F-FDG PET Dissemination Features in Diffuse Large B-Cell Lymphoma Are Predictive of Outcome.

in Journal of nuclear medicine : official publication, Society of Nuclear Medicine by Anne-Ségolène Cottereau, Christophe Nioche, Anne-Sophie Dirand, Jérôme Clerc, Franck Morschhauser, Olivier Casasnovas, Michel Meignan, Irène Buvat

TLDR

  • This study looked at how well certain features on a type of scan called F-FDG PET could predict how well patients with a type of cancer called diffuse large B-cell lymphoma (DLBCL) would do over time.
  • The study used data from a trial called LNH073B and included patients with advanced-stage DLBCL andF-FDG PET/CT images available for review.
  • The study found that combining certain features on the F-FDG PET scan with a measure called metabolic tumor volume (MTV) could help predict how well patients with DLBCL would do over time.

Abstract

We assessed the predictive value of new radiomic features characterizing lesion dissemination in baselineF-FDG PET and tested whether combining them with baseline metabolic tumor volume (MTV) could improve prediction of progression-free survival (PFS) and overall survival (OS) in diffuse large B-cell lymphoma (DLBCL) patients.From the LNH073B trial (NCT00498043), patients with advanced-stage DLCBL andF-FDG PET/CT images available for review were selected. MTV and several radiomic features, including the distance between the 2 lesions that were farthest apart (Dmax), were calculated. Receiver-operating-characteristic analysis was used to determine the optimal cutoff for quantitative variables, and Kaplan-Meier survival analyses were performed.With a median age of 46 y, 95 patients were enrolled, half of them treated with R-CHOP biweekly (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) and the other half with R-ACVBP (rituximab, doxorubicin, cyclophosphamide, vindesine, bleomycin, and prednisone), with no significant impact on outcome. Median MTV and Dmaxwere 375 cmand 45 cm, respectively. The median follow-up was 44 mo. High MTV and Dmaxwere adverse factors for PFS (= 0.027 and= 0.0003, respectively) and for OS (= 0.0007 and= 0.0095, respectively). In multivariate analysis, only Dmaxwas significantly associated with PFS (= 0.0014) whereas both factors remained significant for OS (= 0.037 and= 0.0029, respectively). Combining MTV (>384 cm) and Dmax(>58 cm) yielded 3 risk groups for PFS (= 0.0003) and OS (= 0.0011): high with 2 adverse factors (4-y PFS and OS of 50% and 53%, respectively,= 18), low with no adverse factor (94% and 97%,= 36), and an intermediate category with 1 adverse factor (73% and 88%,= 41).Combining MTV with a parameter reflecting the tumor burden dissemination further improves DLBCL patient risk stratification at staging.

Overview

  • The study assesses the predictive value of new radiomic features characterizing lesion dissemination in baselineF-FDG PET and tests whether combining them with baseline metabolic tumor volume (MTV) could improve prediction of progression-free survival (PFS) and overall survival (OS) in diffuse large B-cell lymphoma (DLBCL) patients.
  • The study uses data from the LNH073B trial (NCT00498043) and includes patients with advanced-stage DLBCL andF-FDG PET/CT images available for review.
  • The primary objective of the study is to determine whether combining MTV and radiomic features can improve risk stratification for PFS and OS in DLBCL patients.

Comparative Analysis & Findings

  • High MTV and Dmaxwere adverse factors for PFS and OS.
  • In multivariate analysis, only Dmaxwas significantly associated with PFS whereas both factors remained significant for OS.
  • Combining MTV (>384 cm) and Dmax(>58 cm) yielded 3 risk groups for PFS and OS.

Implications and Future Directions

  • The study highlights the importance of radiomic features in predicting outcomes in DLBCL patients.
  • The findings suggest that combining MTV and radiomic features can improve risk stratification for PFS and OS in DLBCL patients.
  • Future research should focus on developing more accurate and specific radiomic features for predicting outcomes in DLBCL patients.
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