In this study, we describe a direct fit photon-tissue interaction model to quantitatively analyze reflectance spectra of biological tissue samples. The model rapidly extracts biologically-relevant parameters associated with tissue optical scattering and absorption. This model was employed to analyze reflectance spectra acquired from freshly excised human pancreatic pre-cancerous tissues (intraductal papillary mucinous neoplasm (IPMN), a common precursor lesion to pancreatic cancer). Compared to previously reported models, the direct fit model improved fit accuracy and speed. Thus, these results suggest that such models could serve as real-time, quantitative tools to characterize biological tissues assessed with reflectance spectroscopy.
Peripheral artery disease (PAD) is a common condition with high morbidity. While measurement of tissue oxygen saturation (S t O 2 ) has been demonstrated, this is the first study to assess both S t O 2 and relative blood flow (rBF) in the extremities of PAD patients. Diffuse optics is employed to measure hemodynamic response to treadmill and pedal exercises in 31 healthy controls and 26 patients. For S t O 2 , mild and moderate/severe PAD groups show pronounced differences compared with controls. Pre-exercise mean S t O 2 is lower in PAD groups by 9.3% to 10.6% compared with means of 63.5% to 66.2% in controls. For pedal, relative rate of return of S t O 2 to baseline is more rapid in controls (p<0.05 ). Patterns of rBF also differ among groups. After both exercises, rBF tend to occur at depressed levels among severe PAD patients compared with healthy (p<0.05 ); post-treadmill, rBF tend to occur at elevated levels among healthy compared with severe PAD patients (p<0.05 ). Additionally, relative rate of return to baseline S t O 2 is more rapid among subjects with reduced levels of depression in rBF (p=0.041 ), even after adjustment for ankle brachial index. This suggests a physiologic connection between rBF and oxygenation that can be measured using diffuse optics, and potentially employed as an evaluative tool in further studies.
Pancreatic adenocarcinoma has a five-year survival rate of only 6%, largely because current diagnostic methods cannot
reliably detect the disease in its early stages. Reflectance and fluorescence spectroscopies have the potential to provide
quantitative, minimally-invasive means of distinguishing pancreatic adenocarcinoma from normal pancreatic tissue and
chronic pancreatitis. The first collection of wavelength-resolved reflectance and fluorescence spectra and time-resolved
fluorescence decay curves from human pancreatic tissues was acquired with clinically-compatible instrumentation.
Mathematical models of reflectance and fluorescence extracted parameters related to tissue morphology and
biochemistry that were statistically significant for distinguishing between pancreatic tissue types. These results suggest
that optical spectroscopy has the potential to detect pancreatic disease in a clinical setting.
Pancreatic adenocarcinoma is one of the leading causes of cancer death, in part because of the inability of current diagnostic methods to reliably detect early-stage disease. We present the first assessment of the diagnostic accuracy of algorithms developed for pancreatic tissue classification using data from fiber optic probe-based bimodal optical spectroscopy, a real-time approach that would be compatible with minimally invasive diagnostic procedures for early cancer detection in the pancreas. A total of 96 fluorescence and 96 reflectance spectra are considered from 50 freshly excised tissue sites-including human pancreatic adenocarcinoma, chronic pancreatitis (inflammation), and normal tissues-on nine patients. Classification algorithms using linear discriminant analysis are developed to distinguish among tissues, and leave-one-out cross-validation is employed to assess the classifiers' performance. The spectral areas and ratios classifier (SpARC) algorithm employs a combination of reflectance and fluorescence data and has the best performance, with sensitivity, specificity, negative predictive value, and positive predictive value for correctly identifying adenocarcinoma being 85, 89, 92, and 80%, respectively.
A prototype clinical fluorescence and reflectance spectrometer was developed and employed to detect human pancreatic
adenocarcinoma. For the first time, quantitative pancreatic tissue models and chemometric algorithms were applied to
successfully distinguish among tissue types.
Pancreatic adenocarcinoma has a five-year survival rate of only 4%, largely because an effective procedure for early
detection has not been developed. In this study, mathematical modeling of reflectance and fluorescence spectra was
utilized to quantitatively characterize differences between normal pancreatic tissue, pancreatitis, and pancreatic
adenocarcinoma. Initial attempts at separating the spectra of different tissue types involved dividing fluorescence by
reflectance, and removing absorption artifacts by applying a "reverse Beer-Lambert factor" when the absorption
coefficient was modeled as a linear combination of the extinction coefficients of oxy- and deoxy-hemoglobin. These
procedures demonstrated the need for a more complete mathematical model to quantitatively describe fluorescence and
reflectance for minimally-invasive fiber-based optical diagnostics in the pancreas.
Pancreatic adenocarcinoma, one of the leading causes of cancer death in the United States, has a five-year survival rate of only 4%. Present detection methods do not provide accurate diagnosis in the disease's early stages. To investigate whether optical spectroscopy could potentially aid in early diagnosis and improve survival rates, reflectance and fluorescence spectroscopies were employed for the first time in a limited pilot study to probe freshly excised human pancreatic tissues (normal, pancreatitis, and adenocarcinoma) and in vivo human pancreatic cancer xenografts in nude mice. In human pancreatic tissues, measurements were associated with endogenous fluorophores NAD(P)H and collagen, as well as tissue optical properties, with larger relative collagen content detected in adenocarcinoma and pancreatitis than normal. Good correspondence was observed between spectra from adenocarcinoma and cancer xenograft tissues. Reflectance data indicated that adenocarcinoma had higher reflectance in the 430- to 500-nm range compared to normal and pancreatitis tissues. The observed significant differences between the fluorescence and reflectance properties of normal, pancreatitis, and adenocarcinoma tissues present an opportunity for future statistical validation on a larger patient pool and indicate a potential application of multimodal optical spectroscopy to differentiate between diseased and normal pancreatic tissue states.
Tissue engineered constructs can be employed to graft wounds or replace diseased tissue. Non-invasive methods are
required to assess cellular viability in these constructs both pre- and post-implantation into patients. In this study, Monte
Carlo simulations and fluorescence experiments were executed on ex vivo produced oral mucosa equivalent (EVPOME)
constructs to investigate the fluorescence emitted at 355 nm excitation from these constructs. Both simulations and
experiments indicated the need to investigate alternative excitation wavelengths in order to increase the cellular
fluorescence from these constructs, while decreasing contributions from extra-cellular fluorophores.
The ability of multi-modal optical spectroscopy to detect signals from pancreatic tissue was demonstrated by studying
human pancreatic cancer xenografts in mice and freshly excised human pancreatic tumor tissue. Measured optical spectra
and fluorescence decays were correlated with tissue morphological and biochemical properties. The measured spectral
features and decay times correlated well with expected pathological differences in normal, pancreatitis and
adenocarcinoma tissue states. The observed differences between the fluorescence and reflectance properties of normal,
pancreatitis and adenocarcinoma tissue indicate a possible application of multi-modal optical spectroscopy to
differentiating between the three tissue classifications.