Automatic Extraction of Lung Cancer Staging Information From Computed Tomography Reports: Deep Learning Approach.

ABSTRACT:

Background

Lung cancer is the leading cause of cancer deaths worldwide. Clinical staging of lung cancer plays a crucial role in making treatment decisions and evaluating prognosis. However, in clinical practice, approximately one-half of the clinical stages of lung cancer patients are inconsistent with their pathological stages. As one of the most important diagnostic modalities for staging, chest computed tomography (CT) provides a wealth of information about cancer staging, but the free-text nature of the CT reports obstructs their computerization.

Objective

We aimed to automatically extract the staging-related information from CT reports to support accurate clinical staging of lung cancer.

Methods

In this study, we developed an information extraction (IE) system to

SUBMITTER: Hu D 

PROVIDER: S-EPMC8339987 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature