Combining the Fecal Immunochemical Test with a Logistic Regression Model for Screening Colorectal Neoplasia

Feiyuan Liu; Qiaoyun Long; Hui He; Shaowei Dong; Li Zhao; Chang Zou; Weiqing Wu

doi:10.3389/fphar.2021.635481

Combining the Fecal Immunochemical Test with a Logistic Regression Model for Screening Colorectal Neoplasia

Front Pharmacol. 2021 Mar 17:12:635481. doi: 10.3389/fphar.2021.635481. eCollection 2021.

Authors

Feiyuan Liu¹, Qiaoyun Long^{2

3}, Hui He⁴, Shaowei Dong^{2

3}, Li Zhao⁴, Chang Zou^{2

3}, Weiqing Wu⁴

Affiliations

¹ Department of Scientific Research, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, China.
² Department of Clinical Research Center, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, China.
³ Shenzhen Public Service Platform on Tumor Precision Medicine and Molecular Diagnosis, Shenzhen, China.
⁴ Department of Health Management, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, China.

Abstract

Background: The fecal immunochemical test (FIT) is a widely used strategy for colorectal cancer (CRC) screening with moderate sensitivity. To further increase the sensitivity of FIT in identifying colorectal neoplasia, in this study, we established a classifier model by combining FIT result and other demographic and clinical features. Methods: A total of 4,477 participants were examined with FIT and those who tested positive (over 100 ng/ml) were followed up by a colonoscopy examination. Demographic and clinical information of participants including four domains (basic information, clinical history, diet habits and life styles) that consist of 15 features were retrieved from questionnaire surveys. A mean decrease accuracy (MDA) score was used to select features that are mostly related to CRC. Five different algorithms including logistic regression (LR), classification and regression tree (CART), support vector machine (SVM), artificial neural network (ANN) and random forest (RF) were used to generate a classifier model, through a 10X cross validation process. Area under curve (AUC) and normalized mean squared error (NMSE) were used in the evaluation of the performance of the model. Results: The top six features that are mostly related to CRC include age, gender, history of intestinal adenoma or polyposis, smoking history, gastrointestinal discomfort symptom and fruit eating habit were selected. LR algorithm was used in the generation of the model. An AUC score of 0.92 and an NMSE score of 0.076 were obtained by the final classifier model in separating normal individuals from participants with colorectal neoplasia. Conclusion: Our results provide a new "Funnel" strategy in colorectal neoplasia screening via adding a classifier model filtering step between FIT and colonoscopy examination. This strategy minimizes the need of colonoscopy examination while increases the sensitivity of FIT-based CRC screening.

Keywords: classifier model; colorectal neoplasia screening; fecal immunochemical test; funnel strategy; logistic regression model.