Journal Article

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

Goshen, Ran, O’Keeffe-Rosetti, Maureen, Liles, Elizabeth G., Choman, Eran, Rust, Kristal C., Hornbrook, Mark C., Kinar, Yaron

2017

Overview

Background Machine learning tools identify patients with blood counts indicating greater likelihood of colorectal cancer and warranting colonoscopy referral. Aims To validate a machine learning colorectal cancer detection model on a US community-based insured adult population. Methods Eligible colorectal cancer cases (439 females, 461 males) with complete blood counts before diagnosis were identified from Kaiser Permanente Northwest Region’s Tumor Registry. Control patients ( n  = 9108) were randomly selected from KPNW’s population who had no cancers, received at ≥1 blood count, had continuous enrollment from 180 days prior to the blood count through 24 months after the count, and were aged 40–89. For each control, one blood count was randomly selected as the pseudo-colorectal cancer diagnosis date for matching to cases, and assigned a “calendar year” based on the count date. For each calendar year, 18 controls were randomly selected to match the general enrollment’s 10-year age groups and lengths of continuous enrollment. Prediction performance was evaluated by area under the curve, specificity, and odds ratios. Results Area under the receiver operating characteristics curve for detecting colorectal cancer was 0.80 ± 0.01. At 99% specificity, the odds ratio for association of a high-risk detection score with colorectal cancer was 34.7 (95% CI 28.9–40.4). The detection model had the highest accuracy in identifying right-sided colorectal cancers. Conclusions ColonFlag ® identifies individuals with tenfold higher risk of undiagnosed colorectal cancer at curable stages (0/I/II), flags colorectal tumors 180–360 days prior to usual clinical diagnosis, and is more accurate at identifying right-sided (compared to left-sided) colorectal cancers.

• Publisher: Springer US,Springer,Springer Nature B.V
• Subject: Adult / Age Factors / Aged / Aged, 80 and over / Algorithms / Analysis / Area Under Curve / Artificial intelligence / Biochemistry / Blood / Blood Cell Count / Blood tests / Cancer / Colonoscopy / Colorectal cancer / Colorectal Neoplasms - blood / Colorectal Neoplasms - diagnosis / Colorectal Neoplasms - pathology / Data Mining - methods / Diagnosis / Diagnosis, Computer-Assisted - methods / Early Detection of Cancer - methods / Female / Gastroenterology / Gastrointestinal diseases / Health aspects / Health maintenance organizations / Health risk assessment / Hepatology / Humans / Machine Learning / Male / Medical examination / Medicine / Medicine & Public Health / Middle Aged / Odds Ratio / Oncology / Original Article / Predictive Value of Tests / Referral and Consultation / Registries / Reproducibility of Results / Risk Factors / ROC Curve / Sex Factors / Transplant Surgery