World Lymphoma Day offers us a unique opportunity to recognize the advancements in research and treatment of this disease. On this occasion, we’d like to spotlight a recent study that has shed light on the prognostic significance of genetic alterations and imaging features derived from 18F-FDG PET/CT in diffuse large B cell lymphoma (DLBCL).

Unlocking Predictive Insights: Genetic and Imaging Features in Diffuse Large B Cell Lymphoma (DLBCL). The Predictive Power of Clinical, Imaging, and Genomic Features.

At a Glance: Key Insights about the B-Cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is the leading type of non-Hodgkin lymphoma (NHL) in the West, accounting for roughly one-third of NHL cases. The standard therapy, R-CHOP, has limitations, proving ineffective for about one-third of patients. This underscores the urgency of identifying these patients early on for alternative treatments. Recent research has delved into the predictive value of 18F-FDG PET/CT imaging, clinical data, and genomic parameters in forecasting a complete response to initial DLBCL treatment.

Deep Dive: The Study’s Findings

While R-CHOP therapy can cure 50% to 70% of DLBCL patients, a significant portion remains unresponsive due to primary refractoriness or relapse post-treatment. The International Prognostic Index (IPI) has traditionally been the primary clinical tool for predicting outcomes and stratifying patients for clinical trials. However, its limitations in identifying patients with a survival chance of less than 50% highlight the need for more refined predictors.

It is known that molecular aberrations in tumor cells offer significant prognostic insights. For example, tumors with concurrent MYC rearrangement with BCL2 or BCL6 are known to be particularly aggressive. On the other hand, radiomics, that has experienced rapid growth in recent years, has also emerged as a promising source of information for the prediction of important clinical outcomes.

In this work, we have demonstrated that the inclusion of genomic data to a model composed of imaging features (conventional PET parameters + radiomic features) and clinical data provides the best performance to predict response to front-line therapy in DLBCL patients treated with R-CHOP (AUC = 0.904 and a balanced accuracy of 80%). Importantly, our study identified the amplification of the BCL6 gene as the genetic marker retaining the highest predictive value, in addition to underscoring the potential of a comprehensive panel of imaging features mainly composed of radiomic features, to shape future therapeutic strategies.

The Bigger Picture

Combining clinical, imaging, and genomic features, a holistic approach can significantly enhance our predictive accuracy for first-line therapy responses. The emergence of BCL6 amplification as a pivotal genetic marker, coupled with insights from radiomic data, offers a promising path forward. By harnessing these tools, we can refine patient selection, ensuring that each individual receives the most effective treatment tailored to their unique profile, guiding towards alternative, potentially life-saving therapeutic avenues.