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

• URL: http://arxiv.org/abs/2012.09976v1
• Date: Thu, 17 Dec 2020 23:27:13 GMT
• Title: Handling uncertainty using features from pathology: opportunities in primary care data for developing high risk cancer survival methods
• Authors: Goce Ristanoski, Jon Emery, Javiera Martinez-Gutierrez, Damien Mccarthy, Uwe Aickelin
• Abstract summary: 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. We investigate how past pathology test results can lead to deriving features that can be used to predict cancer outcomes.
• Score: 0.10499611180329804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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