Joannie Desroches

Intraoperative brain cancer detection with Raman spectroscopy in humans

A handheld Raman spectroscopy probe enabled detection of invasive brain cancer intraoperatively in patients with grade 2 to 4 gliomas. Probing for brain tumors Gliomas are invasive cancers, spreading quietly throughout the brain. They pose a formidable challenge to surgeons who try to remove all cancer cells during resection; leaving any cancer behind can lower the patient’s prospects for survival. Jermyn et al. adapted Raman spectroscopy for the operating room by developing an imaging technique that uses a commercially available, handheld contact fiber optic probe. The probe’s optic cables were connected to a near-infrared laser, for stimulating tissue molecules; in turn, these components were linked to a computer to visualize resulting spectra in real time. When held against human brain tissue, the probe measured the Raman scattering signal, which was separated from background signals and differentiated from “normal” tissues using certain algorithms. The authors tested the probe in 17 patients with grade 2 to 4 gliomas who were undergoing surgery and compared imaging results with 161 biopsy samples. Intraoperative Raman imaging allowed the authors to detect both invasive and dense cancer cells with an accuracy of 92%. By comparison, the surgeon, using standard surgical tools like the bright-field microscope and magnetic resonance imaging, identified cancer with 73% accuracy. Such label-free, portable, intraoperative imaging technologies will be important in improving the efficiency of tumor resections and, in turn, for extending survival times of glioma patients. Cancers are often impossible to visually distinguish from normal tissue. This is critical for brain cancer where residual invasive cancer cells frequently remain after surgery, leading to disease recurrence and a negative impact on overall survival. No preoperative or intraoperative technology exists to identify all cancer cells that have invaded normal brain. To address this problem, we developed a handheld contact Raman spectroscopy probe technique for live, local detection of cancer cells in the human brain. Using this probe intraoperatively, we were able to accurately differentiate normal brain from dense cancer and normal brain invaded by cancer cells, with a sensitivity of 93% and a specificity of 91%. This Raman-based probe enabled detection of the previously undetectable diffusely invasive brain cancer cells at cellular resolution in patients with grade 2 to 4 gliomas. This intraoperative technology may therefore be able to classify cell populations in real time, making it an ideal guide for surgical resection and decision-making.

Characterization of a Raman spectroscopy probe system for intraoperative brain tissue classification

A detailed characterization study is presented of a Raman spectroscopy system designed to maximize the volume of resected cancer tissue in glioma surgery based on in vivo molecular tissue characterization. It consists of a hand-held probe system measuring spectrally resolved inelastically scattered light interacting with tissue, designed and optimized for in vivo measurements. Factors such as linearity of the signal with integration time and laser power, and their impact on signal to noise ratio, are studied leading to optimal data acquisition parameters. The impact of ambient light sources in the operating room is assessed and recommendations made for optimal operating conditions. In vivo Raman spectra of normal brain, cancer and necrotic tissue were measured in 10 patients, demonstrating that real-time inelastic scattering measurements can distinguish necrosis from vital tissue (including tumor and normal brain tissue) with an accuracy of 87%, a sensitivity of 84% and a specificity of 89%.

Neural networks improve brain cancer detection with Raman spectroscopy in the presence of operating room light artifacts

Abstract. 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.

Highly Accurate Detection of Cancer In Situ with Intraoperative, Label-Free, Multimodal Optical Spectroscopy

Effectiveness of surgery as a cancer treatment is reduced when all cancer cells are not detected during surgery, leading to recurrences that negatively impact survival. To maximize cancer cell detection during cancer surgery, we designed an in situ intraoperative, label-free, optical cancer detection system that combines intrinsic fluorescence spectroscopy, diffuse reflectance spectroscopy, and Raman spectroscopy. Using this multimodal optical cancer detection system, we found that brain, lung, colon, and skin cancers could be detected in situ during surgery with an accuracy, sensitivity, and specificity of 97%, 100%, and 93%, respectively. This highly sensitive optical molecular imaging approach can profoundly impact a wide range of surgical and noninvasive interventional oncology procedures by improving cancer detection capabilities, thereby reducing cancer burden and improving survival and quality of life. Cancer Res; 77(14); 3942-50. ©2017 AACR.

Raman spectroscopy detects distant invasive brain cancer cells centimeters beyond MRI capability in humans

Surgical treatment of brain cancer is limited by the inability of current imaging capabilities such as magnetic resonance imaging (MRI) to detect the entirety of this locally invasive cancer. This results in residual cancer cells remaining following surgery, leading to recurrence and death. We demonstrate that intraoperative Raman spectroscopy can detect invasive cancer cells centimeters beyond pathological T1-contrast-enhanced and T2-weighted MRI signals. This intraoperative optical guide can be used to detect invasive cancer cells and minimize post-surgical cancer burden. The detection of distant invasive cancer cells beyond MRI signal has the potential to increase the effectiveness of surgery and directly lengthen patient survival.

A new method using Raman spectroscopy for in vivo targeted brain cancer tissue biopsy

Modern cancer diagnosis requires histological, molecular, and genomic tumor analyses. Tumor sampling is often achieved using a targeted needle biopsy approach. Targeting errors and cancer heterogeneity causing inaccurate sampling are important limitations of this blind technique leading to non-diagnostic or poor quality samples, and the need for repeated biopsies pose elevated patient risk. An optical technology that can analyze the molecular nature of the tissue prior to harvesting could improve cancer targeting and mitigate patient risk. Here we report on the design, development, and validation of an in situ intraoperative, label-free, cancer detection system based on high wavenumber Raman spectroscopy. This optical detection device was engineered into a commercially available biopsy system allowing tumor analysis prior to tissue harvesting without disrupting workflow. Using a dual validation approach we show that high wavenumber Raman spectroscopy can detect human dense cancer with >60% cancer cells in situ during surgery with a sensitivity and specificity of 80% and 90%, respectively. We also demonstrate for the first time the use of this system in a swine brain biopsy model. These studies set the stage for the clinical translation of this optical molecular imaging method for high yield and safe targeted biopsy.

Neural networks improve brain cancer detection with Raman spectroscopy in the presence of light artifacts

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.

Brain Tumor Resection Guided with Single-Point Raman Spectroscopy: In-Human Results

A Raman spectroscopy technique was developed and used on 8 glioblastoma patients. We demonstrate that a classification accuracy of 93% is achieved in differentiating normal brain from tumor tissue with low densities of cancer cells.

Mesoscopic characterization of prostate cancer using Raman spectroscopy: potential for diagnostics and therapeutics

To test if Raman spectroscopy (RS) is an appropriate tool for the diagnosis and possibly grading of prostate cancer (PCa).

Imaging system based on diffusive reflectance spectroscopy for blood vessels detection during brain biopsy procedure

During the diagnosis and treatment process for brain cancer, clinicians often need to achieve a brain biopsy to get histological data. However there are risks associated with this procedure: 1) collected samples are not always representative of the tumor and 2) there is a risk of blood vessel rupture when the sample is taken. This type of bleeding occurs between 0.3 and 59.8% of the cases and the mortality rate can be as high as 3.9% (Dammers et al, Woodworth et al). Here we present a diffuse reflectance spectroscopy imaging system directly integrated on a brain biopsy needle to guide surgeon during needle biopsy procedures. To mitigate the risks associated with the procedure, our imaging system combines 18 optical fibers (9 used as white-light sources and 9 used as detectors) to acquire a total of 81 reflectance spectra per acquisition. A tomographic algorithm (Goyette et al) is used to reconstruct an image in the vicinity of the needle based on the optical contrast due to the optical absorption of hemoglobin (Hb) which is the main absorber in brain. The evaluation and characterization tests were first carried out in vitro using tissue-simulating phantoms reproducing human brain optical properties (absorption and scattering).