Scholarlink

Purpose <p>Early colorectal cancer (CRC) detection is crucial for effective treatment; however, traditional screening methods face challenges. Colonoscopy, though highly effective, has limited availability, and fecal immunochemical tests (FIT) are more accessible and cost-effective but suffer from low adherence. Our retrospective study aimed to develop a transparent artificial-intelligence model leveraging routine CBC data as a cost-effective method for CRC detection.</p> Methods <p>We conducted a retrospective analysis of 28,450 individuals aged 45–75 who underwent colonoscopy within six months of a complete blood count (CBC) test. Among them, 439 (1.8%) had CRC, 2,955 (11.8%) had advanced adenomas, and 21,662 (86.5%) had benign findings on colonoscopy. The database was divided into training (70%) and testing (30%) sets. The model was developed using ridge regression.</p> Results <p>Descriptive analysis revealed significant differences between CRC cases and controls across most CBC markers, CBC-derived ratios, and age (<i>P</i> &lt; 0.001), except for lymphocytes. The model, based on red cell distribution width (RDW), systemic inflammation response index (SIRI), hemoglobin, and age, achieved an AUC of 0.77 (95% CI: 0.75–0.77) for CRC, comparable to a deep learning model (TabPFN). Interpretability analysis revealed that older age, elevated RDW and SIRI, and low hemoglobin were associated with CRC. In a subgroup (7.25%) with FIT results, FIT showed higher sensitivity for CRC (88%) than the model (64%), but lower specificity (77% vs. 81%).</p> Conclusion <p>Given CBC's widespread use and accessibility, this approach may be a scalable pre-screening tool to improve CRC risk stratification and optimize resource allocation, demonstrating how explainable AI may augment existing CRC screening programs.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI-driven pre-screening for colorectal cancer using complete blood counts: toward broader population impact

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月18日

查看全文
  • Bruna Los,
  • Bruno Aragão Rocha,
  • Daniel Noce da Silva,
  • Vinicius Moura Ribeiro,
  • Marco Aurelio Kohara,
  • Rodrigo Azevedo Rodrigues,
  • Maria Carolina Tostes Pintão,
  • Otavio Jose Eulalio Pereira,
  • João Vicente de Morais Malvezzi,
  • Flavia Helena da Silva,
  • Pedro Henrique Araújo de Souza,
  • Daniella Castro Araújo

摘要

Purpose

Early colorectal cancer (CRC) detection is crucial for effective treatment; however, traditional screening methods face challenges. Colonoscopy, though highly effective, has limited availability, and fecal immunochemical tests (FIT) are more accessible and cost-effective but suffer from low adherence. Our retrospective study aimed to develop a transparent artificial-intelligence model leveraging routine CBC data as a cost-effective method for CRC detection.

Methods

We conducted a retrospective analysis of 28,450 individuals aged 45–75 who underwent colonoscopy within six months of a complete blood count (CBC) test. Among them, 439 (1.8%) had CRC, 2,955 (11.8%) had advanced adenomas, and 21,662 (86.5%) had benign findings on colonoscopy. The database was divided into training (70%) and testing (30%) sets. The model was developed using ridge regression.

Results

Descriptive analysis revealed significant differences between CRC cases and controls across most CBC markers, CBC-derived ratios, and age (P < 0.001), except for lymphocytes. The model, based on red cell distribution width (RDW), systemic inflammation response index (SIRI), hemoglobin, and age, achieved an AUC of 0.77 (95% CI: 0.75–0.77) for CRC, comparable to a deep learning model (TabPFN). Interpretability analysis revealed that older age, elevated RDW and SIRI, and low hemoglobin were associated with CRC. In a subgroup (7.25%) with FIT results, FIT showed higher sensitivity for CRC (88%) than the model (64%), but lower specificity (77% vs. 81%).

Conclusion

Given CBC's widespread use and accessibility, this approach may be a scalable pre-screening tool to improve CRC risk stratification and optimize resource allocation, demonstrating how explainable AI may augment existing CRC screening programs.