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Ex Vivo Diagnosis of Lung Cancer Using a Raman Miniprobe Nicholas D. Magee,*,† Julien S. Villaumie,‡ Eric T. Marple,§ Madeleine Ennis,† J. Stuart Elborn,†,| and John J. McGarvey‡,| Respiratory Medicine Research Group, Centre for Infection and Immunity, Microbiology Building, Queens UniVersity Belfast, GrosVenor Road, Belfast, BT12 6BN, United Kingdom, School of Chemistry and Chemical Engineering, DaVid Keir Building, Queens UniVersity Belfast, Stranmillis Road, Belfast, BT9 5AG, United Kingdom, and EmVision LLC, Loxahatchee, Florida ReceiVed: January 14, 2009; ReVised Manuscript ReceiVed: March 31, 2009
Lung cancer is the most common cause of cancer death. The conventional method of confirming the diagnosis is bronchoscopy, inspecting the airways of the patient with a fiber optic endoscope. A number of studies have shown that Raman spectroscopy can diagnose lung cancer in vitro. In this study, Raman spectra were obtained from ex vivo normal and malignant lung tissue using a minifiber optic Raman probe suitable for insertion into the working channel of a bronchoscope. Shifted subtracted Raman spectroscopy was used to reduce the fluorescence from the lung tissue. Using principal component analysis with a leave-one-out analysis, the tissues were classified accurately. This novel technique has the potential to obtain Raman spectra from tumors from patients with lung cancer in vivo. Introduction Flexible bronchoscopy is the main tool used by clinicians to confirm the diagnosis of lung cancer, the most common cause of cancer death. It allows the direct visualization and sampling of tumors in the trachea, main bronchi and segmental bronchi, while the patient is lightly sedated. A number of optical techniques have been applied to the diagnosis of lung cancer, including autofluorescence bronchoscopy (AFB), which is a relatively new technique where the inherent fluorescent nature of the bronchi can be analyzed. Areas of malignancy and dysplasia show lower levels of fluorescence and AFB may be used with computed tomography for early detection of lung cancer or to identify high-risk patients.1-3 Although AFB has a much higher sensitivity than conventional white light bronchoscopy (WLB) (57% in WLB vs 83% in AFB), because areas of inflammation also exhibit lower levels of fluorescence, its specificity is lower (62% vs 58%).4,5 Raman spectroscopy contains much more detailed chemical structural information which may be used to enhance the specificity, compared to autofluorescence bronchoscopy. A number of groups are trialling Raman fiber optic probes for various biomedical applications6-10 and obtaining Raman spectra from the bronchial airways is challenging because of several technical issues. Raman scattering is inherently a weak phenomenon, while bronchial tissue and the mucus lining the bronchi generally exhibit high background “fluorescence” and this, coupled with Raman scattering generated as the excitation light passes through the fiber optics, may swamp the much weaker biological Raman signal of interest. It is therefore important to collect Raman spectra from the tissue as efficiently as possible and use filters to reduce the Raman scattering from * Corresponding author;
[email protected]; Tel. (44) 2890632711; Fax (44) 2890632697. † Respiratory Medicine Research Group, Centre for Infection and Immunity, Queens University Belfast. ‡ School of Chemistry and Chemical Engineering, Queens University Belfast. § Emvision LLC. | Joint senior authors.
the fiber optics, in addition to any extraneous non-Raman background signal. Rayleigh-scattered radiation, at the same frequency as the incident laser excitation but generally several orders of magnitude more intense than the Raman signal, must also be filtered out. The ideal Raman fiber optic probe for a relatively inaccessible organ such as the lung, must be small (2.5 mm OD max) and flexible. It should also be capable of capturing good quality Raman spectra and of conveying this signal efficiently to the spectrometer, while at the same time excluding Rayleigh light and Raman scattering originating from the fiber optics.11 These challenges must be met while maintaining a safe level of laser exposure to the patient (BS EN 60825).12 Balancing size and efficiency of the probe is challenging and so most probe studies to date have focused on easily accessible organs such as the skin, mouth and cervix, where a larger probe can be used.6,7,13-16 Before a miniprobe is used clinically, it is necessary to test it in the ex vivo situation to ensure that it can efficiently collect Raman spectra from tissue and use the spectra to classify tissue accurately. Raman fiber optic probes are generally composed of low-OH fused silica because of its low Raman activity,11 although large background signals may still appear in the 700-1300 cm-1 range, which can swamp the Raman scattering in this wavenumber region.11,15 Some authors have used a simple fiber optic probe, ignoring the fingerprint region of the spectrum, and instead use the high wavenumber region to characterize the tissue.17,18 For the analysis of lung tissue in particular, fluorescence remains a significant problem in Raman spectroscopy.19,20 Short et al. used a filtered probe with 23 collection fibers around 1 excitation fiber, but the spectral range below 1500 cm-1 was obscured by high fluorescence from the lung tissue. Despite this, they have provisionally demonstrated that this system can classify in vivo malignant from normal tissue.21 In the investigation reported here, the issue of high fluorescence levels was addressed by means of the technique of shifted subtracted Raman spectroscopy (SSRS)22 (vide infra).
