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(Chest. 2006;129:995-1001.)
© 2006 American College of Chest Physicians

Optical Spectroscopy for the Classification of Malignant Lesions of the Bronchial Tree*

Martin P. L. Bard, MD, PhD; Arjen Amelink, PhD; Marina Skurichina, PhD; Vincent Noordhoek Hegt, MD, PhD; Robert P. W. Duin, PhD; Henricus J. C. M. Sterenborg, PhD; Henk C. Hoogsteden, MD, PhD and Joachim G. J. V. Aerts, MD, PhD

* From the Department of Respiratory Diseases (Drs. Bard and Hoogsteden), Center for Optical Diagnostics and Therapy, Department of Radiation Oncology (Drs. Amelink and Sterenborg), Erasmus University Medical Centre Rotterdam; Faculty of Electrical Engineering, Mathematics and Computer Science (Drs. Skurichina and Duin), Delft University of Technology; and Departments of Pathology (Dr. Noordhoek Hegt) and Respiratory Diseases (Dr. Aerts), Sint Franciscus Hospital, Rotterdam, the Netherlands.

Correspondence to: Joachim Aerts, MD, PhD, Department of Respiratory Diseases, Sint Franciscus Hospital, Kleiweg 500, 3045 PM, Rotterdam, the Netherlands; e-mail: j.aerts{at}sfg.nl

Abstract

Optical spectroscopy may be used for in vivo, noninvasive distinction of malignant from normal tissue. The aim of our study was to analyze the accuracy of various optical spectroscopic techniques for the classification of cancerous lesions of the bronchial tree. We developed a fiberoptic instrument allowing the measurement of autofluorescence spectroscopy (AFS), diffuse reflectance spectroscopy (DRS), and differential path length spectroscopy (DPS) during bronchoscopy. Spectroscopic measurements were obtained from 191 different endobronchial lesions (63 malignant and 128 nonmalignant) in 107 patients. AFS, DRS, and DPS sensitivity/specificity for the distinction between malignant and nonmalignant bronchial lesions were 73%/82%, 86%/81%, and 81%/88%, respectively. All three optical spectroscopic modalities facilitate an increase of the positive predictive value of autofluorescence bronchoscopy for the detection of endobronchial tumors. Even better results were obtained when the three spectroscopic techniques were combined.

Key Words: autofluorescence bronchoscopy • lung cancer • optical spectroscopy

Optical spectroscopy explores the optical phenomena resulting from the interaction of light with biological tissue. Autofluorescence spectroscopy (AFS) investigates the fluorescence of the endogenous fluorophores present in the tissue, but the autofluorescence spectra are also affected by the optical properties (ie, scattering and absorption) of the tissue.1 White light reflectance spectroscopy does not employ fluorophores but directly studies the optical properties of tissue. White light reflectance spectra depend on the presence of light absorbers such as hemoglobin and light scatterers. The nature of these scatterers is still a mater of scientific debate, but most probably several membrane-bound cellular organelles are involved.23 Optical spectroscopy may be particularly useful for the analysis of differences in normal and cancerous tissue because major scattering, absorption, and fluorescence changes are known to occur during the development of cancer.14 Therefore, these noninvasive optical spectroscopic techniques may allow a real-time, in vivo analysis of tissue, and may help to distinguish healthy from cancerous tissue based on their optical differences.

The combination of AFS and diffuse reflectance spectroscopy (DRS) has been shown to facilitate the detection of cancerous tissue with high accuracy in the uterus,567 the breast,8 the ovary,9 the esophagus,10 and the oral cavity.11 In the lung, few data are available, mainly due to the inaccessibility of this organ. A decrease in autofluorescence intensity is a well-known phenomenon in malignant and dysplastic lesions of the bronchi.11213 However, the diagnostic accuracy of AFS and white light reflectance spectroscopy for bronchial tumors has never been prospectively studied in a large cohort of patient.

