Abstract
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). 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 90days. The AI model based on routine laboratory blood tests can provide an easy-to use risk score to predict cancer within 90days. 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. 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.
Highlights
Risk prediction models aim to assist healthcare providers in the process of clinical decision making by estimating the probability of specific outcomes in a population
We evaluate the ability of an artificial intelligence (AI) model to provide an individual cancer risk score based on these routine laboratory tests
The AUC results obtained by the AI analysis in Cohort I and in the “out of time” validation test Cohort II are presented in Table 2
Summary
Risk prediction models aim to assist healthcare providers in the process of clinical decision making by estimating the probability of specific outcomes in a population. Parametric logistic regression analyses (LR) have dominated and improved risk prediction in healthcare for decades [1]. The increased opportunities of managing large and complex datasets have encouraged the application and the development of new models and tools based on artificial intelligence (AI) [2]. Many of the symptoms associated with malignant disease are non-specific, vague or imprecise and relative low risk. Even when it comes to classical “alarm” symptoms, the positive predictive value (PPV) for an underlying
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