Use of Natural Language Processing to Extract and Classify Papillary Thyroid Cancer Features From Surgical Pathology Reports.

ABSTRACT:

Background

We aim to use Natural Language Processing to automate the extraction and classification of thyroid cancer risk factors from pathology reports.

Methods

We analyzed 1410 surgical pathology reports from adult papillary thyroid cancer patients from 2010 to 2019. Structured and nonstructured reports were used to create a consensus-based ground truth dictionary and categorized them into modified recurrence risk levels. Nonstructured reports were narrative, while structured reports followed standardized formats. We developed ThyroPath, a rule-based Natural Language Processing pipeline, to extract and classify thyroid cancer features into risk categories. Training involved 225 reports (150 structured, 75 unstructured), with testing on 170 reports (120 structured, 50 unstr

SUBMITTER: Loor-Torres R 

PROVIDER: S-EPMC11531997 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature