Dataset Information

Prediction of Acquired Taxane Resistance Using a Personalized Pathway-Based Machine Learning Method.

ABSTRACT: PURPOSE:This study was conducted to develop and validate an individualized prediction model for automated detection of acquired taxane resistance (ATR). Materials and Methods:Penalized regression, combinedwith an individualized pathway score algorithm,was applied to construct a predictive model using publically available genomic cohorts of ATR and intrinsic taxane resistance (ITR). To develop a model with enhanced generalizability, we merged multiple ATR studies then updated the learning parameter via robust cross-study validation. RESULTS:For internal cross-study validation, the ATR model produced a perfect performance with an overall area under the receiver operating curve (AUROC) of 1.000 with an area under the precision-recall curve (AUPRC) of 1.000, a Brier score of 0.007, a sensitivity and a specificity of 100%. The model showed an excellent performance on two independent blind ATR cohorts (overall AUROC of 0.940, AUPRC of 0.940, a Brier score of 0.127). When we applied our algorithm to two large-scale pharmacogenomic resources for ITR, the Cancer Genome Project (CGP) and the Cancer Cell Line Encyclopedia (CCLE), an overall ITR cross-study AUROC was 0.70, which is a far better accuracy than an almost random level reported by previous studies. Furthermore, this model had a high transferability on blind ATR cohorts with an AUROC of 0.69, suggesting that general predictive features may be at work across both ITR and ATR. CONCLUSION:We successfully constructed a multi-study-derived personalized prediction model for ATR with excellent accuracy, generalizability, and transferability.

SUBMITTER: Kim YR

PROVIDER: S-EPMC6473276 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Prediction of Acquired Taxane Resistance Using a Personalized Pathway-Based Machine Learning Method.

Kim Young Rae YR Kim Dongha D Kim Sung Young SY

Cancer research and treatment 20180810 2

<h4>Purpose</h4>This study was conducted to develop and validate an individualized prediction model for automated detection of acquired taxane resistance (ATR).<h4>Materials and methods</h4>Penalized regression, combinedwith an individualized pathway score algorithm,was applied to construct a predictive model using publically available genomic cohorts of ATR and intrinsic taxane resistance (ITR). To develop a model with enhanced generalizability, we merged multiple ATR studies then updated the l ...[more]

PMID: 30092623

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Project description:BackgroundAccurate estimation of surgical transfusion risk is essential for efficient allocation of blood bank resources and for other aspects of anesthetic planning. This study hypothesized that a machine learning model incorporating both surgery- and patient-specific variables would outperform the traditional approach that uses only procedure-specific information, allowing for more efficient allocation of preoperative type and screen orders.MethodsThe American College of Surgeons National Surgical Quality Improvement Program Participant Use File was used to train four machine learning models to predict the likelihood of red cell transfusion using surgery-specific and patient-specific variables. A baseline model using only procedure-specific information was created for comparison. The models were trained on surgical encounters that occurred at 722 hospitals in 2016 through 2018. The models were internally validated on surgical cases that occurred at 719 hospitals in 2019. Generalizability of the best-performing model was assessed by external validation on surgical cases occurring at a single institution in 2020.ResultsTransfusion prevalence was 2.4% (73,313 of 3,049,617), 2.2% (23,205 of 1,076,441), and 6.7% (1,104 of 16,053) across the training, internal validation, and external validation cohorts, respectively. The gradient boosting machine outperformed the baseline model and was the best- performing model. At a fixed 96% sensitivity, this model had a positive predictive value of 0.06 and 0.21 and recommended type and screens for 36% and 30% of the patients in internal and external validation, respectively. By comparison, the baseline model at the same sensitivity had a positive predictive value of 0.04 and 0.144 and recommended type and screens for 57% and 45% of the patients in internal and external validation, respectively. The most important predictor variables were overall procedure-specific transfusion rate and preoperative hematocrit.ConclusionsA personalized transfusion risk prediction model was created using both surgery- and patient-specific variables to guide preoperative type and screen orders and showed better performance compared to the traditional procedure-centric approach.Editor’s perspective

Dataset Information

Prediction of Acquired Taxane Resistance Using a Personalized Pathway-Based Machine Learning Method.

Publications

Prediction of Acquired Taxane Resistance Using a Personalized Pathway-Based Machine Learning Method.

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