Hierarchical Deep Learning Classification of Unstructured Pathology Reports to Automate ICD-O Morphology Grading

TL;DR

This paper introduces a hierarchical deep learning approach using CNNs to automate ICD-O morphology classification of unstructured pathology reports, significantly reducing reporting lag in cancer registries, especially in resource-limited settings.

Contribution

The paper presents a novel hierarchical CNN-based method that improves classification accuracy over flat models for ICD-O codes in pathology reports.

Findings

Hierarchical CNN outperforms flat models in accuracy.

Automates classification of 1813 pathology reports.

Reduces reporting lag in cancer data collection.

Abstract

Timely cancer reporting data are required in order to understand the impact of cancer, inform public health resource planning and implement cancer policy especially in Sub Saharan Africa where the reporting lag is behind world averages. Unstructured pathology reports, which contain tumor specific data, are the main source of information collected by cancer registries. Due to manual processing and labelling of pathology reports using the International Classification of Disease for oncology (ICD-O) codes, by human coders employed by cancer registries, has led to a considerable lag in cancer reporting. We present a hierarchical deep learning classification method that employs convolutional neural network models to automate the classification of 1813 anonymized breast cancer pathology reports with applicable ICD-O morphology codes across 9 classes. We demonstrate that the hierarchical deep learning classification method improves on performance in comparison to a flat multiclass CNN model for ICD-O morphology classification of the same reports.