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13 July 2018 Determination of optical properties of human brain tumor tissues from 350 to 1000 nm to investigate the cause of false negatives in fluorescence-guided resection with 5-aminolevulinic acid
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Abstract
The optical properties of human brain tumor tissues, including glioblastoma, meningioma, oligodendroglioma, and metastasis, that were classified into “strong,” “vague,” and “unobservable” fluorescence by a neurosurgeon were measured and compared. The optical properties of the tissues were measured with a double integrating sphere and the inverse Monte Carlo technique from 350 to 1000 nm. Using reasons of ex-vivo measurement, the optical properties at around 420 nm were potentially affected by the hemoglobin content in tissues. Significant differences were not observed between the optical properties of the glioblastoma regions with “strong” and “unobservable” fluorescence. Sections of human brain tumor tissue with “strong” and “unobservable” fluorescence were stained with hematoxylin and eosin. The cell densities [mean ± standard deviation (S.D.)] in regions with “strong” and “unobservable” fluorescence were 31  ±  9  ×  102  per mm2 and 12  ±  4  ×  102  per mm2, respectively, which is a statistically significant difference. The higher fluorescence intensity is associated with higher cell density. The difference in cell density modified the scattering coefficient yet it does not lead to significant differences in the reduced scattering coefficient and thus does not affect the propagation of the diffuse fluorescent light. Hence, the false negatives, which mean a brain tumor only shows “unobservable” fluorescence and is hence classified incorrectly as nontumor, in using 5-ALA for detection of human glioblastoma do not result from the differences in optical properties of human brain glioblastoma tissues. Our results suggest that the primary cause of false negatives may be a lack of PpIX or a low accumulation of PpIX.

1.

Introduction

In 1998, the fluorescence-guided resection of malignant gliomas using 5-aminolevulinic acid (5-ALA) was reported by Stummer et al. in Germany.1 In 2006, Stummer et al. reported that fluorescence-guided resection using 5-ALA not only led to an increase in the complete resection rate but also improved progression-free survival.2 In addition, the median overall from the time of surgery in patients without residual tumors as a result of complete resection achieved by fluorescence-guided resection with 5-ALA was longer than those in patients with residual contrast-enhancing tumor material.3 It was reported that even small volumes of residual tumor [radiation therapy oncology group recursive partitioning analysis (RTOG-RPA) classes III–V based on age] was associated with a worse prognosis compared with there being no visible and residual contrast-enhancing tumor.4 Differences in survival depending on resection status, especially in RPA classes IV and V, support a causal influence of resection on survival. However, fluorescence diagnosis with 5-ALA is very cost effective, whereas there have been problems with false negatives in fluorescence diagnosis; a false negative means that a brain tumor has “unobservable” fluorescence and is hence classified incorrectly as a nontumor.1

Fluorescence intensity from PpIX observed through the surgical microscope has been graded according to intensity in two or three stages.5 On irradiation of violet–blue light to a brain tumor that included PpIX, necrotic tumors usually displayed no or only inhomogeneous red fluorescence. In addition, joining, perfused, and viable tumor tissue was distinguished by its deep red fluorescence, which we call “strong” in this report, whereas normal brain tissue was colored blue. Usually, a transition zone with clearly discernible, but lighter, pink fluorescence was encountered between solidly fluorescing tumor and nonfluorescing blue brain tissue.5 We term this fluorescence impression “vague.” “Strong” fluorescence from the region indicates that the region is highly likely to be tumor. In a primary malignant glioblastoma, the tumor probability (positive predictive value) for tumor tissue that shows strong fluorescence is 100%.1 Moreover, in the detection of primary malignant glioma tissue using 5-ALA, sensitivity and specificity were 85% and 100%, respectively.1 However, while diagnosis with 5-ALA by fluorescence provided an excellent ability to predict the presence of abnormal and tumor tissue, the mechanism of selective accumulation of PpIX in tumor tissue has not yet been understood. Pathological findings have revealed that strong correlation exists between tumor aggressiveness and corresponding intraoperative fluorescence.6 A previous study has indicated that solidly proliferating, central, viable tumors with vascular proliferation are associated with intense red fluorescence, and surrounding infiltrating tumor with moderate to high cell density is associated with pink fluorescence.5 Histologically, solidly fluorescing tissue generally consisted of coalescent tumor cells, whereas vaguely fluorescing tissue usually represented an infiltrating tumor of intermediate or low cellular density.5 Their analysis of tumor biopsy specimens revealed infiltrating tumor also to be present beyond vaguely fluorescing tissue portions. Takahashi et al. reported that the high mRNA level of CPOX expression was significantly correlated with the phenotype of strong 5-ALA-induced fluorescence.7 Elucidation of the cause of false negatives may ameliorate the sensitivity in fluorescence-guided resection for glioblastoma with 5-ALA.

To the best of our knowledge, no reports exist concerning whether the optical properties of false-negative brain tumor tissue differ from those of brain tumor tissue with strong fluorescence, when measured with a double integrating sphere and inverse Monte Carlo methods. If the cell density in the tumor tissue that contains the PpIX increases, the fluorescence intensity of the tumor tissue increases. The purpose of this study is to investigate the effect of optical properties of human brain tumor tissues on the fluorescence intensity. In addition, the cell density of the resected tumor tissue and the tissue that was classified by a neurosurgeon according to observable fluorescence are compared.

2.

Materials and Methods

2.1.

Sample

The specimens were tumor and normal human brain tissues obtained from brain tumor patients undergoing fluorescence-guided resection with 5-ALA at the Department of Neurosurgery, Osaka Medical College. A study protocol approved by the Institutional Review Board of Osaka Medical College was followed and a consent form was signed by each participating patient before the surgery.

2.1.1.

Samples for optical property measurements

Fresh samples were delivered within 1 to 2 h after their acquisition from the operation room. Measurements of diffuse reflectance and total transmittance were conducted within 3 to 12 h after resection. Samples were stored at low temperature (1°C to 4°C) in saline solution until sample preparation for spectroscopic measurement. A total of 32 samples of human brain tumor tissues that were classified by histological type by a pathologist were investigated: glioblastoma (n=14), meningioma (n=9), oligodendroglioma (n=3), and metastasis (n=6). These tumor tissues were classified into three groups according to their fluorescence, “strong” (n=15), “vague” (n=6), and “unobservable” (n=11) by a neurosurgeon. (Fluorescence intensity was also measured using a spectral radiance measuring system. The excitation light from a 405-nm LED was irradiated to samples at an irradiance of 10  mW/cm2. The measurement distance was 70 mm and the diameter of the spot size on the sample was 3 mm.8) In addition, four samples of normal human brain tissue [white matter (n=2) and gray matter (n=2)] were investigated. The materials were stored at low temperature in saline solution until spectroscopic investigation. The sample size is the number of individuals.

2.1.2.

Samples for histological evaluation

Eleven samples of tumor tissues [unclassified glioma “strong” (n=5), unclassified glioma “unobservable” (n=3), meningioma “strong” (n=2), and metastasis “strong” (n=1)] were used. These tumor tissues were classified into three groups according to their fluorescence, “strong” (n=8) and “unobservable” (n=3) by a neurosurgeon. The cell density was evaluated from pathological specimens. The data of cell density are used purely as ancillary data. The number of samples differs between Secs. 2.1.1 and 2.1.2.

2.2.

Sample Preparation for Optical Properties Measurements

Sections for spectroscopic measurement were cut into 0.5- or 1-mm-thick slices using surgical knives and scissors, and positioned between slide glasses. The thickness of the samples was controlled using a spacer.

2.3.

Integrating-Sphere Measurements of Diffuse Reflectance and Transmittance

Optical parameter measurements were performed using a double integrating sphere system as described previously.9 Samples in glass slides were placed between both spheres, which were 100 mm in diameter. The sample port diameter was chosen as 5 or 10 mm according to the sample size. The diameter of the sample was slightly larger than the sample port. A xenon lamp [L2274(GS) and C8849, Hamamatsu Photonics K.K.] used as the light source. The beam size at the sample was around 1 mm in diameter. For diffuse reflectance and transmittance measurements, an integrating sphere (CSTM-3P-GPS-033SL, Labsphere) was used. The reflected (Rd) and transmitted (Tt) light was detected by a spectrometer (MAYP10161, Maya2000-Pro, Ocean Optics) with an order-sorting and blocking filter and stored in a computer. All samples were measured in the wavelength range from 350 to 1000 nm. Diffuse reflectance and total transmittance were measured at a minimum of five spots in the sample and averaged.

2.4.

Evaluation of Optical Properties of Tissues

The optical properties from the measured data were calculated using an inverse Monte Carlo technique as described previously.9 The algorithm consisted of the following steps: (a) estimate a set of optical properties, (b) calculate the reflectance and transmittance with the Monte Carlo code developed by Wang et al.,10 (c) compare the calculated results with the measured values of the Rd and Tt, and (d) reiterate the above steps until the calculated and measured values agree within the specified acceptance margin of 99.5%. This iterative process yields the set of optical properties that most closely match the measured values of reflectance and transmittance of the tissue. The crosstalk between the spheres was not taken into account. In these calculations, the anisotropy factor was fixed at 0.9 because this is the typical value in many tissues.11 Additionally, because the average refractive index of a single cell is 1.38 at 405 nm (as shown in Ref. 12), the refractive index was fixed at 1.38. When considering a photon and several scattering events, the reduced scattering coefficient (μs) can be defined13 to describe a multiple scattering process as μs=μs (1g).

2.5.

Histological Analysis

Sections prepared for histological investigation were stained with hematoxylin and eosin (H&E).

2.6.

Statistical Analysis

The data are presented as the mean with the standard deviation. Statistical analyses were performed using the Student’s t-test with a significance level of P<0.05.

2.7.

Geometry of the Modeled Tissue for Florescence Monte Carlo Simulations

Homogeneous turbid media including fluorescence was assumed. The model of the tissue was divided into 400 elements in the radial direction, each element being 0.005-cm wide. The z-direction was divided into 200 elements, each element being 0.005-cm long. A pencil beam was assumed to enter the phantom at the point (r,z)=(0,0).

2.8.

Fluorescence Monte Carlo Simulations

The Monte Carlo technique was employed to yield a probability distribution of the generated fluorescence inside the volume and an intensity at the surface. The fluorescence Monte Carlo simulation developed by Jacques14 was used. The wavelength of excitation and emission was 405 and 635 nm, respectively. Additional inputs to the Monte Carlo simulations, apart from the optical properties of the different layers and geometry, were the quantum yield, molar extinction constant, and concentration of the fluorescence in the tissue. The glioblastoma of human brain tumor tissues classified as “strong,” “vague,” and “unobservable” was assumed as the tissue model in simulation. The optical properties used were measured values in this study. The numbers of photons used in each simulation for excitation light and fluorescence emission were 4 and 16 million, respectively. In the simulation, the fluorescence quantum yields of PpIX15 and PpIX concentration16 were assumed as 0.011 and 5.8  μmol/L, respectively.

2.9.

Refractive Index and Anisotropy Factor

The refractive index (n) and anisotropy factor (g) of the tissue model were altered slightly to evaluate their effect on μa and μs. Three different simulations were performed, changing the n and g at all wavelengths to 1.40 and 0.95, respectively. In all other aspects, the calculation processes were identical to those described in Sec. 2.4.

3.

Results

3.1.

Optical Properties of Human Brain Tumor Tissues with “Strong,” “Vague,” and “Unobservable” Fluorescence, and Normal Human Brain Tissues

The absorption coefficient (μa) spectra of the human brain tumor tissue from 350 to 1000 nm are shown in Fig. 1(a). There was no significant difference between the μa values of brain tumor tissues in regions with “strong,” “vague,” and “unobservable” fluorescence, as shown in Fig. 1(a). The μs spectra of the brain tumor tissues from 350 to 1000 nm are shown in Fig. 1(c). Again, no statistical difference was observed in the μs of the tumor tissues with “strong,” “vague,” and “unobservable” fluorescence.

Fig. 1

Optical properties of human brain tumor tissues obtained from glioblastoma, metastasis, meningioma, and oligodendroglioma (classified into three classes according to their fluorescence), normal brain gray matter (n=2), and white matter (n=2). (a) Absorption coefficient μa spectra, (b) μa spectra magnified scale between 650 and 1000 nm, and (c) reduced scattering coefficient μs spectra.

JBO_23_7_075006_f001.png

3.2.

Optical Properties of Human Brain Tumor Tissues Classified by Histological Types, and Human Brain Normal Tissues

There was no significant difference between the μa values of tissue with different tumor types (Figs. 2 and 3). The μs values of human brain tumor tissues are shown in Figs. 2 and 3, respectively. The μs values of the glioblastoma tissue were significantly higher than those of the meningioma tissue from 508 to 1000 nm. The μs values of glioblastoma tissue were significantly higher than those of oligodendroglioma tissue at some wavelengths. There was no significant difference between the μs values of glioblastoma and metastasis tissues. The μs values of meningioma tissue at some wavelengths were significantly higher than those of oligodendroglioma tissue. Furthermore, there was no significant difference between the μs values of meningioma and metastasis tissues.

Fig. 2

Optical properties of human glioblastoma (n=14), meningioma (n=9), normal brain gray matter (n=2), and white matter (n=2). (a) Absorption coefficient μa spectra, (b) μa spectra magnified scale between 650 and 1000 nm, and (c) reduced scattering coefficient μs spectra.

JBO_23_7_075006_f002.png

Fig. 3

Optical properties of human oligodendroglioma (n=3) and metastasis (n=6). (a) Absorption coefficient μa spectra, (b) μa spectra magnified scale between 650 and 1000 nm, and (c) reduced scattering coefficient μs spectra.

JBO_23_7_075006_f003.png

3.3.

Optical Properties of Human Brain Tumor Tissues Classified Following to Histological Types and Fluorescence Intensities

The optical properties of glioblastoma and meningioma tissues classified according to their fluorescence are shown in Figs. 4 and 5, respectively. There were no significant differences between the μa values of glioblastoma tissue region with “strong” and “unobservable” fluorescence in Figs. 4(a) and 4(b). The maximum μa values in the absorption band of hemoglobin appeared at 416 and 544 nm in glioblastoma tissue region with “strong” fluorescence. The maximum μa values in the absorption band of hemoglobin appeared at 423 and 551 nm in glioblastoma tissue at the region with “unobservable” fluorescence. There were no significant differences between the μs values of glioblastoma tissue in the regions with “strong” and “unobservable” fluorescence in Fig. 4(c). The maximum μa values in the absorption band of hemoglobin appeared at 423 and 551 nm in meningioma tissue at the regions with “strong” fluorescence [Fig. 5(a)]. The dips observed for μs in Figs. 15 at 420 nm are probably due to artifacts resulting from the hemoglobin absorption.

Fig. 4

Optical properties of human glioblastoma [classified into three classes according to their fluorescence: strong (n=8), vague (n=2), and unobservable (n=4)]. (a) Absorption coefficient μa, (b) μa spectra magnified scale between 650 and 1000 nm, and (c) reduced scattering coefficient μs spectra.

JBO_23_7_075006_f004.png

Fig. 5

Optical properties of human meningioma [classified into three classes according to their fluorescence: strong (n=5), vague (n=2), and unobservable (n=2)]. (a) Absorption coefficient μa spectra, (b) μa spectra magnified scale between 650 and 1000 nm, and (c) reduced scattering coefficient μs spectra.

JBO_23_7_075006_f005.png

3.4.

Correlation Between Visible Fluorescence and Histology

The sections of human brain tumor tissue, classified as meningioma, metastasis, and unclassified glioma with “strong” and “unobservable” fluorescence were stained with H&E, and histological images are shown in Fig. 6, from which the cell density was estimated. The cell densities [mean ± standard deviation (S.D.)] in regions with “strong” and “unobservable” fluorescence were 31±9×102  permm2 and 12±4×102  permm2, respectively. Thus, there is a statistically significant difference in the cell densities between the regions with “strong” and “unobservable” fluorescence. Meningioma showed typical whorl formation [Fig. 6(c)]. Some nuclei contained clear holes. Spindle-shaped tumor cells with elongated nuclei, forming parallel or storiform bundles, were observed.

Fig. 6

H&E staining of a tumor section. (a) Regions with “strong” fluorescence and (b) regions with “unobservable” fluorescence in unclassified glioma. (c) Regions of meningioma with “strong” fluorescence and (d) regions of metastasis with “strong” fluorescence. Scale bar, 100  μm.

JBO_23_7_075006_f006.png

3.5.

Correlation Between Visible Fluorescence and the Optical Property of Tissue

To investigate whether optical properties affect emissions from glioblastoma tissues, the emission was calculated using Monte Carlo simulation (Fig. 7). The maximum fluorescence strength at the surface and the ratio of relative intensity at the surface are shown in Table 1. Although the probability distributions of emitted fluorescence in the three models were different, the intensities at the surface were almost identical.

Fig. 7

Upper row: (a) left, the distribution of the excitation light in the “strong” fluorescence human brain tumor model; (b) middle, in the “vague” fluorescence human brain tumor model; and (c) right, in the “unobservable” fluorescence human brain tumor model. Lower row: (d)–(f), corresponding images for the fluorescence emission light at 635 nm.

JBO_23_7_075006_f007.png

Table 1

Comparison of the maximum emission intensity at the surface of tissues estimated using fluorescence Monte Carlo simulation. The ratio of relative emission intensity is estimated by normalization with the value of the sample classified as “vague.” The ratio of emission to excitation at the tissue surface was estimated. In addition, the 1/(μ1+μ2) of tissues with strong and unobservable fluorescence was compared.

StrongVagueUnobservableP-value
Maximum emission intensity[1/cm2]0.160.210.16
Ratio of relative emission intensity[—]0.781.000.77
J_em/J_ex[×104]8.624.725.92
1/(μ1+μ2)0.21±0.060.18±0.080.18

3.6.

Sensitivity of the Optical Properties to Variations of Reflective Index and Anisotropy Factor

In Fig. 8(a), the absorption coefficient and the reduced scattering coefficient spectra are presented for different refractive indices. Figure 8(b) shows the absorption coefficient and the reduced scattering coefficient spectra when the anisotropy factor is changed. The absorption coefficient and the reduced scattering coefficient were not changed significantly by changing the refractive index and anisotropy factor.

Fig. 8

(a) Absorption coefficient spectra and (b) reduced scattering coefficient spectra of tumor tissue, when the refractive index or the anisotropy factor was changed.

JBO_23_7_075006_f008.png

4.

Discussion

The μa values of glioblastoma tissue in the region with “strong” fluorescence and “unobservable” fluorescence were compared at 405 and 635 nm, and there was no significant difference as can be seen in Fig. 4(a). Our results have been compared with ones reported by other groups (Table 2).1719 The μs values determined by Gebhart et al.19 are about 1.5 times lower than the values obtained in the present work. However, in that study, the tissues were stored at 193 K until in-vitro measurement of the optical properties. Roggan et al.20 stated that the μs of liver tissues is lowered by shock-freezing at 77 K by 21.7% in comparison with the untreated ones. Therefore, this might explain why the μs values estimated in this study are higher than the values reported by Gebhart et al.19 Pitzschke et al.21 reported that tissue freezing reduces the μs.

Table 2

Comparison of (a) mean absorption coefficients, (b) mean reduced scattering coefficients, and (c) optical penetration depths of meningioma and brain tumor. Differences to the means of two measurements are calculated.

Wavelength [nm]MeningiomaBrain tumorRef.
This workReferenceDifferenceThis workReferenceDifference
(a)
6300.190.0350.1618
6300.210.081 (glioma)0.1319
6700.160.030.1318
6700.190.071 (glioma)0.1219
8500.090.0250.0718
8500.110.064 (glioma)0.0519
(b)
6301.880.930.9518
6303.492.2 (glioma)1.319
6701.730.90.8318
6703.312.1 (glioma)1.219
8501.190.650.5418
8502.431.8 (glioma)0.619
(c)
4880.481.40.50.9(0.02)17
5140.521.40.50.90.0217
6301.003.12.118
6300.891.3 (glioma)0.419
6350.912.61.71.7(0.8)17
6701.133.52.418
6700.991.5 (glioma)0.519
8502.004.42.418
8501.651.7 (glioma)0.0519
Note: Svaasand et al.,17 Yaroslavsky et al.,18 and Gebhart et al.19

It is difficult to estimate accurately the experimental error in a study of this type in which many independent measurements are conducted. Experimental contributions to the error included the inaccuracy of the spectrometer, which we estimated to be <1% of the full-scale value. This error becomes more prominent as the measured values of Rd and Tt become smaller, such as in the wavelength range between 350 and 600 nm. Furthermore, there is a significant biological variability between the samples. For example, if the blood of the samples varied, this would be particularly noticeable between 350 and 600 nm. This may have introduced an error in the μs spectra.

Tumors are associated with regions of hypoxia. Solid tumors contain regions at very low oxygen concentrations (hypoxia), often surrounding areas of necrosis.22 Perhaps, tumor tissues at very low oxygen concentration is account for certain curves in μa, mimicking the absorption curve of deoxyhemoglobin, whereas other curves correspond to the oxyhemoglobin form.

The higher fluorescence intensity is associated with higher cell density. However, while a higher cell density was found in “strong” than in “unobservable” fluorescence tissues, this is not reflected in the μa and the μs spectra. Perhaps this is caused by the higher μa of blood than that of PpIX included in mitochondria; thus the μa of blood is prominent in the μa of the brain tumor tissue. In addition, the μs is not linearly related with the concentration of scattering molecules for more strongly scattering materials, such as brain tissue. This leads to there being no difference between the μss of “strong” and “unobservable” brain tumor tissue. Thus, the difference of cell density modified the scattering coefficient yet it does not lead to significant differences in the μs and thus does not affect the propagation of the diffuse fluorescent light.

Diaz et al.23 investigated whether fluorescein had the ability to specifically stain glioma cells in a study of fluorescein uptake by intracranial U87 malignant glioma xenografts in male nonobese diabetic/severe combined immunodeficient mice. In that report, false negatives could be caused by lack of fluorescence in areas of diffuse, low-density cellular infiltration. The results of our study are in agreement with this, as shown in Sec. 3.4. In addition, Bogaards et al.24 reported that in the majority of animals, complete tumor resection was not achieved owing to small tumor cell nests in the surgical cavity, which were undetectable by fluorescence of PpIX. These could be considered as false negatives for the particular device, 5-ALA dose, and administration time. Moreover, in cases where the tumor invaded beyond the surgical cavity, this would not be detectable by fluorescence imaging of the cavity with fluorescent dye as the effective tissue sampling depth of the blue-light excitation below the exposed tissue surface is <0.5  mm.25 These reports suggest the lack of fluorescent dye and/or the decline of the excitation energy in the tumor tissue may have the potential to cause false negatives in fluorescence-guided resection. The optical penetration depth (δ) is one of the indices of light attenuation in biological tissue. Thus, the δ values of human brain tumor tissues were calculated from the optical properties shown in Figs. 15 and compared. Two equations were used to calculate the optical penetration depth (δ). When μa3  μs, the δ can be estimated as26

Eq. (1)

δ=13μa(μa+μs).

When μa is comparable with μs (10  μa3  μs), δ was estimated as27

Eq. (2)

δ=1μa(μa+3  μs).

The δ depends on the wavelength, as shown in Figs. 9Fig. 1011. The δs at 635 nm in brain tumor tissues at the regions with “strong,” “vague,” and “unobservable” fluorescence were about 0.98, 0.97, and 0.78 mm, respectively, as shown in Fig. 9. Moreover, to confirm whether there is a difference in δ in glioblastoma tissues between the regions with “strong” and “unobservable” fluorescence, the δ values of glioblastoma tissues were compared [Fig. 11(a)]. The δs at 405 nm in glioblastoma tissues with “strong” and “unobservable” fluorescence were about 0.28±0.08  mm and 0.20±0.05  mm, respectively, and thus are comparable within the error limits. Assuming the attenuation coefficient to be μ1=1/δ1 at 405 nm and μ2=1/δ2 at 635 nm, the detected fluorescence intensity under a uniform distribution of PpIX of density [PpIX] under pencil illumination is proportional to: [PpIX]/(μ1+μ2). “1/(μ1+μ2)” and p-values comparing “strong” and “unobservable” fluorescence tissue are shown in Table 1. They were comparable within the error limits. From the result of the fluorescence Monte Carlo simulation (Fig. 7), the ratio of the relative intensity of emission at the surface was almost identical (Table 1). The measured approximate fluorescence intensity ratio of “strong,” “vague,” and “unobservable” was 2.51:0.49:0.1 by spectroscopy;8 this explains that this difference is caused by the difference of the concentration of fluorescence, or PpIX.

Fig. 9

Optical penetration depths δ of human brain tumor tissues obtained from glioblastoma, metastasis, meningioma, and oligodendroglioma (classified into three classes according to their fluorescence), normal brain gray matter (n=2), and white matter (n=2).

JBO_23_7_075006_f009.png

Fig. 10

Optical penetration depth δ spectra of human glioblastoma (n=14), meningioma (n=9), oligodendroglioma (n=3), metastasis (n=6), normal brain gray matter (n=2), and white matter (n=2).

JBO_23_7_075006_f010.png

Fig. 11

Optical penetration depth δ spectra of (a) human glioblastoma [classified into three classes according to their fluorescence: strong (n=8), vague (n=2), and unobservable (n=4)], and (b) human meningioma [classified into three classes according to their fluorescence: strong (n=5), vague (n=2), and unobservable (n=2)].

JBO_23_7_075006_f011.png

If in-vivo measurement shows that tissue has higher μa than in this ex-vivo study, the δ would become lower and the detectable fluorescence would be mainly from PpIX included in the tissue in the submillimeter range from the surface. Thus, the effect of optical properties on the false negatives would become smaller or zero as μa increases.

In many cases, optical properties change the wavelength-dependent manner and the corresponding excitation and emission intensity changes. However, this situation is restricted to when δ is high, or incident light can easily penetrate tissues and the intensity of excitation light becomes high and results in the emission light becoming easy to detect. In 2017, Wirth et al.28 reported that the depth of fluorescence was estimated under a bulk phantom, having μs of 1.0  mm1 and μa of 0.001  mm1. Although the results by Wirth et al. suggest that the depth of fluorescence was correctly estimated for shallower inclusions, the error of the predicted depth drastically increased for inclusions at depths greater than δ, even when using a correction based on optical properties. With regard to the optical properties of the human glioblastoma tissues, the δ was 0.2 or 0.3 mm at 405 nm. Whether we can recognize, or detect, the PpIX fluorescence emitted from the glioblastoma tissue does not mainly depend on the optical properties but is more strongly affected by the quantity of PpIX present within 0.2- to 0.3-mm depth, or the δ, from the surface when PpIX is excited by the light at 405 nm. Hence, the small amount of PpIX that is contained in the tissue within the depth of optical penetration more strongly affects the false negatives under the conditions that the tissue has low δ.

Technical advances2931 have increased the sensitivity of tumor tissue detection using 5-ALA-PpIX, but so far, the sensitivity has not reached 100%.32,33 The correction of optical properties for the detection of PpIX validated with a phantom was found to be suitable for quantitative fluorescence spectroscopy and imaging.28,34 If there is a region with high δ in brain tumor tissue caused by local variation, the method of correction using the optical properties would work effectively in quantitative fluorescence spectroscopy and imaging. In vivo measurement of PpIX content in false-negative tissue, such as reported by Valdés et al.,35 it is also important to reveal the correlation in each patient between the presence of tumor tissue and the corresponding local in-vivo optical properties.

Stummer et al.36 reported that “strong” visual fluorescence in individual samples was related to high cell densities and strong fluorescence, whereas samples designated as “weak” by study surgeons had low cell densities and low spectrometric fluorescence. The mRNA level of ABC transporter ABCG2 was lower in malignant glioma cells in the brain tumor that exhibited strong fluorescence of PpIX after 5-ALA treatment, whereas the surrounding cells emitted “vague” fluorescence.37 ABCG2, a porphyrin efflux pump, is downregulated in tumors. These reports and our results suggest that the primary cause of false negatives may be a lack of PpIX or a low accumulation of PpIX in malignant glioblastoma.

5.

Conclusion

In human glioblastoma tissue obtained from fluorescence-guided resection with 5-ALA for malignant tumors, a significant difference was not observed between the optical properties of the glioblastoma regions with “strong” and “unobservable” fluorescence. However, the variation in hemoglobin contents caused by the use of ex-vivo measurement would affect these results. The μa values of glioblastoma tissue in the region with “strong” fluorescence and “unobservable” fluorescence were compared at 405 and 635 nm, and there was no significant difference. There were no significant differences between the μs values of glioblastoma tissues in “strong” and “unobservable” fluorescence at 405 and 635 nm. The fluorescence signal strongly correlated with the tumor cell density. Although a higher cell density was found in “strong” and “unobservable” in fluorescence tissues, this is not reflected in the μa and the μs spectra. In addition, from the result of the fluorescence Monte Carlo simulation, the ratio of the relative intensity of emission at the surface was almost identical. The δs at 405 nm in glioblastoma tissues with “strong” and “unobservable” fluorescence were about 0.28±0.08  mm and 0.20±0.05  mm, respectively. The small amount of PpIX that is contained in the tissue within the depth of optical penetration more strongly affects the false negatives under the conditions that the tissue has low δ. Hence, it was considered that there was no correlation between fluorescence signals and the patient-averaged ex-vivo optical properties of the malignant glioma tissues. Thus, a false negative in using 5-ALA for detection of human glioblastoma does not result from the differences in optical properties of human brain glioblastoma tissues.

Disclosures

The authors have no financial interests and no other potential conflicts of interest to disclose in this article.

Acknowledgments

The Authors wish to express their appreciation to Dr. Takashi Yoneda for sample preparation. This development was supported by SENTAN, JST. This work was supported by JSPS KAKENHI (Grant No. 15K16322).

References

1. 

W. Stummer et al., “Intraoperative detection of malignant gliomas by 5-aminolevulinic acid-induced porphyrin fluorescence,” Neurosurgery, 42 (3), 518 –526 (1998). https://doi.org/10.1097/00006123-199803000-00017 NEQUEB Google Scholar

2. 

W. Stummer et al., “Fluorescence-guided surgery with 5-aminolevulinic acid for resection of malignant glioma: a randomised controlled multicentre phase III trial,” Lancet Oncol., 7 (5), 392 –401 (2006). https://doi.org/10.1016/S1470-2045(06)70665-9 LOANBN 1470-2045 Google Scholar

3. 

W. Stummer et al., “Extent of resection and survival in glioblastoma multiforme: identification of and adjustment for bias,” Neurosurgery, 62 (3), 564 –576 (2008). https://doi.org/10.1227/01.neu.0000317304.31579.17 NEQUEB Google Scholar

4. 

U. Pichlmeier et al., “Resection and survival in glioblastoma multiforme: an RTOG recursive partitioning analysis of ALA study patients,” Neuro-Oncology, 10 (6), 1025 –1034 (2008). https://doi.org/10.1215/15228517-2008-052 Google Scholar

5. 

W. Stummer et al., “Fluorescence-guided resection of glioblastoma multiforme by using 5-aminolevulinic acid-induced porphyrins: a prospective study in 52 consecutive patients,” J. Neurosurg., 93 (6), 1003 –1013 (2000). https://doi.org/10.3171/jns.2000.93.6.1003 JONSAC 0022-3085 Google Scholar

6. 

D. W. Roberts et al., “Coregistered fluorescence-enhanced tumor resection of malignant glioma: relationships between δ-aminolevulinic acid-induced protoporphyrin IX fluorescence, magnetic resonance imaging enhancement, and neuropathological parameters. Clinical article,” J. Neurosurg., 114 (3), 595 –603 (2011). https://doi.org/10.3171/2010.2.JNS091322 JONSAC 0022-3085 Google Scholar

7. 

K. Takahashi et al., “Enhanced expression of coproporphyrinogen oxidase in malignant brain tumors: CPOX expression and 5-ALA-induced fluorescence,” Neuro-Oncology, 13 (11), 1234 –1243 (2011). https://doi.org/10.1093/neuonc/nor116 Google Scholar

8. 

Y. Kajimoto and T. Kuroiwa, “Unsolved problems in 5-aminolevulinic acid based photodynamic diagnosis: quantification of fluorescence and molecular mechanism of porphyrin accumulation,” J. Jpn. Soc. Laser Surg. Med., 32 (2), 143 –148 (2011). https://doi.org/10.2530/jslsm.32.143 Google Scholar

9. 

N. Honda et al., “Optical properties of tumor tissues grown on the chorioallantoic membrane of chicken eggs: tumor model to assay of tumor response to photodynamic therapy,” J. Biomed. Opt., 20 (12), 125001 (2015). https://doi.org/10.1117/1.JBO.20.12.125001 JBOPFO 1083-3668 Google Scholar

10. 

L. Wang et al., “MCML-Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Methods Programs Biomed., 47 (2), 131 –146 (1995). https://doi.org/10.1016/0169-2607(95)01640-F CMPBEK 0169-2607 Google Scholar

11. 

V. V. Tuchin, “Optical properties of tissues with strong (multiple) scattering,” Tissue Optics: Light Scattering Methods and Instruments for Medical Diagnosis, 3 –17 SPIE Press, Bellingham, Washington (2007). Google Scholar

12. 

B. Kemper et al., “Integral refractive index determination of living suspension cells by multifocus digital holographic phase contrast microscopy,” J. Biomed. Opt., 12 (5), 054009 (2007). https://doi.org/10.1117/1.2798639 JBOPFO 1083-3668 Google Scholar

13. 

“Scattering and absorption properties of diffusive media,” Light Propagation Through Biological Tissue and Other Diffusive Media, 9 –28 SPIE Press, Bellingham, Washington (2010). Google Scholar

14. 

S. L. Jacques, “Monte Carlo simulations of fluorescence in turbid media,” Handbook of Biomedical Fluorescence, 61 –107 Marcel-Dekker, New York (2003). Google Scholar

15. 

G. I. Lozovaya et al., “Protoporphyrin IX as a possible ancient photosensitizer: spectral and photochemical studies,” Origins Life Evol. Biosphere, 20 321 –330 (1990). https://doi.org/10.1007/BF01808114 OLEBEM 0169-6149 Google Scholar

16. 

A. Johansson et al., “5-aminolevulinic acid-induced protoporphyrin IX levels in tissue of human malignant brain tumors,” Photochem. Photobiol., 86 1373 –1378 (2010). https://doi.org/10.1111/php.2010.86.issue-6 PHCBAP 0031-8655 Google Scholar

17. 

L. O. Svaasand and R. Eixingsen, “Optical penetration in human intracranial tumors,” Photochem. Photobiol., 41 73 –76 (1985). https://doi.org/10.1111/j.1751-1097.1985.tb03450.x PHCBAP 0031-8655 Google Scholar

18. 

A. N. Yaroslavsky et al., “Optical properties of selected native and coagulated human brain tissues in vitro in the visible and near infrared spectral range,” Phys. Med. Biol., 47 (12), 2059 –2073 (2002). https://doi.org/10.1088/0031-9155/47/12/305 PHMBA7 0031-9155 Google Scholar

19. 

S. C. Gebhart et al., “In vitro determination of normal and neoplastic human brain tissue optical properties using inverse adding-doubling,” Phys. Med. Biol., 51 2011 –2027 (2006). https://doi.org/10.1088/0031-9155/51/8/004 PHMBA7 0031-9155 Google Scholar

20. 

A. Roggan et al., “The effect of preparation technique on the optical parameters of biological tissue,” Appl. Phys. B, 69 (5), 445 –453 (1999). https://doi.org/10.1007/s003400050833 Google Scholar

21. 

A. Pitzschke et al., “Optical properties of rabbit brain in the red and near-infrared: changes observed under in vivo, postmortem, frozen, and formalin-fixated conditions,” J. Biomed. Opt., 20 (2), 025006 (2015). https://doi.org/10.1117/1.JBO.20.2.025006 JBOPFO 1083-3668 Google Scholar

22. 

J. M. Brown and W. R. Wilson, “Exploiting tumor hypoxia in cancer treatment,” Nat. Rev. Cancer, 4 (6), 437 –447 (2004). https://doi.org/10.1038/nrc1367 NRCAC4 1474-175X Google Scholar

23. 

R. J. Diaz et al., “Study of the biodistribution of fluorescein in glioma-infiltrated mouse brain and histopathological correlation of intraoperative findings in high-grade gliomas resected under fluorescein fluorescence guidance,” J. Neurosurg., 122 (6), 1360 –1369 (2015). https://doi.org/10.3171/2015.2.JNS132507 JONSAC 0022-3085 Google Scholar

24. 

A. Bogaards et al., “Increased brain tumor resection using fluorescence image guidance in a preclinical model,” Lasers Surg. Med., 35 (3), 181 –190 (2004). https://doi.org/10.1002/(ISSN)1096-9101 LSMEDI 0196-8092 Google Scholar

25. 

V. X. Yang et al., “A multispectral fluorescence imaging system: design and initial clinical tests in intra-operative photofrin-photodynamic therapy of brain tumors,” Lasers Surg. Med., 32 (3), 224 –232 (2003). https://doi.org/10.1002/(ISSN)1096-9101 LSMEDI 0196-8092 Google Scholar

26. 

R. Splinter, B. A. Hooper, “Light-tissue interaction variables,” An Introduction to Biomedical Optics, 121 –154 Taylor and Francis, London (2007). Google Scholar

27. 

S. L. Jacques and S. A. Prahl, “Limits of diffusion theory,” (1998) http://omlc.org/classroom/ece532/class5/limits.html October 2015). Google Scholar

28. 

D. Wirth et al., “Fluorescence depth estimation from wide-field optical imaging data for guiding brain tumor resection: a multi-inclusion phantom study,” Biomed. Opt. Express, 8 (8), 3656 –3670 (2017). https://doi.org/10.1364/BOE.8.003656 BOEICL 2156-7085 Google Scholar

29. 

P. A. Valdés et al., “Quantitative, spectrally-resolved intraoperative fluorescence imaging,” Sci. Rep., 2 798 (2012). https://doi.org/10.1038/srep00798 SRCEC3 2045-2322 Google Scholar

30. 

P. A. Valdés et al., “qF-SSOP: real-time optical property corrected fluorescence imaging,” Biomed. Opt. Express, 8 (8), 3597 –3605 (2017). https://doi.org/10.1364/BOE.8.003597 BOEICL 2156-7085 Google Scholar

31. 

M. Sibai et al., “Quantitative spatial frequency fluorescence imaging in the sub-diffusive domain for image-guided glioma resection,” Biomed. Opt. Express, 6 (12), 4923 –4933 (2015). https://doi.org/10.1364/BOE.6.004923 BOEICL 2156-7085 Google Scholar

32. 

H. A. Leroy et al., “Fluorescence guided resection and glioblastoma in 2015: a review,” Lasers Surg. Med., 47 (5), 441 –451 (2015). https://doi.org/10.1002/lsm.v47.5 LSMEDI 0196-8092 Google Scholar

33. 

S. Eljamel, “5-ALA fluorescence image guided resection of glioblastoma multiforme: a meta-analysis of the literature,” Int. J. Mol. Sci., 16 (5), 10443 –10456 (2015). https://doi.org/10.3390/ijms160510443 1422-0067 Google Scholar

34. 

J. Bravo et al., “Hyperspectral data processing improves PpIX contrast during fluorescence guided surgery of human brain tumors,” Sci. Rep., 7 9455 (2017). https://doi.org/10.1038/s41598-017-09727-8 Google Scholar

35. 

P. A. Valdés et al., “Combined fluorescence and reflectance spectroscopy for in vivo quantification of cancer biomarkers in low- and high-grade glioma surgery,” J. Biomed. Opt., 16 (11), 116007 (2011). https://doi.org/10.1117/1.3646916 JBOPFO 1083-3668 Google Scholar

36. 

W. Stummer et al., “5-Aminolevulinic acid-derived tumor fluorescence: the diagnostic accuracy of visible fluorescence qualities as corroborated by spectrometry and histology and postoperative imaging,” Neurosurgery, 74 (3), 310 –320 (2014). https://doi.org/10.1227/NEU.0000000000000267 NEQUEB Google Scholar

37. 

T. Ishikawa et al., “Key role of human ABC transporter ABCG2 in photodynamic therapy and photodynamic diagnosis,” Adv. Pharmacol. Sci., 2010 587306 (2010). https://doi.org/10.1155/2010/587306 Google Scholar

Biographies for the authors are not available.

© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE) 1083-3668/2018/$25.00 © 2018 SPIE
Norihiro Honda, Katsunori Ishii, Yoshinaga Kajimoto, Toshihiko Kuroiwa, and Kunio Awazu "Determination of optical properties of human brain tumor tissues from 350 to 1000 nm to investigate the cause of false negatives in fluorescence-guided resection with 5-aminolevulinic acid," Journal of Biomedical Optics 23(7), 075006 (13 July 2018). https://doi.org/10.1117/1.JBO.23.7.075006
Received: 9 December 2017; Accepted: 22 June 2018; Published: 13 July 2018
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