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.
Materials and Methods
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.
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 (), meningioma (), oligodendroglioma (), and metastasis (). These tumor tissues were classified into three groups according to their fluorescence, “strong” (), “vague” (), and “unobservable” () 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 . 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 () and gray matter ()] were investigated. The materials were stored at low temperature in saline solution until spectroscopic investigation. The sample size is the number of individuals.
Samples for histological evaluation
Eleven samples of tumor tissues [unclassified glioma “strong” (), unclassified glioma “unobservable” (), meningioma “strong” (), and metastasis “strong” ()] were used. These tumor tissues were classified into three groups according to their fluorescence, “strong” () and “unobservable” () 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.
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.
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 () and transmitted () 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.
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 and , 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 () can be defined13 to describe a multiple scattering process as ().
Sections prepared for histological investigation were stained with hematoxylin and eosin (H&E).
The data are presented as the mean with the standard deviation. Statistical analyses were performed using the Student’s -test with a significance level of .
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 -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 .
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 , respectively.
Refractive Index and Anisotropy Factor
The refractive index () and anisotropy factor () of the tissue model were altered slightly to evaluate their effect on and . Three different simulations were performed, changing the and 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.
Optical Properties of Human Brain Tumor Tissues with “Strong,” “Vague,” and “Unobservable” Fluorescence, and Normal Human Brain Tissues
The absorption coefficient () 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 values of brain tumor tissues in regions with “strong,” “vague,” and “unobservable” fluorescence, as shown in Fig. 1(a). The 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 of the tumor tissues with “strong,” “vague,” and “unobservable” fluorescence.
Optical Properties of Human Brain Tumor Tissues Classified by Histological Types, and Human Brain Normal Tissues
There was no significant difference between the values of tissue with different tumor types (Figs. 2 and 3). The values of human brain tumor tissues are shown in Figs. 2 and 3, respectively. The values of the glioblastoma tissue were significantly higher than those of the meningioma tissue from 508 to 1000 nm. The values of glioblastoma tissue were significantly higher than those of oligodendroglioma tissue at some wavelengths. There was no significant difference between the values of glioblastoma and metastasis tissues. The values of meningioma tissue at some wavelengths were significantly higher than those of oligodendroglioma tissue. Furthermore, there was no significant difference between the values of meningioma and metastasis tissues.
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 values of glioblastoma tissue region with “strong” and “unobservable” fluorescence in Figs. 4(a) and 4(b). The maximum values in the absorption band of hemoglobin appeared at 416 and 544 nm in glioblastoma tissue region with “strong” fluorescence. The maximum 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 values of glioblastoma tissue in the regions with “strong” and “unobservable” fluorescence in Fig. 4(c). The maximum 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 in Figs. 1–5 at 420 nm are probably due to artifacts resulting from the hemoglobin absorption.
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 and , 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.
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.
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.
|Maximum emission intensity||||0.16||0.21||0.16||—|
|Ratio of relative emission intensity||[—]||0.78||1.00||0.77||—|
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.
The 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).17–19 The 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 of liver tissues is lowered by shock-freezing at 77 K by in comparison with the untreated ones. Therefore, this might explain why the 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 .
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]||Meningioma||Brain tumor||Ref.|
|This work||Reference||Difference||This work||Reference||Difference|
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 of the full-scale value. This error becomes more prominent as the measured values of and 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 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 , 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 and the spectra. Perhaps this is caused by the higher of blood than that of PpIX included in mitochondria; thus the of blood is prominent in the of the brain tumor tissue. In addition, the 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 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 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 .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 () is one of the indices of light attenuation in biological tissue. Thus, the values of human brain tumor tissues were calculated from the optical properties shown in Figs. 1–5 and compared. Two equations were used to calculate the optical penetration depth (). When , the can be estimated as26
When is comparable with (), was estimated as27
The depends on the wavelength, as shown in Figs. 9Fig. 10–11. The 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 in glioblastoma tissues between the regions with “strong” and “unobservable” fluorescence, the values of glioblastoma tissues were compared [Fig. 11(a)]. The at 405 nm in glioblastoma tissues with “strong” and “unobservable” fluorescence were about and , respectively, and thus are comparable within the error limits. Assuming the attenuation coefficient to be at 405 nm and at 635 nm, the detected fluorescence intensity under a uniform distribution of PpIX of density [PpIX] under pencil illumination is proportional to: . “” and -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.
If in-vivo measurement shows that tissue has higher than in this ex-vivo study, the 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 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 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 of and of . 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 , even when using a correction based on optical properties. With regard to the optical properties of the human glioblastoma tissues, the 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 , 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 .
Technical advances29–31 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 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.
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 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 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 and the 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 at 405 nm in glioblastoma tissues with “strong” and “unobservable” fluorescence were about and , 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 . 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.
The authors have no financial interests and no other potential conflicts of interest to disclose in this article.
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).