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Timely cancer reporting data are required in order to understand the impactof cancer, inform public health resource planning and implement cancer policyespecially in Sub Saharan Africa where the reporting lag is behind worldaverages. Unstructured pathology reports, which contain tumor specific data,are the main source of information collected by cancer registries. Due tomanual processing and labelling of pathology reports using the InternationalClassification of Disease for oncology (ICD-O) codes, by human coders employedby cancer registries, has led to a considerable lag in cancer reporting. Wepresent a hierarchical deep learning classification method that employsconvolutional neural network models to automate the classification of 1813anonymized breast cancer pathology reports with applicable ICD-O morphologycodes across 9 classes. We demonstrate that the hierarchical deep learningclassification method improves on performance in comparison to a flatmulticlass CNN model for ICD-O morphology classification of the same reports.

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