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More than 144 000 Australians were diagnosed with cancer in 2019. Themajority will first present to their GP symptomatically, even for cancer forwhich screening programs exist. Diagnosing cancer in primary care ischallenging due to the non-specific nature of cancer symptoms and its lowprevalence. Understanding the epidemiology of cancer symptoms and patterns ofpresentation in patient's medical history from primary care data could beimportant to improve earlier detection and cancer outcomes. As past medicaldata about a patient can be incomplete, irregular or missing, this createsadditional challenges when attempting to use the patient's history for any newdiagnosis. Our research aims to investigate the opportunities in a patient'spathology history available to a GP, initially focused on the results withinthe frequently ordered full blood count to determine relevance to a futurehigh-risk cancer prognosis, and treatment outcome. We investigated how pastpathology test results can lead to deriving features that can be used topredict cancer outcomes, with emphasis on patients at risk of not surviving thecancer within 2-year period. This initial work focuses on patients with lungcancer, although the methodology can be applied to other types of cancer andother data within the medical record. Our findings indicate that even in casesof incomplete or obscure patient history, hematological measures can be usefulin generating features relevant for predicting cancer risk and survival. Theresults strongly indicate to add the use of pathology test data for potentialhigh-risk cancer diagnosis, and the utilize additional pathology metrics orother primary care datasets even more for similar purposes.

Handling uncertainty using features from pathology: opportunities in primary care data for developing high risk cancer survival methods