Raman spectroscopy detects and distinguishes neuroblastoma and related tissues in fresh and (banked) frozen specimens

Abstract

Background: Raman spectroscopy has been shown to accurately distinguish different neural crest-derived pediatric tumors. This study tests the ability of Raman spectroscopy to accurately identify cryopreserved tissue specimens using a classification algorithm designed from fresh tumor data and vice versa.

Methods: Fresh specimens of neuroblastoma and other pediatric neural crest tumors were analyzed with Raman spectroscopy. After analysis, the specimens were stored at -80 degrees C. At a later date, the specimens were thawed and reanalyzed by Raman spectroscopy. A computer algorithm was used to classify the spectra from the frozen tissue against a computer model built on the fresh tissue data. This classification process was then reversed, testing fresh spectra against a model built from frozen data.

Results: We collected 1114 spectra (862 fresh and 252 frozen) from 62 tissue samples, including 8 normal adrenal glands, 29 neuroblastomas, 14 ganglioneuromas, 8 nerve sheath tumors, and 3 pheochromocytomas. At the tissue level, frozen neuroblastoma, ganglioneuroma, nerve sheath tumor, and pheochromocytoma were distinguished from normal adrenal tissue with 100% sensitivity and specificity. Fresh tissue had the same results except for the misclassification of one specimen of nerve sheath tumor.

Conclusions: The representative spectra show a high correlation between fresh and frozen tissue, and a clear difference between pathologic conditions. Spectra from frozen tissue can be accurately classified against spectra from fresh tissue and vice versa. This modality makes it possible to determine in a few minutes a result that often takes 12 to 36 hours for tissue processing and consideration by a trained pathologist to achieve.