxy-6-[18F]-fluoro-phenyl-alanine (18F-FDOPA) positron emission tomography (PET) images, to non-invasively identify isocitrate dehydrogenase (IDH) mutated and/or 1p/19q codeleted gliomas. Methods: Fifty-seven newly diagnosed glioma patients underwent dynamic 18F-FDOPA imaging on the same digital PET system. All images were reconstructed with and without PSFd. An L1-penalized (Lasso) logistic regression model, with 5-fold cross-validation and 20 repetitions, was trained with radiomics features extracted from the static tumor-to-background-ratio (TBR) and dynamic time-to-peak (TTP) parametric images, as well as a combination of both. Feature importance was assessed using Shapley additive explanation values. Results: The PSFd significantly modified 95% of TBR, but only 79% of TTP radiomics features. Applying the PSFd significantly improved the ability to identify IDH-mutated and/or 1p/19q codeleted gliomas, compared to PET images not processed with PSFd, with respective areas under the curve of 0.83 versus 0.79 and 0.75 versus 0.68 for a combination of static and dynamic radiomics features (p < 0.001). Without the PSFd, four and eight radiomics features contributed to 50% of the model for detecting IDH-mutated and/or 1p/19q codeleted gliomas, respectively. Application of the PSFd reduced this to three and seven contributive radiomics features. Conclusion: Application of the PSFd to dynamic 18F-FDOPA PET imaging significantly improves the detection of molecular parameters in newly diagnosed gliomas, most notably by modifying TBR radiomics features.
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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, F-54000 Nancy, France
- Nancyclotep Imaging Platform, Université de Lorraine, F-54000 Nancy, France
| | - Timothée Zaragori
- Imagerie Adaptative Diagnostique et Interventionnelle, Institut National de la Santé et de la Recherche Médicale U1254, Université de Lorraine, F-54000 Nancy, France
- Nancyclotep Imaging Platform, Université de Lorraine, F-54000 Nancy, France
| | - Marie Bros
- Department of Nuclear Medicine, Centre Hospitalier Régional Universitaire de Nancy, F-54000 Nancy, France
| | - Julien Oster
- Imagerie Adaptative Diagnostique et Interventionnelle, Institut National de la Santé et de la Recherche Médicale U1254, Université de Lorraine, F-54000 Nancy, France
| | - Laetitia Imbert
- Imagerie Adaptative Diagnostique et Interventionnelle, Institut National de la Santé et de la Recherche Médicale U1254, Université de Lorraine, F-54000 Nancy, France
- Nancyclotep Imaging Platform, Université de Lorraine, F-54000 Nancy, France
- Department of Nuclear Medicine, Centre Hospitalier Régional Universitaire de Nancy, F-54000 Nancy, France
| | - Antoine Verger
- Imagerie Adaptative Diagnostique et Interventionnelle, Institut National de la Santé et de la Recherche Médicale U1254, Université de Lorraine, F-54000 Nancy, France
- Nancyclotep Imaging Platform, Université de Lorraine, F-54000 Nancy, France
- Department of Nuclear Medicine, Centre Hospitalier Régional Universitaire de Nancy, F-54000 Nancy, France
- Correspondence:
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A Survey of Radiomics in Precision Diagnosis and Treatment of Adult Gliomas. J Clin Med 2022; 11:jcm11133802. [PMID: 35807084 PMCID: PMC9267404 DOI: 10.3390/jcm11133802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/18/2022] [Accepted: 06/29/2022] [Indexed: 02/04/2023] Open
Abstract
Glioma is the most common primary malignant tumor of the adult central nervous system (CNS), which mostly shows invasive growth. In most cases, surgery is often difficult to completely remove, and the recurrence rate and mortality of patients are high. With the continuous development of molecular genetics and the great progress of molecular biology technology, more and more molecular biomarkers have been proved to have important guiding significance in the individualized diagnosis, treatment, and prognosis evaluation of glioma. With the updates of the World Health Organization (WHO) classification of tumors of the CNS in 2021, the diagnosis and treatment of glioma has entered the era of precision medicine in the true sense. Due to its ability to non-invasively achieve accurate identification of glioma from other intracranial tumors, and to predict the grade, genotyping, treatment response, and prognosis of glioma, which provides a scientific basis for the clinical application of individualized diagnosis and treatment model of glioma, radiomics has become a research hotspot in the field of precision medicine. This paper reviewed the research related to radiomics of adult gliomas published in recent years and summarized the research proceedings of radiomics in differential diagnosis, preoperative grading and genotyping, treatment and efficacy evaluation, and survival prediction of adult gliomas.
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Barca C, Foray C, Zinnhardt B, Winkeler A, Herrlinger U, Grauer OM, Jacobs AH. In Vivo Quantitative Imaging of Glioma Heterogeneity Employing Positron Emission Tomography. Cancers (Basel) 2022; 14:cancers14133139. [PMID: 35804911 PMCID: PMC9264799 DOI: 10.3390/cancers14133139] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/22/2022] [Accepted: 06/22/2022] [Indexed: 11/16/2022] Open
Abstract
Glioblastoma is the most common primary brain tumor, highly aggressive by being proliferative, neovascularized and invasive, heavily infiltrated by immunosuppressive glioma-associated myeloid cells (GAMs), including glioma-associated microglia/macrophages (GAMM) and myeloid-derived suppressor cells (MDSCs). Quantifying GAMs by molecular imaging could support patient selection for GAMs-targeting immunotherapy, drug target engagement and further assessment of clinical response. Magnetic resonance imaging (MRI) and amino acid positron emission tomography (PET) are clinically established imaging methods informing on tumor size, localization and secondary phenomena but remain quite limited in defining tumor heterogeneity, a key feature of glioma resistance mechanisms. The combination of different imaging modalities improved the in vivo characterization of the tumor mass by defining functionally distinct tissues probably linked to tumor regression, progression and infiltration. In-depth image validation on tracer specificity, biological function and quantification is critical for clinical decision making. The current review provides a comprehensive overview of the relevant experimental and clinical data concerning the spatiotemporal relationship between tumor cells and GAMs using PET imaging, with a special interest in the combination of amino acid and translocator protein (TSPO) PET imaging to define heterogeneity and as therapy readouts.
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Affiliation(s)
- Cristina Barca
- European Institute for Molecular Imaging (EIMI), University of Münster, D-48149 Münster, Germany; (C.F.); (B.Z.)
- Correspondence: (C.B.); (A.H.J.)
| | - Claudia Foray
- European Institute for Molecular Imaging (EIMI), University of Münster, D-48149 Münster, Germany; (C.F.); (B.Z.)
| | - Bastian Zinnhardt
- European Institute for Molecular Imaging (EIMI), University of Münster, D-48149 Münster, Germany; (C.F.); (B.Z.)
- Biomarkers & Translational Technologies (BTT), Pharma Research & Early Development (pRED), F. Hoffmann-La Roche Ltd., CH-4070 Basel, Switzerland
| | - Alexandra Winkeler
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMaps, Service Hospitalier Frédéric Joliot, F-91401 Orsay, France;
| | - Ulrich Herrlinger
- Division of Clinical Neuro-Oncology, Department of Neurology, University Hospital Bonn, D-53105 Bonn, Germany;
- Centre of Integrated Oncology (CIO), University Hospital Bonn, D-53127 Bonn, Germany
| | - Oliver M. Grauer
- Department of Neurology with Institute of Translational Neurology, University Hospital Münster, D-48149 Münster, Germany;
| | - Andreas H. Jacobs
- European Institute for Molecular Imaging (EIMI), University of Münster, D-48149 Münster, Germany; (C.F.); (B.Z.)
- Centre of Integrated Oncology (CIO), University Hospital Bonn, D-53127 Bonn, Germany
- Department of Geriatrics with Neurology, Johanniter Hospital, D-53113 Bonn, Germany
- Correspondence: (C.B.); (A.H.J.)
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22
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The Role of [ 68Ga]Ga-DOTA-SSTR PET Radiotracers in Brain Tumors: A Systematic Review of the Literature and Ongoing Clinical Trials. Cancers (Basel) 2022; 14:cancers14122925. [PMID: 35740591 PMCID: PMC9221214 DOI: 10.3390/cancers14122925] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/10/2022] [Accepted: 06/12/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary [68Ga]Ga-DOTA-SSTR PET imaging has recently been introduced in the management of patients with brain tumors, mostly meningiomas and pituitary adenomas or carcinomas. The current literature demonstrated the superior diagnostic accuracy of this imaging modality, especially for lesions difficult to be detected or characterized on conventional imaging protocols, such as skull base or transosseous meningiomas. [68Ga]Ga-DOTA-SSTR PET tracers also seem to provide superior volume contouring for radiotherapy planning and may also be used to evaluate the tumor’s overexpression of somatostatin receptors for devising patient-tailored peptide receptor radionuclide therapy. In this review, we comprehensively analyzed the current literature discussing the implementation of [68Ga]Ga-DOTA-SSTR PET imaging in brain tumors, further presenting ongoing clinical trials and suggesting potential future applications. Abstract Background: The development of [68Ga]Ga-DOTA-SSTR PET tracers has garnered interest in neuro-oncology, to increase accuracy in diagnostic, radiation planning, and neurotheranostics protocols. We systematically reviewed the literature on the current uses of [68Ga]Ga-DOTA-SSTR PET in brain tumors. Methods: PubMed, Scopus, Web of Science, and Cochrane were searched in accordance with the PRISMA guidelines to include published studies and ongoing trials utilizing [68Ga]Ga-DOTA-SSTR PET in patients with brain tumors. Results: We included 63 published studies comprising 1030 patients with 1277 lesions, and 4 ongoing trials. [68Ga]Ga-DOTA-SSTR PET was mostly used for diagnostic purposes (62.5%), followed by treatment planning (32.7%), and neurotheranostics (4.8%). Most lesions were meningiomas (93.6%), followed by pituitary adenomas (2.8%), and the DOTATOC tracer (53.2%) was used more frequently than DOTATATE (39.1%) and DOTANOC (5.7%), except for diagnostic purposes (DOTATATE 51.1%). [68Ga]Ga-DOTA-SSTR PET studies were mostly required to confirm the diagnosis of meningiomas (owing to their high SSTR2 expression and tracer uptake) or evaluate their extent of bone invasion, and improve volume contouring for better radiotherapy planning. Some studies reported the uncommon occurrence of SSTR2-positive brain pathology challenging the diagnostic accuracy of [68Ga]Ga-DOTA-SSTR PET for meningiomas. Pre-treatment assessment of tracer uptake rates has been used to confirm patient eligibility (high somatostatin receptor-2 expression) for peptide receptor radionuclide therapy (PRRT) (i.e., neurotheranostics) for recurrent meningiomas and pituitary carcinomas. Conclusion: [68Ga]Ga-DOTA-SSTR PET studies may revolutionize the routine neuro-oncology practice, especially in meningiomas, by improving diagnostic accuracy, delineation of radiotherapy targets, and patient eligibility for radionuclide therapies.
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Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers. Diagnostics (Basel) 2022; 12:1329. [PMID: 35741138 PMCID: PMC9221970 DOI: 10.3390/diagnostics12061329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 12/10/2022] Open
Abstract
Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.
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Affiliation(s)
- David Morland
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
| | - Elizabeth Katherine Anna Triumbari
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
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Comte V, Schmutz H, Chardin D, Orlhac F, Darcourt J, Humbert O. Development and validation of a radiomic model for the diagnosis of dopaminergic denervation on [18F]FDOPA PET/CT. Eur J Nucl Med Mol Imaging 2022; 49:3787-3796. [PMID: 35567626 PMCID: PMC9399031 DOI: 10.1007/s00259-022-05816-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 04/23/2022] [Indexed: 11/30/2022]
Abstract
Purpose FDOPA PET shows good performance for the diagnosis of striatal dopaminergic denervation, making it a valuable tool for the differential diagnosis of Parkinsonism. Textural features are image biomarkers that could potentially improve the early diagnosis and monitoring of neurodegenerative parkinsonian syndromes. We explored the performances of textural features for binary classification of FDOPA scans. Methods We used two FDOPA PET datasets: 443 scans for feature selection, and 100 scans from a different PET/CT system for model testing. Scans were labelled according to expert interpretation (dopaminergic denervation versus no dopaminergic denervation). We built LASSO logistic regression models using 43 biomarkers including 32 textural features. Clinical data were also collected using a shortened UPDRS scale. Results The model built from the clinical data alone had a mean area under the receiver operating characteristics (AUROC) of 63.91. Conventional imaging features reached a maximum score of 93.47 but the addition of textural features significantly improved the AUROC to 95.73 (p < 0.001), and 96.10 (p < 0.001) when limiting the model to the top three features: GLCM_Correlation, Skewness and Compacity. Testing the model on the external dataset yielded an AUROC of 96.00, with 95% sensitivity and 97% specificity. GLCM_Correlation was one of the most independent features on correlation analysis, and systematically had the heaviest weight in the classification model. Conclusion A simple model with three radiomic features can identify pathologic FDOPA PET scans with excellent sensitivity and specificity. Textural features show promise for the diagnosis of parkinsonian syndromes. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-022-05816-7.
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Affiliation(s)
- Victor Comte
- Department of Nuclear Medicine, Centre Antoine Lacassagne, Université Côte d'Azur, Nice, France.
| | - Hugo Schmutz
- Laboratoire TIRO UMR E4320, Université Côte d'Azur, Nice, France
| | - David Chardin
- Department of Nuclear Medicine, Centre Antoine Lacassagne, Université Côte d'Azur, Nice, France.,Laboratoire TIRO UMR E4320, Université Côte d'Azur, Nice, France
| | - Fanny Orlhac
- Laboratoire d'Imagerie Translationnelle en Oncologie (LITO) U1288, Institut Curie, Inserm, Université Paris-Saclay, Orsay, France
| | - Jacques Darcourt
- Department of Nuclear Medicine, Centre Antoine Lacassagne, Université Côte d'Azur, Nice, France.,Laboratoire TIRO UMR E4320, Université Côte d'Azur, Nice, France
| | - Olivier Humbert
- Department of Nuclear Medicine, Centre Antoine Lacassagne, Université Côte d'Azur, Nice, France.,Laboratoire TIRO UMR E4320, Université Côte d'Azur, Nice, France
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Afridi M, Jain A, Aboian M, Payabvash S. Brain Tumor Imaging: Applications of Artificial Intelligence. Semin Ultrasound CT MR 2022; 43:153-169. [PMID: 35339256 PMCID: PMC8961005 DOI: 10.1053/j.sult.2022.02.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Artificial intelligence has become a popular field of research with goals of integrating it into the clinical decision-making process. A growing number of predictive models are being employed utilizing machine learning that includes quantitative, computer-extracted imaging features known as radiomic features, and deep learning systems. This is especially true in brain-tumor imaging where artificial intelligence has been proposed to characterize, differentiate, and prognostication. We reviewed current literature regarding the potential uses of machine learning-based, and deep learning-based artificial intelligence in neuro-oncology as it pertains to brain tumor molecular classification, differentiation, and treatment response. While there is promising evidence supporting the use of artificial intelligence in neuro-oncology, there are still more investigations needed on a larger, multicenter scale along with a streamlined and standardized image processing workflow prior to its introduction in routine clinical decision-making protocol.
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Affiliation(s)
- Muhammad Afridi
- School of Osteopathic Medicine, Rowan University, Stratford, NJ
| | - Abhi Jain
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Mariam Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT.
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Clément A, Zaragori T, Filosa R, Ovdiichuk O, Beaumont M, Collet C, Roeder E, Martin B, Maskali F, Barberi-Heyob M, Pouget C, Doyen M, Verger A. Multi-tracer and multiparametric PET imaging to detect the IDH mutation in glioma: a preclinical translational in vitro, in vivo, and ex vivo study. Cancer Imaging 2022; 22:16. [PMID: 35303961 PMCID: PMC8932106 DOI: 10.1186/s40644-022-00454-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 03/03/2022] [Indexed: 11/16/2022] Open
Abstract
Background This translational study explores multi-tracer PET imaging for the non-invasive detection of the IDH1 mutation which is a positive prognostic factor in glioma. Methods U87 human high-grade glioma (HGG) isogenic cell lines with or without the IDH1 mutation (CRISP/Cas9 method) were stereotactically grafted into rat brains, and examined, in vitro, in vivo and ex vivo. PET imaging sessions, with radiotracers specific for glycolytic metabolism ([18F]FDG), amino acid metabolism ([18F]FDopa), and inflammation ([18F]DPA-714), were performed sequentially during 3–4 days. The in vitro radiotracer uptake was expressed as percent per million cells. For each radiotracer examined in vivo, static analyses included the maximal and mean tumor-to-background ratio (TBRmax and TBRmean) and metabolic tumor volume (MTV). Dynamic analyses included the distribution volume ratio (DVR) and the relative residence time (RRT) extracted from a reference Logan model. Ex vivo analyses consisted of immunological analyses. Results In vitro, IDH1+ cells (i.e. cells expressing the IDH1 mutation) showed lower levels of [18F]DPA-714 uptake compared to IDH1- cells (p < 0.01). These results were confirmed in vivo with lower [18F]DPA-714 uptake in IDH+ tumors (3.90 versus 5.52 for TBRmax, p = 0.03). Different values of [18F]DPA-714 and [18F] FDopa RRT (respectively 11.07 versus 22.33 and 2.69 versus − 1.81 for IDH+ and IDH- tumors, p < 0.02) were also observed between the two types of tumors. RRT [18F]DPA-714 provided the best diagnostic performance to discriminate between the two cell lines (AUC of 100%, p < 0.01). Immuno-histological analyses revealed lower expression of Iba-1 and TSPO antibodies in IDH1+ tumors. Conclusions [18F]DPA-714 and [18F] FDopa both correlate with the presence of the IDH1 mutation in HGG. These radiotracers are therefore good candidates for translational studies investigating their clinical applications in patients. Supplementary Information The online version contains supplementary material available at 10.1186/s40644-022-00454-6.
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Affiliation(s)
- Alexandra Clément
- Nancyclotep Molecular and Experimental Imaging Platform, CHRU-Nancy, 05 rue du Morvan, 54500, Vandoeuvre-Les-Nancy, France. .,Lorraine University, INSERM, IADI UMR 1254, Nancy, France.
| | - Timothee Zaragori
- Nancyclotep Molecular and Experimental Imaging Platform, CHRU-Nancy, 05 rue du Morvan, 54500, Vandoeuvre-Les-Nancy, France.,Lorraine University, INSERM, IADI UMR 1254, Nancy, France
| | - Romain Filosa
- Nancyclotep Molecular and Experimental Imaging Platform, CHRU-Nancy, 05 rue du Morvan, 54500, Vandoeuvre-Les-Nancy, France
| | - Olga Ovdiichuk
- Nancyclotep Molecular and Experimental Imaging Platform, CHRU-Nancy, 05 rue du Morvan, 54500, Vandoeuvre-Les-Nancy, France
| | - Marine Beaumont
- Lorraine University, INSERM, IADI UMR 1254, Nancy, France.,Lorraine University, CIC-IT UMR 1433, CHRU-Nancy, Nancy, France
| | - Charlotte Collet
- Nancyclotep Molecular and Experimental Imaging Platform, CHRU-Nancy, 05 rue du Morvan, 54500, Vandoeuvre-Les-Nancy, France.,Lorraine University, INSERM, IADI UMR 1254, Nancy, France
| | - Emilie Roeder
- Nancyclotep Molecular and Experimental Imaging Platform, CHRU-Nancy, 05 rue du Morvan, 54500, Vandoeuvre-Les-Nancy, France
| | - Baptiste Martin
- Nancyclotep Molecular and Experimental Imaging Platform, CHRU-Nancy, 05 rue du Morvan, 54500, Vandoeuvre-Les-Nancy, France
| | - Fatiha Maskali
- Nancyclotep Molecular and Experimental Imaging Platform, CHRU-Nancy, 05 rue du Morvan, 54500, Vandoeuvre-Les-Nancy, France
| | | | - Celso Pouget
- Department of Pathology, CHRU-Nancy, Nancy, France
| | - Matthieu Doyen
- Nancyclotep Molecular and Experimental Imaging Platform, CHRU-Nancy, 05 rue du Morvan, 54500, Vandoeuvre-Les-Nancy, France.,Lorraine University, INSERM, IADI UMR 1254, Nancy, France
| | - Antoine Verger
- Nancyclotep Molecular and Experimental Imaging Platform, CHRU-Nancy, 05 rue du Morvan, 54500, Vandoeuvre-Les-Nancy, France.,Lorraine University, INSERM, IADI UMR 1254, Nancy, France.,Department of Nuclear Medicine, CHRU-Nancy, Nancy, France
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PET Imaging in Neuro-Oncology: An Update and Overview of a Rapidly Growing Area. Cancers (Basel) 2022; 14:cancers14051103. [PMID: 35267411 PMCID: PMC8909369 DOI: 10.3390/cancers14051103] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/08/2022] [Accepted: 02/19/2022] [Indexed: 12/21/2022] Open
Abstract
Simple Summary Positron emission tomography (PET) is a functional imaging technique which plays an increasingly important role in the management of brain tumors. Owing different radiotracers, PET allows to image different metabolic aspects of the brain tumors. This review outlines currently available PET radiotracers and their respective indications in neuro-oncology. It specifically focuses on the investigation of gliomas, meningiomas, primary central nervous system lymphomas as well as brain metastases. Recent advances in the production of PET radiotracers, image analyses and translational applications to peptide radionuclide receptor therapy, which allow to treat brain tumors with radiotracers, are also discussed. The objective of this review is to provide a comprehensive overview of PET imaging’s potential in neuro-oncology as an adjunct to brain magnetic resonance imaging (MRI). Abstract PET plays an increasingly important role in the management of brain tumors. This review outlines currently available PET radiotracers and their respective indications. It specifically focuses on 18F-FDG, amino acid and somatostatin receptor radiotracers, for imaging gliomas, meningiomas, primary central nervous system lymphomas as well as brain metastases. Recent advances in radiopharmaceuticals, image analyses and translational applications to therapy are also discussed. The objective of this review is to provide a comprehensive overview of PET imaging’s potential in neuro-oncology as an adjunct to brain MRI for all medical professionals implicated in brain tumor diagnosis and care.
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Ahrari S, Zaragori T, Rozenblum L, Oster J, Imbert L, Kas A, Verger A. Relevance of Dynamic 18F-DOPA PET Radiomics for Differentiation of High-Grade Glioma Progression from Treatment-Related Changes. Biomedicines 2021; 9:biomedicines9121924. [PMID: 34944740 PMCID: PMC8698938 DOI: 10.3390/biomedicines9121924] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/14/2021] [Accepted: 12/14/2021] [Indexed: 12/22/2022] Open
Abstract
This study evaluates the relevance of 18F-DOPA PET static and dynamic radiomics for differentiation of high-grade glioma (HGG) progression from treatment-related changes (TRC) by comparing diagnostic performances to the current PET imaging standard of care. Eighty-five patients with histologically confirmed HGG and investigated by dynamic 18F-FDOPA PET in two institutions were retrospectively selected. ElasticNet logistic regression, Random Forest and XGBoost machine models were trained with different sets of features-radiomics extracted from static tumor-to-background-ratio (TBR) parametric images, radiomics extracted from time-to-peak (TTP) parametric images, as well as combination of both-in order to discriminate glioma progression from TRC at 6 months from the PET scan. Diagnostic performances of the models were compared to a logistic regression model with TBRmean ± clinical features used as reference. Training was performed on data from the first center, while external validation was performed on data from the second center. Best radiomics models showed only slightly better performances than the reference model (respective AUCs of 0.834 vs. 0.792, p < 0.001). Our current results show similar findings at the multicentric level using different machine learning models and report a marginal additional value for TBR static and TTP dynamic radiomics over the classical analysis based on TBR values.
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Affiliation(s)
- Shamimeh Ahrari
- Université de Lorraine, IADI, INSERM, UMR 1254, F-54000 Nancy, France; (S.A.); (T.Z.); (J.O.); (L.I.)
| | - Timothée Zaragori
- Université de Lorraine, IADI, INSERM, UMR 1254, F-54000 Nancy, France; (S.A.); (T.Z.); (J.O.); (L.I.)
| | - Laura Rozenblum
- Sorbonne Université, AP-HP, Hôpitaux Universitaires Pitié-Salpêtrière Charles Foix, Service de Médecine Nucléaire and LIB, INSERM U1146, F-75013 Paris, France; (L.R.); (A.K.)
| | - Julien Oster
- Université de Lorraine, IADI, INSERM, UMR 1254, F-54000 Nancy, France; (S.A.); (T.Z.); (J.O.); (L.I.)
| | - Laëtitia Imbert
- Université de Lorraine, IADI, INSERM, UMR 1254, F-54000 Nancy, France; (S.A.); (T.Z.); (J.O.); (L.I.)
- Department of Nuclear Medicine & Nancyclotep Imaging Platform, Université de Lorraine, CHRU-Nancy, F-54000 Nancy, France
| | - Aurélie Kas
- Sorbonne Université, AP-HP, Hôpitaux Universitaires Pitié-Salpêtrière Charles Foix, Service de Médecine Nucléaire and LIB, INSERM U1146, F-75013 Paris, France; (L.R.); (A.K.)
| | - Antoine Verger
- Université de Lorraine, IADI, INSERM, UMR 1254, F-54000 Nancy, France; (S.A.); (T.Z.); (J.O.); (L.I.)
- Department of Nuclear Medicine & Nancyclotep Imaging Platform, Université de Lorraine, CHRU-Nancy, F-54000 Nancy, France
- Correspondence:
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Effects of Carbidopa Premedication on 18F-FDOPA PET Imaging of Glioma: A Multiparametric Analysis. Cancers (Basel) 2021; 13:cancers13215340. [PMID: 34771504 PMCID: PMC8582429 DOI: 10.3390/cancers13215340] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 10/21/2021] [Indexed: 01/03/2023] Open
Abstract
Simple Summary 18F-FDOPA PET imaging is routinely used and recommended to assess gliomas. Carbidopa is a peripheral enzyme inhibitor. Carbidopa premedication increases the radiotracer uptake on static images. None of the evidence-based data available to date recommends carbidopa premedication. Our study therefore determined the impact of carbidopa premedication on static, radiomics and dynamic parameters for 18F-FDOPA PET brain tumor imaging. We show that carbidopa premedication leads to higher SUV and TTP dynamic parameters and impacts SUV-dependent radiomics by the same magnitude in healthy brains and tumors. The carbidopa effect is therefore compensated for by correcting for the tumor-to-healthy-brain ratio, a significant advantage for harmonizing data for multicentric studies. Results were obtained from simulations of time-activity curves using compartmental modeling. Abstract Purpose: This study aimed to determine the impact of carbidopa premedication on static, dynamic and radiomics parameters of 18F-FDOPA PET in brain tumors. Methods: The study included 54 patients, 18 of whom received carbidopa, who underwent 18F-FDOPA PET for newly diagnosed gliomas. SUV-derived, 105 radiomics features and TTP dynamic parameters were extracted from volumes of interest in healthy brains and tumors. Simulation of the effects of carbidopa on time-activity curves were generated. Results: All static and TTP dynamic parameters were significantly higher in healthy brain regions of premedicated patients (ΔSUVmean = +53%, ΔTTP = +48%, p < 0.001). Furthermore, carbidopa impacted 81% of radiomics features, of which 92% correlated with SUVmean (absolute correlation coefficient ≥ 0.4). In tumors, premedication with carbidopa was an independent predictor of SUVmean (ΔSUVmean = +52%, p < 0.001) and TTP (ΔTTP = +24%, p = 0.025). All parameters were no longer significantly modified by carbidopa premedication when using ratios to healthy brain. Simulated data confirmed that carbidopa leads to higher tumor TTP values, corrected by the ratios. Conclusion: In 18F-FDOPA PET, carbidopa induces similarly higher SUV and TTP dynamic parameters and similarly impacts SUV-dependent radiomics in healthy brain and tumor regions, which is compensated for by correcting for the tumor-to-healthy-brain ratio. This is a significant advantage for multicentric study harmonization.
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Zaragori T, Doyen M, Rech F, Blonski M, Taillandier L, Imbert L, Verger A. Dynamic 18F-FDopa PET Imaging for Newly Diagnosed Gliomas: Is a Semiquantitative Model Sufficient? Front Oncol 2021; 11:735257. [PMID: 34676168 PMCID: PMC8523996 DOI: 10.3389/fonc.2021.735257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 09/06/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose Dynamic amino acid positron emission tomography (PET) has become essential in neuro-oncology, most notably for its prognostic value in the noninvasive prediction of isocitrate dehydrogenase (IDH) mutations in newly diagnosed gliomas. The 6-[18F]fluoro-l-DOPA (18F-FDOPA) kinetic model has an underlying complexity, while previous studies have predominantly used a semiquantitative dynamic analysis. Our study addresses whether a semiquantitative analysis can capture all the relevant information contained in time–activity curves for predicting the presence of IDH mutations compared to the more sophisticated graphical and compartmental models. Methods Thirty-seven tumour time–activity curves from 18F-FDOPA PET dynamic acquisitions of newly diagnosed gliomas (median age = 58.3 years, range = 20.3–79.9 years, 16 women, 16 IDH-wild type) were analyzed with a semiquantitative model based on classical parameters, with (SQ) or without (Ref SQ) a reference region, or on parameters of a fit function (SQ Fit), a graphical Logan model with input function (Logan) or reference region (Ref Logan), and a two-tissue compartmental model previously reported for 18F-FDOPA PET imaging of gliomas (2TCM). The overall predictive performance of each model was assessed with an area under the curve (AUC) comparison using multivariate analysis of all the parameters included in the model. Moreover, each extracted parameter was assessed in a univariate analysis by a receiver operating characteristic curve analysis. Results The SQ model with an AUC of 0.733 for predicting IDH mutations showed comparable performance to the other models with AUCs of 0.752, 0.814, 0.693, 0.786, and 0.863, respectively corresponding to SQ Fit, Ref SQ, Logan, Ref Logan, and 2TCM (p ≥ 0.10 for the pairwise comparisons with other models). In the univariate analysis, the SQ time-to-peak parameter had the best diagnostic performance (75.7% accuracy) compared to all other individual parameters considered. Conclusions The SQ model circumvents the complexities of the 18F-FDOPA kinetic model and yields similar performance in predicting IDH mutations when compared to the other models, most notably the compartmental model. Our study provides supportive evidence for the routine clinical application of the SQ model for the dynamic analysis of 18F-FDOPA PET images in newly diagnosed gliomas.
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Affiliation(s)
- Timothée Zaragori
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU-Nancy, Université de Lorraine, Nancy, France.,IADI UMR 1254, INSERM, Université de Lorraine, Nancy, France
| | - Matthieu Doyen
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU-Nancy, Université de Lorraine, Nancy, France.,IADI UMR 1254, INSERM, Université de Lorraine, Nancy, France
| | - Fabien Rech
- Department of Neurosurgery, CHRU-Nancy, Université de Lorraine, Nancy, France.,Centre de Recherche en Automatique de Nancy CRAN UMR 7039, CNRS, Université de Lorraine, Nancy, France
| | - Marie Blonski
- Centre de Recherche en Automatique de Nancy CRAN UMR 7039, CNRS, Université de Lorraine, Nancy, France.,Department of Neuro-Oncology, CHRU-Nancy, Université de Lorraine, Nancy, France
| | - Luc Taillandier
- Centre de Recherche en Automatique de Nancy CRAN UMR 7039, CNRS, Université de Lorraine, Nancy, France.,Department of Neuro-Oncology, CHRU-Nancy, Université de Lorraine, Nancy, France
| | - Laëtitia Imbert
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU-Nancy, Université de Lorraine, Nancy, France.,IADI UMR 1254, INSERM, Université de Lorraine, Nancy, France
| | - Antoine Verger
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU-Nancy, Université de Lorraine, Nancy, France.,IADI UMR 1254, INSERM, Université de Lorraine, Nancy, France
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31
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Verger A, Imbert L, Zaragori T. Dynamic amino-acid PET in neuro-oncology: a prognostic tool becomes essential. Eur J Nucl Med Mol Imaging 2021; 48:4129-4132. [PMID: 34518904 DOI: 10.1007/s00259-021-05530-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Antoine Verger
- Department of Nuclear Medicine & Nancyclotep Imaging Platform, CHRU-Nancy, Université de Lorraine, F-54000, Nancy, France.
- INSERM, IADI, UMR 1254 Université de Lorraine, F-54000, Nancy, France.
- Médecine Nucléaire, Hôpital de Brabois, CHRU-Nancy, Allée du Morvan, 54500, Vandoeuvre-les-Nancy, France.
| | - Laëtitia Imbert
- Department of Nuclear Medicine & Nancyclotep Imaging Platform, CHRU-Nancy, Université de Lorraine, F-54000, Nancy, France
- INSERM, IADI, UMR 1254 Université de Lorraine, F-54000, Nancy, France
| | - Timothée Zaragori
- Department of Nuclear Medicine & Nancyclotep Imaging Platform, CHRU-Nancy, Université de Lorraine, F-54000, Nancy, France
- INSERM, IADI, UMR 1254 Université de Lorraine, F-54000, Nancy, France
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Li Z, Kaiser L, Holzgreve A, Ruf VC, Suchorska B, Wenter V, Quach S, Herms J, Bartenstein P, Tonn JC, Unterrainer M, Albert NL. Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic [ 18F]FET PET radiomics. Eur J Nucl Med Mol Imaging 2021; 48:4415-4425. [PMID: 34490493 PMCID: PMC8566644 DOI: 10.1007/s00259-021-05526-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 08/05/2021] [Indexed: 12/22/2022]
Abstract
Purpose To evaluate radiomic features extracted from standard static images (20–40 min p.i.), early summation images (5–15 min p.i.), and dynamic [18F]FET PET images for the prediction of TERTp-mutation status in patients with IDH-wildtype high-grade glioma. Methods A total of 159 patients (median age 60.2 years, range 19–82 years) with newly diagnosed IDH-wildtype diffuse astrocytic glioma (WHO grade III or IV) and dynamic [18F]FET PET prior to surgical intervention were enrolled and divided into a training (n = 112) and a testing cohort (n = 47) randomly. First-order, shape, and texture radiomic features were extracted from standard static (20–40 min summation images; TBR20–40), early static (5–15 min summation images; TBR5–15), and dynamic (time-to-peak; TTP) images, respectively. Recursive feature elimination was used for feature selection by 10-fold cross-validation in the training cohort after normalization, and logistic regression models were generated using the radiomic features extracted from each image to differentiate TERTp-mutation status. The areas under the ROC curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive value were calculated to illustrate diagnostic power in both the training and testing cohort. Results The TTP model comprised nine selected features and achieved highest predictability of TERTp-mutation with an AUC of 0.82 (95% confidence interval 0.71–0.92) and sensitivity of 92.1% in the independent testing cohort. Weak predictive capability was obtained in the TBR5–15 model, with an AUC of 0.61 (95% CI 0.42–0.80) in the testing cohort, while no predictive power was observed in the TBR20–40 model. Conclusions Radiomics based on TTP images extracted from dynamic [18F]FET PET can predict the TERTp-mutation status of IDH-wildtype diffuse astrocytic high-grade gliomas with high accuracy preoperatively. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05526-6.
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Affiliation(s)
- Zhicong Li
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Lena Kaiser
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Adrien Holzgreve
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Viktoria C Ruf
- Center for Neuropathology and Prion Research, LMU Munich, Munich, Germany
| | - Bogdana Suchorska
- Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany
- Department of Neurosurgery, Sana Hospital, Duisburg, Germany
| | - Vera Wenter
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Stefanie Quach
- Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany
| | - Jochen Herms
- Center for Neuropathology and Prion Research, LMU Munich, Munich, Germany
| | - Peter 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), Heidelberg, Germany
| | - Jörg-Christian Tonn
- Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Marcus Unterrainer
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Nathalie 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), Heidelberg, Germany.
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