Using Big Data Techniques to Improve Prostate Cancer Reporting in the Gauteng Province, South Africa

Prostate cancer (PCa) data is of public health importance in South Africa. Biopsy data is recorded as semi-structured narrative text that is not easily analysed. Our study reports a pilot study that applied predictive analytics and text mining techniques to extract prognostic information that guides patient management. In particular, the Gleason score (GS) reported in a number of formats were extracted successfully. Our study reports that predominantly older men were diagnosed with PCa reporting a high-risk GS (8-10). Where cell differentiation was reported, 64% of biopsies reported poor differentiation. The approaches demonstrated in our study should be extended to a larger dataset to assess whether it has the potential to scale up to the national level.