Artificial Intelligence Approach for Variant Reporting
|Number of pages||13|
|Journal / Publication||JCO Clinical Cancer Informatics|
|Publication status||Published - 22 Mar 2018|
|Link to Scopus||https://www.scopus.com/record/display.uri?eid=2-s2.0-85060547064&origin=recordpage|
Methods We used an artificial intelligence approach to capture the collective clinical sign-out experience of six board-certified molecular pathologists to build and validate a decision support tool for variant reporting. We extracted all reviewed and reported variants from our clinical database and tested several machine learning models. We used 10-fold cross-validation for our variant call prediction model, which derives a contiguous prediction score from 0 to 1 (no to yes) for clinical reporting.
Results For each of the 19,594 initial training variants, our pipeline generates approximately 500 features, which results in a matrix of > 9 million data points. From a comparison of naive Bayes, decision trees, random forests, and logistic regression models, we selected models that allow human interpretability of the prediction score. The logistic regression model demonstrated 1% false negativity and 2% false positivity. The final models’ Youden indices were 0.87 and 0.77 for screening and confirmatory cutoffs, respectively. Retraining on a new assay and performance assessment in 16,123 independent variants validated our approach (Youden index, 0.93). We also derived individual pathologist-centric models (virtual consensus conference function), and a visual drill-down functionality allows assessment of how underlying features contributed to a particular score or decision branch for clinical implementation.
Conclusion: Our decision support tool for variant reporting is a practically relevant artificial intelligence approach to harness the next-generation sequencing bioinformatics pipeline output when the complexity of data interpretation exceeds human capabilities.
Artificial Intelligence Approach for Variant Reporting. / Zomnir, Michael G.; Lipkin, Lev; Pacula, Maciej; Dominguez Meneses, Enrique; MacLeay, Allison; Duraisamy, Sekhar; Nadhamuni, Nishchal; Al Turki, Saeed H.; Zheng, Zongli; Rivera, Miguel; Nardi, Valentina; Dias-Santagata, Dora; Iafrate, A. John; Le, Long P.; Lennerz, Jochen K.In: JCO Clinical Cancer Informatics, Vol. 2, 22.03.2018.