Development and validation of a hierarchical approach for lymphoma classification using immunohistochemical markers.

ABSTRACT:

Background

Accurate lymphoma classification is critical for effective treatment and immunohistochemistry is a cost-effective and time-saving approach. Although several machine learning algorithms showed effective results, they focused on a specific task of classification but not the whole classification workflow, thus impractical to be applied in clinical settings. Thus, we aim to develop an effective and economic machine learning-assisted system that can streamline the lymphoma differential diagnostic workflow using EBER in situ hybridization and immunohistochemical markers.

Methods

We included pathological reports diagnosed as lymphomas from two cancer centers (Sun Yat-sen University Cancer Center and Peking University Cancer Hospital & Institute). We proposed a hierarchic

SUBMITTER: Xu J 

PROVIDER: S-EPMC11499568 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature