Implementation of new clinical technologies that provide precise molecular diagnosis of tissues are highly desirable to guide treatment strategies and improve cancer patient care. Molecular technologies offer the exciting opportunity to incorporate cancer-specific biomarkers into clinical decision making for improved cancer detection and diagnosis. In particular, ambient ionization mass spectrometry (MS) techniques provide the specificity and sensitivity necessary to perform <i>in situ</i> chemical analysis of tissue samples for near real time assessment of their molecular signatures. Ambient ionization MS also provides a wealth of chemical information, which calls for the use of refined statiscal methods to properly interpret data and derive classification models. Inspired by Dick Zare's passion and creativity merging chemistry and medical research, in this talk I will describe my lab’s research efforts applying ambient ionization MS techniques integrated to machine learning classifiers to address critical problem in the clinical management cancer patients. Focus will be given to our research developing a method to radidly and accurately diagnose thyroid cancer in indeterminate fine needle aspiration biopsies, and in defining markers of treatment outcome in ovarian cancer.

Ambient ionization mass spectrometry and machine learning to guide clinical decisions and improve patient outcome

2019 Fall National Meeting

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