Development of a screening model for APL using cell population data and deep learning-extracted WBC scattergram features.

ABSTRACT:

Background

Acute promyelocytic leukemia (APL), a high-risk subtype of acute myeloid leukemia, necessitates rapid diagnosis upon hospital admission to mitigate early mortality. Current diagnosing approaches relying on time-consuming genetic testing or morphological expertise are particularly challenging in resource-limited settings. Herein, this study introduces a novel machine learning approach leveraging routine lab data to enable immediate APL suspicion, offering a new diagnostic possibility for under-resourced hospitals.

Methods

We developed a two-stage machine learning model using multi-center retrospective data. The cohort included 94 confirmed APL patients (2020-2024) from three tertiary hospitals, with an external validation set (n = 541) from an independent center. U

SUBMITTER: Cai Q 

PROVIDER: S-EPMC12593920 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature