Open Access
1 March 2010 Intraoperative delineation of primary brain tumors using time-resolved fluorescence spectroscopy
Pramod V. Butte, Fang Qiyin M.D., Jo A. Javier, William H. Yong, Brian K. Pikul, Keith L. Black M.D., Laura Marcu
Author Affiliations +
The goal of this study is to determine the potential of time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) as an adjunctive tool for delineation of brain tumor from surrounding normal tissue in order to assist the neurosurgeon in near-complete tumor excision. A time-domain TR-LIFS prototype apparatus (gated photomultiplier detection, fast digitizer) was used for recording tissue autofluorescence in normal cortex (NC), normal white matter (NWM), and various grades of gliomas intraoperatively. Tissue fluorescence was induced with a pulsed nitrogen laser (337 nm, 700 ps), and the intensity decay profiles were recorded in the 360- to 550-nm spectral range (10-nm interval). Histopathological analysis (hematoxylin & eosin) of the biopsy samples taken from the site of TR-LIFS measurements was used for validation of spectroscopic results. Preliminary results on 17 patients demonstrate that normal cortex (N=16) and normal white matter (N=3) show two peaks of fluorescence emission at 390 nm (lifetime=1.8±0.3 ns) and 460 nm (lifetime=0.8±0.1 ns). The 390-nm emission peak is absent in low-grade glioma (N=5; lifetime=1.1 ns) and reduced in high-grade glioma (N=9; lifetime=1.7±0.4 ns). The emission characteristics at 460 nm in all tissues correlated with the nicotinamide adenine dinucleotide fluorescence (peak: 440 to 460 nm; lifetime: 0.8 to 1.0 ns). These findings demonstrate the potential of using TR-LIFS as a tool for enhanced delineation of brain tumors during surgery. In addition, this study evaluates similarities and differences between TR-LIFS signatures of brain tumors obtained in vivo and those previously reported in ex vivo brain tumor specimens.



Gliomas represent 40% of all primary brain tumors, and treatment of glioma poses a challenge due to their tendency to infiltrate the surrounding normal brain. Astrocytoma and glioblastoma are the two most common types of gliomas. The current 18-month survival rate of glioblastoma patients treated with surgery for biopsy only, partial resection, and complete resection is 15%, 25%, and 34% respectively, making it one of the most lethal tumors.1 The standard treatment for brain tumor patients is surgery followed by radiation or chemotherapy. The extent of surgical resection is a primary determinant of outcome, and it was shown that the extent of tumor resection is a single most important factor for longer survival.2, 3 The surgical resection is complicated by location of these tumors, as they may be embedded in or near the eloquent areas of the brain such as motor cortex, sensory cortex, and speech centers. Determining the functional areas and the resectable tumor during the surgery has presented a great challenge.

In order to overcome such problems, currently several techniques are employed by surgeons in their quest for near complete removal of the tumor. This includes stereotactic image-guided surgery that delineates the tumor and intraoperative ultrasound or electric mapping to determine the functional areas from the tumor. Stereotactic image-guided surgery based on pre operative magnetic resonance imaging (MRI) scans could be used to determine the position of excision with respect to the position of tumor. However, this technique has intrinsic confounding factors such as the brain shift that occurs during the surgery. This shift may be due to loss of cerebral spinal fluid or due to release of pressure; this can result in significant inaccuracies in resection of the tumor. More recently, in order to eliminate these limitations, many major medical centers have employed intraoperative MRI (iMRI). This technique requires, however, a major investment and modification of operating room settings as well as surgical instrumentation. Moreover, the actual pathological diagnosis can be provided only by biopsy and frozen section, which requires 15to20min and can be used only in limited capacity. Even in the hands of the most skilled surgeon, complete excision is limited by the tactile and visual feedback. Thus, in order to enhance the ability of the surgeon to excise completely the tumor with more accuracy without sacrificing safety, new technologies need to be developed. Such new technologies should be able to distinguish accurately between the tumor and normal brain and guide the tumor resection in near-real time.

Laser-induced fluorescence spectroscopy (LIFS) represents a promising new adjunctive technique in the diagnosis of tissue pathologies.4, 5, 6, 7, 8 Fluorescence spectroscopy involves the use of lasers to excite endogenous autofluorescence (label-free) within tissues. Either the steady-state5, 9, 10 or the time-resolved6, 11, 12 fluorescence spectroscopy technique can be used to measure or monitor these changes. The time-resolved measurement resolves fluorescence intensity decay in terms of excited-state lifetimes and thus provides additional information about the underlying fluorescence intensity decay dynamics and improves its specificity. Earlier work has demonstrated that label-free steady-state LIFS of endogenous fluorophores (autofluorescence) can be used as a tool for diagnosis of neoplasm in the brain. Several types of brain tumors have been investigated, both in vivo and ex vivo, using this technique. These include studies of glioblastoma, 5, 6, 9 astrocytoma,9 oligodendroglioma,9 and metastatic carcinoma.9 In addition, our group has previously reported results on the utility of time-resolved LIFS (TR-LIFS) for distinguishing glioblastoma from uninvolved cerebral cortex and white matter and on its increased specificity relative to steady-state fluorescence spectroscopy.6, 11, 12

The overall objective of this study is to investigate the use of TR-LIFS as a rapid diagnostic tool for intraoperative delineation of tumor from surrounding normal tissue. Such a tool has a potential to enhance the ability of the neurosurgeon-neuropathologist to distinguish between tumor and normal brain during surgery. Extending on our earlier work conducted in ex vivo specimens,6, 12 The current study is designed (1) to determine the spectrum- and time-dependent autofluorescence emission characteristics of gliomas (high- and low-grade), normal cortex (NC), and normal white matter (NWM) measured in vivo in patients undergoing surgical resection of brain tumors; and (2) to identify the main autofluorescence signatures that provide means of discrimination between tumor and normal tissue. In addition, this study evaluates the possible differences between fluorescence signatures measured in vivo versus those measured ex vivo in tumor specimens as reported earlier.6, 12


Materials and Methods



A total of 17 patients diagnosed with a primary brain tumor (glioma) were included in this study. The patients were scheduled for surgical removal of the brain tumor and underwent the planned operation. During this procedure, the TR-LIFS fiber-optic probe was positioned above the areas of interest using a Greenburg retractor, and the brain tissue was spectroscopically investigated (total: 34 locations). While for this study, we recorded the fluorescence emission for multiple sites (n=123) , the biopsy was performed at the surgeon’s decision based on the suitability of each location for biopsy. Thus, we report here only data from the sites where biopsy was available. Areas with distinct pathologic features were selected based on the gross visual evaluation by the neurosurgeon who-performed the surgery and preoperative MRI scans typically used to guide the surgery. For future validation, intraoperative biopsy was conducted at each point of spectroscopic investigation, except for areas considered normal or not considered suitable for physical biopsy due to risks posed to the patient. The study was carried out with approval of the Cedars-Sinai Medical Center Institutional Review Board.



Experiments were conducted with an instrumental setup, which allowed for spectrally resolved fluorescence lifetime measurements. A schematic of the optical and electronic layout of the apparatus is shown in Fig. 1 . A detailed account of this apparatus and its performance is given elsewhere.13 Briefly, it consisted of (1) a pulsed nitrogen laser (Lasertechnik Berlin, Berlin, Model MNL200-ATM205, λ=337nm , pulse width=700ps FWHM), which was used as the excitation source; (2) a custom-made sterilizable bifurcated fiber-optic probe (CeramOptec, East Longmeadow, Massachusetts); (3) an imaging spectrometer/monochromator (Chromex, Inc., Albuquerque, New Mexico, Model 250is/sm, F/4.4, 600grmm grating, blazed at 450nm ); (4) a gated multichannel plate photomultiplier tube (MCP-PMT; Hamamatsu, Bridgewater, New Jersey, Model R5916-50, rise time=180ps ) with a fast preamplifier (Hamamatsu, Bridgewater, New Jersey, Model C5594, 1.5GHz ); (5) a digital phosphor oscilloscope (TDS5104, Tektronix, Beaverton, Oregon, 5Gigasampless ); and (6) a computer workstation and peripheral electronics. The instrument allowed for mobility, as it was contained in a standard endoscopic cart (70×70×150cm3) , internally modified to accommodate the individual devices.

Fig. 1

Schematic of the pulse-sampling TR-LIFS apparatus, including a fast digitizer and gated detection used for the in vivo evaluation of brain tumor time-resolved fluorescence emission. Photo shows the tip of the optical probe used on the brain tissue (cortex of a patient undergoing brain tumor surgery).



Delivery catheter

Light delivery and collection were implemented with a custom-made bifurcated sterilizable probe. The probe consisted of nonsolarizing silica/silica step index fibers of 0.11 numerical aperture (NA; CeramOptec, East Longmeadow, Massachusetts). It had a central excitation fiber of 600-μm core diameter, surrounded by a collection ring of twelve 200-μm -core-diameter fibers. The collection fibers were beveled at a 10-deg angle in order to improve excitation/collection overlap for small tissue-to-probe distances. The center-to-center separation between the excitation and collection fibers was 480μm . The probe was flexible throughout its entire length (3m) except for a 7-cm distal part consisting of a rigid stainless steel tube. This facilitated the mounting and micromanipulation of the probe. The laser light was coupled into the illumination channel of the probe with a standard subminiature version A (SMA) connector, while the distal end of the collection channel was formed into a straight line in order to facilitate coupling to the spectrograph. After sample excitation, the emitted fluorescence light was collected and directed into the entrance slit of the spectrometer via the collection channel of the probe. A long-pass filter (WB360, Optima, Tokyo, λ=345nm ) was placed before the entrance slit of the monochromator to prevent contamination of the fluorescence signal by the excitation light. The signal was then detected by the MCP-PMT, amplified by the fast preamplifier, and finally digitized at 8bits resolution by the digital oscilloscope. The overall time resolution of the systems was approximately 300ps .


Experimental Procedure

The fiber-optic probe was positioned at 3mm above the exposed brain tissue specimen by the Greenburg retractor to optimize the probe light collection efficiency as previously reported14 and to steady the probe over the tissue. Time-resolved emission of each sample was recorded in the 370-to550-nm spectral range and scanned at 5-nm intervals (Fig. 2 ). The total amount of time required to obtain a complete time-resolved spectra by scanning through all the wavelengths is approximately 30s . Five consecutive measurements of the fluorescence pulse emission at 390 and 460nm were performed to assess the reproducibility of the fluorescence lifetime measurement. The energy output of the laser (at the tip of the fiber) for sample excitation was adjusted to 3.0μJ /pulse. The area illuminated by the probe (defined by the NA of the excitation fiber and the probe-to-tissue distance) was approximately 2mm2 ; thus, the total fluence per pulse at the tissue level was approximately 1.4μJmm2 , which is very well within the safety limits. After the spectroscopic analysis, the tissue was biopsied at the exact site and sent for pathological investigation.

Fig. 2

Typical fluorescence impulse response function (FIRF) from (a) normal cortex and (b) low-grade glioma after deconvolving the laser signal using the Laguerre deconvolution technique.



Histopathological Analysis

Except for the normal cortex (NC), tissue was biopsied from the areas where fluorescence was recorded. Each biopsy sample was fixed in 10% buffered formalin. The tissue samples were then cut and fixed on the slides; the slides were stained by the standard hematoxylin & eosin (H&E) stain. All slides were studied by the pathologist and correlated with original fluorescence spectroscopy measurements results. Histologically, gliomas are categorized in low-grade (grade I), intermediate-grades (grades II and III), and high-grade gliomas or glioblastoma multiforme (grade IV), which is based on the WHO grading (Kernohan and Sayred).15 In the current study, however, we grouped the gliomas in two sets: low-grade glioma (LGG), including grades I and II, and high-grade glioma (HGG), including grades III and IV.


TR-LIFS Data Analysis

In the context of TR-LIFS, the intrinsic fluorescence impulse response functions (FIRFs), h(n) , describe the real dynamics of the fluorescence decay. The IRFs were recovered by numerical deconvolution of the measured input laser pulse from the measured fluorescence response pulse transients. The Laguerre expansion of kernels technique.16 was used for deconvolution. This method allows a direct recovery of the intrinsic properties of a dynamic system from the experimental input-output data. The technique uses the orthonormal Laguerre functions bja(n) to expand the IRFs and to estimate the Laguerre expansion coefficients (LECs) cf . Once the fluorescence IRFs were estimated for each emission wavelength, the steady-state spectrum (I2) was computed by integrating each intensity decay curve as a function of time. Further, to characterize the temporal dynamics of the fluorescence decay, two sets of parameters were used: (1) the average lifetime (τf) computed as the interpolated time at which the IRF decays to 1/e of its maximum value; and (2) the normalized value of the corresponding LECs. Thus, a complete description of the fluorescence emission from each sample as a function of emission wavelength, λE , was given by the variation of a set of spectroscopic parameters ( Iλ , τf , and LECs). This analytical approach for characterization of fluorescence decay was recently developed by our research group, and its advantages over other deconvolution methods are described in detail elsewhere.16, 17


Statistical Analysis

Parameters derived from the time-resolved fluorescence spectrum were used for the discrimination of distinct types of tissue. These parameters were based on the discrete fluorescence intensity (Iλ) , average lifetime (τλ) , and Laguerre coefficient (LECλ) values at all recorded wavelengths. A univariate statistical analysis (one-way ANOVA) was used to identify a set of spectroscopic parameters that best discriminate between distinct tissue types as defined by the histopathologic analysis. This was achieved by comparing time-resolved derived parameters ( τf and LECs) at every λE and intensity ratios (Ir) at discrete wavelengths. A P -value of <0.05 was assumed to indicate statistically significant differences. A systematic comparison of the P -values obtained for each parameter at every λE allowed us to identify a set of spectroscopic parameters ( Ir , τf and LECs at specific λEs yielding the lowest P -values) likely to provide means of discrimination among different compositional features of the brain tissue. All statistical analysis was conducted using SPSS (SPSS, Inc., Chicago, Illinois).





The histological examination revealed the presence of the following tissue types: NC=normal cortex (N=16) ; NWM=normal white matter (N=3) ; LGG=low -grade glioma with WHO grade I or II (N=5) ; and HGG=high -grade glioma with WHO grade III or IV (N=9) . In addition, the histological analysis revealed that the cells underlying the areas of laser excitation were not visibly altered post-excitation and had a well-preserved morphology.


Spectroscopic Features

Figure 2 shows the typical fluorescence response IRF from which the fluorescence emission spectra and the average lifetime were recovered. The NC (Fig. 3 ) autofluorescence was characterized by a broad emission spectrum with two well-defined peaks, one within the 440-to460-nm wavelength range, and the other at shorter emission wavelengths at approximately 390nm . The former peak is consistent with the fluorescence emission of enzyme cofactor nicotinamide adenine dinucleotide (NADH). Attenuation of fluorescence intensity was observed at about 415nm that corresponds to the hemoglobin absorption band. The lifetime (τλ) or the intensity decay characteristics (Fig. 3) in the normal cortex showed that the emission was significantly (p<0.001) longer lasting at blue-shifted (τ390=1.8±0.3ns) when compared with the red-shifted (τ460=0.8±0.1ns) wavelengths. The lifetimes observed at 460nm are consistent also with the NADH lifetime reported in the literature.4, 18, 19 The NWM was characterized by a similar broad fluorescence emission spectrum (Fig. 3) with two well-defined peaks around 390 and 440nm , the latter corresponding to emission enzyme cofactor NADH, respectively. The attenuation of fluorescence intensity observed at about 415nm corresponds to the hemoglobin absorption band.10 The average fluorescence lifetime (Fig. 3) showed similar characteristics as NC with longer lasting emission values in the blue-shifted (τ390=1.9±0.5ns) when compared with the red-shifted (τ460=1.0±0.04ns) wavelengths (p=0.02) . The fluorescence emission spectra of LGG samples showed a relatively narrow broadband emission characterized by a single well-defined peak at 460-nm wavelength [Fig. 3] lasting for less than 1ns . Across the entire spectrum, the fluorescence emission decay was short-lasting, with an average lifetime mainly at about 1.1ns . As clearly observed, the LGG shows a very distinct average lifetime when compared with NC (p=0.0009) , NWM (p=0.015) , and HGG (p=0.005) at 390nm . HGG demonstrated similar fluorescence emission spectrum to NC with a well-defined peak between 440-to460-nm wavelengths, which is consistent4, 18, 20 with emission of enzyme cofactor NADH, and a second smaller peak at 390nm . Similar attenuation of fluorescence intensity at about 415nm that corresponds to the hemoglobin absorption band was also observed. The fluorescence emission closely followed the trend observed in normal cortex τ390 at 1.7±0.4ns and τ460 at 1.0nm±0.9ns (p<.001) .

Fig. 3

Fluorescence emission spectra (top), average lifetimes (middle), and Laguerre coefficients (LEC-1) (bottom) across the emission wavelengths for distinct brain tissue types. (a) Normal cortex (NC), normal white matter (NWM), and high-grade glioma (HGG). (b) Normal cortex, normal white matter and low-grade glioma (LGG). Note: The average lifetimes of the LGG is relatively constant across the wavelength compared to the average lifetimes of NC, NWM, and HGG.



Statistical Analysis

The results of the ANOVA demonstrated that the following parameters were within the statistical significance ( p -value <0.01 ): I390I370 , I440I370 , I460I370 , I390I500 , I390I440 , I390I460 , τ390 , τ440 , τ460 , LEC- 1390 , LEC- 1440 , LEC- 1460 , LEC- 2390 , LEC- 2440 , LEC- 2460 , and LEC- 3390 . This set of parameters including Ir , τf , LECs were found able to discriminate between the four type of tissues of interest (LGG, HGG, NC, and NWM). Figure 4 demonstrates the mean with the dispersion and skewness. Spectroscopic parameters derived from both spectral domain (Ir) and time domain ( τf , LECs) were able to distinguish LGG from healthy brain tissue (NC, NWM). These parameters were generally characterized by a narrow distribution around the mean value. In contrast, the parameters derived for HGG exhibited a broad distribution. A few parameters were able to distinguish HGG tissue from NWM, but not from NC. Overall, based on this analysis of variance test, we determined that most of the time-resolved fluorescence parameters that help in distinguishing between the four tissue types were derived from three wavelengths: 390nm , 440nm , and 460nm . In addition, intensity ratios including intensity values derived from the main peak emissions ( 390nm and 440to460nm ) are important for tissue discrimination.

Fig. 4

Representative examples of spectroscopic parameters (mean±SE) that allows for the differentiation of distinct tissue types (a) Intensity ratio 390nm versus 440nm ; (b) intensity ratio 390 versus 460; (c) average lifetime at 390nm ; and (d) Laguerre coefficients at 390nm . ( NC=normal cortex; NWM=normal white matter; HGG=high -grade glioma; LGG=low -Grade glioma.) Note: LGG demonstrates low variance as compared with high-grade glioma and can be readily distinguished from normal brain tissue. On each box, the central mark is the median, the edges of the box are the 25th and 75th percentiles, the whiskers extend to the most extreme data points not considered outliers, and outliers are plotted individually.




By examining the ex vivo brain tumor tissue using TR-LIFS, we demonstrated previously6, 12 that this technique has the potential to characterize and diagnose brain tumors. Extending on these early results, in this study, we examined for the first time in vivo the fluorescence emission of low- and high-grade gliomas and compare this with that of normal brain tissue. We demonstrated that TR-LIFS could be used to distinguish between the glioma tissue versus NWM and NC based on specific spectral and temporal fluorescence characteristics.


TR-LIFS Features in the Characterization of Brain Tumors versus Normal Brain Tissue

We determined that when the tissue is excited using ultraviolet light (e.g., 337nm ), the emission from two wavelength ranges appeared important for diagnosing gliomas from normal brain tissue: 380to400nm and 440to470nm (the region of main peak emission for NC and LGG and HGG). Striking and distinct fluorescence parameters were observed in LGG when compared with the NC or NWM. One important observation was that LGG shows mainly a single peak of fluorescence emission at 460nm of wavelength, whereas the NC and NWM demonstrate two fluorescence emission peaks centered at 390nm and 460nm . This is interpreted as the presence of a single fluorophore in LGG. This is further reinforced by inspecting the fluorescence lifetimes, which clearly show fast-intensity decay and nearly constant average lifetime values across the fluorescence spectrum. This particular emission with peak at 460nm and lifetime of around 0.8to1.0ns is attributed to NADH, which demonstrates spectral10 and temporal18, 19, 21 characteristics as described in previous studies (Table 1 ).

Table 1

Summary of fluorescence properties listing all the fluorophores significant in the brain.

Pyridoxaminephosphate3303900.110.18Liver, brain,kidneyRef. 23
Pyridoxalphosphate330430NA0.03Liver, brain,kidneyRef. 23
NADH3404600.4–1.21 0.40.69±0.02 All cellsRefs. 4, 18, 19
FAD/FMN 370440 510–5206–10NABrain, liverRef. 23
GAD335380NANABrainRef. 27

In contrast to these findings for LGG, normal tissue (i.e., NC and NWM) and HGG demonstrated additional fluorescence emission peak and longer fluorescence lifetimes at 390nm (1.7to1.8ns) . This longer lasting emission at blue-shifted wavelengths when compared to that at red-shifted emission wavelengths suggests that this additional emission peak originates from a distinct fluorophore, other than NADH. While relatively scarce information on the origin of the fluorescence emission of brain tissue at 390nm on UV (337nm) has been reported, we identified two possible fluorophores known to emit fluorescence within this wavelength band: Pyridoxamine-5-phosphate (PMP) and glutamate decarboxylase (GAD).22 Both of these fluorophores are metabolically affected in gliomas and are briefly discussed in the following.

Pyridoxine or vitamin B6 is required by the body to convert carbohydrates to glucose. In addition, the function of pyridoxine is the formation of RNA and DNA, and it plays a role in the removal of the toxic chemical homocysteine. Pyridoxamine phosphate (PMP) along with Pyridoxine phosphate (PN) is converted by pyridoxine- 5 -phosphate oxidase (PNPO) into pyridoxal phosphate (PLP). PMP fluoresces with peak emission at 390nm , along with PLP, which fluoresces with emission peak between 315 and 340nm (Ref. 23). But in hydrated form at neutral pH, it shows similar fluorescence characteristics as PMP. The PLP mechanism is known to be disturbed in cancers.24, 25, 26 Our TR-LIFS studies of gliomas and normal brain tissue show that there is a decrease/absence of the emission peak at 390nm of wavelength in glioma when compared with normal cortex tissue. We hypothesize that this may be due to altered metabolism in the PLP mechanism that leads to decrease in the PMP levels in the brain.

The second fluorophore possibly responsible for changes observed at emission peak at 390nm is glutamate decarboxylase, which is an enzyme essential in the formation of neurotransmitter GABA ( γ -amino butyric acid) from amino acid L-glutamate in a single step. Glutamate decarboxylase shows fluorescence emission maxima at 380nm of wavelength when excited at 335nm at neutral pH.27 GABA is a most important inhibitory neurotransmitter found in abundance in the brain.28 The absorption maxima of GAD are 330nm and 420nm . GABAA-benzodiazepine receptors are present in 13 of brain, and the α1 subunit of a GABAA receptor is expressed throughout the brain.29 Most of the GAD is present in the neurons at the synapses. It is known that the activity of this enzyme increases with the differentiation of the neuroblastoma cells. It has been shown that activities of GAD are much lower in the C6 glioblastoma and C1300 neuroblastoma cells than in normal mouse cerebral cortex.30 C6 glioma cells may represent a neuroglial precursor at an early developmental.31 Moreover, it is known that malignant glioma cells in rats actively secrete glutamate in vitro and that the extracellular levels of glutamate have been shown increased both in and around glioma implants in vivo.32 This may suggest an absence of glutamate decarboxylase in its active form in neoplastic tissue.

The ANOVA test performed on various TR-LIFS-derived parameters demonstrated that a small subset of spectral and time-resolved parameters (total: 16) retrieved primarily from a few wavelengths (390, 440, and 460nm ) provide means of discrimination of distinct brain tissue types. This finding is important, as it allows for future design of TR-LIFS devices that collect fluorescence decay transients at a limited number of spectral bands, as recently reported.33 In turn, such design will allow for fast data acquisition time (less than 1s for each point measurement). HGG samples demonstrated a high degree of variability of both fluorescence emission intensity and average lifetime at 390nm . This variability may be due to a high degree of variation in the genetic as well as metabolic makeup of HGGs. This finding suggests that future studies focused on HGG have to also evaluate the changes in fluorescence parameters as a function of tumor heterogeneity.

While the in vivo measurements of fluorescence intensity data are often affected by various factors such as the presence of endogenous absorbers (e.g., blood hemoglobin), variation in fiber-optic probe excitation collection-collection geometry (e.g., moving artifacts due to arterial pulsation or breathing) and photobleaching, we determined that the use of time-resolved (lifetime) information has the potential to account for factors that nonlinearly affect the fluorescence measurement. Figure 5 , for example, demonstrates that while in some cases the emission spectrum was significantly altered by breathing artifacts and blood absorption, the average fluorescence lifetime is virtually unaffected. This underscores the importance of using fluorescence decay information as an additional means for tissue discrimination.

Fig. 5

Example of the fluorescence emission spectrum (intensity values at distinct wavelengths) modulated by the movement of the brain due to breathing. The changes in intensity are due to changes in the excitation-collection during the approximately 30-s data recording (wavelength scanning) time. The figure depicts the variability in fluorescence emission spectrum from NC tissue due to movement of the brain (top). Note: The average lifetime (bottom) from the same tissue samples is not affected by such movements.



Ex Vivo versus In Vivo TR-LIFS Measurements

Current results demonstrate that the brain tumor fluorescence signatures obtained in vivo (in patients undergoing brain tumor surgery) are distinct from those we previously reported for ex vivo measurements (brain tumor specimens).6, 11, 12, 34 When the fluorescence emission from the ex vivo NC tissue was compared with the in vivo samples, we noticed that the ex vivo samples show a single peak of emission. This suggests that the additional fluorophore emitting at the 390-nm wavelength band is absent in the ex vivo samples. The peak emission of NADH also appeared blue-shifted to 440nm . The difference in the emission characteristics becomes even more evident when the average lifetime at 390nm and 460nm are taken into consideration. While the tissue fluorescence measured in ex vivo samples demonstrated a uniform average lifetime value across the emission spectrum, the fluorescence measurements in the in vivo samples showed a decrease (approximate 2.0ns at 390nm versus 1.0ns at 460nm ) of lifetime values as a function of emission wavelength. The emission spectrum of ex vivo NWM showed similar trends (two distinct emission peaks at 390nm and 460nm ) to the in vivo samples. However, the lifetime information at these peaks was found to have slightly longer lifetime at 460nm in ex vivo tissue (1.8ns) when compared with the in vivo samples (1ns) . The lifetime at 390nm is similar in both ex vivo and in vivo samples of normal white matter (2.0ns) . LGG fluorescence emission (Fig. 6 ) in both ex vivo and in vivo tissue is characterized by a single peak at 460nm and a uniform lifetime along the emission wavelengths.

Fig. 6

Group values (mean±SE) of a set of spectroscopic parameters derived from ex vivo and in vivo TR-LIFS measurements: normalized emission spectrum (top) and average lifetime along the emission wavelengths (bottom) of normal cortex, (a) Normal cortex; (b) low-grade glioma.


While the exact cause of differences in the brain tissue fluorescence emission ex vivo versus in vivo is still to be determined, we hypothesize that such changes are determined by difference in the environment and/or metabolic changes that may occur after tissue excision. These differences, however, are expected to provide additional insight for future studies targeted at understanding the origin of fluorescence emission from brain tissue.



This study has demonstrated for the first time the ability of a time-resolved fluorescence spectroscopy technique to characterize in vivo the biochemical features of primary brain tumors that are clinically relevant. Although this is a pilot study conducted on a small number of patients, we have shown that normal brain tissue (NC, NWM) and LGG are well-characterized by distinct fluorescence emission features. Fluorescence temporal features including average lifetime and Laguerre coefficients derived from three spectral emission bands centered at 390, 440, and 460nm can be used to distinguish LGG from normal brain tissue. Thus, future optimization of TR-LIFS data acquisition at these predefined wavelength bands33 centered around these wavelengths combined with the fast analysis of the fluorescence intensity decay facilitated by the Laguerre expansion of kernels deconvolution approach,16, 17 will enable near-real time (less than 1s ) tissue diagnosis. We also determined that while fluorescence of LGG is distinct and can be used to clearly delineate from normal tissue, HGG fluorescence characteristics have a large variability, which appears to be correlated with heterogeneous composition of high-grade glioma, a factor that is not taken into account in the conventional gross histopathological diagnosis or classification of the brain biopsy sample. We also observed differences in the emission characteristics between the ex vivo and in vivo tissues that indicate a possible change in the biochemical composition of tissue after excision. This finding suggests that studies conducted in brain tissue specimens cannot be fully translated to the in vivo situation. In turn, this emphasizes the need for further validation of fluorescence-based diagnostic devices directly in vivo in a large number of patients and development of optimized TR-LIFS devices that allow for clinical investigations. Current study has provided the base of knowledge that will enable further development of such TR-LIFS devices.


This work was supported in part by the Whitaker Foundation (RG-01-0346, PI: L. Marcu) and the Maxine-Dunitz Neurosurgical Institute at Cedars-Sinai Medical Center and by the WISE Fund at the University of Southern California.



A. A. Brandes, A. Tosoni, E. Franceschi, M. Reni, G. Gatta, and C. Vecht, “Glioblastoma in adults,” Crit. Rev. Oncol. Hematol., 67 (2), 139 –152 (2008). 1040-8428 Google Scholar


J. T. Fazekas, “Treatment of grades I and II brain astrocytomas. The role of radiotherapy,” Int. J. Radiat. Oncol., Biol., Phys., 2 (7–8), 661 –666 (1977). 0360-3016 Google Scholar


S. A. Leibel, G. E. Sheline, W. M. Wara, E. B. Boldrey, and S. L. Nielsen, “The role of radiation therapy in the treatment of astrocytomas,” Cancer, 35 (6), 1551 –1557 (1975).<1551::AID-CNCR2820350612>3.0.CO;2-V 0008-543X Google Scholar


Y. G. Chung, J. A. Schwartz, C. M. Gardner, R. E. Sawaya, and S. L. Jacques, “Diagnostic potential of laser-induced autofluorescence emission in brain tissue,” J. Korean Med. Sci., 12 (2), 135 –142 (1997). 1011-8934 Google Scholar


A. C. Croce, S. Fiorani, D. Locatelli, R. Nano, M. Ceroni, F. Tancioni, E. Giombelli, E. Benericetti, and G. Bottiroli, “Diagnostic potential of autofluorescence for an assisted intraoperative delineation of glioblastoma resection margins,” Photochem. Photobiol., 77 (3), 309 –318 (2003).<0309:DPOAFA>2.0.CO;2 0031-8655 Google Scholar


L. Marcu, J. A. Jo, P. V. Butte, W. H. Yong, B. K. Pikul, K. L. Black, and R. C. Thompson, “Fluorescence lifetime spectroscopy of glioblastoma multiforme,” Photochem. Photobiol., 80 98 –103 (2004). 0031-8655 Google Scholar


W. S. Poon, K. T. Schomacker, T. F. Deutsch, and R. L. Martuza, “Laser-induced fluorescence: experimental intraoperative delineation of tumor resection margins,” J. Neurosurg., 76 (4), 679 –686 (1992). 0022-3085 Google Scholar


G. A. Wagnieres, W. M. Star, and B. C. Wilson, “In vivo fluorescence spectroscopy and imaging for oncological applications,” Photochem. Photobiol., 68 (5), 603 –632 (1998). 0031-8655 Google Scholar


W. C. Lin, S. A. Toms, M. Johnson, E. D. Jansen, and A. Mahadevan-Jansen, “In vivo brain tumor demarcation using optical spectroscopy,” Photochem. Photobiol., 73 (4), 396 –402 (2001).<0396:IVBTDU>2.0.CO;2 0031-8655 Google Scholar


W. C. Lin, S. A. Toms, M. Motamedi, E. D. Jansen, and A. Mahadevan-Jansen, “Brain tumor demarcation using optical spectroscopy: an in vitro study,” J. Biomed. Opt., 5 (2), 214 –220 (2000). 1083-3668 Google Scholar


P. V. Butte, B. K. Pikul, A. Hever, W. H. Yong, K. L. Black, and L. Marcu, “Diagnosis of meningioma by time-resolved fluorescence spectroscopy,” J. Biomed. Opt., 10 (6), 064026 (2005). 1083-3668 Google Scholar


W. H. Yong, P. V. Butte, B. K. Pikul, J. A. Jo, Q. Fang, T. Papaioannou, K. Black, and L. Marcu, “Distinction of brain tissue, low grade, and high grade glioma with time-resolved fluorescence spectroscopy,” Front. Biosci., 11 1255 –1263 (2006). 1093-4715 Google Scholar


Q. Fang, T. Papaioannou, J. A. Jo, K. Vaitha, and L. Marcu, “Time-domain laser-induced fluorescence spectroscopy apparatus for clinical diagnostics,” Rev. Sci. Instrum., 75 (1), 151 –162 (2004). 0034-6748 Google Scholar


T. Papaioannou, N. Preyer, Q. Fang, M. Carnohan, R. Ross, A. Brightwell, G. Cottone, L. Jones, and L. Marcu, “Effects of fiber-optic probe design and probe-to-target distance on diffuse reflectance measurements of turbidmedia: an experimental and computational study at 337nm,” Appl. Opt., 43 (14), 2846 –2860 (2004). 0003-6935 Google Scholar


D. N. Louis, H. Ohgaki, O. D. Wiestler W. K. Cavenee, P. C. Burger, A. Jouvet, B. W. Scheithauer, and P. Kleihues, “The 2007 WHO classification of tumors of the central nervous system,” Acta Neuropathol., 114 (2), 97 –109 (2007). 0001-6322 Google Scholar


J. A. Jo, Q. Fang, T. Papaioannou, and L. Marcu, “Fast model-free deconvolution of fluorescence decay for analysis of biological systems,” J. Biomed. Opt., 9 (4), 743 –752 (2007). 1083-3668 Google Scholar


J. A. Jo, Q. Fang, T. Papaioannou, J. H. Qiao, M. C. Fishbein, B. Beseth, A. H. Dorafshar, T. Reil, D. Baker, J. Freischlag, and L. Marcu, “Application of the Laguerre deconvolution method for time-resolved fluorescence spectroscopy to the characterization of atherosclerotic plaques,” 6559 –6562 (2005). Google Scholar


A. Pradhan, P. Pal, G. Durocher, L. Villeneuve, A. Balassy, F. Babai, L. Gaboury, and L. Blanchard, “Steady state and time-resolved fluorescence properties of metastatic and non-metastatic malignant cells from different species,” J. Photochem. Photobiol., B, 31 (3), 101 –112 (1995). 1011-1344 Google Scholar


K. C. Reinert, R. L. Dunbar, W. Gao, G. Chen, and T. J. Ebner, “Flavoprotein autofluorescence imaging of neuronal activation in the cerebellar cortex in vivo,” J. Neurophysiol., 92 (1), 199 –211 (2004). 0022-3077 Google Scholar


D. M. Jameson, V. Thomas, and D. M. Zhou, “Time-resolved fluorescence studies on NADH bound to mitochondrial malate dehydrogenase,” Biochim. Biophys. Acta, 994 (2), 187 –190 (1989). 0006-3002 Google Scholar


K. Konig, M. W. Berns, and B. J. Tromberg, “Time-resolved and steady-state fluorescence measurements of beta-nicotinamide adenine dinucleotide-alcohol dehydrogenase complex during UVA exposure,” J. Photochem. Photobiol., B, 37 (1–2), 91 –95 (1997). 1011-1344 Google Scholar


R. Shukuya and G. W. Schwert, “Glutamic acid decarboxylase. II. The spectrum of the enzyme,” J. Biol. Chem., 235 1653 –1657 (1960). 0021-9258 Google Scholar


W. R. Zipfel, R. M. Williams, R. Christie, A. Y. Nikitin, B. T. Hyman, and W. W. Webb, “Live tissue intrinsic emission microscopy using multiphoton-excited native fluorescence and second harmonic generation,” Proc. Natl. Acad. Sci. U.S.A., 100 (12), 7075 –7080 (2003). 0027-8424 Google Scholar


E. O. Ngo, G. R. LePage, J. W. Thanassi, N. Meisler, and L. M. Nutter, “Absence of pyridoxine-5-phosphate oxidase (PNPO) activity in neoplastic cells: isolation, characterization, and expression of PNPO cDNA,” Biochemistry, 37 (21), 7741 –7748 (1998). 0006-2960 Google Scholar


S. K. Sharma and K. Dakshinamurti, “Determination of vitamin B6 vitamers and pyridoxic acid in biological samples,” J. Chromatogr., 578 (1), 45 –51 (1992). 0021-9673 Google Scholar


K. O. Honikel and N. B. Madsen, “Comparison of the absorbance spectra and fluorescence behavior of phosphorylase b with that of model pyridoxal phosphate derivatives in various solvents,” J. Biol. Chem., 247 (4), 1057 –1064 (1972). 0021-9258 Google Scholar


S. Udenfriend, Fluorescence Assay in Biology and Medicine, I Academic Press, New York (1969). Google Scholar


P. Flace, V. Benagiano, L. Lorusso, F. Girolamo, A. Rizzi, D. Virgintino, L. Roncali, and G. Ambrosi, “Glutamic acid decarboxylase immunoreactive large neuron types in the granular layer of the human cerebellar cortex,” Anat. Embryol. (Berl), 208 (1), 55 –64 (2004). 0340-2061 Google Scholar


H. K. Wolf, R. Buslei, I. Blumcke, O. D. Wiestler, and T. Pietsch, “Neural antigens in oligodendrogliomas and dysembryoplastic neuroepithelial tumors,” Acta Neuropathol., 94 (5), 436 –443 (1997). Google Scholar


L. Ossola, M. Maitre, J. M. Blindermann, and P. Mandel, “Enzymes of GABA metabolism in tissue culture,” Adv. Exp. Med. Biol., 123 139 –157 (1979). 0065-2598 Google Scholar


J. Segovia, G. M. Lawless, N. J. Tillakaratne, M. Brenner, and A. J. Tobin, “Cyclic AMP decreases the expression of a neuronal marker (GAD67) and increases the expression of an astroglial marker (GFAP) in C6 cells,” J. Neurochem., 63 (4), 1218 –1225 (1994). 0022-3042 Google Scholar


T. Takano, J. H. Lin, G. Arcuino, Q. Gao, J. Yang, and M. Nedergaard, “Glutamate release promotes growth of malignant gliomas,” Nat. Med., 7 (9), 1010 –1015 (2001). 1078-8956 Google Scholar


Y. Sun, R. Liu, D. S. Elson, C. W. Hollars, J. A. Jo, J. Park, and L. Marcu, “Simultaneous time- and wavelength-resolved fluorescence spectroscopy for near real-time tissue diagnosis,” Opt. Lett., 33 (6), 630 –632 (2008). 0146-9592 Google Scholar


L. Marcu, M. C. Fishbein, J. M. Maarek, and W. S. Grundfest, “Discrimination of human coronary artery atherosclerotic lipid-rich lesions by time-resolved laser-induced fluorescence spectroscopy,” Arterioscler., Thromb., Vasc. Biol., 21 (7), 1244 –1250 (2001). 1079-5642 Google Scholar
©(2010) Society of Photo-Optical Instrumentation Engineers (SPIE)
Pramod V. Butte, Fang Qiyin M.D., Jo A. Javier, William H. Yong, Brian K. Pikul, Keith L. Black M.D., and Laura Marcu "Intraoperative delineation of primary brain tumors using time-resolved fluorescence spectroscopy," Journal of Biomedical Optics 15(2), 027008 (1 March 2010).
Published: 1 March 2010 Logo
Cited by 73 scholarly publications and 3 patents.
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In vivo imaging

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