Refining the Feasibility of Machine-Learning-Based Diagnostic Model Utilizing Gut Microbiota Analysis for Colorectal...

Recently, we developed a colorectal cancer (CRC) diagnostic model based on a machine learning algorithm with gut microbiota analysis. In this study, we evaluated the reproducibility of the diagnostic accuracy of the gut microbiota model, compared the diagnostic accuracy of the gut microbiota model with that of the fecal immunochemical test (FIT), and investigated the practical application potential of the gut microbiota model.

Fecal samples were collected from both CRC patients and healthy individuals (HI) who underwent FIT. Gut microbiota analysis was performed using the same pipeline as that used in our previous study. Study subjects were diagnosed using the machine-learning-based gut microbiota model (ml-GMM) with the same cut-off value as in our previous study and by FIT.

The true positive rates of ml-GMM and FIT were 53.1% and 86.4%, respectively, among 81 CRC patients, whereas the false positive rates among 245 HI cases were 7.3% and 2.4%, respectively. Evaluation of the proportion of either ml-GMM or FIT being positive revealed a rate of 91.4% among CRC patients (Stage 0/I 78.3%; Stage II, 95.5%; Stage III, 96.6%; stage IV, 100.0%), whereas it was 9.4% among HI. Furthermore, we demonstrated a possible synergistic effect of ml-GMM with FIT for detection of more CRC patients.

The reproducibility of the diagnostic accuracy of ml-GMM was confirmed. It was suggested that ml-GMM in combination with FIT could detect more CRC patients than FIT alone.