Improving Interpretability of Radiology Report-based Pediatric Brain Tumor Pathology Classification and Key-phrases Extraction Using Large Language Models

Radiology reports are crucial for bridging the expertise of radiologists and other clinicians. Machine Learning models trained on these reports have shown promising performance in various downstream clinical tasks, such as predicting the necessity of future follow-up procedures, based on past radiology reports. However, for clinicians to adopt these models and for radiologists to validate the results, interpretability of the model is essential. In this study, we train BERT models on radiology reports to classify pediatric brain tumor pathologies. These large language models enable accurate report-level classification, without the need for costly word-level annotations. To identify and extract keywords and key-phrases related to distinct pathologies from radiology reports, we used a modified Term Frequency-Inverse Document Frequency to determine phrase importance based on prevalence and attributions scores. We achieved an overall multiclass Area Under Receiver Operating Characteristic Curve (AUROC) of 79.57% using ClinicaiBERT. Moreover, the per-class AUROC values were 86%, 71.2%, and 81.5%, for ‘Pilocytic Astrocytoma’, ‘Low-Grade Astrocytoma’, and ‘Other’ pathologies, respectively. Our explainability analysis identified hypotonia and mesencephalon as the most important terms for ‘Pilocytic Astrocytoma’ and ‘Low-Grade Astrocytoma’, respectively.