Classifying Human Brain Tumors by Lipid Imaging with Mass Spectrometry. Eberlin, L. S., Norton, I., Dill, A. L., Golby, A. J., Ligon, K. L., Santagata, S., Cooks, R. G., & Agar, N. Y. Cancer Research, 72(3):645–654, American Association for Cancer Research, 2012.
Classifying Human Brain Tumors by Lipid Imaging with Mass Spectrometry [link]Paper  doi  abstract   bibtex   
Brain tissue biopsies are required to histologically diagnose brain tumors, but current approaches are limited by tissue characterization at the time of surgery. Emerging technologies such as mass spectrometry imaging can enable a rapid direct analysis of cancerous tissue based on molecular composition. Here, we illustrate how gliomas can be rapidly classified by desorption electrospray ionization-mass spectrometry (DESI-MS) imaging, multivariate statistical analysis, and machine learning. DESI-MS imaging was carried out on 36 human glioma samples, including oligodendroglioma, astrocytoma, and oligoastrocytoma, all of different histologic grades and varied tumor cell concentration. Gray and white matter from glial tumors were readily discriminated and detailed diagnostic information could be provided. Classifiers for subtype, grade, and concentration features generated with lipidomic data showed high recognition capability with more than 97% cross-validation. Specimen classification in an independent validation set agreed with expert histopathology diagnosis for 79% of tested features. Together, our findings offer proof of concept that intraoperative examination and classification of brain tissue by mass spectrometry can provide surgeons, pathologists, and oncologists with critical and previously unavailable information to rapidly guide surgical resections that can improve management of patients with malignant brain tumors. Cancer Res; 72(3); 645\textendash54. \textcopyright2011 AACR.
@Article{eberlin12classifying,
  author    = {Eberlin, Livia S. and Norton, Isaiah and Dill, Allison L. and Golby, Alexandra J. and Ligon, Keith L. and Santagata, Sandro and Cooks, R. Graham and Agar, Nathalie Y.R.},
  title     = {Classifying Human Brain Tumors by Lipid Imaging with Mass Spectrometry},
  journal   = {Cancer Research},
  year      = {2012},
  volume    = {72},
  number    = {3},
  pages     = {645--654},
  issn      = {0008-5472},
  abstract  = {Brain tissue biopsies are required to histologically diagnose brain tumors, but current approaches are limited by tissue characterization at the time of surgery. Emerging technologies such as mass spectrometry imaging can enable a rapid direct analysis of cancerous tissue based on molecular composition. Here, we illustrate how gliomas can be rapidly classified by desorption electrospray ionization-mass spectrometry (DESI-MS) imaging, multivariate statistical analysis, and machine learning. DESI-MS imaging was carried out on 36 human glioma samples, including oligodendroglioma, astrocytoma, and oligoastrocytoma, all of different histologic grades and varied tumor cell concentration. Gray and white matter from glial tumors were readily discriminated and detailed diagnostic information could be provided. Classifiers for subtype, grade, and concentration features generated with lipidomic data showed high recognition capability with more than 97\% cross-validation. Specimen classification in an independent validation set agreed with expert histopathology diagnosis for 79\% of tested features. Together, our findings offer proof of concept that intraoperative examination and classification of brain tissue by mass spectrometry can provide surgeons, pathologists, and oncologists with critical and previously unavailable information to rapidly guide surgical resections that can improve management of patients with malignant brain tumors. Cancer Res; 72(3); 645{\textendash}54. {\textcopyright}2011 AACR.},
  doi       = {10.1158/0008-5472.CAN-11-2465},
  eprint    = {http://cancerres.aacrjournals.org/content/72/3/645.full.pdf},
  owner     = {Purva},
  publisher = {American Association for Cancer Research},
  timestamp = {2016-12-21},
  url       = {http://cancerres.aacrjournals.org/content/72/3/645},
}
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