  Automated classification of cancer pathology reports can extract information
from unstructured reports and categorize each report into structured diagnosis
and severity categories. Thus, such system can reduce the burden for populating
tumor registries, help registration for clinical trial as well as developing
large dataset for deep learning model development using true pathologic ground
truth. However, the content of breast pathology reports can be difficult for
categorize due to the high linguistic variability in content and wide variety
of potential diagnoses >50. Existing NLP models are primarily focused on
developing classifier for primary breast cancer types (e.g. IDC, DCIS, ILC) and
tumor characteristics, and ignore the rare diagnosis of cancer subtypes. We
then developed a hierarchical hybrid transformer-based pipeline (59 labels) -
Hierarchical Classification System for Breast Cancer Specimen Report (HCSBC),
which utilizes the potential of the transformer context-preserving NLP
technique and compared our model to several state of the art ML and DL models.
We trained the model on the EUH data and evaluated our model's performance on
two external datasets - MGH and Mayo Clinic. We publicly release the code and a
live application under Huggingface spaces repository
