首页|University of British Columbia Reports Findings in Acute Myeloid Leukemia (MAGIC-DR:An interpretable machine-learning guided approach for acute myeloid leukemi a measurable residual disease analysis)
University of British Columbia Reports Findings in Acute Myeloid Leukemia (MAGIC-DR:An interpretable machine-learning guided approach for acute myeloid leukemi a measurable residual disease analysis)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Acute Myelo id Leukemia is the subject of a report.According to news reporting originating from Vancouver,Canada,by NewsRx correspondents,research stated,"Multiparamet er flow cytometry is widely used for acute myeloid leukemia minimal residual dis ease testing (AML MRD) but is time consuming and demands substantial expertise.Machine learning offers potential advancements in accuracy and efficiency,but h as yet to be widely adopted for this application." Financial support for this research came from Faculty of Medicine,University of British Columbia.Our news editors obtained a quote from the research from the University of Briti sh Columbia,"To explore this,we trained single cell XGBoost classifiers from 9 8 diagnostic AML cell populations and 30 MRD negative samples.Performance was a ssessed by cross-validation.Predictions were integrated with UMAP as a heatmap parameter for an augmented/interactive AML MRD analysis framework,which was ben chmarked against traditional MRD analysis for 25 test cases.The results showed that XGBoost achieved a median AUC of 0.97,effectively distinguishing diverse A ML cell populations from normal cells.When integrated with UMAP,the classifier s highlighted MRD populations against the background of normal events.Our pipel ine,MAGIC-DR,incorporated classifier predictions and UMAP into flow cytometry standard (FCS) files.This enabled a human-in-the-loop machine learning guided M RD workflow.Validation against conventional analysis for 25 MRD samples showed 100% concordance in myeloid blast detection,with MAGIC-DR also id entifying several immature monocytic populations not readily found by convention al analysis."
VancouverCanadaNorth and Central Ame ricaAcute Myeloid LeukemiaCancerCyborgsCytometryEmerging TechnologiesHealth and MedicineHematologyLeukemiaMachine LearningMyeloid LeukemiaOncology
NETL
NSTL
