The near-infrared autofluorescence fingerprint of the brain

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.

Intraoperative microscopic autofluorescence detection and characterization in brain tumors using stimulated Raman histology and two-photon fluorescence

Introduction

The intrinsic autofluorescence of biological tissues interferes with the detection of fluorophores administered for fluorescence guidance, an emerging auxiliary technique in oncological surgery. Yet, autofluorescence of the human brain and its neoplasia is sparsely examined. This study aims to assess autofluorescence of the brain and its neoplasia on a microscopic level by stimulated Raman histology (SRH) combined with two-photon fluorescence.

Methods

With this experimentally established label-free microscopy technique unprocessed tissue can be imaged and analyzed within minutes and the process is easily incorporated in the surgical workflow. In a prospective observational study, we analyzed 397 SRH and corresponding autofluorescence images of 162 samples from 81 consecutive patients that underwent brain tumor surgery. Small tissue samples were squashed on a slide for imaging. SRH and fluorescence images were acquired with a dual wavelength laser (790 nm and 1020 nm) for excitation. In these images tumor and non-tumor regions were identified by a convolutional neural network that reliably differentiates between tumor, healthy brain tissue and low quality SRH images. The identified areas were used to define regions.of- interests (ROIs) and the mean fluorescence intensity was measured.

Results

In healthy brain tissue, we found an increased mean autofluorescence signal in the gray (11.86, SD 2.61, n=29) compared to the white matter (5.99, SD 5.14, n=11, p<0.01) and in the cerebrum (11.83, SD 3.29, n=33) versus the cerebellum (2.82, SD 0.93, n=7, p<0.001), respectively. The signal of carcinoma metastases, meningiomas, gliomas and pituitary adenomas was significantly lower (each p<0.05) compared to the autofluorescence in the cerebrum and dura, and significantly higher (each p<0.05) compared to the cerebellum. Melanoma metastases were found to have a higher fluorescent signal (p<0.01) compared to cerebrum and cerebellum.

Discussion

In conclusion we found that autofluorescence in the brain varies depending on the tissue type and localization and differs significantly among various brain tumors. This needs to be considered for interpreting photon signal during fluorescence-guided brain tumor surgery.

Clinical applications of non-invasive multi and hyperspectral imaging of cell and tissue autofluorescence beyond oncology

Hyperspectral and multispectral imaging of cell and tissue autofluorescence employs fluorescence imaging, without exogenous fluorophores, across multiple excitation/emission combinations (spectral channels). This produces an image stack where each pixel (matched by location) contains unique information about the sample's spectral properties. Analysis of this data enables access to a rich, molecularly specific data set from a broad range of cell-native fluorophores (autofluorophores) directly reflective of biochemical status, without use of fixation or stains. This non-invasive, non-destructive technology has great potential to spare the collection of biopsies from sensitive regions. As both staining and biopsy may be impossible, or undesirable, depending on the context, this technology great diagnostic potential for clinical decision making. The main research focus has been on the identification of neoplastic tissues. However, advances have been made in diverse applications-including ophthalmology, cardiovascular health, neurology, infection, assisted reproduction technology and organ transplantation.

In vivo grading of lipids in fatty liver by near-infrared autofluorescence and reflectance

The prevalence of nonalcoholic fatty liver (NAFLD) is rapidly increasing worldwide. When untreated, it may lead to complications such as liver cirrhosis or hepatocarcinoma. The diagnosis of NAFLD is usually obtained by ultrasonography, a technique that can underestimate its prevalence. For this reason, physicians aspire for an accurate, cost-effective, and noninvasive method to determine both the presence and the specific stage of the NAFLD. In this paper, we report an integrated approach for the quantitative estimation of the density of triglycerides in the liver based on the use of autofluorescence and reflectance signals generated by the abdomen of obese C57BL6/J mice. Singular value decomposition is applied to the generated spectra and its corresponding regression model provided a determination coefficient of 0.99 and a root mean square error of 240 mg/dl. This, in turn, enabled the quantitative imaging of triglycerides density in the livers of mice under in vivo conditions.

Second near-infrared (NIR-II) imaging: a novel diagnostic technique for brain diseases

Imaging in the second near-infrared II (NIR-II) window, a kind of biomedical imaging technology with characteristics of high sensitivity, high resolution, and real-time imaging, is commonly used in the diagnosis of brain diseases. Compared with the conventional visible light (400-750 nm) and NIR-I (750-900 nm) imaging, the NIR-II has a longer wavelength of 1000-1700 nm. Notably, the superiorities of NIR-II can minimize the light scattering and autofluorescence of biological tissue with the depth of brain tissue penetration up to 7.4 mm. Herein, we summarized the main principles of NIR-II in animal models of traumatic brain injury, cerebrovascular visualization, brain tumor, inflammation, and stroke. Simultaneously, we encapsulated the in vivo process of NIR-II probes and their in vivo and in vitro toxic effects. We further dissected its limitations and following optimization measures.

Nanoprobes-Assisted Multichannel NIR-II Fluorescence Imaging-Guided Resection and Photothermal Ablation of Lymph Nodes

Lymph node metastasis is a major metastatic route of cancer and significantly influences the prognosis of cancer patients. Radical lymphadenectomy is crucial for a successful surgery. However, iatrogenic normal organ injury during lymphadenectomy is a troublesome complication. Here, this paper reports a kind of organic nanoprobes (IDSe-IC2F nanoparticles (NPs)) with excellent second near-infrared (NIR-II) fluorescence and photothermal properties. IDSe-IC2F NPs can effectively label lymph nodes and helped achieve high-contrast lymphatic imaging. More importantly, by jointly using IDSe-IC2F nanoparticles and other kinds of nanoparticles with different excitation/emission properties, a multichannel NIR-II fluorescence imaging modality and imaging-guided lymphadenectomy is proposed. With the help of this navigation system, the iatrogenic injury can be largely avoided. In addition, NIR-II fluorescence imaging-guided photothermal treatment ("hot" strategy) can ablate those metastatic lymph nodes which are difficult to deal with during resection ("cold" strategy). Nanoprobes-assisted and multichannel NIR-II fluorescence imaging-guided "cold" and "hot" treatment strategy provides a general new basis for the future precision surgery.

Autofluorescence-Free In Vivo Imaging Using Polymer-Stabilized Nd3+-Doped YAG Nanocrystals

Neodymium-doped yttrium aluminum garnet (YAG:Nd³⁺) has been widely developed during roughly the past 60 years and has been an outstanding fluorescent material. It has been considered as the gold standard among multipurpose solid-state lasers. Yet, the successful downsizing of this system into the nanoregimen has been elusive, so far. Indeed, the synthesis of a garnet structure at the nanoscale, with enough crystalline quality for optical applications, was found to be quite challenging. Here, we present an improved solvothermal synthesis method producing YAG:Nd³⁺ nanocrystals of remarkably good structural quality. Adequate surface functionalization using asymmetric double-hydrophilic block copolymers, constituted of a metal-binding block and a neutral water-soluble block, provides stabilized YAG:Nd³⁺ nanocrystals with long-term colloidal stability in aqueous suspensions. These newly stabilized nanoprobes offer spectroscopic quality (long lifetimes, narrow emission lines, and large Stokes shifts) close to that of bulk YAG:Nd³⁺. The narrow emission lines of YAG:Nd³⁺ nanocrystals are exploited by differential infrared fluorescence imaging, thus achieving an autofluorescence-free in vivo readout. In addition, nanothermometry measurements, based on the ratiometric fluorescence of the stabilized YAG:Nd³⁺ nanocrystals, are demonstrated. The progress here reported paves the way for the implementation of this new stabilized YAG:Nd³⁺ system in the preclinical arena.