10.1021/jp900379w CCC: $40.75 2009 American Chemical Society Published on Web 05/19/2009
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Figure 3. Purpose-built Raman Spectrometer.
Figure 1. Raman fiber optic miniprobe.
Figure 2. Magnified schematic of probe tip.
Aim The aim of this study was to obtain Raman spectra from ex vivo malignant and normal bronchial tissue using a minifiber optic probe coupled with SSRS and to employ multivariate analysis to distinguish tumor from healthy lung tissue. Experimental Methods Mini Raman Probe. A Raman fiber optic miniprobe was designed specifically for this application (Emvision LLC, Loxahatchee, Florida). The probe (Figure 1) consisted of a central excitation fiber optic (diameter 400 µm) surrounded by seven collection fibers (diameter 300 µm), made of low-OH silica (Figure 2). The minifiber optic probe had an external diameter of 2.1 mm, including the casing, and a length of 3 m. A band-pass filter was incorporated into the tip of the probe to eliminate the Raman scattering from the fiber optics alone, while simultaneously allowing the 785 nm excitation laser beam only to be directed onto the sample. A long pass filter was also incorporated into the tip of the collection fibers to reduce Rayleigh scattering from the tissue. After collimation, the signal from the seven collection fibers underwent additional filtering through a long pass filter to improve the elimination of Rayleigh scattering, and the filtered signal was then refocused onto an additional fiber bundle (each fiber measuring 100 µm in diameter). The 100 µm fibers of the bundle were then aligned into a slit to maintain the intensity of the Raman signal received by a custom-built Raman spectrometer, while improving the spectral resolution. Purpose-Built Raman SSRS Spectrometer. The system is based on the Czerny Turner design (Figure 3). Light entering the spectrometer is collimated, reflected by a mirror onto the diffraction grating before impinging on the charge-coupled Detector (Model DV420, Andor, Belfast, Northern Ireland). SSRS22 involves obtaining a Raman spectrum of the sample,
then shifting the spectrum by approximately 20 pixels along the detector and retaking the Raman spectrum at the same spatial position on the sample. In the present setup, the spectrum shift was achieved by inserting a thin quartz flat between the entrance of the fiber optic probe into the spectrometer and the collimating lens of the spectrometer. This flat piece of quartz was rotated horizontally to change the incident angle of the Raman beam on the grating, thereby changing the wavelength dispersion. The position of a strong peak from a neon bulb was used as a guide. Since the so-called fixed pattern response (FPR) from the detector and the sample fluorescence vary little between the two spectra, subtracting one spectrum from another virtually eliminates the FPR and fluorescence, leaving the characteristic Raman peaks of the sample in the form of a derivative-like spectrum. This method is similar to shifted excitation difference Raman spectroscopy23 but does not require a tunable laser excitation source. Each SSRS spectrum was normalized by dividing by the difference between the maximum and minimum intensities of the spectrum. A total of seven patients undergoing lung resection for nonsmall lung cancer were recruited and consented to participation in this study. Immediately after resection, the resected lung was dissected by a pathologist and a 3 × 2 × 2 cm block of tumor was obtained, followed by a similar sized block of normal main bronchus distant from the tumor. These were both stored at -80 °C and thawed to room temperature prior to Raman analysis. An average spectrum from five spectral points (12 s total acquisition time at each point) across each sample of tissue was used for tissue classification. Five spectral points were analyzed to mirror the number of biopsies that is sufficient to diagnose lung cancer in conventional bronchoscopy. Results Prior to using the minifiber optic probe, a fresh piece of human lung tissue was analyzed using a simple, single channel fiber optic probe (with no filters) connected to a conventional Raman spectrometer. This demonstrated that obtaining Raman spectra of sufficient quality for accurate tissue classification from lung tissue in the absence of filters is prohibitively difficult. Examples of such spectra, obtained for a sample of human bronchus using a single fiber optic cable made from low-OH silica with a diameter of 100 µm (Thorlabs), are shown in Figure 4. The excitation wavelength was 785 nm and the acquisition time was 4 min. Figure 4a compares a Raman spectrum from the fiber optic cable itself with that from the piece of bronchus using the same fiber optic cable. Clearly, Rayleigh light from the excitation laser is responsible for a large degree of interference in the low wavenumber region of the spectrum, causing part of the fingerprint area of the spectrum to be masked. The resultant difference spectrum (Figure 4b) is greatly distorted below 1500 cm-1. While there are visible Raman bands at higher wavenumbers, a serious drawback is that the acquisition time
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Figure 4. (a) Raman spectra of a simple, unfiltered fiber optic (purple) and human bronchus obtained using the simple, unfiltered fiber optic (blue) (total acquisition time of 4 min). (b) Difference spectrum from these two spectra.
