The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Classifying Human Brain Tumors by Lipid Imaging with Mass Spectrometry
Open accessAbstract
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); 645–54. ©2011 AACR.
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The 2007 WHO Classification of Tumours of the Central Nervous System
Louis, D. N., Ohgaki, H., Wiestler, O. D., Cavenee, W. K., Burger, P. C., Jouvet, A., Scheithauer, B. W., & Kleihues, P. (2007) · Acta Neuropathologica · doi:10.1007/s00401-007-0243-4 · Cited by 15,397
Ambient Sampling/Ionization Mass Spectrometry: Applications and Current Trends
Harris, G. A., Galhena, A. S., & Fernández, F. M. (2011) · Analytical Chemistry · doi:10.1021/ac200918u · Cited by 527
Heterogeneity Maintenance in Glioblastoma: A Social Network
Bonavia, R., Inda, M.-D.-M., Cavenee, W. K., & Furnari, F. B. (2011) · Cancer Research · doi:10.1158/0008-5472.can-11-0153 · Cited by 439
Profiling Critical Cancer Gene Mutations in Clinical Tumor Samples
MacConaill, L. E., Campbell, C. D., Kehoe, S. M., Bass, A. J., Hatton, C., Niu, L., Davis, M., Yao, K., Hanna, M., Mondal, C., et al. (2009) · PLoS ONE · doi:10.1371/journal.pone.0007887 · Cited by 438
Self-citationAmbient mass spectrometry: bringing MS into the “real world”
Alberici, R. M., Simas, R. C., Sanvido, G. B., Romão, W., Lalli, P. M., Benassi, M., Cunha, I. B. S., & Eberlin, M. N. (2010) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-010-3808-3 · Cited by 340
Craniotomy for Tumor Treatment in an Intraoperative Magnetic Resonance Imaging Unit
Black, P. M., Alexander, E., Martin, C., Moriarty, T., Nabavi, A., Wong, T. Z., Schwartz, R. B., & Jolesz, F. (1999) · Neurosurgery · doi:10.1097/00006123-199909000-00001 · Cited by 301
Eberlin, L. S., Ferreira, C. R., Dill, A. L., Ifa, D. R., & Cooks, R. G. (2011) · Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids · doi:10.1016/j.bbalip.2011.05.006 · Cited by 257
Self-citationSchwartz, S. A., Weil, R. J., Thompson, R. C., Shyr, Y., Moore, J. H., Toms, S. A., Johnson, M. D., & Caprioli, R. M. (2005) · Cancer Research · doi:10.1158/0008-5472.can-04-3016 · Cited by 235
Eberlin, L. S., Dill, A. L., Costa, A. B., Ifa, D. R., Cheng, L., Masterson, T., Koch, M., Ratliff, T. L., & Cooks, R. G. (2010) · Analytical Chemistry · doi:10.1021/ac9029482 · Cited by 192
Self-citationAmbient molecular imaging by desorption electrospray ionization mass spectrometry
Wiseman, J. M., Ifa, D. R., Venter, A., & Cooks, R. G. (2008) · Nature Protocols · doi:10.1038/nprot.2008.11 · Cited by 171
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Cited by
Lipids and cancer: Emerging roles in pathogenesis, diagnosis and therapeutic intervention
Butler, L. M., Perone, Y., Dehairs, J., Lupien, L. E., de Laat, V., Talebi, A., Loda, M., Kinlaw, W. B., & Swinnen, J. V. (2020) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2020.07.013 · Cited by 589
Overview of Mass Spectrometry-Based Metabolomics: Opportunities and Challenges
Gowda, G. A. N., & Djukovic, D. (2014) · Methods in Molecular Biology · doi:10.1007/978-1-4939-1258-2_1 · Cited by 334
An introduction and overview of machine learning in neurosurgical care
Senders, J. T., Zaki, M. M., Karhade, A. V., Chang, B., Gormley, W. B., Broekman, M. L., Smith, T. R., & Arnaout, O. (2018) · Acta Neurochirurgica · doi:10.1007/s00701-017-3385-8 · Cited by 178
Contrast Agent Mass Spectrometry Imaging Reveals Tumor Heterogeneity
Tata, A., Zheng, J., Ginsberg, H. J., Jaffray, D. A., Ifa, D. R., & Zarrine-Afsar, A. (2015) · Analytical Chemistry · doi:10.1021/acs.analchem.5b01992 · Cited by 34
Metadata
- Authors
- Eberlin, L. S., Norton, I., Dill, A. L., Golby, A. J., Ligon, K. L., Santagata, S., Cooks, R. G., & Agar, N. Y. R.
- Venue
- Cancer Research
- Published
- 2012-01-31
- Volume
- 72
- Issue
- 3
- Pages
- 645-654
- ISSN
- 0008-5472 (ISSN-L)
- 1538-7445
- OpenAlex
- W2102681122
- Cited by
- 294
- References
- 20
- FWCI
- 9.44
- Type
- journal article
- Language
- en
- Open access
- green
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