| Sumario: | Simple Summary: In this manuscript, we present a statistical model for reliable and early prediction of treatment failure in patients with diffuse large B-cell lymphoma. The model combines measurable parameters—namely, the metabolic tumor volume and the metabolic heterogeneity, from baseline PET/CT with the presence or absence of mutations in SOCS1 and CREBBP/EP300 and represents a promising tool for the design of clinical trials focused on tailoring treatment to the individual risk. According to our bioinformatics analysis, mutation profiling may not be needed in patients with high-risk PET/CT metrics. Hence, the proposed approach may help optimize economic resources avoiding costly, and likely unnecessary, DNA analysis in many patients. Accurate estimation of the progression risk after first-line therapy represents an unmet clinical need in diffuse large B-cell lymphoma (DLBCL). Baseline (18)F-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) parameters, together with genetic analysis of lymphoma cells, could refine the prediction of treatment failure. We evaluated the combined impact of mutation profiling and baseline PET/CT functional parameters on the outcome of DLBCL patients treated with the R-CHOP14 regimen in the SAKK38/07 clinical trial (NCT00544219). The concomitant presence of mutated SOCS1 with wild-type CREBBP and EP300 defined a group of patients with a favorable prognosis and 2-year progression-free survival (PFS) of 100%. Using an unsupervised recursive partitioning approach, we generated a classification-tree algorithm that predicts treatment outcomes. Patients with elevated metabolic tumor volume (MTV) and high metabolic heterogeneity (MH) (15%) had the highest risk of relapse. Patients with low MTV and favorable mutational profile (9%) had the lowest risk, while the remaining patients constituted the intermediate-risk group (76%). The resulting model stratified patients among three groups with 2-year PFS of 100%, 82%, and 42%, respectively (p < 0.001).
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