Abstract
Cancer is one of the most common and serious medical conditions, with significant challenges in the detection of cancer originating from the non-specific nature of symptoms and very low prevalence. For general practitioners (GPs), this can be particularly important, as they are the primary contact for patients for most medical conditions. This places high significance on using the data available to a GP to design decision support tools that will aid GPs in detecting cancer as early as possible. With pathology data being one of the datasets available in the GP electronic medical record (EMR), our work targets this type of data in an attempt to incorporate an early cancer detection tool in existing GP practices. We focus on utilizing full blood count pathology results to design features that can be used in an early cancer detection model 3 to 6 months ahead of standard diagnosis. This research focuses initially on lung cancer but can be extended to other types of cancer. Additional challenges are present in this type of data due to the irregular and infrequent nature of doing pathology tests, which are also considered in designing the AI solution. Our findings demonstrate that hematological measures from pathology data are a suitable choice for a cancer detection tool that can deliver early cancer diagnosis up to 6 months ahead for up to 8 out of 10 patients, in a way that is easily incorporated in current GP practice.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hamilton, W.: The CAPER studies: five case-control studies aimed at identifying and quantifying the risk of cancer in symptomatic primary care patients. Br. J. Cancer 101, S80–S86 (2009)
Hippisley-Cox, J., Coupland, C.: Identifying patients with suspected lung cancer in primary care: derivation and validation of an algorithm. Br. J. Gen. Pract. 61(592), e715–e723 (2011)
Corner, J., Hopkinson, J., Fitzsimmons, D., et al.: Is late diagnosis of lung cancer inevitable? Interview study of patients’ recollections of symptoms before diagnosis. Thorax 60, 314–319 (2005)
Iyen-Omofoman, B., et al.: Using socio-demographic and early clinical features in general practice to identify people with lung cancer earlier. Thorax 68(5), 451–459 (2013)
Hannah, T.P., et al.: Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ 371, m4087 (2020)
Bailey, S.E.R., Ukoumunne, O.C., Shephard, E., Hamilton, W.: How useful is thrombocytosis in predicting an underlying cancer in primary care? A systematic review. Fam. Pract. 34(1), 4–10 (2017)
Bailey, S.E.R., Ukoumunne, O.C., Shephard, E.A., Hamilton, W.: Clinical relevance of thrombocytosis in primary care: a prospective cohort study of cancer incidence using English electronic medical records and cancer registry data. Br. J. Gen. Pract. 67(659), e405–e413 (2017)
Shapley, M., Mansell, G., Jordan, J.L., Jordan, K.P.: Positive predictive values of ≥5% in primary care for cancer: systematic review. Br. J. Gen. Pract. 60(578), e366–e377 (2010)
Bjerager, M., Palshof, T., Dahl, R., et al.: Delay in diagnosis of lung cancer in general practice. Br. J. Gen. Pract. 56, 863–868 (2006)
World Health Organization: Cancer Fact Sheets. https://www.who.int/news-room/fact-sheets/detail/cancer
Victoria Cancer Council: I-PACED (Implementing Pathways for Cancer Early Diagnosis). https://www.cancervic.org.au/downloads/resources/factsheets/AL1720_OCP_I-PACED_Lung_FINAL.pdf
ten Haaf, K., et al.: Risk prediction models for selection of lung cancer screening candidates: a retrospective validation study. PLoS Med. 14(4), e1002277 (2017)
O’Dowd, E.L., et al.: What characteristics of primary care and patients are associated with early death in patients with lung cancer in the UK? Thorax 70(2), 161–168 (2015)
Weller, D.P., Peake, M.D., Field, J.K.: Presentation of lung cancer in primary care. NPJ Prim. Care Respir. Med. 29(1), 1–5 (2019)
Goldstein, B.A., et al.: Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J. Am. Med. Inform. Assoc. 24(1), 198–208 (2017)
Schmidt-Hansen, M., et al.: Lung cancer in symptomatic patients presenting in primary care: a systematic review of risk prediction tools. Br. J. Gen. Pract. 67(659), e396–e404 (2017)
Bradley, S.H., Martyn, P.T.K., Richard, D.N.: Recognizing lung cancer in primary care. Adv. Ther. 36(1), 19–30 (2019)
NPS MedicineWise Annual Report 2019–20. https://www.nps.org.au/about-us/reports-evaluation
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Ristanoski, G., Emery, J., Gutierrez, J.M., McCarthy, D., Aickelin, U. (2021). Primary Care Datasets for Early Lung Cancer Detection: An AI Led Approach. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P., Riaño, D. (eds) Artificial Intelligence in Medicine. AIME 2021. Lecture Notes in Computer Science(), vol 12721. Springer, Cham. https://doi.org/10.1007/978-3-030-77211-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-77211-6_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77210-9
Online ISBN: 978-3-030-77211-6
eBook Packages: Computer ScienceComputer Science (R0)