A machine learning approach in a monocentric cohort for predicting primary refractory disease in Diffuse Large B-cell lymphoma patients.

ABSTRACT:

Introduction

Primary refractory disease affects 30-40% of patients diagnosed with DLBCL and is a significant challenge in disease management due to its poor prognosis. Predicting refractory status could greatly inform treatment strategies, enabling early intervention. Various options are now available based on patient and disease characteristics. Supervised machine-learning techniques, which can predict outcomes in a medical context, appear highly suitable for this purpose.

Design

Retrospective monocentric cohort study.

Patient population

Adult patients with a first diagnosis of DLBCL admitted to the hematology unit from 2017 to 2022.

Aim

We evaluated in our Center five supervised machine-learning (ML) models as a tool for the prediction of primary refractory D

SUBMITTER: Detrait MY 

PROVIDER: S-EPMC11444388 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature