Prognostic Value of 18 F-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.

التفاصيل البيبلوغرافية
العنوان:	Prognostic Value of ¹⁸ F-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.
المؤلفون:	Carlier T; Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France.; Nuclear Medicine Department, University Hospital, Nantes, France., Frécon G; Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France.; Nuclear Medicine Department, University Hospital, Nantes, France., Mateus D; Laboratoire des Sciences Numériques de Nantes, Ecole Centrale de Nantes, CNRS UMR 6004, Nantes, France., Rizkallah M; Laboratoire des Sciences Numériques de Nantes, Ecole Centrale de Nantes, CNRS UMR 6004, Nantes, France., Kraeber-Bodéré F; Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France.; Nuclear Medicine Department, University Hospital, Nantes, France., Kanoun S; Nuclear Medicine, Georges-François Leclerc Center, Dijon, France., Blanc-Durand P; Nuclear Medicine, CHU Henri Mondor, Paris-Est University, Créteil, France., Itti E; Nuclear Medicine, CHU Henri Mondor, Paris-Est University, Créteil, France., Le Gouill S; Haematology Department, University Hospital, Nantes, France; and., Casasnovas RO; Hematology, CHU Dijon Bourgogne, Dijon, France., Bodet-Milin C; Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France.; Nuclear Medicine Department, University Hospital, Nantes, France., Bailly C; Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France; clement.bailly@chu-nantes.fr.; Nuclear Medicine Department, University Hospital, Nantes, France.
المصدر:	Journal of nuclear medicine : official publication, Society of Nuclear Medicine [J Nucl Med] 2024 Jan 02; Vol. 65 (1), pp. 156-162. Date of Electronic Publication: 2024 Jan 02.
نوع المنشور:	Journal Article; Research Support, Non-U.S. Gov't
اللغة:	English
بيانات الدورية:	Publisher: Society of Nuclear Medicine Country of Publication: United States NLM ID: 0217410 Publication Model: Electronic Cited Medium: Internet ISSN: 1535-5667 (Electronic) Linking ISSN: 01615505 NLM ISO Abbreviation: J Nucl Med Subsets: MEDLINE
أسماء مطبوعة:	Publication: Reston, VA : Society of Nuclear Medicine Original Publication: [Chicago, Ill.] : S.N. Turiel & Assoc.
مواضيع طبية MeSH:	Fluorodeoxyglucose F18* , Lymphoma, Large B-Cell, Diffuse/diagnostic imaging , Lymphoma, Large B-Cell, Diffuse/therapy, Humans ; Prognosis ; Positron Emission Tomography Computed Tomography/methods ; Radiomics ; Neoplasm Recurrence, Local ; Retrospective Studies
مستخلص:	The results of the GA in Newly Diagnosed Diffuse Large B-Cell Lymphoma (GAINED) study demonstrated the success of an ¹⁸ F-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 ¹⁸ F-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. (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)
فهرسة مساهمة:	Keywords: 18F-FDG PET; DLBCL; GAINED study; radiomics
المشرفين على المادة:	0Z5B2CJX4D (Fluorodeoxyglucose F18)
تواريخ الأحداث:	Date Created: 20231109 Date Completed: 20240104 Latest Revision: 20240724
رمز التحديث:	20240725
DOI:	10.2967/jnumed.123.265872
PMID:	37945379
قاعدة البيانات:	MEDLINE

الوصف
تدمد:	1535-5667
DOI:	10.2967/jnumed.123.265872