Machine learning in the differentiation of follicular lymphoma from diffuse large B-cell lymphoma with radiomic [18F]FDG PET/CT features.
Background: One of the challenges in the management of patients with follicular lymphoma (FL) is the identification of individuals with histological transformation, most commonly into diffuse large B-cell lymphoma (DLBCL). [18F]FDG-PET/CT is used for staging of patients with lymphoma, but visual int...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 49; no. 5; pp. 1535 - 1544 |
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| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155912926&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155912926 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Apr2022 vid: 49 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 155912926 153873164 155912926 155912926 10.1007/s00259-021-05626-3 155912926 ppf: 1535 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning in the differentiation of follicular lymphoma from diffuse large B-cell lymphoma with radiomic [18F]FDG PET/CT features. aug: au: de Jesus, F. Montes Yin, Y. Mantzorou-Kyriaki, E. Kahle, X. U. de Haas, R. J. Yakar, D. Glaudemans, A. W. J. M. Noordzij, W. Kwee, T. C. Nijland, M. affil: Universitair Medisch Centrum Groningen, Groningen, Netherlands sug: subj: Machine Learning Utilization Lymphoma, Non-Hodgkin's Diagnosis Lymphoma, B-Cell Diagnosis Cell Differentiation Fludeoxyglucose F 18 Diagnostic Use Positron-Emission Tomography Utilization Tomography, X-Ray Computed Utilization Human Histocytochemistry Algorithms Descriptive Statistics Logistic Regression Sensitivity and Specificity ab: Background: One of the challenges in the management of patients with follicular lymphoma (FL) is the identification of individuals with histological transformation, most commonly into diffuse large B-cell lymphoma (DLBCL). [18F]FDG-PET/CT is used for staging of patients with lymphoma, but visual interpretation cannot reliably discern FL from DLBCL. This study evaluated whether radiomic features extracted from clinical baseline [18F]FDG PET/CT and analyzed by machine learning algorithms may help discriminate FL from DLBCL. Materials and methods: Patients were selected based on confirmed histopathological diagnosis of primary FL (n=44) or DLBCL (n=76) and available [18F]FDG PET/CT with EARL reconstruction parameters within 6 months of diagnosis. Radiomic features were extracted from the volume of interest on co-registered [18F]FDG PET and CT images. Analysis of selected radiomic features was performed with machine learning classifiers based on logistic regression and tree-based ensemble classifiers (AdaBoosting, Gradient Boosting, and XG Boosting). The performance of radiomic features was compared with a SUVmax-based logistic regression model. Results: From the segmented lesions, 121 FL and 227 DLBCL lesions were included for radiomic feature extraction. In total, 79 radiomic features were extracted from the SUVmap, 51 from CT, and 6 shape features. Machine learning classifier Gradient Boosting achieved the best discrimination performance using 136 radiomic features (AUC of 0.86 and accuracy of 80%). SUVmax-based logistic regression model achieved an AUC of 0.79 and an accuracy of 70%. Gradient Boosting classifier had a significantly greater AUC and accuracy compared to the SUVmax-based logistic regression (p≤0.01). Conclusion: Machine learning analysis of radiomic features may be of diagnostic value for discriminating FL from DLBCL tumor lesions, beyond that of the SUVmax alone. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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