Titre :

Radiomics reflecting both tumor and host features improves outcome prediction in follicular lymphoma

Auteur(s) :

Rebaud, L. [Auteur]
Siemens Healthcare [France]
Capobianco, N. [Auteur]
Siemens Healthcare Technology Center [Erlangen]
Spottiswoode, B. [Auteur]
Siemens Molecular Imaging [Knoxville]
Cottereau, A. [Auteur]
Hôpital Cochin [AP-HP]
Trotman, J. [Auteur]
Feugier, P. [Auteur]
Centre Hospitalier Régional Universitaire de Nancy [CHRU Nancy]
Nastoupil, L. J. [Auteur]
MD Anderson Cancer Center [Houston]
Bachy, E. [Auteur]
Service d’Hématologie [Centre Hospitalier Lyon Sud - HCL]
Flinn, I. W. [Auteur]
Sarah Cannon Research Institute [Nashville, Tennessee]
Haioun, C. [Auteur]
CHU Henri Mondor [Créteil]
Ysebaert, L. [Auteur]
Service Hématologie - IUCT-Oncopole [CHU Toulouse]
Bartlett, N. L. [Auteur]
Washington University School of Medicine [Saint Louis, MO]
Tilly, H. [Auteur]
Centre de Lutte Contre le Cancer Henri Becquerel Normandie Rouen [CLCC Henri Becquerel]
Casasnovas, R. [Auteur]
Service d'Hématologie Clinique (CHU de Dijon)
Ricci, R. [Auteur]
The Lymphoma Academic Research Organisation [Lyon] [LYSARC]
Portugues, C. [Auteur]
The Lymphoma Academic Research Organisation [Lyon] [LYSARC]
Meignan, M. [Auteur]
Hôpital Henri Mondor
Morschhauser, Franck [Auteur]
Groupe de Recherche sur les formes Injectables et les Technologies Associées (GRITA) - ULR 7365
Service des Maladies du Sang [CHU Lille] [SMS]
Buvat, I. [Auteur]
Laboratoire d'Imagerie Translationnelle en Oncologie [LITO ]

Titre de la manifestation scientifique :

17th International Conference on Malignant Lymphoma

Ville :

Lugano

Pays :

Suisse

Date de début de la manifestation scientifique :

2023-06-13

Titre de la revue :

Hematological Oncology

Nom court de la revue :

Hematol Oncol

Éditeur :

Wiley

Date de publication :

2023-06-09

ISSN :

1099-1069

Mot(s)-clé(s) en anglais :

diagnostic and prognostic biomarkers
indolent non-Hodgkin lymphoma
PET-CT

Discipline(s) HAL :

Sciences du Vivant [q-bio]

Résumé en anglais : [en]

Introduction: To date, several indices widely based on simple clinical or biologic parameters have been proposed to refine prognosis of follicular lymphoma (FL). The prognostic value of 18F-FDG PET/CT parameters such as ...
Lire la suite >Introduction: To date, several indices widely based on simple clinical or biologic parameters have been proposed to refine prognosis of follicular lymphoma (FL). The prognostic value of 18F-FDG PET/CT parameters such as Total Metabolic Tumor Volume (TMTV) remains controversial. Here, we explored the prognostic impact of additional features obtained from 18F-FDG PET/CT images in patients included in the phase III RELEVANCE trial (Morschhauser, NEJM 2018, JCO 2022), which compared rituximab-chemotherapy (R-chemo) with rituximab-lenalidomide (R2) in patients with previously untreated, high tumor burden FL. Methods: Baseline 18F-FDG PET/CT scans and clinical information (ECOG, age, Ann Arbor stage, and FLIPI) were available for 351 follicular lymphoma patients. Lesions were segmented semi-automatically by expert physicians on the PET/CT scans. Deep learning tools (TotalSegmentator and MOOSE) were used to automatically segment organs from PET-registered CT scans. A total of 7437 PET and CT features were calculated, including tumor radiomics from segmented lesions and host radiomics from segmented organs and correlated to PFS and OS. To select predictive features, a permutation test was used to ensure that less than one false positive was selected. Highly correlated features were dropped to reduce feature redundancy and only features significantly predictive of both PFS and OS were selected. Finally, a Cox model was trained and evaluated in a 10x10 nested cross-validation with feature selection and hyperparameters tuning performed in the inner loop. Averaged time-dependent ROC-AUC (tAUC) was used to assess the prognostic value of the different features and models. Three models with different feature sets were built: basic (clinical features and TMTV), tumor (clinical features, TMTV, and tumor radiomics), and global (clinical features, TMTV, tumor radiomics, and host radiomics). Results: Median number of selected tumor features was 5, reflecting tumor metabolic activity, and tissue densities measured on CT in lesion surroundings. They had an average univariate tAUC of 0.56 ± 0.03 for PFS and 0.59 ± 0.01 for OS. Median number of selected organ features was 2 with an averaged tAUC of 0.56 ± 0.04 for PFS and 0.60±0.06 for OS. Selected features reflected FDG uptake magnitude in liver, lung and kidney density. The basic model reached a tAUC of 0.58±0.04 for PFS and 0.65±0.05 for OS. The tumor model led to tAUC of 0.59 ± 0.04 for PFS and 0.67 ± 0.06 for OS. The global model yielded to a tAUC of 0.63 ± 0.04 for PFS and 0.72 ± 0.05 for OS. Global model was significantly better than clinical on both PFS and OS (p < 0.01) while tumor model was significantly better than basic model on PFS (p < 0.01) but not on OS (p < 0.27). Conclusions: Our study suggests that radiomics features complementary to TMTV derived from baseline 18F-FDG PET/CT scans can improve outcome prediction for follicular lymphoma patients.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
CHU Lille

Collections :

• Groupe de Recherche sur les formes Injectables et les Technologies Associées (GRITA) - ULR 7365

Date de dépôt :

2024-09-06T21:05:16Z
2024-09-26T14:12:10Z