ArticleCancer medicine2022
Development and evaluation of a colorectal cancer screening method using machine learning-based gut microbiota analysis.
Article in Cancer medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 31 citations in OpenAlex.
- Gut microbiota signatures and machine learning-based candidate feature prioritization in advanced colorectal cancer.Archives of microbiology · 2026Article
- Modifications of microbiome-derived cell-free RNA in plasma discriminates colorectal cancer samples.Nature biotechnology · 2026Article
- A novel prognostic biomarker for colorectal cancer: the ratio of red cell distribution width to lymphocytes.Scientific reports · 2026Article
- Unveiling the unique gut microbial signatures in colorectal adenomas: establishment and validation of a cross-kingdom microbiome predictive model.Frontiers in microbiology · 2026Article
- Clinical strains isolated from early-stage colorectal cancer patients promote tumorigenesis.PeerJ · 2026Article
- Refining the Feasibility of Machine-Learning-Based Diagnostic Model Utilizing Gut Microbiota Analysis for Colorectal Cancer Screening.Cancer medicine · 2025Article
- Cardiometabolic disease risk in gorillas is associated with altered gut microbial metabolism.NPJ biofilms and microbiomes · 2025Article
- B cell senescence promotes age-related changes in oral microbiota.Aging cell · 2024Article
- A review of machine learning methods for cancer characterization from microbiome data.NPJ precision oncology · 2024Review
- Research progress on the correlation between intestinal flora and colorectal cancer.Frontiers in oncology · 2024Review
- Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment.Journal of hematology & oncology · 2023Review
- Intracellular bacteria in cancer-prospects and debates.NPJ biofilms and microbiomes · 2023Review
- Crohn's Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome.Diagnostics (Basel, Switzerland) · 2023Article
- Gut Microbial and Associated Metabolite Markers for Colorectal Cancer Diagnosis.Microorganisms · 2023Review
- Bacterial induction of B cell senescence promotes age-related changes in the gut microbiota.Nature cell biology · 2023Article
- Article
- The global research of microbiota in colorectal cancer screening: a bibliometric and visualization analysis.Frontiers in oncology · 2023Article
- Development and evaluation of a colorectal cancer screening method using machine learning-based gut microbiota analysis.Cancer medicine · 2022Article
- Do we need to change our perspective about gut biomarkers? A public data mining approach to identify differentially abundant bacteria in intestinal inflammatory diseases.Frontiers in cellular and infection microbiology · 2022Article
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accumulating evidence indicates that alterations of gut microbiota are associated with colorectal cancer (CRC). Therefore, the use of gut microbiota for the diagnosis of CRC has received attention. Recently, several studies have been conducted to detect the differences in the gut microbiota between healthy individuals and CRC patients using machine learning-based gut bacterial DNA meta-sequencing analysis, and to use this information for the development of CRC diagnostic model. However, to date, most studies had small sample sizes and/or only cross-validated using the training dataset that was used to create the diagnostic model, rather than validated using an independent test dataset. Since machine learning-based diagnostic models cause overfitting if the sample size is small and/or an independent test dataset is not used for validation, the reliability of these diagnostic models needs to be interpreted with caution. To circumvent these problems, here we have established a new machine learning-based CRC diagnostic model using the gut microbiota as an indicator. Validation using independent test datasets showed that the true positive rate of our CRC diagnostic model increased substantially as CRC progressed from Stage I to more than 60% for CRC patients more advanced than Stage II when the false positive rate was set around 8%. Moreover, there was no statistically significant difference in the true positive rate between samples collected in different cities or in any part of the colorectum. These results reveal the possibility of the practical application of gut microbiota-based CRC screening tests.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.