We present a macroscopic line scanning Raman imaging system which has been modified to be suitable for intraoperative use. A sterilizable probe muzzle was designed to flatten the biological tissue ensuring its position at the focal plane of the Raman probe optics, removing the need for probe sterilization. The system uses a flexible imaging probe with a 1cm2 field of view to record fingerprint Raman images, mounted on an articulated arm that supports the probe weight and allows gentle contact with the tissue. Validation results obtained on porcine tissues show >95% classification accuracy between different tissue types.
Blood vessel injury during image-guided brain biopsy poses a risk of hemorrhage. Approaches that reduce this risk may minimize related patient morbidity. We present here an intraoperative imaging device that has the potential to detect the brain vasculature in situ. The device uses multiple diffuse reflectance spectra acquired in an outward-viewing geometry to detect intravascular hemoglobin, enabling the construction of an optical image in the vicinity of the biopsy needle revealing the proximity to blood vessels. This optical detection system seamlessly integrates into a commercial biopsy system without disrupting the neurosurgical clinical workflow. Using diffusive brain tissue phantoms, we show that this device can detect 0.5-mm diameter absorptive carbon rods up to ∼2 mm from the biopsy window. We also demonstrate feasibility and practicality of the technique in a clinical environment to detect brain vasculature in an in vivo model system. In situ brain vascular detection may add a layer of safety to image-guided biopsies and minimize patient morbidity.
Surgical excision of the whole prostate through a radical prostatectomy procedure is part of the standard of care for prostate cancer. Positive surgical margins (cancer cells having spread into surrounding nonresected tissue) occur in as many as 1 in 5 cases and strongly correlate with disease recurrence and the requirement of adjuvant treatment. Margin assessment is currently only performed by pathologists hours to days following surgery and the integration of a real-time surgical readout would benefit current prostatectomy procedures. Raman spectroscopy is a promising technology to assess surgical margins: its in vivo use during radical prostatectomy could help insure the extent of resected prostate and cancerous tissue is maximized. We thus present the design and development of a dual excitation Raman spectroscopy system (680- and 785-nm excitations) integrated to the robotic da Vinci surgical platform for in vivo use. Following validation in phantoms, spectroscopic data from 20 whole human prostates immediately following radical prostatectomy are obtained using the system. With this dataset, we are able to distinguish prostate from extra prostatic tissue with an accuracy, sensitivity, and specificity of 91%, 90.5%, and 96%, respectively. Finally, the integrated Raman spectroscopy system is used to collect preliminary spectroscopic data at the surgical margin in vivo in four patients.
Brain cancer diagnosis requires histological, molecular, and genomic tumor analyses. Since conventional imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) don’t provide molecular characterisation, tumor sampling is often achieved using a targeted needle biopsy approach. Targeting errors and cancer heterogeneity are important limitations of this technique, causing inaccurate sampling resulting in non-diagnostic or poor quality samples leading and the need for repeated biopsies, which poses an elevated patient risk because of infections and potential hemorrhages. Previously, we presented the design of an optically-guided brain biopsy needle using high wavenumber Raman spectroscopy (RS) to characterize tissue prior to sample collection with demonstrated efficacy in a live animal. Using an intraoperative probe we further demonstrated in vivo high wavenumber or fingerprint RS can distinguish cancer and normal brain tissue with >90% accuracy. Here we report on the design, development, and validation of a new intraoperative cancer detection optical needle system based on the combination of fingerprint and high wavenumber RS for highly accurate brain biopsy targeting based on molecular tissue features. This optical cancer detection device was engineered into the internal cannula of a widely used commercially available biopsy needle allowing tumor analysis prior to tissue harvesting with minimal workflow disruption. First in-human results are presented setting the stage for the clinical translation of this optical molecular imaging method for high yield and safe targeted brain biopsy.
Invasive brain cancer cells cannot be visualized during surgery and so they are often not removed. These residual cancer cells give rise to recurrences. In vivo Raman spectroscopy can detect these invasive cancer cells in patients with grade 2 to 4 gliomas. The robustness of this Raman signal can be dampened by spectral artifacts generated by lights in the operating room. We found that artificial neural networks (ANNs) can overcome these spectral artifacts using nonparametric and adaptive models to detect complex nonlinear spectral characteristics. Coupling ANN with Raman spectroscopy simplifies the intraoperative use of Raman spectroscopy by limiting changes required to the standard neurosurgical workflow. The ability to detect invasive brain cancer under these conditions may reduce residual cancer remaining after surgery and improve patient survival.
Brain needle biopsy (BNB) is performed to collect tissue when precise neuropathological diagnosis is required to provide information about tumor type, grade, and growth patterns. The principal risks associated with this procedure are intracranial hemorrhage (due to clipping blood vessels during tissue extraction), incorrect tumor typing/grading due to non-representative or non-diagnostic samples (e.g. necrotic tissue), and missing the lesion. We present an innovative device using sub-diffuse optical tomography to detect blood vessels and Raman spectroscopy to detect molecular differences between tissue types, in order to reduce the risks of misdiagnosis, incorrect tumour grading, and non-diagnostic samples. The needle probe integrates optical fibers directly onto the external cannula of a commercial BNB needle, and can perform measurements for both optical techniques through the same fibers. This integrated optical spectroscopy system uses diffuse reflectance signals to perform a 360-degree reconstruction of the tissue adjacent to the biopsy needle, based on the optical contrast associated with hemoglobin light absorption, thereby localizing blood vessels. Raman spectra measurements are also performed interstitially for tissue characterization. A detailed sensitivity of the system is presented to demonstrate that it can detect absorbers with diameters <300 µm located up to ∼2 mm from the biopsy needle core, for bulk optical properties consistent with brain tissue. Results from animal experiments are presented to validate blood vessel detection and Raman spectrum measurement without disruption of the surgical workflow. We also present phantom measurements of Raman spectra with the needle probe and a comparison with a clinically validated Raman spectroscopy probe.
It is often difficult to identify cancer tissue during brain cancer (glioma) surgery. Gliomas invade into areas of normal
brain, and this cancer invasion is frequently not detected using standard preoperative magnetic resonance imaging
(MRI). This results in enduring invasive cancer following surgery and leads to recurrence. A hand-held Raman
spectroscopy is able to rapidly detect cancer invasion in patients with grade 2-4 gliomas. However, ambient light sources
can produce spectral artifacts which inhibit the ability to distinguish between cancer and normal tissue using the spectral
information available. To address this issue, we have demonstrated that artificial neural networks (ANN) can accurately
classify invasive cancer versus normal brain tissue, even when including measurements with significant spectral artifacts
from external light sources. The non-parametric and adaptive model used by ANN makes it suitable for detecting
complex non-linear spectral characteristics associated with different tissues and the confounding presence of light
artifacts. The use of ANN for brain cancer detection with Raman spectroscopy, in the presence of light artifacts,
improves the robustness and clinical translation potential for intraoperative use. Integration with the neurosurgical
workflow is facilitated by accounting for the effect of light artifacts which may occur, due to operating room lights,
neuronavigation systems, windows, or other light sources. The ability to rapidly detect invasive brain cancer under these
conditions may reduce residual cancer remaining after surgery, and thereby improve patient survival.
Cancer tissue is frequently impossible to distinguish from normal brain during surgery. Gliomas are a class of brain cancer which invade into the normal brain. If left unresected, these invasive cancer cells are the source of glioma recurrence. Moreover, these invasion areas do not show up on standard-of-care pre-operative Magnetic Resonance Imaging (MRI). This inability to fully visualize invasive brain cancers results in subtotal surgical resections, negatively impacting patient survival. To address this issue, we have demonstrated the efficacy of single-point in vivo Raman spectroscopy using a contact hand-held fiber optic probe for rapid detection of cancer invasion in 8 patients with low and high grade gliomas. Using a supervised machine learning algorithm to analyze the Raman spectra obtained in vivo, we were able to distinguish normal brain from the presence of cancer cells with sensitivity and specificity greater than 90%. Moreover, by correlating these results with pre-operative MRI we demonstrate the ability to detect low density cancer invasion up to 1.5cm beyond the cancer extent visible using MRI. This represents the potential for significant improvements in progression-free and overall patient survival, by identifying previously undetectable residual cancer cell populations and preventing the resection of normal brain tissue. While the importance of maximizing the volume of tumor resection is important for all grades of gliomas, the impact for low grade gliomas can be dramatic because surgery can even be curative. This convenient technology can rapidly classify cancer invasion in real-time, making it ideal for intraoperative use in brain tumor resection.
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