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- W2922275337 abstract "Introduction: Despite population screening and availability of several stool-based, non-invasive screening methods, over 20% of colorectal cancers (CRC) in the US are metastatic at the time of diagnosis. Blood-based methods using cell-free DNA (cfDNA) are under development as an alternative to stool-based tests. However, early stage detection of cancer using tumor-derived mutations in cfDNA (circulating tumor DNA, or ctDNA) has proven challenging because of the small proportion of cfDNA derived from tumor tissue (tumor fraction, ctDNA/cfDNA ratio) in early stage disease. Using an artificial intelligence-driven approach based on machine learning (ML) to discover signatures in cfDNA potentially reflective of both tumor and immune contributions may represent a promising direction for the early detection of cancer. Methods: De-identified plasma samples (N=1,040) were received from academic clinical studies and commercial biobanks (n=579 CRC patients; n=461 controls). Whole-genome sequencing was performed to >50M reads on cfDNA extracted from plasma. Reads aligning to expressed sequences in the genome were extracted and read counts were normalized to account for variability in read depth, sequencecontent bias, and technical batch effects. cfDNA tumor fraction was estimated using IchorCNA. ML models were trained using 10-fold cross-validation stratified by sequencing batch to mitigate bias from sequencing batch effects. Results: In a cohort heavily weighted towards early stage cancer (82% stage I/II), our method achieves a sensitivity in cross-validation of 81% (Clopper-Pearson 95% confidence interval, 77-84%) at 85% specificity. Sensitivity generally increased by tumor stage. Stratification by sequencing batch was required for reliable generalization. Further analyses revealed susceptibility to additional confounders, including variation in preanalytical and analytical processes such as institution-specific blood collection protocols. Downsampling the dataset to balance with respect to such confounders can reduce sensitivity at 85% specificity by 20-30%. Conclusion: An ML approach using a single analyte was able to achieve high sensitivity and specificity in an predominantly early-stage CRC cohort. The observation of systematic technical and site-specific biases warrants similar confounder analyses in other retrospective studies. Prospective validation of the presented ML method and evaluation of a previously presented multi-analyte approach are underway." @default.
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- W2922275337 date "2018-10-01" @default.
- W2922275337 modified "2023-10-18" @default.
- W2922275337 title "Early Stage Colorectal Cancer Detection Using Artificial Intelligence and Whole-Genome Sequencing of Cell-Free DNA in a Retrospective Cohort of 1,040 Patients" @default.
- W2922275337 doi "https://doi.org/10.14309/00000434-201810001-00307" @default.
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