An automatic pipeline for temporal monitoring of radiotherapy-induced toxicities in head and neck cancer patients.

ABSTRACT: Radiotherapy for head and neck cancer often causes a spectrum of toxicities. Such toxicities are usually unavailable as structured data and are reported within textual clinical reports. To reduce the burden of manual assessment of toxicities, we propose a language processing model for the automatic extraction of toxicities. The cohort consists of 384 patients with head and neck cancer who underwent radiotherapy, either as monotherapy or in combination with chemotherapy or surgery. A total of 3510 notes were extracted. The toxicities were then manually annotated. Two tasks of toxicity mention detection and toxicity extraction were defined. Pre-trained language models such as BERT, Clinical BioBERT, and Clinical Longformer were fine-tuned. Our best model achieves an F1 score of 90% for autom

SUBMITTER: Bagherzadeh P 

PROVIDER: S-EPMC11805912 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature