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Carlier T, Frécon G, Mateus D, Rizkallah M, Kraeber-Bodéré F, Kanoun S, Blanc-Durand P, Itti E, Le Gouill S, Casasnovas RO, Bodet-Milin C, Bailly C. Prognostic Value of 18F-FDG PET Radiomics Features at Baseline in PET-Guided Consolidation Strategy in Diffuse Large B-Cell Lymphoma: A Machine-Learning Analysis from the GAINED Study. J Nucl Med 2024; 65:156-162. [PMID: 37945379 DOI: 10.2967/jnumed.123.265872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 10/17/2023] [Indexed: 11/12/2023] Open
Abstract
The results of the GA in Newly Diagnosed Diffuse Large B-Cell Lymphoma (GAINED) study demonstrated the success of an 18F-FDG PET-driven approach to allow early identification-for intensification therapy-of diffuse large B-cell lymphoma patients with a high risk of relapse. Besides, some works have reported the prognostic value of baseline PET radiomics features (RFs). This work investigated the added value of such biomarkers on survival of patients involved in the GAINED protocol. Methods: Conventional PET features and RFs were computed from 18F-FDG PET at baseline and extracted using different volume definitions (patient level, largest lesion, and hottest lesion). Clinical features and the consolidation treatment information were also considered in the model. Two machine-learning pipelines were trained with 80% of patients and tested on the remaining 20%. The training was repeated 100 times to highlight the test set variability. For the 2-y progression-free survival (PFS) outcome, the pipeline included a data augmentation and an elastic net logistic regression model. Results for different feature groups were compared using the mean area under the curve (AUC). For the survival outcome, the pipeline included a Cox univariate model to select the features. Then, the model included a split between high- and low-risk patients using the median of a regression score based on the coefficients of a penalized Cox multivariate approach. The log-rank test P values over the 100 loops were compared with a Wilcoxon signed-ranked test. Results: In total, 545 patients were included for the 2-y PFS classification and 561 for survival analysis. Clinical features alone, consolidation features alone, conventional PET features, and RFs extracted at patient level achieved an AUC of, respectively, 0.65 ± 0.07, 0.64 ± 0.06, 0.60 ± 0.07, and 0.62 ± 0.07 (0.62 ± 0.07 for the largest lesion and 0.54 ± 0.07 for the hottest). Combining clinical features with the consolidation features led to the best AUC (0.72 ± 0.06). Adding conventional PET features or RFs did not improve the results. For survival, the log-rank P values of the model involving clinical and consolidation features together were significantly smaller than all combined-feature groups (P < 0.007). Conclusion: The results showed that a concatenation of multimodal features coupled with a simple machine-learning model does not seem to improve the results in terms of 2-y PFS classification and PFS prediction for patient treated according to the GAINED protocol.
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Affiliation(s)
- Thomas Carlier
- Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France
- Nuclear Medicine Department, University Hospital, Nantes, France
| | - Gauthier Frécon
- Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France
- Nuclear Medicine Department, University Hospital, Nantes, France
| | - Diana Mateus
- Laboratoire des Sciences Numériques de Nantes, Ecole Centrale de Nantes, CNRS UMR 6004, Nantes, France
| | - Mira Rizkallah
- Laboratoire des Sciences Numériques de Nantes, Ecole Centrale de Nantes, CNRS UMR 6004, Nantes, France
| | - Françoise Kraeber-Bodéré
- Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France
- Nuclear Medicine Department, University Hospital, Nantes, France
| | - Salim Kanoun
- Nuclear Medicine, Georges-François Leclerc Center, Dijon, France
| | - Paul Blanc-Durand
- Nuclear Medicine, CHU Henri Mondor, Paris-Est University, Créteil, France
| | - Emmanuel Itti
- Nuclear Medicine, CHU Henri Mondor, Paris-Est University, Créteil, France
| | - Steven Le Gouill
- Haematology Department, University Hospital, Nantes, France; and
| | | | - Caroline Bodet-Milin
- Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France
- Nuclear Medicine Department, University Hospital, Nantes, France
| | - Clément Bailly
- Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France;
- Nuclear Medicine Department, University Hospital, Nantes, France
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Godefroy T, Frécon G, Asquier-Khati A, Mateus D, Lecomte R, Rizkallah M, Piriou N, Jamet B, Le Tourneau T, Pallardy A, Boutoille D, Eugène T, Carlier T. 18F-FDG-Based Radiomics and Machine Learning: Useful Help for Aortic Prosthetic Valve Infective Endocarditis Diagnosis? JACC Cardiovasc Imaging 2023:S1936-878X(23)00093-1. [PMID: 37052569 DOI: 10.1016/j.jcmg.2023.01.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 01/25/2023] [Indexed: 04/14/2023]
Abstract
BACKGROUND Fluorine-18 fluorodeoxyglucose (18F-FDG)-positron emission tomography (PET)/computed tomography (CT) results in better sensitivity for prosthetic valve endocarditis (PVE) diagnosis, but visual image analysis results in relatively weak specificity and significant interobserver variability. OBJECTIVES The primary objective of this study was to evaluate the performance of a radiomics and machine learning-based analysis of 18F-FDG PET/CT (PET-ML) as a major criterion for the European Society of Cardiology score using machine learning as a major imaging criterion (ESC-ML) in PVE diagnosis. The secondary objective was to assess performance of PET-ML as a standalone examination. METHODS All 18F-FDG-PET/CT scans performed for suspected aortic PVE at a single center from 2015 to 2021 were retrospectively included. The gold standard was expert consensus after at least 3 months' follow-up. The machine learning (ML) method consisted of manually segmenting each prosthetic valve, extracting 31 radiomics features from the segmented region, and training a ridge logistic regressor to predict PVE. Training and hyperparameter tuning were done with a cross-validation approach, followed by an evaluation on an independent test database. RESULTS A total of 108 patients were included, regardless of myocardial uptake, and were divided into training (n = 68) and test (n = 40) cohorts. Considering the latter, PET-ML findings were positive for 13 of 22 definite PVE cases and 3 of 18 rejected PVE cases (59% sensitivity, 83% specificity), thus leading to an ESC-ML sensitivity of 72% and a specificity of 83%. CONCLUSIONS The use of ML for analyzing 18F-FDG-PET/CT images in PVE diagnosis was feasible and beneficial, particularly when ML was included in the ESC 2015 criteria. Despite some limitations and the need for future developments, this approach seems promising to optimize the role of 18F-FDG PET/CT in PVE diagnosis.
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Affiliation(s)
- Thomas Godefroy
- Nantes Université, CHU Nantes, INSERM, Nuclear Médicine, Nantes, France
| | - Gauthier Frécon
- Nantes Université, CHU Nantes, INSERM, Nuclear Médicine, Nantes, France; ECN, LS2N, Nantes, France
| | - Antoine Asquier-Khati
- Nantes Université, CHU Nantes, INSERM, Infectious Diseases Department, Nantes, France
| | | | - Raphaël Lecomte
- Nantes Université, CHU Nantes, INSERM, Infectious Diseases Department, Nantes, France
| | | | - Nicolas Piriou
- Nantes Université, CHU Nantes, INSERM, Nuclear Médicine, Nantes, France; Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, Nantes, France
| | - Bastien Jamet
- Nantes Université, CHU Nantes, INSERM, Nuclear Médicine, Nantes, France
| | - Thierry Le Tourneau
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, Nantes, France
| | - Amandine Pallardy
- Nantes Université, CHU Nantes, INSERM, Nuclear Médicine, Nantes, France
| | - David Boutoille
- Nantes Université, CHU Nantes, INSERM, Infectious Diseases Department, Nantes, France
| | - Thomas Eugène
- Nantes Université, CHU Nantes, INSERM, Nuclear Médicine, Nantes, France.
| | - Thomas Carlier
- Nantes Université, CHU Nantes, INSERM, Nuclear Médicine, Nantes, France
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