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

OBJECTIVE: To test if Raman spectroscopy (RS) is an appropriate tool for the diagnosis and possibly grading of prostate cancer (PCa). PATIENTS AND METHODS: Between 20 and 50 Raman spectra were acquired from 32 fresh and non-processed post-prostatectomy specimens using a macroscopic handheld RS probe. Each measured area was characterized and categorized according to histopathological criteria: tissue type (extraprostatic or prostatic); tissue malignancy (benign or malignant); cancer grade (Grade Groups [GGs] 1-5); and tissue glandular level. The data were analysed using machine-learning classification with neural network. RESULTS: The RS technique was able to distinguish prostate from extraprostatic tissue with a sensitivity of 82% and a specificity of 83% and benign from malignant tissue with a sensitivity of 87% and a specificity of 86%. In an exploratory fashion, RS differentiated benign from GG1 in 726/801 spectra (91%; sensitivity 80%, specificity 91%), from GG2 in 588/805 spectra (73%; sensitivity 76%, specificity 73%), from GG3 in 670/797 spectra (84%; sensitivity 86%, specificity 84%), from GG4 in 711/802 spectra (88%; sensitivity 77%, specificity 89%) and from GG5 in 729/818 spectra (89%; sensitivity 90%, specificity 89%). CONCLUSION: Current diagnostic approaches of PCa using needle biopsies have suboptimal cancer detection rates and a significant risk of infection. Standard non-targeted random sampling results in false-negative biopsies in 15-30% of patients, which affects clinical management. RS, a non-destructive tissue interrogation technique providing vibrational molecular information, resolved the highly complex architecture of the prostate and detect cancer with high accuracy using a fibre optic probe to interrogate radical prostatectomy (RP) specimens from 32 patients (947 spectra). This proof-of-principle paves the way for the development of in vivo tumour targeting spectroscopy tools for informed biopsy collection to address the clinical need for accurate PCa diagnosis and possibly to improve surgical resection during RP as a complement to histopathological analysis.

Development and characterization of a handheld hyperspectral Raman imaging probe system for molecular characterization of tissue on mesoscopic scales.

PURPOSE: Raman spectroscopy is a promising cancer detection technique for surgical guidance applications. It can provide quantitative information relating to global tissue properties associated with structural, metabolic, immunological, and genetic biochemical phenomena in terms of molecular species including amino acids, lipids, proteins, and nucleic acid (DNA). To date in vivo Raman spectroscopy systems mostly included probes and biopsy needles typically limited to single-point tissue interrogation over a scale between 100 and 500 microns. The development of wider field handheld systems could improve tumor localization for a range of open surgery applications including brain, ovarian, and skin cancers. METHODS: Here we present a novel Raman spectroscopy implementation using a coherent imaging bundle of fibers to create a probe capable of reconstructing molecular images over mesoscopic fields of view. Detection is performed using linear scanning with a rotation mirror and an imaging spectrometer. Different slits widths were tested at the entrance of the spectrometer to optimize spatial and spectral resolution while preserving sufficient signal-to-noise ratios to detect the principal Raman tissue features. The nonbiological samples, calcite and polytetrafluoroethylene (PTFE), were used to characterize the performance of the system. The new wide-field probe was tested on ex vivo samples of calf brain and swine tissue. Raman spectral content of both tissue types were validated with data from the literature and compared with data acquired with a single-point Raman spectroscopy probe. The single-point probe was used as the gold standard against which the new instrument was benchmarked as it has already been thoroughly validated for biological tissue characterization. RESULT: We have developed and characterized a practical noncontact handheld Raman imager providing tissue information at a spatial resolution of 115 microns over a field of view >14 mm2 and a spectral resolution of 6 cm-1 over the whole fingerprint region. Typical integration time to acquire an entire Raman image over swine tissue was set to approximately 100 s. Spectra acquired with both probes (single-point and wide-field) showed good agreement, with a Pearson correlation factor >0.85 over different tissue categories. Protein and lipid content of imaged tissue were manifested into the measured spectra which correlated well with previous findings in the literature. An example of quantitative molecular map is presented for swine tissue and calf brain based on the ratio of protein-to-lipid content showing clear delineations between white and gray matter as well as between adipose and muscle tissue. CONCLUSION: We presented the development of a Raman imaging probe with a field of view of a few millimeters and a spatial resolution consistent with standard surgical imaging methods using an imaging bundle. Spectra acquired with the newly developed system on swine tissue and calf brain correlated well with an establish single-point probe and observed spectral features agreed with previous finding in the literature. The imaging probe has demonstrated its ability to reconstruct molecular images of soft tissues. The approach presented here has a lot of potential for the development of surgical Raman imaging probe to guide the surgeon during cancer surgery.

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 review of Raman spectroscopy advances with an emphasis on clinical translation challenges in oncology.

There is an urgent need for improved techniques for disease detection. Optical spectroscopy and imaging technologies have potential for non- or minimally-invasive use in a wide range of clinical applications. The focus here, in vivo Raman spectroscopy (RS), measures inelastic light scattering based on interaction with the vibrational and rotational modes of common molecular bonds in cells and tissue. The Raman 'signature' can be used to assess physiological status and can also be altered by disease. This information can supplement existing diagnostic (e.g. radiological imaging) techniques for disease screening and diagnosis, in interventional guidance for identifying disease margins, and in monitoring treatment responses. Using fiberoptic-based light delivery and collection, RS is most easily performed on accessible tissue surfaces, either on the skin, in hollow organs or intra-operatively. The strength of RS lies in the high biochemical information content of the spectra, that characteristically show an array of very narrow peaks associated with specific chemical bonds. This results in high sensitivity and specificity, for example to distinguish malignant or premalignant from normal tissues. A critical issue is that the Raman signal is often very weak, limiting clinical use to point-by-point measurements. However, non-linear techniques using pulsed-laser sources have been developed to enable in vivo Raman imaging. Changes in Raman spectra with disease are often subtle and spectrally distributed, requiring full spectral scanning, together with the use of tissue classification algorithms that must be trained on large numbers of independent measurements. Recent advances in instrumentation and spectral analysis have substantially improved the clinical feasibility of RS, so that it is now being investigated with increased success in a wide range of cancer types and locations, as well as for non-oncological conditions. This review covers recent advances and continuing challenges, with emphasis on clinical translation.

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

Invasive brain cancer cells cannot be visualized during surgery and so they are often not removed. These residual cancer cells give rise to recurrences.

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%.