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. 2011 Nov;23(11):1036-41.
doi: 10.1097/MEG.0b013e32834a2882.

Diagnostic performance of quantitative fecal immunochemical test and multivariate prediction model for colorectal neoplasms in asymptomatic individuals

Fumio Omata  1 , Ayumi Shintani, Mitsuhiro Isozaki, Katsunori Masuda, Yoshiyuki Fujita, Tsuguya Fukui
Affiliations

Diagnostic performance of quantitative fecal immunochemical test and multivariate prediction model for colorectal neoplasms in asymptomatic individuals

Fumio Omata et al. Eur J Gastroenterol Hepatol. 2011 Nov.

Abstract

Objective: Quantitative fecal immunochemical test (QTFIT) has the advantage of being able to describe test characteristics on a scaled rather than binary system. The aims of this study were to decide the optimal cut-off points of QTFIT and to make a multivariate prediction model for colorectal neoplasms in asymptomatic adults.

Materials and methods: We retrospectively analyzed 1085 consecutive asymptomatic individuals who completed both full colonoscopy and QTFIT at a general health checkup clinic. Advanced adenomatous polyps (AP) were defined as APs of at least 1 cm in diameter; adenomas with villous component or high-grade dysplasia; and significant neoplasia (SN) including both advanced AP and colorectal cancer.

Results: The ideal cut-off value of QTFIT was chosen based on a value that maximized the sum of both sensitivity and specificity, and clinical utility. For AP, 25 ng/ml was chosen as the optimal cut-off value and provided a sensitivity of 31% [95% confidence interval (CI): 27-36] and specificity of 79% (95% CI: 76-82). For SN, the ideal QTFIT cut-off value was 25 ng/ml, providing a sensitivity of 51% (95% CI: 39-62) and specificity of 77% (95% CI: 74-80). For colorectal cancer, the optimal cut-off point was 50 ng/ml, offering a sensitivity of 75% (95% CI: 41-93) and specificity of 86% (95% CI: 85-86). The multivariate prediction model was represented by nomogram and was validated by bootstrap method.

Conclusion: The diagnostic performance of QTFIT for CRC is promising, although its sensitivity for AP and SN is unsatisfactory. BMI, in addition to age and sex improves the accuracy of SN screening by QTFIT.

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