Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer deaths worldwide. Early diagnosis of CRC, which saves lives and enables better outcomes, is generally implemented through a two-step population screening approach based on the use of Fecal Immunochemical Test (FIT) followed by colonoscopy if the test is positive. However, the FIT step has a high false positive rate, and there is a need for new predictive biomarkers to better prioritize cases for colonoscopy. Here we used 16S rRNA metabarcoding from FIT positive samples to uncover microbial taxa, taxon co-occurrence and metabolic features significantly associated with different colonoscopy outcomes, underscoring a predictive potential and revealing changes along the path from healthy tissue to carcinoma. Finally, we used machine learning to develop a two-phase classifier which reduces the current false positive rate while maximizing the inclusion of CRC and clinically relevant samples.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9817783 | PMC |
| http://dx.doi.org/10.3390/cancers15010120 | DOI Listing |
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COLOFIT: Development and Internal-External Validation of Models Using Age, Sex, Faecal Immunochemical and Blood Tests to Optimise Diagnosis of Colorectal Cancer in Symptomatic Patients.
Aliment Pharmacol Ther
January 2025
Gastrointestinal and Liver Theme, National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre (BRC), Nottingham University Hospitals NHS Trust and the University of Nottingham, School of Medicine, Queen's Medical Centre, Nottingham, UK.
Background: Colorectal cancer (CRC) is the third most common cancer in the United Kingdom and the second largest cause of cancer death.
Aim: To develop and validate a model using available information at the time of faecal immunochemical testing (FIT) in primary care to improve selection of symptomatic patients for CRC investigations.
Methods: We included all adults (≥ 18 years) referred to Nottingham University Hospitals NHS Trust between 2018 and 2022 with symptoms of suspected CRC who had a FIT.
Effect of the COVID-19 pandemic on colorectal cancer screening in two university-affiliated health care systems.
PLoS One
January 2025
Center for Health Services Research, Regenstrief Institute Inc, Indianapolis, IN, United States of America.
Objectives: In two large university affiliated healthcare systems, we examined trends in colorectal cancer (CRC) screening both prior to and during the COVID-19 pandemic to compare the trends in non-invasive screening tests and colonoscopy.
Materials And Methods: In this retrospective time-trend analysis, we obtained the numbers of colonoscopies and non-invasive tests performed monthly during the pandemic and the year prior to it. We obtained colonoscopy data from five endoscopy units with the indication determined by dual independent review.
Rebound in Fecal Testing, Colonoscopy Utilization, and Colorectal Neoplasia Detection During the COVID-19 Pandemic.
Am J Gastroenterol
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Kaiser Permanente Northern California Division of Research, Oakland, CA 94612.
Objectives: The COVID-19 pandemic reduced colorectal cancer (CRC) screening, but the rebound in testing and outcomes following the pandemic has not been widely reported. We evaluated CRC test utilization and colorectal neoplasia detection among screening eligible patients in a large health system in 2020 and 2021, compared to 2019 (pre-pandemic).
Methods: Using a retrospective cohort study design, fecal immunochemical test (FIT) and colonoscopy utilization, FIT positivity, and neoplasia detection were evaluated annually in 2019-2021 among Kaiser Permanente Northern California patients aged 50-75 years overall and by sex, age, race and ethnicity, and spoken language preference.
Text Messaging Versus Postal Reminders to Improve Participation in a Colorectal Cancer Screening Program: Randomized Controlled Trial.
JMIR Mhealth Uhealth
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see Acknowledgments, .
Article Synopsis
- The study evaluated SMS text reminders for colorectal cancer screening in Catalonia, finding that they were less effective than standard postal reminders in increasing participation rates.
- The trial involved over 24,000 participants aged 50 to 69 and was halted early due to lower than expected participation in the SMS group (17.2% vs. 21.9% for control).
- Despite initial low participation, a recovery strategy of sending additional postal reminders to nonparticipants in the SMS group improved overall participation rates to 29.3%.
Artificial Intelligence Models Could Enhance the Diagnostic Accuracy (DA) of Fecal Immunochemical Test (FIT) in the Detection of Colorectal Adenoma in a Screening Setting.
Anticancer Res
January 2025
Department of Surgery, Kuopio University Hospital and School of Medicine, University of Eastern Finland, Kuopio, Finland.
Background/aim: This study evaluated the diagnostic accuracy (DA) for colorectal adenomas (CRA), screened by fecal immunochemical test (FIT), using five artificial intelligence (AI) models: logistic regression (LR), support vector machine (SVM), neural network (NN), random forest (RF), and gradient boosting machine (GBM). These models were tested together with clinical features categorized as low-risk (lowR) and high-risk (highR).
Patients And Methods: The colorectal neoplasia (CRN) screening cohort of 5,090 patients included 222 CRA patients and 264 non-CRA patients.
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