Scientists to present research on RMI ability to classify lung, kidney tumors at USCAP meeting

ChemImageFeb 16 2011

Clinical researchers from Cornell Weil Medical School and University of Chicago Medical School will present research conducted on Raman Molecular Imaging's (RMI) ability to differentiate diagnostic dilemmas in lung and kidney tumors at the United States and Canadian Academy of Pathology's (USCAP) 100th Annual Meeting, Feb. 26-March 4 at the Henry B. Gonzalez Convention Center, San Antonio, Texas.

RMI, a technique developed by scientists at ChemImage Corporation, combines Raman spectroscopy with digital imaging to generate chemical-specific data imprints of the sample in each pixel of the digital image.

"ChemImage is proud to support the research conducted by Cornell Weil Medical School and University of Chicago Medical School through the use of our Raman Molecular Imaging instrumentation," said Dr. John Maier, Vice President Biomedical Research at ChemImage Corporation. "Although additional research is needed in both studies, we are encouraged by the results RMI yields."

Presentation highlights include:

Raman Spectroscopy of Oncocytic Kidney Tumors
Authored by Maria Tretiakova, Shona Stewart and John Maier
Presented by Maria Tretiakova, March 2

RMI was used to classify 32 kidney tumor cases as either malignant chromophobe renal cell carcinoma (ChRCC) or benign renal oncocytoma (ONC), both sometimes challenging diagnoses for practitioners to make. Evaluation of 615 spectra from 25 ChRCC cases and 7 ONC cases yielded the correct molecular classification in 92% of ChRCC and 71% of ONC cases, forming the basis of a first attempt at differentiating between the two similar tumors.

Raman Molecular Imaging Differentiates Epithelioid Mesothelioma from Metastatic-to-Pleura Bronchogenic Adenocarcinoma
Authored by Andrew Schreiner, John Maier, Amy Drauch, Paul S.
Presented by Andrew Schreiner, March 2

In this preliminary study, 10 lung tissue samples obtained from Cornell Weil Medical School were analyzed using RMI to distinguish epithelioid mesothelioma from metastatic-to-pleura bronchogenic adenocarcinoma. Principal component analysis (PCA), a multivariate statistical technique used for data analysis, revealed that a difference between the two groups can be seen using RMI.  These deviations enabled the creation of a digital stain, indicating the present disease.

In addition to kidney and lung cancer research, RMI is also be used to predict disease progression in patients with prostate cancer. For more information on RMI's role in pathology, visit: http://Ez.com/USCAPpresentations.