Abstract
We report a pilot study designed to test elastic light-scattering (ELS) spectroscopy for characterizing normal, tumor, and tumor-infiltrated brain tissues. ELS spectra were measured from 393 sites on 36 ex vivo tissue specimen obtained from 29 patients. We employed and compared the performances of three methods of spectral classification for tissue characterization, including spectral slope analysis, principle component analysis (PCA), and artificial neural network (ANN) classification. The ANN classifier yielded the best correlation between spectral pattern and histopathological diagnosis, with a typical sensitivity of 80% and specificity of 93% for differentiating tumor from normal brain tissues. We also demonstrate that all three classification methods discriminate between tumor and normal tissue and have the potential to identify and quantitatively characterize tumor-infiltrated brain tissues.
Highlights
Malignant brain tumors continue to trouble the neuro-oncology community with their poor prognosis despite aggressive surgical and adjuvant therapy [1]
We observed that spectral slope of the 600–670 nm segment gave the most accurate classification of tissue types, and this wavelength range was empirically chosen for statistical analysis for diagnosis
We report a pilot study designed to test elastic light-scattering (ELS) spectroscopy for characterizing normal, tumor, and tumor-infiltrated brain tissues
Summary
Malignant brain tumors continue to trouble the neuro-oncology community with their poor prognosis despite aggressive surgical and adjuvant therapy [1]. One of the variables shown to result in longer patient survival and improved quality of life after the diagnosis of malignant brain tumor is maximal tumor resection at the time of surgery [2,3]. The intraoperative ultrasonography machine is a fairly inexpensive tool and offers the surgical team real-time anatomic analysis capabilities; it is hampered by the lack of sensitivity and resolution, especially adjacent to dense bony structures [4,5,6,7]. The magnetic resonance imaging (MRI) technology offers superior soft tissue resolution than ultrasonography or CT, and real-time surgical navigation tools were developed that allowed surgeons to verify the position of a surgical instrument in relation to structures seen on the imaging study [12]. Due to the static nature of the image, acquired prior to commencing surgery, the navigation system lost reliability with the onset of brain “shift” during surgery [13], at times resulting in shift of tumor margins by more than a centimeter in imaging space
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