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Senen, Central Jakarta, Daerah Khusus Ibukota, Postal code: 10430, Jakarta, Indonesia  \nTel: +68 21 31930373  \nEmail: [kolitgastro@gmail.com](kolitgastro@gmail.com)  \n[Received: 09 January 2024](Received: 09 January 2024)  \n[Revised: 01 April 2024](Revised: 01 April 2024)  \n[Accepted: 12 April 2024](Accepted: 12 April 2024)  \n\n| \u003Cbr>What’s Known\u003Cbr>• Detecting asymptomatic individuals for colorectal cancer (CRC) screening remains a challenging task. ColonFlag is a machine learning algorithm, incorporating age, gender, and 20 complete blood count (CBC) parameters from routine lab data.\u003Cbr>• Machine learning techniques offera valuable supplementary avenue, yet their feasibility for translation into clinical practice remains uncertain.\u003Cbr>\u003Cbr>What’s New |\n| --- |\n| • ColonFlag demonstrated the ability to detect CRC in asymptomatic patients, yet it exhibited variability in performance across diverse populations.\u003Cbr>• While ColonFlag is not intended to replace traditional screening programs, its potential to identify CRC before clinical diagnosis suggests an opportunity to detect more cases than regular screening alone. |\n\nAbstract  \nBackground: Colorectal cancer (CRC) screening is essential to reduce incidence and mortality rates. However, participation in screening remains suboptimal. ColonFlag, a machine learning algorithm using complete blood count (CBC), identifies individuals at high CRC risk using routinely performed tests. This study aims to review the existing literature assessing the efficacy of ColonFlag across diverse populations in multiple countries. Methods: The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) were followed in reporting this systematic review. Searches were conducted on PubMed, Cochrane, ScienceDirect, and Google Scholar for English articles, using keywords related to CBC, machine learning, ColonFlag, and CRC, covering the first development study from 2016 to August 2023. The Cochrane Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias.  \nResults: A total of 949 articles were identified during the literature search. Ten studies were found to be eligible. ColonFlag yielded Area Under the Curve (AUC) values ranging from 0.736 to 0.82. The sensitivity and specificity ranged from 3.91% to 35.4% and 82.73% to 94%, respectively. The positive predictive values ranged between 2.6% and 9.1%, while the negative predictive values ranged from 97.6% to 99.9%. ColonFlag performed better in shorter time windows, tumors located more proximally, in advanced stages, and in cases of CRC compared to adenoma. Conclusion: While ColonFlag exhibits low sensitivity compared to established screening methods such as the fecal immunochemical test (FIT) or colonoscopy, its potential to detect CRC before clinical diagnosis suggests an opportunity for identifying more cases than regular screening alone.  \nPlease cite this article as: Putri RD, Sujana SA, Hanifa NN, Santoso TA, Abdullah M. Efficacy of ColonFlag as a Complete Blood Count-Based Machine Learning Algorithm for Early Detection of Colorectal Cancer: ","cbCaifLI3NmdTLe7","https://ap.wps.com/l/cbCaifLI3NmdTLe7","pdf",708916,13,"English","# What’s Known\n# What’s New\n# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Introduction\n# Materials and Methods\n## Data Sources and Search Strategy","[{\"question\":\"What problem does ColonFlag aim to address in colorectal cancer screening?\",\"answer\":\"ColonFlag targets the challenge of detecting CRC in asymptomatic individuals by using routinely available CBC-based information to estimate CRC risk.\"},{\"question\":\"How was ColonFlag evaluated in the systematic review?\",\"answer\":\"The review followed PRISMA reporting and searched PubMed, Cochrane, ScienceDirect, and Google Scholar for English articles (2016 to August 2023). Risk of bias was assessed with PROBAST.\"},{\"question\":\"What key performance outcomes were reported for ColonFlag?\",\"answer\":\"ColonFlag showed AUC values ranging from 0.736 to 0.82, with sensitivity and specificity varying widely across studies, and predictive values reflecting low positive predictive values but high negative predictive values.\"}]","Efficacy of ColonFlag as a Complete Blood Count-Based Machine Learning Algorithm for Early Detection of Colorectal Cancer - A Systematic Review | PDF",1789993278,33]