Baseline radiomics features and MYC rearrangement status predict progression in aggressive B-cell lymphoma.

We investigated whether outcome prediction of aggressive B-cell lymphoma patients can be improved by combining clinical, molecular genotype and radiomics features. MYC, BCL2 and BCL6 rearrangements were assessed using fluorescence in situ hybridization. Seventeen radiomics features were extracted from the baseline PET/CT of 323 patients: maximum standardized uptake value (SUVmax), SUVpeak, SUVmean, metabolic tumor volume (MTV), total lesion glycolysis and 12 dissemination features pertaining to distance, differences in uptake and volume between lesions, respectively. Logistic regression with backward feature selection was used to predict progression after 2 years. The predictive value of 1) international prognostic index (IPI), 2) IPI+MYC (wild type, single hit or double/triple hit), 3) IPI, MYC and MTV, 4) radiomics and 5) MYC+radiomics models was tested using the cross-validated area under the curve (CV-AUC) and positive predictive values (PPV). IPI yielded a CV-AUC of 0.65±0.07 with a PPV of 29.6%. The IPI+MYC model yielded a CV-AUC of 0.68±0.08. IPI, MYC and MTV yielded a CV-AUC of 0.74±0.08. The highest model performance of the radiomics model was observed for MTV combined with the maximum distance between the largest lesion and another lesion, the maximum difference in SUVpeak between 2 lesions and the sum of distances between all lesions, yielding an improved CV-AUC of 0.77±0.07. The same radiomics features were retained when adding MYC (CV-AUC:0.77±0.07). PPV was highest for the MYC+radiomics model (50.0%) and increased with 20% compared to the IPI (29.6%). Adding radiomics features improved model performance and PPV and can therefore aid in identifying poor prognosis patients.