A machine learning tool for identifying non-metastatic colorectal cancer in primary care. (March 2023)

• Record Type: Journal Article
• Authors: Nemlander, Elinor; Ewing, Marcela; Abedi, Eliya; Hasselström, Jan; Sjövall, Annika; Carlsson, Axel C.; Rosenblad, Andreas
• Is Part Of: European journal of cancer. Volume 182(2023)
• Page Start: 100
• Page End: 106
• Publication Date: 2023-03
• Subjects: Artificial intelligence -- Cancer detection -- Family practice -- Gradient boosting -- Normalised relative influence -- Prediction; Cancer -- Periodicals; Neoplasms -- Periodicals; Cancer -- Périodiques; Cancer; Tumors; Electronic journals; Periodicals; Electronic journals; 616.994
• DOI: 10.1016/j.ejca.2023.01.011
• Languages: English
• ISSNs: 0959-8049

Abstract

Abstract: Background: Primary health care (PHC) is often the first point of contact when diagnosing colorectal cancer (CRC). Human limitations in processing large amounts of information warrant the use of machine learning as a diagnostic prediction tool for CRC. Aim: To develop a predictive model for identifying non-metastatic CRC (NMCRC) among PHC patients using diagnostic data analysed with machine learning. Design and setting: A case–control study containing data on PHC visits for 542 patients >18 years old diagnosed with NMCRC in the Västra Götaland Region, Sweden, during 2011, and 2, 139 matched controls. Method: Stochastic gradient boosting (SGB) was used to construct a model for predicting the presence of NMCRC based on diagnostic codes from PHC consultations during the year before the date of cancer diagnosis and the total number of consultations. Variables with a normalised relative influence (NRI) >1% were considered having an important contribution to the model. Risks of having NMCRC were calculated using odds ratios of marginal effects. Results: Of the 361 variables used as predictors in the stochastic gradient boosting model, 184 had non-zero influence, with 16 variables having NRI >1% and a combined NRI of 63.3%. Variables representing anaemia and bleeding had a combined NRI of 27.6%. The model had a sensitivity of 73.3% and a specificity of 83.5%. Change in bowel habit had the highest odds ratios of marginal effects at 28.8. Conclusion: Machine learning is useful for identifying variables of importance for predicting NMCRC in PHC. Malignant diagnoses may be hidden behind benign symptoms such as haemorrhoids. Highlights: We used an AI-method to predict the presence of non-metastatic colorectal cancer. A case–control study based on all diagnostic codes in primary health care. Malignant diagnoses may be hidden behind perceived symptoms such as hemorrhoids. The prediction tool had a sensitivity of 73.3% and a specificity of 83.5%.