Dataset Information

SAKK38/07 study: integration of baseline metabolic heterogeneity and metabolic tumor volume in DLBCL prognostic model.

ABSTRACT: Several functional parameters from baseline (18)F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography have been proposed as promising biomarkers of treatment efficacy in diffuse large B-cell lymphoma (DLBCL). We tested their ability to predict outcome in 2 cohorts of DLBCL patients receiving conventional immunochemotherapy (rituximab, cyclophosphamide, doxorubicin hydrochloride, vincristine sulfate, and prednisone [R-CHOP] regimen), either every 14 (R-CHOP14) or 21 days (R-CHOP21). Baseline PET analysis was performed in 141 patients with DLBCL treated with R-CHOP14 in the prospective SAKK38/07 study (NCT00544219) of the Swiss Group for Clinical Cancer Research (testing set). Reproducibility was examined in a validation set of 113 patients treated with R-CHOP21. In the SAKK38/07 cohort, progression-free survival (PFS) at 5 years was 83% for patients with low metabolic tumor volume (MTV) and 59% for those with high MTV (hazard ratio [HR], 3.4; 95% confidence interval [CI], 1.6-7.0; P = .0005), whereas overall survival (OS) was 91% and 64%, respectively (HR, 4.4; 95% CI, 1.9-10; P = .0001). MTV was the most powerful predictor of outcome also in the validation set. Elevated metabolic heterogeneity (MH) significantly predicted poorer outcomes in the subgroups of patients with elevated MTV. A model integrating MTV and MH identified high-risk patients with shorter PFS (testing set: HR, 5.6; 95% CI, 1.8-17; P < .0001; validation set: HR, 5.6; 95% CI, 1.7-18; P = .0002) and shorter OS (testing set: HR, 9.5; 95% CI, 1.7-52; P < .0001; validation set: HR, 7.6; 95% CI, 2.0-28; P = .0003). This finding was confirmed by an unsupervised regression tree analysis indicating that prognostic models based on MTV and MH may allow early identification of refractory patients who might benefit from treatment intensification. This trial was registered at www.clinicaltrials.gov as #NCT00544219.

SUBMITTER: Ceriani L

PROVIDER: S-EPMC7094027 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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SAKK38/07 study: integration of baseline metabolic heterogeneity and metabolic tumor volume in DLBCL prognostic model.

Ceriani Luca L Gritti Giuseppe G Cascione Luciano L Pirosa Maria Cristina MC Polino Angela A Ruberto Teresa T Stathis Anastasios A Bruno Andrea A Moccia Alden A AA Giovanella Luca L Hayoz Stefanie S Schär Sämi S Dirnhofer Stefan S Rambaldi Alessandro A Martinelli Giovanni G Mamot Christoph C Zucca Emanuele E

Blood advances 20200301 6

Several functional parameters from baseline (18)F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography have been proposed as promising biomarkers of treatment efficacy in diffuse large B-cell lymphoma (DLBCL). We tested their ability to predict outcome in 2 cohorts of DLBCL patients receiving conventional immunochemotherapy (rituximab, cyclophosphamide, doxorubicin hydrochloride, vincristine sulfate, and prednisone [R-CHOP] regimen), either every 14 (R-CHOP14) or 21 days (R- ...[more]

PMID: 32196557

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Project description:BackgroundCurrent radiological assessments of 18fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging data in diffuse large B-cell lymphoma (DLBCL) can be time consuming, do not yield real-time information regarding disease burden and organ involvement, and hinder the use of FDG-PET to potentially limit the reliance on invasive procedures (e.g. bone marrow biopsy) for risk assessment.MethodsOur aim is to enable real-time assessment of imaging-based risk factors at a large scale and we propose a fully automatic artificial intelligence (AI)-based tool to rapidly extract FDG-PET imaging metrics in DLBCL. On availability of a scan, in combination with clinical data, our approach generates clinically informative risk scores with minimal resource requirements. Overall, 1268 patients with previously untreated DLBCL from the phase III GOYA trial (NCT01287741) were included in the analysis (training: n = 846; hold-out: n = 422).ResultsOur AI-based model comprising imaging and clinical variables yielded a tangible prognostic improvement compared to clinical models without imaging metrics. We observed a risk increase for progression-free survival (PFS) with hazard ratios [HR] of 1.87 (95% CI: 1.31-2.67) vs 1.38 (95% CI: 0.98-1.96) (C-index: 0.59 vs 0.55), and a risk increase for overall survival (OS) (HR: 2.16 (95% CI: 1.37-3.40) vs 1.40 (95% CI: 0.90-2.17); C-index: 0.59 vs 0.55). The combined model defined a high-risk population with 35% and 42% increased odds of a 4-year PFS and OS event, respectively, versus the International Prognostic Index components alone. The method also identified a subpopulation with a 2-year Central Nervous System (CNS)-relapse probability of 17.1%.ConclusionOur tool enables an enhanced risk stratification compared with IPI, and the results indicate that imaging can be used to improve the prediction of central nervous system relapse in DLBCL. These findings support integration of clinically informative AI-generated imaging metrics into clinical workflows to improve identification of high-risk DLBCL patients.Trial registrationRegistered clinicaltrials.gov number: NCT01287741.

Dataset Information

SAKK38/07 study: integration of baseline metabolic heterogeneity and metabolic tumor volume in DLBCL prognostic model.

Publications

SAKK38/07 study: integration of baseline metabolic heterogeneity and metabolic tumor volume in DLBCL prognostic model.

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