Hierarchical Deep Learning Ensemble to Automate the Classification of Breast Cancer Pathology Reports by ICD-O Topography

TL;DR

This paper introduces a hierarchical deep learning ensemble that automates the classification of breast cancer pathology reports by ICD-O topography, significantly improving accuracy over existing CNN models and reducing manual coding effort.

Contribution

The study presents a novel hierarchical deep learning ensemble approach that outperforms state-of-the-art CNN models in classifying ICD-O topography codes for breast cancer pathology reports.

Findings

Over 14% improvement in F1 micro score

55% increase in F1 macro score

Enhanced classification accuracy over flat models

Abstract

Like most global cancer registries, the National Cancer Registry in South Africa employs expert human coders to label pathology reports using appropriate International Classification of Disease for Oncology (ICD-O) codes spanning 42 different cancer types. The annotation is extensive for the large volume of cancer pathology reports the registry receives annually from public and private sector institutions. This manual process, coupled with other challenges results in a significant 4-year lag in reporting of annual cancer statistics in South Africa. We present a hierarchical deep learning ensemble method incorporating state of the art convolutional neural network models for the automatic labelling of 2201 de-identified, free text pathology reports, with appropriate ICD-O breast cancer topography codes across 8 classes. Our results show an improvement in primary site classification over the state of the art CNN model by greater than 14% for F1 micro and 55% for F1 macro scores. We demonstrate that the hierarchical deep learning ensemble improves on state-of-the-art models for ICD-O topography classification in comparison to a flat multiclass model for predicting ICD-O topography codes for pathology reports.