Figure 5. Raman spectrum of human bronchus obtained using the minifiber optic probe without any SSRS (acquisition time of 6 s).
of 4 min, necessary to achieve this signal level, is too long to be clinically acceptable. When the Emvision Raman miniprobe is employed in place of the single channel fiber, the filters incorporated into the probe tip obviate both Rayleigh light and also Raman scattering from the probe itself. A spectrum of lung tissue obtained using the miniprobe with 785 nm excitation is shown in Figure 5. Although Raman bands are now visible across this spectrum, in contrast to that in Figure 4, they are dominated by an overwhelming background that largely originates from the lung tissue. With SSRS incorporated into the system in order to remove this background, the Raman spectra obtained using the same fiber optic probe are shown in Figure 6. Results for seven individual patients are displayed. Each spectrum is an average of five individual sampling points obtained from each sample. Two spectra were recorded at each sample point (each with an acquisition time of 6 s), with slightly different grating positions, as described above. A difference spectrum was then calculated from these two spectra by linear subtraction, giving the resultant SSRS spectrum a derivative-like appearance. Raman spectra were reconstructed using the average SSRS spectra from all the healthy samples and all the tumor samples (Figure 7) by applying a reconstruction algorithm24 modified to function with noisy data. The algorithm was first applied to the baseline corrected SSRS spectra, giving Raman spectra with a large element of noise, due to the integration of both the Raman derivative-like features and the noise from SSRS data. The large noise level, which is periodic due to the nature of the reconstruction algorithm, was then removed by averaging the reconstructed spectra over a period centered on the pixel of interest, for each pixel of the spectrum. The length of each period was equal to the SSRS shift at the pixel of interest. On average, the total noise over each period is essentially the same. There was also some loss of spectral resolution, which relates to the
signal averaging over a relatively large shift (10-20 pixels) between the SSRS spectra. This could be reduced by using a smaller SSRS shift. Examination of Figure 7 shows that there are a number of noteworthy differences between these reconstructed spectra for the healthy and tumor samples: The intensity of the peaks at 1070, 1300, and 1445 cm-1 are greater in the healthy samples, whereas the peaks at 855, 920, 935, 1002, 1260, and 1655 cm-1 exhibit greater intensity in the tumor spectra. There is an additional broad shoulder between 1310 and 1370 cm-1 and a shoulder at 1615 cm-1 in the tumor spectrum. The Raman SSRS spectra from each sample were averaged so that each of the 7 patients was represented by a healthy spectrum and a tumor spectrum. These 14 spectra were analyzed using principal component analysis with a leave-one-out crossvalidation. The scores from principal component 5 classified the spectra most accurately. Figure 8 shows these scores and illustrates that using a cutoff score of 0.1, the normal and tumor spectra were correctly classified with an accuracy of 100%. The loading from this component explained 6% of the variance in the analysis and is shown in the Supporting Information, Figure 1. This illustrates that the main differences in the average reconstructed spectra match the peaks where the tumor spectrum is more intense and the troughs where it is less intense. Discussion Many of the spectral features that distinguish healthy and tumor tissue in the reconstructed spectra (Figure 7) are consistent with previous lung cancer studies.21,25 These include the greater intensity in the tumor spectrum at 1655 cm-1 (amide I) and 1260 cm-1 (amide III), the CH2CH3 bending modes at 1300 and 1445 cm-1 (possibly due to collagen or phospholipids), the ring-breathing mode of phenylalanine at 1002 cm-1, and the shoulder at 1615 cm-1 (C–C stretch of either tyrosine, phenylalanine, or tryptophan). The peak at 1070 cm-1 (possibly due to the C-C or C-O or C-N stretching modes) was greater in healthy tissue. Similar trends in the foregoing features have also been reported by Huang et al.25 and in other studies of cancer using Raman spectroscopy.26-28 In this study, we have reduced the fluorescence by the use of shifted subtracted Raman spectroscopy.22 Short et al. demonstrated the use of an endoscopic Raman probe via a bronchoscopic working channel in human subjects.21 The authors used an excitation wavelength of 785 nm and a Raman miniprobe of similar design to ours with some modifications. While this report also demonstrates the feasibility of using Raman spectroscopy in the diagnosis of lung cancer, the analysis did not employ any method to remove background fluorescence,
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Figure 6. Average Raman spectra using the miniprobe and SSRS (total acquisition time of 12 s) from (a) healthy bronchial specimens (b) lung cancer specimens.
Figure 8. Principal Component 5 Scores (H ) healthy sample, T ) tumor sample).
Figure 7. Reconstruction of average healthy and malignant SSRS spectra.
and hence the accessible spectral region was restricted to 1500-3400 cm-1 with fluorescence levels in the lower wavenumber region precluding the acquisition of Raman spectra there. As demonstrated in the present work, we also encountered difficulties with fluorescence but used SSRS to circumvent this problem. Although the SSRS method requires additional data processing, in practice this can be accomplished by the computer
program within a few seconds following acquisition of the raw spectral data.24 It is not clear whether the PCA model developed on this ex vivo tissue will be transferable to an in vivo situation. Previous studies have suggested that this may not always be the case. Molchovsky et al. found that the ex vivo classifier did not perform well and indeed the PCA analyses of ex vivo and in vivo tissue are different.29 It is usual for surgeons to isolate the blood supply to the lung prior to resection of the tumor. There is approximately thirty minutes between the surgeon removing the blood supply and the excision of the tumor and this hypoxic
Ex Vivo Diagnosis of Lung Cancer Using a Raman Miniprobe effect may have chemical consequences, which may be reflected in the Raman spectra. In vivo Raman spectroscopy may hold promise for a realtime diagnosis of lung cancer without the need for biopsy, thus shortening the diagnostic journey for the patient. The short acquisition time of 6 s used in this study demonstrates the feasibility to obtain spectra of sufficient quality using this system and to classify tissue accurately. We did not attempt to measure the sampling depth of the probe in this pilot study, but this would be important in further studies. Additionally, the molecular analysis provided by Raman spectroscopy may provide some functional information about the tumor and possibly about the aggressiveness of the tumor.30,31 This may be used to guide treatment, leading to a more personalized approach for the patient. If combined with autofluorescence bronchoscopy, it may also improve its specificity and allow the more accurate diagnosis of premalignant lesions. Conclusions In conclusion, the use of Raman spectroscopy in the realtime in vivo diagnosis of medical disease is arguably among the most exciting and clinically relevant applications of Raman spectroscopy being pursued at present and many research groups are investigating similar techniques in other organ systems.10,21,29 Each organ system presents its own difficulties in the application of Raman to the clinical setting, but with advances in probe design, Raman spectroscopy is being brought closer to the endoscopy suite and may play an important part in the diagnosis of many cancers. We have demonstrated the potential to apply this technique to lung cancer diagnosis using a minifiber optic probe suitable for insertion into the working channel of a bronchoscope. Although this is an ex vivo pilot study, it has shown the potential for Raman spectroscopy to provide realtime diagnosis. It will be necessary to obtain a larger independent set of spectra from malignant and normal tissue in the clinical setting to validate this model. Acknowledgment. This project was funded by the Research and Development Office for Health and Personal Social Services in Northern Ireland (EAT/2538/03). J.S.V. thanks Perkin-Elmer Inc. and The Faraday Institute for financial support. We thank Dr. Steven Bell for use of a Raman spectrometer for initial studies. Supporting Information Available: This material is available free of charge via the Internet at http://pubs.acs.org. References and Notes (1) Evans, C. L.; Potma, E. O.; Puoris’haag, M.; Cote, D.; Lin, C. P.; Xie, X. S. Proc. Natl. Acad. Sci. U.S.A 2005, 102, 16807.
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