Double dose of 5-aminolevulinic acid and its effect on protoporphyrin IX accumulation in low-grade glioma

Current and Future Applications of 5-Aminolevulinic Acid in Neurosurgical Oncology

Maximal safe surgical resection is the gold standard in brain tumor surgery. Fluorescence-guided surgery (FGS) is one of many intraoperative techniques that have been designed with the intention of accomplishing this goal. 5-aminolevulinic acid (5-ALA) is one of the main fluorophores that facilitates FGS in neurosurgical oncology. Multiple different types of brain tumors can take in and metabolize 5-ALA into protoporphyrin IX (PpIX) through the mitochondria heme biosynthesis pathway. PpIX then selectively accumulates in brain tumor cells due to decreased ferrochelatase activity and emits red fluorescence (630-720 nm) when excited with blue light (375-440 nm). This mechanism allows neurosurgeons to better visualize tumor burden and increase extent of resection while preserving non-cancerous brain parenchyma and, specifically, eloquent white matter tracts, if combined with mapping techniques, thereby minimizing morbidity while improving survival. While 5-ALA use is well established in the treatment of high-grade gliomas, its applicability in recurrent high-grade and non-enhancing IDH-mutant low-grade gliomas, as well as non-glial tumors, is less established or limited by certain features of their cellular and molecular biology. This review aims to discuss the current landscape of 5-ALA utility across the diverse range of brain tumors, practical considerations that optimize its current use in neurosurgery, modern clinical limitations of 5-ALA, and how its application can be expanded by combining its use with other techniques that overcome current limitations.

Predicting intraoperative 5-ALA-induced tumor fluorescence via MRI and deep learning in gliomas with radiographic lower-grade characteristics

PURPOSE

Lower-grade gliomas typically exhibit 5-aminolevulinic acid (5-ALA)-induced fluorescence in only 20-30% of cases, a rate that can be increased by doubling the administered dose of 5-ALA. Fluorescence can depict anaplastic foci, which can be precisely sampled to avoid undergrading. We aimed to analyze whether a deep learning model could predict intraoperative fluorescence based on preoperative magnetic resonance imaging (MRI).

METHODS

We evaluated a cohort of 163 glioma patients categorized intraoperatively as fluorescent (n = 83) or non-fluorescent (n = 80). The preoperative MR images of gliomas lacking high-grade characteristics (e.g., necrosis or irregular ring contrast-enhancement) consisted of T1, T1-post gadolinium, and FLAIR sequences. The preprocessed MRIs were fed into an encoder-decoder convolutional neural network (U-Net), pre-trained for tumor segmentation using those three MRI sequences. We used the outputs of the bottleneck layer of the U-Net in the Variational Autoencoder (VAE) as features for classification. We identified and utilized the most effective features in a Random Forest classifier using the principal component analysis (PCA) and the partial least square discriminant analysis (PLS-DA) algorithms. We evaluated the performance of the classifier using a tenfold cross-validation procedure.

RESULTS

Our proposed approach's performance was assessed using mean balanced accuracy, mean sensitivity, and mean specificity. The optimal results were obtained by employing top-performing features selected by PCA, resulting in a mean balanced accuracy of 80% and mean sensitivity and specificity of 84% and 76%, respectively.

CONCLUSIONS

Our findings highlight the potential of a U-Net model, coupled with a Random Forest classifier, for pre-operative prediction of intraoperative fluorescence. We achieved high accuracy using the features extracted by the U-Net model pre-trained for brain tumor segmentation. While the model can still be improved, it has the potential for evaluating when to administer 5-ALA to gliomas lacking typical high-grade radiographic features.

Deep learning-based hyperspectral image correction and unmixing for brain tumor surgery

Hyperspectral imaging for fluorescence-guided brain tumor resection improves visualization of tissue differences, which can ameliorate patient outcomes. However, current methods do not effectively correct for heterogeneous optical and geometric tissue properties, leading to less accurate results. We propose two deep learning models for correction and unmixing that can capture these effects. While one is trained with protoporphyrin IX (PpIX) concentration labels, the other is semi-supervised. The models were evaluated on phantom and pig brain data with known PpIX concentration; the supervised and semi-supervised models achieved Pearson correlation coefficients (phantom, pig brain) between known and computed PpIX concentrations of (0.997, 0.990) and (0.98, 0.91), respectively. The classical approach achieved (0.93, 0.82). The semi-supervised approach also generalizes better to human data, achieving a 36% lower false-positive rate for PpIX detection and giving qualitatively more realistic results than existing methods. These results show promise for using deep learning to improve hyperspectral fluorescence-guided neurosurgery.

Utility and Safety of 5-ALA Guided Surgery in Pediatric Brain Tumors: A Systematic Review

Background: 5-Aminolevulinic acid-guided surgery for adult gliomas has been approved by the European Medicines Agency and the US Food and Drug Administration, becoming a reliable tool for improving gross total resection rates and patient outcomes. This has led several medical centers to explore the off-label use of 5-ALA in the resection of pediatric brain tumors, assessing its efficacy and safety across various tumor types. However, given the differences between children and adults, the appropriateness of 5-ALA use in pediatric populations has not yet been fully established. Methods: We collected eligible publications from Embase, Scopus, PubMed, and Proquest, ultimately selecting 27 studies. Data extraction and retrospective analysis of 249 surgical cases were conducted to determine the current efficacy and safety of 5-ALA in pediatric brain tumors. The fluorescence rate and utility stratified by several clinical features, including WHO grade, tumor classification, and tumor location, were analyzed. Results: Most studies suggest that 5-ALA can enhance tumor identification in high-grade tumors, including glioblastomas and anaplastic astrocytomas. Changes in survival or recurrence rates associated with 5-ALA-guided resection have not been reported. None of the cases reported significant postoperative complications related to the use of 5-ALA. Conclusions: 5-ALA can aid in the resection of high-grade gliomas in pediatric patients. The efficacy of 5-ALA in low-grade gliomas and other tumors may require enhancement with additional tools or modified administration protocols. The safety of 5-ALA has reached a preliminary consensus, although further randomized controlled trials and data on survival and molecular characteristics are needed.

Detection of a Water-Soluble Hypericin Formulation in Glioblastoma Tissue with Fluorescence Lifetime and Intensity Using a Dual-Tap CMOS Camera System

BACKGROUND

High hypericin-loaded polyvinylpyrrolidone (HHL-PVP) constitutes a novel approach to utilize the promising characteristics of hypericin for photodynamic diagnosis (PDD) and therapy (PDT) of brain tumors in an orally bioavailable formulation. The aim of this study was to investigate the ability of a Complementary Metal-Oxide-Semiconductor (CMOS) camera-based fluorescence imaging system to selectively visualize HHL-PVP in glioblastoma tissue even in the presence of 5-Aminolvevulinic acid (5-ALA) induced fluorescence, which is widely utilized in brain tumor surgery.

METHODS

We applied a previously established system with a non-hypericin specific filter for 5-ALA fluorescence visualization and a newly introduced hypericin-specific filter at 575-615 nm that transmits the spectrum of hypericin, but not 5-ALA fluorescence. Glioblastoma specimens obtained from 12 patients (11 with preoperative 5-ALA intake) were ex vivo incubated with HHL-PVP. Subsequently, fluorescence intensity and lifetime changes using both the non-hypericin specific filter and hypericin-specific filter were measured before and after HHL-PVP incubation and after subsequent rinsing.

RESULTS

While no significant differences in fluorescence signal were observed using the non-hypericin specific filter, statistically significant increases in fluorescence intensity (p = 0.001) and lifetime (p = 0.028) after HHL-PVP incubation were demonstrated using the hypericin-specific filter. In consequence, specimens treated with HHL-PVP could be identified according to the fluorescence signal with high diagnostic sensitivity (87.5%) and specificity (100%).

CONCLUSIONS

Our CMOS camera-based system with a hypericin-specific filter is capable of selectively visualizing hypericin fluorescence in glioblastoma tissue after ex vivo HHL-PVP incubation. In the future, this technique could facilitate clinical investigations of HHL-PVP for PDD and PDT while maintaining the current standard of care with 5-ALA guidance.

5-Aminolevulonic Acid, a New Tumor Contrast Agent: Anesthesia Considerations in Patients Undergoing Craniotomy

5-aminolevulinic acid (ALA) is used during resection of malignant gliomas due to its fluorescence properties and has been shown to render resection more effective than resection without ALA guidance. The aim of this narrative review is to categorize the adverse effects of ALA relevant to anesthesia providers. Intraoperative hypotension, porphyria-related side effects, alterations in blood chemistry and coagulation, photosensitivity, and increased levels of liver enzymes have all been reported. We also sought to examine the impact of dosage and timing of oral administration on efficacy of ALA and on these side effects. Twenty-seven studies met our inclusion criteria of patients undergoing craniotomy for glioma resection using ALA and occurrence of at least one adverse effect. The results of these studies showed that there was heterogeneity in levels of intraoperative hypotension, with some reporting an incidence as high as 32%, and that hypotension was associated with antihypertensive medication use. Clinical symptoms of porphyria, such as gastrointestinal disturbance, were less commonly reported. Photosensitivity of the skin after 5-ALA administration was well documented particularly in patients exposed to light; however, adverse effects on the eye were not adequately studied. Elevation in liver enzymes was a common finding postoperatively but was often clinically insignificant. The timing of oral administration presents practical issues for the preoperative management of patients undergoing resection with ALA. We provide guidance for perioperative management of patients who receive ALA for brain tumor resection. Controlled studies with adequate statistical power are required to further understand and prevent the adverse effects of ALA.

Spectral library and method for sparse unmixing of hyperspectral images in fluorescence guided resection of brain tumors

Through spectral unmixing, hyperspectral imaging (HSI) in fluorescence-guided brain tumor surgery has enabled the detection and classification of tumor regions invisible to the human eye. Prior unmixing work has focused on determining a minimal set of viable fluorophore spectra known to be present in the brain and effectively reconstructing human data without overfitting. With these endmembers, non-negative least squares regression (NNLS) was commonly used to compute the abundances. However, HSI images are heterogeneous, so one small set of endmember spectra may not fit all pixels well. Additionally, NNLS is the maximum likelihood estimator only if the measurement is normally distributed, and it does not enforce sparsity, which leads to overfitting and unphysical results. In this paper, we analyzed 555666 HSI fluorescence spectra from 891 ex vivo measurements of patients with various brain tumors to show that a Poisson distribution indeed models the measured data 82% better than a Gaussian in terms of the Kullback-Leibler divergence, and that the endmember abundance vectors are sparse. With this knowledge, we introduce (1) a library of 9 endmember spectra, including PpIX (620 nm and 634 nm photostates), NADH, FAD, flavins, lipofuscin, melanin, elastin, and collagen, (2) a sparse, non-negative Poisson regression algorithm to perform physics-informed unmixing with this library without overfitting, and (3) a highly realistic spectral measurement simulation with known endmember abundances. The new unmixing method was then tested on the human and simulated data and compared to four other candidate methods. It outperforms previous methods with 25% lower error in the computed abundances on the simulated data than NNLS, lower reconstruction error on human data, better sparsity, and 31 times faster runtime than state-of-the-art Poisson regression. This method and library of endmember spectra can enable more accurate spectral unmixing to aid the surgeon better during brain tumor resection.

Fluorescence-Guided Surgical Techniques in Adult Diffuse Low-Grade Gliomas: State-of-the-Art and Emerging Techniques: A Systematic Review

Diffuse low-grade gliomas are infiltrative tumors whose margins are not distinguishable from the adjacent healthy brain parenchyma. The aim was to precisely examine the results provided by the intraoperative use of macroscopic fluorescence in diffuse low-grade gliomas and to describe the new fluorescence-based techniques capable of guiding the resection of low-grade gliomas. Only about 20% and 50% of low-grade gliomas are macroscopically fluorescent after 5-amino-levulinic acid (5-ALA) or fluorescein sodium intake, respectively. However, 5-ALA is helpful for detecting anaplastic foci, and thus choosing the best biopsy targets in diffuse gliomas. Spectroscopic detection of 5-ALA-induced fluorescence can detect very low and non-macroscopically visible concentrations of protoporphyrin IX, a 5-ALA metabolite, and, consequently, has excellent performances for the detection of low-grade gliomas. Moreover, these tumors have a specific spectroscopic signature with two fluorescence emission peaks, which is useful for distinguishing them not only from healthy brain but also from high-grade gliomas. Confocal laser endomicroscopy can generate intraoperative optic biopsies, but its sensitivity remains limited. In the future, the coupled measurement of autofluorescence and induced fluorescence, and the introduction of fluorescence detection technologies providing a wider field of view could result in the development of operator-friendly tools implementable in the operative routine.

Towards machine learning-based quantitative hyperspectral image guidance for brain tumor resection

BACKGROUND

Complete resection of malignant gliomas is hampered by the difficulty in distinguishing tumor cells at the infiltration zone. Fluorescence guidance with 5-ALA assists in reaching this goal. Using hyperspectral imaging, previous work characterized five fluorophores' emission spectra in most human brain tumors.

METHODS

In this paper, the effectiveness of these five spectra was explored for different tumor and tissue classification tasks in 184 patients (891 hyperspectral measurements) harboring low- (n = 30) and high-grade gliomas (n = 115), non-glial primary brain tumors (n = 19), radiation necrosis (n = 2), miscellaneous (n = 10) and metastases (n = 8). Four machine-learning models were trained to classify tumor type, grade, glioma margins, and IDH mutation.

RESULTS

Using random forests and multilayer perceptrons, the classifiers achieve average test accuracies of 84-87%, 96.1%, 86%, and 91% respectively. All five fluorophore abundances vary between tumor margin types and tumor grades (p < 0.01). For tissue type, at least four of the five fluorophore abundances are significantly different (p < 0.01) between all classes.

CONCLUSIONS

These results demonstrate the fluorophores' differing abundances in different tissue classes and the value of the five fluorophores as potential optical biomarkers, opening new opportunities for intraoperative classification systems in fluorescence-guided neurosurgery.

Protoporphyrin IX in serum of high-grade glioma patients: A novel target for disease monitoring via liquid biopsy

High-grade gliomas (HGG) carry a dismal prognosis. Diagnosis comprises MRI followed by histopathological evaluation of tissue; no blood biomarker is available. Patients are subjected to serial MRIs and, if unclear, surgery for monitoring of tumor recurrence, which is laborious. MRI provides only limited diagnostic information regarding the differentiation of true tumor progression from therapy-associated side effects. 5-aminolevulinic acid (5-ALA) is routinely used for induction of protoporphyrin IX (PpIX) accumulation in malignant glioma tissue, enabling improved tumor visualization during fluorescence-guided resection (FGR). We investigated whether PpIX can also serve as a serum HGG marker to monitor relapse. Patients (HGG: n = 23 primary, pHGG; n = 5 recurrent, rHGG) undergoing FGR received 5-ALA following standard clinical procedure. The control group of eight healthy volunteers (HCTR) also received 5-ALA. Serum was collected before and repeatedly up to 72 h after drug administration. Significant PpIX accumulation in HGG was observed after 5-ALA administration (ANOVA: p = 0.005, post-hoc: HCTR vs. pHGG p = 0.029, HCTR vs. rHGG p = 0.006). Separation of HCTR from pHGG was possible when maximum serum PpIX levels were reached (CI95% of tMax). ROC analysis of serum PpIX within CI95% of tMax showed successful classification of HCTR and pHGG (AUCROC 0.943, CI95% 0.884-1.000, p < 0.001); the optimal cut-off for diagnosis was 1275 pmol PpIX/ml serum, reaching 87.0% accuracy, 90.5% positive predictive and 84.0% negative predictive value. Baseline PpIX level was similar in patient and control groups. Thus, 5-ALA is required for PpIX induction, which is safe at the standard clinical dosage. PpIX is a new target for liquid biopsy in glioma. More extensive clinical studies are required to characterize its full potential.

Machine and Deep Learning in Hyperspectral Fluorescence-Guided Brain Tumor Surgery

Malignant glioma resection is often the first line of treatment in neuro-oncology. During glioma surgery, the discrimination of tumor's edges can be challenging at the infiltration zone, even by using surgical adjuncts such as fluorescence guidance (e.g., with 5-aminolevulinic acid). Challenging cases in which there is no visible fluorescence include lower-grade gliomas, tumor cells infiltrating beyond the margin as visualized on pre- and/or intraoperative MRI, and even some high-grade tumors. One field of research aiming to address this problem involves inspecting in detail the light emission spectra from different tissues (e.g., tumor vs. normal brain vs. brain parenchyma infiltrated by tumor cells). Hyperspectral imaging measures the emission spectrum at every image pixel level, thus combining spatial and spectral information. Assuming that different tissue types have different "spectral footprints," eventually related to higher or lower abundances of fluorescent dyes or auto-fluorescing molecules, the tissue can then be segmented according to type, providing surgeons a detailed spatial map of what they see. However, processing from raw hyperspectral data cubes to maps or overlays of tissue labels and potentially further molecular information is complex. This chapter will explore some of the classical methods for the various steps of this process and examine how they can be improved with machine learning approaches. While preliminary work on machine learning in hyperspectral imaging has had relatively limited success in brain tumor surgery, more recent research combines this with fluorescence to obtain promising results. In particular, this chapter describes a pipeline that isolates biopsies in ex vivo hyperspectral fluorescence images for efficient labeling, extracts all the relevant emission spectra, preprocesses them to correct for various optical properties, and determines the abundance of fluorophores in each pixel, which correspond directly with the presence of cancerous tissue. Each step contains a combination of classical and deep learning-based methods. Furthermore, the fluorophore abundances are then used in four machine learning models to classify tumor type, WHO grade, margin tissue type, and isocitrate dehydrogenase (IDH) mutation status in brain tumors. The classifiers achieved average test accuracies of 87%, 96.1%, 86%, and 93%, respectively, thus greatly outperforming prior work both with and without fluorescence. This field is new, but these early results show great promise for the feasibility of data-driven hyperspectral imaging for intraoperative classification of brain tumors during fluorescence-guided surgery.

Supramaximal Resection Can Prolong the Survival of Patients with Cortical Glioblastoma: A Volumetric Study

We aimed to retrospectively determine the resection rate of fluid-attenuated inversion recovery (FLAIR) lesions to evaluate the clinical effects of supramaximal resection (SMR) on the survival of patients with glioblastoma (GBM). Thirty-three adults with newly diagnosed GBM who underwent gross total tumor resection were enrolled. The tumors were classified into cortical and deep-seated groups according to their contact with the cortical gray matter. Pre- and postoperative FLAIR and gadolinium-enhanced T1-weighted imaging tumor volumes were measured using a three-dimensional imaging volume analyzer, and the resection rate was calculated. To evaluate the association between SMR rate and outcome, we subdivided patients whose tumors were totally resected into the SMR and non-SMR groups by moving the threshold value of SMR in 10% increments from 0% and compared their overall survival (OS) change. An improvement in OS was observed when the threshold value of SMR was 30% or more. In the cortical group (n = 23), SMR (n = 8) tended to prolong OS compared with gross total resection (GTR) (n = 15), with the median OS of 69.6 and 22.1 months, respectively (p = 0.0945). Contrastingly, in the deep-seated group (n = 10), SMR (n = 4) significantly shortened OS compared with GTR (n = 6), with median OS of 10.2 and 27.9 months, respectively (p = 0.0221). SMR could help prolong OS in patients with cortical GBM when 30% or more volume reduction is achieved in FLAIR lesions, although the impact of SMR for deep-seated GBM must be validated in larger cohorts.

5-Aminolevulinic Acid as a Theranostic Agent for Tumor Fluorescence Imaging and Photodynamic Therapy

5-Aminolevulinic acid (ALA) is a naturally occurring amino acid synthesized in all nucleated mammalian cells. As a porphyrin precursor, ALA is metabolized in the heme biosynthetic pathway to produce protoporphyrin IX (PpIX), a fluorophore and photosensitizing agent. ALA administered exogenously bypasses the rate-limit step in the pathway, resulting in PpIX accumulation in tumor tissues. Such tumor-selective PpIX disposition following ALA administration has been exploited for tumor fluorescence diagnosis and photodynamic therapy (PDT) with much success. Five ALA-based drugs have now received worldwide approval and are being used for managing very common human (pre)cancerous diseases such as actinic keratosis and basal cell carcinoma or guiding the surgery of bladder cancer and high-grade gliomas, making it the most successful drug discovery and development endeavor in PDT and photodiagnosis. The potential of ALA-induced PpIX as a fluorescent theranostic agent is, however, yet to be fully fulfilled. In this review, we would like to describe the heme biosynthesis pathway in which PpIX is produced from ALA and its derivatives, summarize current clinical applications of ALA-based drugs, and discuss strategies for enhancing ALA-induced PpIX fluorescence and PDT response. Our goal is two-fold: to highlight the successes of ALA-based drugs in clinical practice, and to stimulate the multidisciplinary collaboration that has brought the current success and will continue to usher in more landmark advances.

Challenges in, and recommendations for, hyperspectral imaging in ex vivo malignant glioma biopsy measurements

The visualization of protoporphyrin IX (PPIX) fluorescence with the help of surgical microscopes during 5-aminolevulinic acid-mediated fluorescence-guided resection (FGR) of gliomas is still limited at the tumor margins. Hyperspectral imaging (HI) detects PPIX more sensitively but is not yet ready for intraoperative use. We illustrate the current status with three experiments and summarize our own experience using HI: (1) assessment of HI analysis algorithm using pig brain tissue, (2) a partially retrospective evaluation of our experience from HI projects, and (3) device comparison of surgical microscopy and HI. In (1), we address the problem that current algorithms for evaluating HI data are based on calibration with liquid phantoms, which have limitations. Their pH is low compared to glioma tissue; they provide only one PPIX photo state and only PPIX as fluorophore. Testing the HI algorithm with brain homogenates, we found proper correction for optical properties but not pH. Considerably more PPIX was measured at pH 9 than at pH 5. In (2), we indicate pitfalls and guide HI application. In (3), we found HI superior to the microscope for biopsy diagnosis (AUC = 0.845 ± 0.024 (cut-off 0.75 µg PPIX/ml) vs. 0.710 ± 0.035). HI thus offers potential for improved FGR.