Prediction of clinical outcomes using the pyrolysis, gas chromatography, and differential mobility spectrometry (Py-GC-DMS) system

摘要: Abstract Biological and molecular heterogeneity of human diseases especially cancers contributes to variations in treatment response, clinical outcome, survival. The addition new disease- condition-specific biomarkers existing markers track cancer provides possibilities for further assisting clinicians predicting outcomes making choices options. Ionization patterns derived from biological specimens can be adapted use with early detection, patient risk stratification, decision making, monitoring disease progression. In order demonstrate the application pyrolysis, gas chromatography, differential mobility spectrometry (Py-GC-DMS) predict outcome diseases, we analyzed ionized spectral signals generated by instrument ACB2000 ( ACB irox universal detector 2000, ACBirox LLC, NJ, USA) serum samples Mantle Cell Lymphoma (MCL) patients. Here, have used mantle cell lymphoma as a model conceptual study only based on ionization samples, developed multivariate algorithm comprised variable selection reduction steps followed receiver operating characteristic curve (ROC) analysis probability good or poor means estimating likely success particular option. Our preliminary performed small cohort proof concept demonstrating ability this system high accuracy suggesting promising field medicine.