TCGA-Reports: A machine-readable pathology report resource for benchmarking text-based AI models.

ABSTRACT: In cancer research, pathology report text is a largely untapped data source. Pathology reports are routinely generated, more nuanced than structured data, and contain added insight from pathologists. However, there are no publicly available datasets for benchmarking report-based models. Two recent advances suggest the urgent need for a benchmark dataset. First, improved optical character recognition (OCR) techniques will make it possible to access older pathology reports in an automated way, increasing the data available for analysis. Second, recent improvements in natural language processing (NLP) techniques using artificial intelligence (AI) allow more accurate prediction of clinical targets from text. We apply state-of-the-art OCR and customized post-processing to report PDFs from The C

SUBMITTER: Kefeli J 

PROVIDER: S-EPMC10935496 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature