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Putri, Raeni Dwi, Sujana, Syifa Alfiah, Hanifa, Nadhira Nizza, Santoso, Tiffanie Almas, Abdullah, Murdani. (1403). Efficacy of ColonFlag as a Complete Blood Count-Based Machine Learning Algorithm for Early Detection of Colorectal Cancer: A Systematic Review. سامانه مدیریت نشریات علمی, 49(10), 610-622. doi: 10.30476/ijms.2024.101219.3400
Raeni Dwi Putri; Syifa Alfiah Sujana; Nadhira Nizza Hanifa; Tiffanie Almas Santoso; Murdani Abdullah. "Efficacy of ColonFlag as a Complete Blood Count-Based Machine Learning Algorithm for Early Detection of Colorectal Cancer: A Systematic Review". سامانه مدیریت نشریات علمی, 49, 10, 1403, 610-622. doi: 10.30476/ijms.2024.101219.3400
Putri, Raeni Dwi, Sujana, Syifa Alfiah, Hanifa, Nadhira Nizza, Santoso, Tiffanie Almas, Abdullah, Murdani. (1403). 'Efficacy of ColonFlag as a Complete Blood Count-Based Machine Learning Algorithm for Early Detection of Colorectal Cancer: A Systematic Review', سامانه مدیریت نشریات علمی, 49(10), pp. 610-622. doi: 10.30476/ijms.2024.101219.3400
Putri, Raeni Dwi, Sujana, Syifa Alfiah, Hanifa, Nadhira Nizza, Santoso, Tiffanie Almas, Abdullah, Murdani. Efficacy of ColonFlag as a Complete Blood Count-Based Machine Learning Algorithm for Early Detection of Colorectal Cancer: A Systematic Review. سامانه مدیریت نشریات علمی, 1403; 49(10): 610-622. doi: 10.30476/ijms.2024.101219.3400

Efficacy of ColonFlag as a Complete Blood Count-Based Machine Learning Algorithm for Early Detection of Colorectal Cancer: A Systematic Review

Iranian Journal of Medical Sciences
مقاله 1، دوره 49، شماره 10، دی 2024، صفحه 610-622    اصل مقاله (692.3 K)
نوع مقاله: Review Article
شناسه دیجیتال (DOI): 10.30476/ijms.2024.101219.3400
نویسندگان
Raeni Dwi Putri1؛ Syifa Alfiah Sujana1؛ Nadhira Nizza Hanifa1؛ Tiffanie Almas Santoso1؛ Murdani Abdullah* 2، 3
1Faculty of Medicine, University of Padjadjaran, Bandung, Indonesia
2Division of Gastroenterology, Pancreatobilliary and Digestive Endoscopy, Department of Internal Medicine, Faculty of Medicine, University of Indonesia Dr. Cipto Mangunkusumo National General Hospital, Jakarta, Indonesia
3Human Cancer Research Center, Indonesian Medical Education and Research Institute, Faculty of Medicine, University of Indonesia, Jakarta, Indonesia
چکیده
Background: 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.
Results: 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. 
کلیدواژه‌ها
Blood cell count؛ Colorectal neoplasms؛ Electronic health records؛ Machine learning؛ Mass screening
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