Large-scale deep learning for metastasis detection in pathology reports.

ABSTRACT:

Objectives

No existing algorithm can reliably identify metastasis from pathology reports across multiple cancer types and the entire US population. In this study, we develop a deep learning model that automatically detects patients with metastatic cancer by using pathology reports from many laboratories and of multiple cancer types.

Materials and methods

We use 60 471 unstructured pathology reports from 4 Surveillance, Epidemiology, and End Results (SEER) registries. The reports were coded into 1 of 3 labels: metastasis negative, metastases positive, or metastasis undetermined. We utilize a task-specific deep neural network trained from scratch and compare its performance with a widely used large language model (LLM).

Results

Our deep learning architecture trained on

SUBMITTER: Krawczuk P 

PROVIDER: S-EPMC12249207 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature