Performance analysis of a machine learning flagging system used to identify a group of individuals at a high risk for colorectal cancer

Abstract

Individuals with colorectal cancer (CRC) have a tendency to intestinal bleeding which may result in mild to severe iron deficiency anemia, but for many colon cancer patients hematological abnormalities are subtle. The fecal occult blood test (FOBT) is used as a pre-screening test whereby those with a positive FOBT are referred to colonscopy. We sought to determine if information contained in the complete blood count (CBC) report coud be processed automatically and used to predict the presence of occult colorectal cancer (CRC) in the setting of a large health services plan. Using the health records of the Maccabi Health Services (MHS) we reviewed CBC reports for 112,584 study subjects of whom 133 were diagnosed with CRC in 2008 and analysed these with the MeScore tool. The odds ratio for being diagnosed with CRC in 2008 was calculated with regards to the MeScore, using cutoff levels of 97% and 99% percentiles. For individuals in the highest one percentile, the odds ratio for CRC was 21.8 (95% CI 13.8 to 34.2). For the majority of the individuals with cancer, CRC was not suspected at the time of the blood draw. Frequent use of anticoagulants, the presence of other gastrointestinal pathologies and non-GI malignancies were assocaitged with false positive MeScores. The MeScore can help identify individuals in the population who would benefit most from CRC screening, including those with no clinical signs or symptoms of CRC.

Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data

“…This is the first US-based study of the ColonFlag Ò early CRC detection model. Previous validations have been performed on members of the MHS in Israel and on a British National Health Services population [20,21]. Performance of the ColonFlag Ò CRC detection model with the KPNW validation data is similar to these previous foreign studies; the algorithm-based analysis performed best in detecting CRC tumors in the cecum and ascending colon.…”

“…An efficient CRC screening program has a high compliance rate, but also targets patients at increased risk for CRC. The reported compliance rate varies tremendously between CRC screening programs worldwide (10-71%), depending on socioeconomic status, ethnicity, age, gender, psychological factors, and other factors [21]. Whereas newer CRC screening programs based on mailed fecal immunochemical tests and screening colonoscopy can reach a majority of patients in some settings [35,36], there is concern that fecal immunochemical tests may be less sensitive than colonoscopy for right-sided colorectal cancers [37].…”

“…Kinar et al [20,21] developed a novel method for identifying individuals at increased risk of having CRC through empirically derived detection models of their blood counts, age, and sex. Based on machine learning methods-decision trees and cross-validation techniquesthis method, called ColonFlag Ò , enabled generation and evaluation of data-driven detection models [20,21].…”

“…Based on machine learning methods-decision trees and cross-validation techniquesthis method, called ColonFlag Ò , enabled generation and evaluation of data-driven detection models [20,21]. ColonFlag Ò was developed using data from Maccabi Healthcare Services (MHS) and the Israel National Cancer Register (INCR) [22].…”

“…This statistical detection model was validated using the UK's The Health Information Network (THIN) database-an anonymized UK primary care database derived from General Practitioners in the UK, which is broadly representative of the UK population in terms of sex, age, and major condition prevalence [23]. Individuals in the highest one percentile of ColonFlag Ò scores faced a 20-fold higher risk of being diagnosed with CRC in the subsequent 12-18 month period [20,21]. This performance of ColonFlag Ò on Israeli and UK patient populations suggests that it should be estimated for other defined populations.…”