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. 2022 Nov 30;60(12):2005-2016.
doi: 10.1515/cclm-2021-1015. Print 2022 Nov 25.

Using artificial intelligence in a primary care setting to identify patients at risk for cancer: a risk prediction model based on routine laboratory tests

Patricia Diana Soerensen  1 , Henry Christensen  1 , Soeren Gray Worsoe Laursen  2 , Christian Hardahl  3 , Ivan Brandslund  4 , Jonna Skov Madsen  1   4
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Free article

Using artificial intelligence in a primary care setting to identify patients at risk for cancer: a risk prediction model based on routine laboratory tests

Patricia Diana Soerensen et al. Clin Chem Lab Med. .
Free article

Abstract

Objectives: To evaluate the ability of an artificial intelligence (AI) model to predict the risk of cancer in patients referred from primary care based on routine blood tests. Results obtained with the AI model are compared to results based on logistic regression (LR).

Methods: An analytical profile consisting of 25 predefined routine laboratory blood tests was introduced to general practitioners (GPs) to be used for patients with non-specific symptoms, as an additional tool to identify individuals at increased risk of cancer. Consecutive analytical profiles ordered by GPs from November 29th 2011 until March 1st 2020 were included. AI and LR analysis were performed on data from 6,592 analytical profiles for their ability to detect cancer. Cohort I for model development included 5,224 analytical profiles ordered by GP's from November 29th 2011 until the December 31st 2018, while 1,368 analytical profiles included from January 1st 2019 until March 1st 2020 constituted the "out of time" validation test Cohort II. The main outcome measure was a cancer diagnosis within 90 days.

Results: The AI model based on routine laboratory blood tests can provide an easy-to use risk score to predict cancer within 90 days. Results obtained with the AI model were comparable to results from the LR model. In the internal validation Cohort IB, the AI model provided slightly better results than the LR analysis both in terms of the area under the receiver operating characteristics curve (AUC) and PPV, sensitivity/specificity while in the "out of time" validation test Cohort II, the obtained results were comparable.

Conclusions: The AI risk score may be a valuable tool in the clinical decision-making. The score should be further validated to determine its applicability in other populations.

Keywords: artificial intelligence (AI) blood; cancer; predictive; score.

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References

    1. Shipe, ME, Deppen, SA, Farjah, F, Grogan, EL. Developing prediction models for clinical use using logistic regression: an overview. J Thorac Dis 2019;11:S574–84. https://doi.org/10.21037/jtd.2019.01.25.
    1. Fei, Y, Li, WQ. Improve artificial neural network for medical analysis, diagnosis and prediction. J Crit Care 2017;40:293. https://doi.org/10.1016/j.jcrc.2017.06.012.
    1. Sud, A, Torr, B, Jones, ME, Broggio, J, Scott, S, Loveday, C, et al.. Effect of delays in the 2-week-wait cancer referral pathway during the COVID-19 pandemic on cancer survival in the UK: a modelling study. Lancet Oncol 2020;21:1035–44. https://doi.org/10.1016/s1470-2045(20)30392-2.
    1. Watson, J, Mounce, L, Bailey, SE, Cooper, SL, Hamilton, W. Blood markers for cancer. BMJ 2019;367:l5774. https://doi.org/10.1136/bmj.l5774.
    1. Cohen, JD, Li, L, Wang, Y, Thoburn, C, Afsari, B, Danilova, L, et al.. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 2018;359:926–30. https://doi.org/10.1126/science.aar3247.

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