Improving the predictive value of end-of-treatment PET/ CT in diffuse large B-cell lymphoma.

The 5-point Deauville score (DS) assesses end-of-treatment (EOT) response on positron emission tomography-computed tomography (PET/CT) in diffuse large B-cell lymphoma patients, categorizing scans as 'positive' or 'negative' for complete metabolic response. However, the positive predictive value (PPV) is suboptimal at 60%. We evaluated whether quantitative PET parameters combined with clinical data could improve prediction of treatment failure in EOT PET-positive patients. Baseline and EOT PET/CT scans of 138 patients in DS groups 4-5 were analyzed. Lesions were segmented using a semi-automated adaptive method (SUV4.0 or MV3). PET parameters, including total metabolic tumor volume (TMTV), number of lesions (NOL), tumorSUV/liverSUV-ratio (TLR), the maximum distance between the largest and any other lesion (DmaxBulk), and changes over time, were obtained. Two Cox regression models predicted 2-year progression-free survival. Clinical data were combined with EOT PET in model 1, and baseline, EOT, and delta values in model 2. After internal bootstrapping, models were evaluated for classification using different risk-of-progression cutoffs. Sensitivity, specificity, PPV, and negative predictive values (NPV) were determined. Using forward selection, model 1 comprised two variables: the NOL and the tumorSUVpeak/liverSUVmean (TLRpeakmean) at EOT (AIC=690.072, c-index=0.747). Model 2 incorporated NOL, TLRpeakmean (EOT) and baseline SUVmean (AIC=687.064, c-index=0.762). The PPV improved to over 85% without compromising the NPV. False positives dropped from 54 (39%, by DS) to 9 (7%) and 6 (4%) for models 1 and 2, respectively. Adding baseline features did not notably impact the models' performance. Our models could support more accurate response-adapted treatment decisions, reducing unnecessary subsequent false positive-directed treatments to just 7%.