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Three-Year Interval for the Multi-Target Stool DNA Test for Colorectal Cancer Screening: A Longitudinal Study.

Thomas F ImperialePhilip T LavinTara N MartiDebbie JakubowskiSteven H ItzkowitzFolasade P MayPaul J LimburgSeth SweetserAnas DaghestaniBarry M Berger
Published in: Cancer prevention research (Philadelphia, Pa.) (2022)
Data supporting the clinical utility of mt-sDNA at the guideline-recommended 3-year interval have not been reported. Between April 2015 and July 2016, candidates for CRC screening whose providers prescribed the mt-sDNA test were enrolled. Participants with a positive baseline test were recommended for colonoscopy and completed the study. Those with a negative baseline test were followed annually for three years. In year 3, the mt-sDNA test was repeated and colonoscopy was recommended independent of results. Data were analyzed using the Predictive Summary Index (PSI), a measure of the gain in certainty for dichotomous diagnostic tests (where a positive value indicates a net gain), and by comparing observed versus expected CRCs and advanced precancerous lesions. Of 2,404 enrolled subjects, 2,044 (85%) had a valid baseline mt-sDNA result (284 [13.9%] positive and 1,760 [86.1%] negative). Following participant attrition, the year 3 intention-to-screen (ITS) cohort included 591 of 1,760 (33.6%) subjects with valid mt-sDNA and colonoscopy results, with no CRCs and 63 advanced precancerous lesions (22 [34.9%] detected by mt-sDNA) and respective PSI values of 0% (P=1) and 9.3% (P=0.01). The observed 3-year CRC yield was lower than expected (one-sided p=0.09), while that for advanced precancerous lesions was higher than expected (two-sided p=0.009). Repeat mt-sDNA screening at a 3-year interval resulted in a statistically significant gain in detection of advanced precancerous lesions. Due to absence of year 3 CRCs, the PSI estimate for CRC was underpowered and could not be reliably quantified. Larger studies are required to assess the CRC study findings.
Keyphrases
  • colorectal cancer screening
  • electronic health record
  • machine learning
  • circulating tumor cells