Advancements in comprehending the biogenesis, dependencies, and mutational landscape of multiple myeloma dramatically expanded over the past two decades. This knowledge progress was paralleled by similar surge in novel therapeutics discovery, including the unveiling of new targets and classes of drugs. These advancements presented us, however, with novel challenges to prognosticate the disease with greater precision accounting for its individual biologic, genomic, and therapeutic features.

In the article that accompanies this editorial, Maura et al. assisted by artificial intelligence and deep neuronal networks, have introduced the first individualized risk-prediction model for newly diagnosed multiple myeloma. This risk-prediction approach is the way forward for the dynamic integration of an ever-widening array of complex genomic, biologic, and soon immunologic features and will permit us to offer patients with myeloma a comprehensive individualized risk prediction adapted to the therapy they will receive

READ THE ARTICLE – https://ascopubs.org/doi/10.1200/JCO.23.02781