Machine Learning-Based Survival Analysis Reveals Prognostic Clinical and Genetic Insights for Patients with Cutaneous T-Cell Lymphoma

Introduction:Cutaneous T-cell lymphomas (CTCL) are heterogeneous lymphoproliferative disorders on a spectrum of disease presentation and severity. Around two-thirds of cutaneous T-cell lymphomas can be classified as mycosis fungoides (MF) or Sézary syndrome (SS). While advanced stages of MF and SS are associated with decreased survival and worse outcomes, even early-stage patients can possess a variable course. Numerous deep sequencing studies have fallen short in identifying genetic abnormalities that drive disease pathogenesis and predict prognosis. Large-cell transformation and elevated lactate dehydrogenase levels are associated with worse prognosis in SS; however, such features cannot accurately prognosticate patient survival. There is a need for investigation that may assist in the prognostication of survival in patients with CTCL. Machine learning methods may help to elucidate correlations between clinical and genetic factors to predict disease progression and outcomes.