  More than 144 000 Australians were diagnosed with cancer in 2019. The
majority will first present to their GP symptomatically, even for cancer for
which screening programs exist. Diagnosing cancer in primary care is
challenging due to the non-specific nature of cancer symptoms and its low
prevalence. Understanding the epidemiology of cancer symptoms and patterns of
presentation in patient's medical history from primary care data could be
important to improve earlier detection and cancer outcomes. As past medical
data about a patient can be incomplete, irregular or missing, this creates
additional challenges when attempting to use the patient's history for any new
diagnosis. Our research aims to investigate the opportunities in a patient's
pathology history available to a GP, initially focused on the results within
the frequently ordered full blood count to determine relevance to a future
high-risk cancer prognosis, and treatment outcome. We investigated how past
pathology test results can lead to deriving features that can be used to
predict cancer outcomes, with emphasis on patients at risk of not surviving the
cancer within 2-year period. This initial work focuses on patients with lung
cancer, although the methodology can be applied to other types of cancer and
other data within the medical record. Our findings indicate that even in cases
of incomplete or obscure patient history, hematological measures can be useful
in generating features relevant for predicting cancer risk and survival. The
results strongly indicate to add the use of pathology test data for potential
high-risk cancer diagnosis, and the utilize additional pathology metrics or
other primary care datasets even more for similar purposes.
