Optical coherence tomography for precision brain imaging, neurosurgical guidance and minimally invasive theranostics

Optical Methods for Brain Tumor Detection: A Systematic Review

Background: In brain tumor surgery, maximal tumor resection is typically desired. This is complicated by infiltrative tumor cells which cannot be visually distinguished from healthy brain tissue. Optical methods are an emerging field that can potentially revolutionize brain tumor surgery through intraoperative differentiation between healthy and tumor tissues. Methods: This study aimed to systematically explore and summarize the existing literature on the use of Raman Spectroscopy (RS), Hyperspectral Imaging (HSI), Optical Coherence Tomography (OCT), and Diffuse Reflectance Spectroscopy (DRS) for brain tumor detection. MEDLINE, Embase, and Web of Science were searched for studies evaluating the accuracy of these systems for brain tumor detection. Outcome measures included accuracy, sensitivity, and specificity. Results: In total, 44 studies were included, covering a range of tumor types and technologies. Accuracy metrics in the studies ranged between 54 and 100% for RS, 69 and 99% for HSI, 82 and 99% for OCT, and 42 and 100% for DRS. Conclusions: This review provides insightful evidence on the use of optical methods in distinguishing tumor from healthy brain tissue.

Delineation of three-dimensional tumor margins based on normalized absolute difference mapping via volumetric optical coherence tomography

The extent of surgical resection is an important prognostic factor in the treatment of patients with glioblastoma. Optical coherence tomography (OCT) imaging is one of the adjunctive methods available to achieve the maximal surgical resection. In this study, the tumor margins were visualized with the OCT image obtained from a murine glioma model. A commercialized human glioblastoma cell line (U-87) was employed to develop the orthotopic murine glioma model. A swept-source OCT (SS-OCT) system of 1300 nm was used for three-dimensional imaging. Based on the OCT intensity signal, which was obtained via accumulation of each A-scan data, an en-face optical attenuation coefficient (OAC) map was drawn. Due to the limited working distance of the focused beam, OAC values decrease with depth, and using the OAC difference in the superficial area was chosen to outline the tumor boundary, presenting a challenge in analyzing the tumor margin along the depth direction. To overcome this and enable three-dimensional tumor margin detection, we converted the en-face OAC map into an en-face difference map with x- and y-directions and computed the normalized absolute difference (NAD) at each depth to construct a volumetric NAD map, which was compared with the corresponding H&E-stained image. The proposed method successfully revealed the tumor margin along the peripheral boundaries as well as the margin depth. We believe this method can serve as a useful adjunct in glioma surgery, with further studies necessary for real-world practical applications.

The State-of-the-Art and Perspectives of Laser Ablation for Tumor Treatment

Tumors significantly impact individuals' physical well-being and quality of life. With the ongoing advancements in optical technology, information technology, robotic technology, etc., laser technology is being increasingly utilized in the field of tumor treatment, and laser ablation (LA) of tumors remains a prominent area of research interest. This paper presents an overview of the recent progress in tumor LA therapy, with a focus on the mechanisms and biological effects of LA, commonly used ablation lasers, image-guided LA, and robotic-assisted LA. Further insights and future prospects are discussed in relation to these aspects, and the paper proposed potential future directions for the development of tumor LA techniques.

Optimizing maximum resection of glioblastoma: Raman spectroscopy versus 5-aminolevulinic acid

OBJECTIVE

The objective of this study was to assess and compare the potential of 5-aminolevulinic acid (5-ALA) and Raman spectroscopy (RS) in detecting tumor-infiltrated brain in patients with glioblastoma (GBM).

METHODS

Between July 2020 and October 2021, the authors conducted a prospective clinical trial with 15 patients who underwent neurosurgical treatment of newly diagnosed and histologically verified GBM. A solid contrast-enhancing tumor core and peritumoral tissue were investigated intraoperatively for cancer cells by using 5-ALA and RS to achieve pathology-tailored maximum resection. In each case, a minimum of 10 biopsies were sampled from navigation-guided areas. Two neuropathologists examined the biopsies for the presence of neoplastic cells. The detection performance of 5-ALA and RS alone and in combination was assessed. Pre- and postoperative MRI, Karnofsky Performance Status (KPS), and National Institutes of Health Stroke Scale (NIHSS) scores were compared, and median progression-free survival (PFS) was evaluated.

RESULTS

A total of 185 biopsy samples were harvested from the contrast-enhancing tumor core (n = 19) and peritumoral tissue (n = 166). In the tumor core, 5-ALA and RS each showed a sensitivity of 100%. In the peritumoral tissue, 5-ALA was less sensitive than RS in detecting cancer (46% vs 69%) but showed higher specificity (81% vs 57%). When the two methods were combined, the accuracy of tumor detection was increased by about 10%. Pathology-tailored resection led to a 52% increase in resection volume comparing the volume of preoperative contrast enhancement with the postoperative resection cavity on MRI (p = 0.0123). Eloquent brain involvement prevented gross-total resection in 4 patients. Four weeks after surgery, mean KPS (p = 0.7637) and NIHSS scores (p = 0.3146) were not significantly different from preoperative values. Of the 13 patients who had received postoperative chemoradiotherapy, 4 did not show any progression after a median follow-up of 14 months. The remaining 9 patients had a median PFS of 8 months.

CONCLUSIONS

According to the study data, RS is capable of detecting tumor-infiltrated brain with higher sensitivity but lower specificity than the current standard of 5-ALA. With further technological and workflow advancements, RS in combination with protoporphyrin IX fluorescence may contribute to pathology-tailored glioma resection in the future.

Artificial Intelligence-Aided Optical Imaging for Cancer Theranostics

The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-making for individualized cancer medicine. In particular, optical imaging methods are able to visualize both the structural and functional information of tumors tissues with high contrast, low cost, and noninvasive property. However, no systematic work has been performed to inspect the recent advances on AI-aided optical imaging for cancer theranostics. In this review, we demonstrated how AI can guide optical imaging methods to improve the accuracy on tumor detection, automated analysis and prediction of its histopathological section, its monitoring during treatment, and its prognosis by using computer vision, deep learning and natural language processing. By contrast, the optical imaging techniques involved mainly consisted of various tomography and microscopy imaging methods such as optical endoscopy imaging, optical coherence tomography, photoacoustic imaging, diffuse optical tomography, optical microscopy imaging, Raman imaging, and fluorescent imaging. Meanwhile, existing problems, possible challenges and future prospects for AI-aided optical imaging protocol for cancer theranostics were also discussed. It is expected that the present work can open a new avenue for precision oncology by using AI and optical imaging tools.

Noninvasive, continuous fluorescence monitoring of bilirubin photodegradation

Nowadays phototherapy is widely used for treatment of various diseases. However, efficient application of phototherapy requires an understanding of light interactions with main endogenous chromophores (e.g., hemoglobin, bilirubin, and water) in tissue. In particular, bilirubin is the target chromophore in the treatment of neonatal jaundice, which is the most common disease affecting up to 80% of preterm infants. The most frequently recommended treatment technique for this disease is phototherapy with blue light in combination with conventional drug therapy. To follow threshold total serum bilirubin (TSB) concentration guidelines, it is essential to estimate TSB concentration accurately. The gold standard biochemical analysis is invasive and bulky. Moreover, noninvasive methods do not provide sufficient reproducibility and accuracy. In this research, the fluorescence sensing of bilirubin with human serum albumin complexes was studied. The fluorescence time course during light irradiation (central wavelength: 467 nm and power density: 12.13 mW cm^-2) was demonstrated to depend on the initial concentration. Specifically, for the bilirubin concentration C = 18.65 μM, an insignificant fluorescence signal increase was observed during the first 30 minutes of light irradiation, while for bilirubin concentration C = 373 μM, the fluorescence signal did not reach maximum during 2.5 hours of light irradiation. Thus, fluorescence sensing might show increased accuracy when used with other noninvasive bilirubin sensing methods.

Vascular elasticity measurement of the great saphenous vein based on optical coherence elastography

Vascular elasticity is important in physiological and clinical problems. The mechanical properties of the great saphenous vein (GSV) deserve attention. This research aims to measure the radial elasticity of ex vivo GSV using the optical coherence elasticity (OCE). The finite element model of the phantom is established, the displacement field is calculated, the radial mechanical characteristics of the simulation body are obtained. Furthermore, we performed OCE on seven isolated GSVs. The strain field is obtained by combining the relationship between strain and displacement to obtain the radial elastic modulus of GSVs. In the phantom experiment, the strain of the experimental region of interest is mainly between 0.1 and 0.4, while the simulation result is between 0.06 and 0.40. The radial elastic modulus of GSVs ranged from 3.83 kPa to 7.74 kPa. This study verifies the feasibility of the OCE method for measuring the radial elastic modulus of blood vessels.

Research progress on the application of optical coherence tomography in the field of oncology

Optical coherence tomography (OCT) is a non-invasive imaging technique which has become the “gold standard” for diagnosis in the field of ophthalmology. However, in contrast to the eye, nontransparent tissues exhibit a high degree of optical scattering and absorption, resulting in a limited OCT imaging depth. And the progress made in the past decade in OCT technology have made it possible to image nontransparent tissues with high spatial resolution at large (up to 2mm) imaging depth. On the one hand, OCT can be used in a rapid, noninvasive way to detect diseased tissues, organs, blood vessels or glands. On the other hand, it can also identify the optical characteristics of suspicious parts in the early stage of the disease, which is of great significance for the early diagnosis of tumor diseases. Furthermore, OCT imaging has been explored for imaging tumor cells and their dynamics, and for the monitoring of tumor responses to treatments. This review summarizes the recent advances in the OCT area, which application in oncological diagnosis and treatment in different types: (1) superficial tumors:OCT could detect microscopic information on the skin’s surface at high resolution and has been demonstrated to help diagnose common skin cancers; (2) gastrointestinal tumors: OCT can be integrated into small probes and catheters to image the structure of the stomach wall, enabling the diagnosis and differentiation of gastrointestinal tumors and inflammation; (3) deep tumors: with the rapid development of OCT imaging technology, it has shown great potential in the diagnosis of deep tumors such in brain tumors, breast cancer, bladder cancer, and lung cancer.

Intelligent Ultra-Light Deep Learning Model for Multi-Class Brain Tumor Detection

The diagnosis and surgical resection using Magnetic Resonance (MR) images in brain tumors is a challenging task to minimize the neurological defects after surgery owing to the non-linear nature of the size, shape, and textural variation. Radiologists, clinical experts, and brain surgeons examine brain MRI scans using the available methods, which are tedious, error-prone, time-consuming, and still exhibit positional accuracy up to 2–3 mm, which is very high in the case of brain cells. In this context, we propose an automated Ultra-Light Brain Tumor Detection (UL-BTD) system based on a novel Ultra-Light Deep Learning Architecture (UL-DLA) for deep features, integrated with highly distinctive textural features, extracted by Gray Level Co-occurrence Matrix (GLCM). It forms a Hybrid Feature Space (HFS), which is used for tumor detection using Support Vector Machine (SVM), culminating in high prediction accuracy and optimum false negatives with limited network size to fit within the average GPU resources of a modern PC system. The objective of this study is to categorize multi-class publicly available MRI brain tumor datasets with a minimum time thus real-time tumor detection can be carried out without compromising accuracy. Our proposed framework includes a sensitivity analysis of image size, One-versus-All and One-versus-One coding schemes with stringent efforts to assess the complexity and reliability performance of the proposed system with K-fold cross-validation as a part of the evaluation protocol. The best generalization achieved using SVM has an average detection rate of 99.23% (99.18%, 98.86%, and 99.67%), and F-measure of 0.99 (0.99, 0.98, and 0.99) for (glioma, meningioma, and pituitary tumors), respectively. Our results have been found to improve the state-of-the-art (97.30%) by 2%, indicating that the system exhibits capability for translation in modern hospitals during real-time surgical brain applications. The method needs 11.69 ms with an accuracy of 99.23% compared to 15 ms achieved by the state-of-the-art to earlier to detect tumors on a test image without any dedicated hardware providing a route for a desktop application in brain surgery.

Brain white matter morphological structure correlation with its optical properties estimated from optical coherence tomography (OCT) data

A pilot post-mortem study identifies a strong correlation between the attenuation coefficient estimated from the OCT data and some morphological features of the sample, namely the number of nuclei in the field of view of the histological image and the fiber structural parameter introduced in the study to quantify the difference in the myelinated fibers arrangements. The morphological features were identified from the histopathological images of the sample taken from the same locations as the OCT images and stained with the immunohistochemical (IHC) staining specific to the myelin. It was shown that the linear regression of the IHC quantitative characteristics allows adequate prediction of the attenuation coefficient of the sample. This discovery opens the opportunity for the usage of the OCT as a neuronavigation tool.

Classification of gastric cancerous tissues by a residual network based on optical coherence tomography images

Optical coherence tomography (OCT) is a noninvasive, radiation-free, and high-resolution imaging technology. The intraoperative classification of normal and cancerous tissue is critical for surgeons to guide surgical operations. Accurate classification of gastric cancerous OCT images is beneficial to improve the effect of surgical treatment based on the deep learning method. The OCT system was used to collect images of cancerous tissues removed from patients. An intelligent classification method of gastric cancerous tissues based on the residual network is proposed in this study and optimized with the ResNet18 model. Four residual blocks are used to reset the model structure of ResNet18 and reduce the number of network layers to identify cancerous tissues. The model performance of different residual networks is evaluated by accuracy, precision, recall, specificity, F1 value, ROC curve, and model parameters. The classification accuracies of the proposed method and ResNet18 both reach 99.90%. Also, the model parameters of the proposed method are 44% of ResNet18, which occupies fewer system resources and is more efficient. In this study, the proposed deep learning method was used to automatically recognize OCT images of gastric cancerous tissue. This artificial intelligence method could help promote the clinical application of gastric cancerous tissue classification in the future.

Molecular Contrast Optical Coherence Tomography and Its Applications in Medicine

The growing need to understand the molecular mechanisms of diseases has prompted the revolution in molecular imaging techniques along with nanomedicine development. Conventional optical coherence tomography (OCT) is a low-cost in vivo imaging modality that provides unique high spatial and temporal resolution anatomic images but little molecular information. However, given the widespread adoption of OCT in research and clinical practice, its robust molecular imaging extensions are strongly desired to combine with anatomical images. A range of relevant approaches has been reported already. In this article, we review the recent advances of molecular contrast OCT imaging techniques, the corresponding contrast agents, especially the nanoparticle-based ones, and their applications. We also summarize the properties, design criteria, merit, and demerit of those contrast agents. In the end, the prospects and challenges for further research and development in this field are outlined.

OCT-Guided Surgery for Gliomas: Current Concept and Future Perspectives

Optical coherence tomography (OCT) has been recently suggested as a promising method to obtain in vivo and real-time high-resolution images of tissue structure in brain tumor surgery. This review focuses on the basics of OCT imaging, types of OCT images and currently suggested OCT scanner devices and the results of their application in neurosurgery. OCT can assist in achieving intraoperative precision identification of tumor infiltration within surrounding brain parenchyma by using qualitative or quantitative OCT image analysis of scanned tissue. OCT is able to identify tumorous tissue and blood vessels detection during stereotactic biopsy procedures. The combination of OCT with traditional imaging such as MRI, ultrasound and 5-ALA fluorescence has the potential to increase the safety and accuracy of the resection. OCT can improve the extent of resection by offering the direct visualization of tumor with cellular resolution when using microscopic OCT contact probes. The theranostic implementation of OCT as a part of intelligent optical diagnosis and automated lesion localization and ablation could achieve high precision, automation and intelligence in brain tumor surgery. We present this review for the increase of knowledge and formation of critical opinion in the field of OCT implementation in brain tumor surgery.

Theranostic applications of optical coherence tomography in neurosurgery?

In light of our own experiences, we value the existing literature to critically point out possible “near” future applications of optical coherence tomography (OCT) as an intraoperative neurosurgical guidance tool. “Pub Med”, “Cochrane Library”, “Crossref Metadata Search”, and “IEEE Xplore” databases as well as the search engine “Google Scholar” were screened for “optical coherence tomography + neurosurgery”, “optical coherence tomography + intraoperative imaging + neurosurgery”, and “microscope integrated optical coherence tomography + neurosurgery”. n = 51 articles related to the use of OCT as an imaging technique in the field of neurosurgery or neurosurgical research. n = 7 articles documented the intraoperative use of OCT in patients. n = 4 articles documented the use of microscope-integrated optical coherence tomography as a neurosurgical guidance tool. The Results demonstrate that OCT is the first imaging technique to study microanatomy in vivo. Postoperative analysis of intraoperative scans holds promise to enrich our physiological and pathophysiological understanding of the human brain. No data exists to prove that OCT-guided surgery minimizes perioperative morbidity or extends tumor resection. But results suggest that regular use of microscope-integrated OCT could increase security during certain critical microsurgical steps like, e.g., dural dissection at cavernous sinus, transtentorial approaches, or aneurysm clip placement. Endoscopy integration could aid surgery in regions which are not yet accessible to real-time imaging modalities like the ventricles or hypophysis. Theranostic instruments which combine OCT with laser ablation might gain importance in the emerging field of minimal invasive tumor surgery. OCT depicts vessel wall layers and its pathologies uniquely. Doppler OCT could further visualize blood flow in parallel. These abilities shed light on promising future applications in the field of vascular neurosurgery.

Intelligent optical diagnosis and treatment system for automated image-guided laser ablation of tumors

PURPOSE

For tumor resections near critical structures, accurate identification of tumor boundaries and maximum removal are the keys to improve surgical outcome and patient survival rate, especially in neurosurgery. In this paper, we propose an intelligent optical diagnosis and treatment system for tumor removal, with automated lesion localization and laser ablation.

METHODS

The proposed system contains a laser ablation module, an optical coherence tomography (OCT) unit, and a robotic arm along with a stereo camera. The robotic arm can move the OCT sample arm and the laser ablation front-end to the suspected lesion area. The corresponding diagnosis and treatment procedures include computer-aided lesion segmentation using OCT, automated ablation planning, and laser control. The ablation process is controlled by a deflectable mirror, and a non-common-path ablation planning algorithm based on the transformation from lesion positions to mirror deflection angles is presented.

RESULTS

Phantom and animal experiments are carried out for system verification. The robot could reach the planned position with high precision, which is approximately 1.16 mm. Tissue classification with OCT images achieves 91.7% accuracy. The error of OCT-guided automated laser ablation is approximately 0.74 mm. Experiments on mouse brain tumors show that the proposed system is capable of clearing lesions efficiently and precisely. We also conducted an ex vivo porcine brain experiment to verify the whole process of the system.

CONCLUSION

An intelligent optical diagnosis and treatment system is proposed for tumor removal. Experimental results show that the proposed system and method are promising for precise and intelligent theranostics. Compared to conventional cancer diagnosis and treatment, the proposed system allows for automated operations monitored in real-time, with higher precision and efficiency.

Applying machine learning to optical coherence tomography images for automated tissue classification in brain metastases

Purpose

A precise resection of the entire tumor tissue during surgery for brain metastases is essential to reduce local recurrence. Conventional intraoperative imaging techniques all have limitations in detecting tumor remnants. Therefore, there is a need for innovative new imaging methods such as optical coherence tomography (OCT). The purpose of this study is to discriminate brain metastases from healthy brain tissue in an ex vivo setting by applying texture analysis and machine learning algorithms for tissue classification to OCT images.

Methods

Tumor and healthy tissue samples were collected during resection of brain metastases. Samples were imaged using OCT. Texture features were extracted from B-scans. Then, a machine learning algorithm using principal component analysis (PCA) and support vector machines (SVM) was applied to the OCT scans for classification. As a gold standard, an experienced pathologist examined the tissue samples histologically and determined the percentage of vital tumor, necrosis and healthy tissue of each sample. A total of 14.336 B-scans from 14 tissue samples were included in the classification analysis.

Results

We were able to discriminate vital tumor from healthy brain tissue with an accuracy of 95.75%. By comparing necrotic tissue and healthy tissue, a classification accuracy of 99.10% was obtained. A generalized classification between brain metastases (vital tumor and necrosis) and healthy tissue was achieved with an accuracy of 96.83%.

Conclusions

An automated classification of brain metastases and healthy brain tissue is feasible using OCT imaging, extracted texture features and machine learning with PCA and SVM. The established approach can prospectively provide the surgeon with additional information about the tissue, thus optimizing the extent of tumor resection and minimizing the risk of local recurrences.

Optical coherence tomography for the early detection of colorectal dysplasia and cancer: validation in a murine model

BACKGROUND

There is an urgent need to develop a non-invasive imaging technique for detecting colorectal dysplasia and cancer. Technology for early and real-time microscopic assessments to select the most representative biopsy sites would also be of clinical value. In this study, we explored the sensitivity of optical coherence tomography (OCT) in detecting local lesions to demonstrate its potential for the early detection of colorectal dysplasia and cancer.

METHODS

An azoxymethane/dextran sodium sulfate mouse model of colorectal carcinogenesis was utilized. Mice were imaged by OCT, and colorectal tissue sections were observed with hematoxylin and eosin staining. The results of the parallel analyses were compared to evaluate the performance of OCT in imaging and early screening of colorectal lesions.

RESULTS

Dysplasia and cancer could be distinguished from normal colon tissues based on the OCT images. However, simple morphological changes observed in the OCT images were not sufficient to distinguish different degrees of dysplasia or distinguish dysplasia from cancerous tissues. The Youden index and diagnostic efficiency of OCT for colorectal dysplasia and cancer were 62.50% and 82.14%, respectively, while the sensitivity and specificity were 87.50% and 75.00%, respectively. Further, the positive and negative predictive values were 82.35% and 81.82%, respectively.

CONCLUSIONS

Based on our findings, we predict that OCT is a promising non-invasive imaging technique that can offer excellent positive detection rates and diagnostic accuracy for early colorectal dysplasia and cancer. This technique is expected to be valuable in realizing real-time qualitative analysis and guided targeted biopsy.

The Potential Value of Mixed Reality in Neurosurgery

Mixed reality (MR) merges virtual information into the real world through computer technology, in which the real environment and virtual objects can get spliced in the same image or space at real time so that it can effectively express and integrate the virtual and real worlds and allow high feedback interaction. This technology combines the many advantages of virtual realityand augmented reality, and has a promising future in the medical field. At present, MR technology is just at the beginning stage in the medical field in the world, whose application in neurosurgery is also rarely reported. Given this, the authors described the research progress of MR in neurosurgery including preoperative planning and intraoperative guidance, doctor-patient communication, teaching rounds, physician training, and so on.

Line-field confocal optical coherence tomography for three-dimensional skin imaging

This paper reports on the latest advances in line-field confocal optical coherence tomography (LC-OCT), a recently invented imaging technology that now allows the generation of either horizontal (x × y) section images at an adjustable depth or vertical (x × z) section images at an adjustable lateral position, as well as three-dimensional images. For both two-dimensional imaging modes, images are acquired in real-time, with real-time control of the depth and lateral positions. Three-dimensional (x × y × z) images are acquired from a stack of horizontal section images. The device is in the form of a portable probe. The handle of the probe has a button and a scroll wheel allowing the user to control the imaging modes. Using a supercontinuum laser as a broadband light source and a high numerical microscope objective, an isotropic spatial resolution of ∼1 µm is achieved. The field of view of the three-dimensional images is 1.2 mm × 0.5 mm × 0.5 mm (x × y × z). Images of skin tissues are presented to demonstrate the potential of the technology in dermatology.

Optical coefficients as tools for increasing the optical coherence tomography contrast for normal brain visualization and glioblastoma detection

The methods used for digital processing of optical coherence tomography (OCT) and crosspolarization (CP) OCT images are focused on improving the contrast ratio of native structural OCT images. Such advances are particularly important for the intraoperative detection of glioma margins where the visual assessment of OCT images can be difficult and lead to errors. The aim of the study was to investigate the application of optical coefficients obtained from CP OCT data for the differentiation of glial tumorous tissue from a normal brain. Pseudocolor en-face OCT maps based on two optical coefficients (the commonly used rate of attenuation in the cochannel, and in addition, the interchannel attenuation difference) were constructed for normal rat brain coronal cross sections and for brains with a 101.8 rat glioblastoma model. It was shown that the use of optical coefficients significantly increased the available information from the OCT data in comparison with unprocessed images. As a result, this allowed contrasting of the white matter from the gray matter and tumorous tissue ex vivo, and for this purpose, the interchannel attenuation difference worked better. The interchannel attenuation difference values of white matter were at least seven and two times higher than corresponding values of the cortex and tumorous tissue, whereas the same parameter for cochannel attenuation coefficient values of white matter are about 4 and 1.4. However, quantitative analysis shows that both coefficients are suitable for the purpose of glioblastoma detection from normal brain tissue regardless of whether a necrotic component was present (in all compared groups p < 0.001 ).

Laser brain cancer surgery in a xenograft model guided by optical coherence tomography

Higher precision surgical devices are needed for tumor resections near critical brain structures. The goal of this study is to demonstrate feasibility of a system capable of precise and bloodless tumor ablation. An image-guided laser surgical system is presented for excision of brain tumors in vivo in a murine xenograft model. The system combines optical coherence tomography (OCT) guidance with surgical lasers for high-precision tumor ablation (Er:YAG) and microcirculation coagulation (Thulium (Tm) fiber laser).

Methods: A fluorescent human glioblastoma cell line was injected into mice and allowed to grow four weeks. Craniotomies were performed and tumors were imaged with confocal fluorescence microscopy. The mice were subsequently OCT imaged prior, during and after laser coagulation and/or ablation. The prior OCT images were used to compute three-dimensional tumor margin and angiography images, which guided the coagulation and ablation steps. Histology of the treated regions was then compared to post-treatment OCT images.

Results: Tumor sizing based on OCT margin detection matched histology to within experimental error. Although fluorescence microscopy imaging showed the tumors were collocated with OCT imaging, margin assessment using confocal microscopy failed to see the extent of the tumor beyond ~ 250 µm in depth, as verified by OCT and histology. The two-laser approach to surgery utilizing Tm wavelength for coagulation and Er:YAG for ablation yielded bloodless resection of tumor regions with minimal residual damage as seen in histology.

Conclusion: Precise and bloodless tumor resection under OCT image guidance is demonstrated in the murine xenograft brain cancer model. Tumor margins and vasculature are accurately made visible without need for exogenous contrast agents.

Dual-modality optical diagnosis for precise in vivo identification of tumors in neurosurgery

In neurosurgery, the precise diagnosis and treatment of tumor diseases are challenging to realize. Current clinical diagnoses lack fast and accurate intraoperative information. Therefore, the development of new methods and techniques to assist neurosurgeons intraoperatively is necessary. Optical diagnosis is a promising method to provide accurate information about biological tissues in a short time. Therefore, in this study, we proposed a dual-modality optical diagnostic method through point-to-face registration fusion in the optical system. We incorporated quantitative autofluorescence spectroscopy and optical coherence tomography (OCT) and evaluated our methods in an animal model.

Methods: A mouse model consisting of 16 nude mice was built by injecting the mouse brains with human glioma cells. Preoperative bioluminescence imaging was used to evaluate the growth states of tumors and locate the tumor sites. Quantitative autofluorescence spectroscopy, which provided local biochemical information with single-point detection, and OCT, which provided relatively global structural information with en face mapping scanning, were combined using the point-to-face registration fusion method to provide precise diagnostic information for identifying the brain tumors. Postoperative pathology was performed to evaluate the sensitivity and specificity of optical diagnosis.

Results: Ex vivo quantitative autofluorescence spectroscopy and OCT imaging were first performed in eight mice to acquire the optimal measuring parameters for tumor staging and identification. We then performed in vivo quantitative autofluorescence spectroscopy and OCT imaging. The results showed that tumor staging could be realized through quantitative autofluorescence spectroscopy, and fusion images could be used to precisely identify tumors. The autofluorescence spectral map, OCT en face map, and fused diagnostic map had average sensitivities of 91.7%, 86.1%, and 95.9% and specificities of 93.2%, 96.0%, and 88.7%, respectively, for tumor identification.

Conclusion: The dual-modality optical point-to-face registration fusion method and system we proposed could provide both biochemical information and structural information. The in vivo experimental results validated that the sensitivity (95.9%) of the fused map was higher than that of either single diagnostic modality (86.1% or 91.7%). Tumor staging was realized through quantitative autofluorescence spectroscopy. The proposed method will be applicable to future intelligent theranostic systems and improve many clinical neurosurgeries.

Bioluminescence imaging and two-photon microscopy guided laser ablation of GBM decreases tumor burden

Brain tumor delineation and treatment are the main concerns of neurosurgeons in neurosurgical operations. Bridging the gap between imaging/diagnosis and treatment will provide great convenience for neurosurgeons. Here, we developed an optical theranostics platform that helps to delineate the boundary and quantitatively analyze glioblastoma multiforms (GBMs) with bioluminescence imaging (BLI) to guide laser ablation, and we imaged the GBM cells with two-photon microscopy (TPM) to visualize the laser ablation zone in vivo.

Methods: Laser ablation, using the method of coupled ablated path planning with the guidance of BLI, was implemented in vivo for mouse brain tumors. The mapping relationship between semi-quantitative BLI and the laser ablation path was built through the quantitative tumor burden. The mapping was reflected through coupled ablated path planning. The BLI quantitatively and qualitatively evaluated treatment using laser ablation with the appropriate laser parameters and laser-tissue parameters. These parameters were measured after treatment. Furthermore, histopathological analysis of the brain tissue was conducted to compare the TPM images before and after laser ablation and to evaluate the results of in vivo laser ablation. The local recurrences were measured with three separate cohorts. The weights of all of the mice were measured during the experiment.

Results: Our in vivo BLI data show that the tumor cell numbers were significantly attenuated after treatment with the optical theranostics platform, and the delineation of GBM margins had clear views to guide the laser resection; the fluorescence intensity in vivo of GBMs quantitatively analyzed the rapid progression of GBMs. The laser-tissue parameters under guidance of multimodality imaging ranged between 1.0 mm and 0.1 mm. The accuracy of the laser ablation reached a submillimeter level, and the resection ratio reached more than 99% under the guidance of BLI. The histopathological sections were compared to TPM images, and the results demonstrated that these images highly coincided. The weight index and local recurrence results demonstrated that the therapeutic effect of the optical theranostics platform was significant.

Conclusion: We propose an optical multimodality imaging-guided laser ablation theranostics platform for the treatment of GBMs in an intravital mouse model. The experimental results demonstrated that the integration of multimodality imaging can precisely guide laser ablation for the treatment of GBMs. This preclinical research provides a possibility for the precision treatment of GBMs. The study also provides some theoretical support for clinical research.