Inferring cancer disease response from radiology reports using large language models with data augmentation and prompting.

ABSTRACT:

Objective

To assess large language models on their ability to accurately infer cancer disease response from free-text radiology reports.

Materials and methods

We assembled 10 602 computed tomography reports from cancer patients seen at a single institution. All reports were classified into: no evidence of disease, partial response, stable disease, or progressive disease. We applied transformer models, a bidirectional long short-term memory model, a convolutional neural network model, and conventional machine learning methods to this task. Data augmentation using sentence permutation with consistency loss as well as prompt-based fine-tuning were used on the best-performing models. Models were validated on a hold-out test set and an external validation set based on Response Ev

SUBMITTER: Tan RSYC 

PROVIDER: S-EPMC10531105 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature