Colorectal Cancer Detected by Machine Learning Models Using Conventional Laboratory Test Data

• Published in: Technology in cancer research & treatment Vol. 20; p. 15330338211058352
• Main Authors: Li, Hui, Lin, Jianmei, Xiao, Yanhong, Zheng, Wenwen, Zhao, Lu, Yang, Xiangling, Zhong, Minsheng, Liu, Huanliang
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 2021; Sage Publications Ltd; SAGE Publishing
• Subjects: Adult; Aged; Area Under Curve; Bayesian analysis; Biomarkers, Tumor; Carcinoembryonic antigen; Case-Control Studies; Colon cancer; Colorectal cancer; Colorectal carcinoma; Colorectal Neoplasms - diagnosis; Diagnostic Tests, Routine; Electronic Health Records; Electronic medical records; Female; Hemoglobin; High density lipoprotein; Humans; Invasiveness; Laboratories; Learning algorithms; Logistic Models; Machine Learning; Male; Medical records; Middle Aged; Original; Patients; Regression analysis; Retrospective Studies; ROC Curve; Sigmoidoscopy; Support Vector Machine; Tumors; diagnosis; colorectal cancer; clinical laboratory techniques; machine learning; logistic regression
• ISSN: 1533-0346; 1533-0338; 1533-0338
• DOI: 10.1177/15330338211058352

Background: Current diagnostic methods for colorectal cancer (CRC) are colonoscopy and sigmoidoscopy, which are invasive and complex procedures with possible complications. This study aimed to determine models for CRC identification that involve minimally invasive, affordable, portable, and accurate screening variables. Methods: This was a retrospective study that used data from electronic medical records of patients with CRC and healthy individuals between July 2017 and June 2018. Laboratory data, including liver enzymes, lipid profiles, complete blood counts, and tumor biomarkers, were extracted from the electronic medical records. Five machine learning models (logistic regression, random forest, k-nearest neighbors, support vector machine [SVM], and naïve Bayes) were used to identify CRC. The performances were evaluated using the areas under the curve (AUCs), sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV). Results: A total of 1164 electronic medical records (CRC patients: 582; healthy controls: 582) were included. The logistic regression model achieved the highest performance in identifying CRC (AUC: 0.865, sensitivity: 89.5%, specificity: 83.5%, PPV: 84.4%, NPV: 88.9%). The first four weighted features in the model were carcinoembryonic antigen (CEA), hemoglobin (HGB), lipoprotein (a) (Lp(a)), and high-density lipoprotein (HDL). A diagnostic model for CRC was established based on the four indicators, with an AUC of 0.849 (0.840-0.860) for identifying all CRC patients, and it performed best in discriminating patients with late colon cancer from healthy individuals with an AUC of 0.905 (0.889-0.929). Conclusions: The logistic regression model based on CEA, HGB, Lp(a), and HDL might be a powerful, noninvasive, and cost-effective method to identify CRC.