Automated Detection of Cancer-Suspicious Findings in Japanese Radiology Reports with Natural Language Processing: A Multicenter Study.

ABSTRACT: Missed critical imaging findings, particularly those indicating cancer, are a common issue that can result in delays in patient follow-up and treatment. To address this, we developed a rule-based natural language processing (NLP) algorithm to detect cancer-suspicious findings from Japanese radiology reports. The dataset used consisted of chest and abdomen CT reports from six institutions. Reports from our institution were used for algorithm development and internal evaluation, while reports from the other five institutions were used for external evaluation. To create the gold standard, reports were annotated by two experienced physicians. Data were statistically analyzed using precision, recall and F1 score with 1000 bootstrap iterations. BERT was used as a baseline deep learning model, an

SUBMITTER: Sugimoto K 

PROVIDER: S-EPMC12572415 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature