Dataset Information

Machine Learning-Assisted Evaluation of Circulating DNA Quantitative Analysis for Cancer Screening.

ABSTRACT: While the utility of circulating cell-free DNA (cfDNA) in cancer screening and early detection have recently been investigated by testing genetic and epigenetic alterations, here, an original approach by examining cfDNA quantitative and structural features is developed. First, the potential of cfDNA quantitative and structural parameters is independently demonstrated in cell culture, murine, and human plasma models. Subsequently, these variables are evaluated in a large retrospective cohort of 289 healthy individuals and 983 patients with various cancer types; after age resampling, this evaluation is done independently and the variables are combined using a machine learning approach. Implementation of a decision tree prediction model for the detection and classification of healthy and cancer patients shows unprecedented performance for 0, I, and II colorectal cancer stages (specificity, 0.89 and sensitivity, 0.72). Consequently, the methodological proof of concept of using both quantitative and structural biomarkers, and classification with a machine learning method are highlighted, as an efficient strategy for cancer screening. It is foreseen that the classification rate may even be improved by the addition of such biomarkers to fragmentomics, methylation, or the detection of genetic alterations. The optimization of such a multianalyte strategy with this machine learning method is therefore warranted.

SUBMITTER: Tanos R

PROVIDER: S-EPMC7509651 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Publications

Machine Learning-Assisted Evaluation of Circulating DNA Quantitative Analysis for Cancer Screening.

Tanos Rita R Tosato Guillaume G Otandault Amaelle A Al Amir Dache Zahra Z Pique Lasorsa Laurence L Tousch Geoffroy G El Messaoudi Safia S Meddeb Romain R Diab Assaf Mona M Ychou Marc M Du Manoir Stanislas S Pezet Denis D Gagnière Johan J Colombo Pierre-Emmanuel PE Jacot William W Assénat Eric E Dupuy Marie M Adenis Antoine A Mazard Thibault T Mollevi Caroline C Sayagués José María JM Colinge Jacques J Thierry Alain R AR

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 20200729 18

While the utility of circulating cell-free DNA (cfDNA) in cancer screening and early detection have recently been investigated by testing genetic and epigenetic alterations, here, an original approach by examining cfDNA quantitative and structural features is developed. First, the potential of cfDNA quantitative and structural parameters is independently demonstrated in cell culture, murine, and human plasma models. Subsequently, these variables are evaluated in a large retrospective cohort of 2 ...[more]

PMID: 32999827

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Project description:BACKGROUND:Machine learning tools can expedite systematic review (SR) processes by semi-automating citation screening. Abstrackr semi-automates citation screening by predicting relevant records. We evaluated its performance for four screening projects. METHODS:We used a convenience sample of screening projects completed at the Alberta Research Centre for Health Evidence, Edmonton, Canada: three SRs and one descriptive analysis for which we had used SR screening methods. The projects were heterogeneous with respect to search yield (median 9328; range 5243 to 47,385 records; interquartile range (IQR) 15,688 records), topic (Antipsychotics, Bronchiolitis, Diabetes, Child Health SRs), and screening complexity. We uploaded the records to Abstrackr and screened until it made predictions about the relevance of the remaining records. Across three trials for each project, we compared the predictions to human reviewer decisions and calculated the sensitivity, specificity, precision, false negative rate, proportion missed, and workload savings. RESULTS:Abstrackr's sensitivity was > 0.75 for all projects and the mean specificity ranged from 0.69 to 0.90 with the exception of Child Health SRs, for which it was 0.19. The precision (proportion of records correctly predicted as relevant) varied by screening task (median 26.6%; range 14.8 to 64.7%; IQR 29.7%). The median false negative rate (proportion of records incorrectly predicted as irrelevant) was 12.6% (range 3.5 to 21.2%; IQR 12.3%). The workload savings were often large (median 67.2%, range 9.5 to 88.4%; IQR 23.9%). The proportion missed (proportion of records predicted as irrelevant that were included in the final report, out of the total number predicted as irrelevant) was 0.1% for all SRs and 6.4% for the descriptive analysis. This equated to 4.2% (range 0 to 12.2%; IQR 7.8%) of the records in the final reports. CONCLUSIONS:Abstrackr's reliability and the workload savings varied by screening task. Workload savings came at the expense of potentially missing relevant records. How this might affect the results and conclusions of SRs needs to be evaluated. Studies evaluating Abstrackr as the second reviewer in a pair would be of interest to determine if concerns for reliability would diminish. Further evaluations of Abstrackr's performance and usability will inform its refinement and practical utility.

Dataset Information

Machine Learning-Assisted Evaluation of Circulating DNA Quantitative Analysis for Cancer Screening.

Publications

Machine Learning-Assisted Evaluation of Circulating DNA Quantitative Analysis for Cancer Screening.

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