For this study, we developed a fiberoptic instrument allowing the measurement of AFS, DRS, and differential path length spectroscopy (DPS) during bronchoscopy. DPS is a new spectroscopic technique developed in our group for the purpose of studying the optical properties of the most superficial layer of the bronchial mucosa, ie, the epithelium.141516 These three spectroscopic techniques were tested in a large group of patients in order to examine their accuracy to distinguish malignant from nonmalignant bronchial tissue. The ability to decrease the rate of false-positive findings of autofluorescence bronchoscopy was also studied for each spectroscopic technique separately and for their combination.

Materials and Methods

Study Population
Patients with known or suspected malignancies of the lung and with a medical indication for a bronchoscopy were invited to participate in the study. All patients were > 18 years old and signed informed consent. The study was approved by the Medical Ethics Review Board of Erasmus Medical Centre Rotterdam. Spectroscopic measurements were performed on 191 different endobronchial lesions in 107 patients studied over an 18-month period. The histologic repartition of these bronchial lesions were normal mucosa (n = 54), metaplastic mucosa (n = 67), mild-grade dysplastic mucosa (n = 7), severe dysplastic mucosa/carcinoma in situ (CIS) [n = 6], and invasive carcinoma (n = 57). The small amount of data for premalignant lesions such as the dysplastic mucosa and CIS hampered the accurate evaluation of the quality of discrimination of the spectroscopic techniques for these lesions. Concerning the inflammatory/metaplastic mucosa that were frequently found in our patient cohort, their cancerous risk is still a matter of discussion because they are very commonly observed in smokers or in case of bronchial infection and can disappear spontaneously.17 In our study, we have chosen to focus our analysis on two groups of lesions with indisputable clinical relevance: a group with "low-grade" lesions (n = 128 lesions, including normal, metaplastic, and mild dysplastic mucosa), and a group with "high-grade" lesions (n = 63 lesions, including severe dysplastic mucosa/CIS and invasive carcinomas).

Examination Procedure
The endoscopic examination of the bronchial tree was performed using a commercially available flexible fluorescence bronchoscope (11004BI; Karl Storz; Tuttlingen, Germany) or, when the fluorescence bronchoscope was unavailable, a flexible white light videobronchoscope (BF-T160; Olympus; Zoeterwoude, the Netherlands). All lesions that appeared abnormal at blue and/or white light imaging were measured. Additionally, some spectra of macroscopically normal bronchial mucosa (corresponding to healthy carina systematically sampled for preoperative staging purposes) were obtained. An average of three measurements was performed on slightly different locations within each lesion in order to take into account possible tissue heterogeneity. During the spectral acquisition (roughly 1 s), the light source of the bronchoscope was switched off. Biopsy specimens were taken from all measured lesions, transported in formaldehyde, and fixed in paraffin. Hematoxylin-eosin stained slides were evaluated without knowledge of bronchoscopic findings. The pathologic diagnoses were coded referring to the World Health Organization lung cancer classification.18

Optical Probe
Spectra were acquired with a custom-made instrument using a fiberoptic probe small enough to be led through the working channel of the bronchoscope (Fig 1 ). The measurements were performed with the tip of the fiberprobe gently touching the tissue under examination. The fiberprobe consisted of three identical 400-µm fibers fitted into a small metal tube. The first fiber was used for delivery of blue light to the tissue (delivery fiber), the second fiber was used for both delivery of white light and detection of reflected white light from the tissue (delivery and collection fiber), and the third fiber was used for the detection of both autofluorescence and reflected white light (collection fiber). A blue diode laser (407 nm; Nichia; Tokyo, Japan) and a tungsten-halogen lamp (HL-2000-FHSA; Ocean Optics; Duiven, the Netherlands) were used as excitation sources. The reflected light from the bronchial mucosa was analyzed in a dual-channel spectrometer (SD2000; Ocean Optics). In order to attenuate the strong 407-nm background due to the blue-violet light source, light collected in the collection fiber was filtered through a long-pass glass filter (GG435; Schott; Tiel, the Netherlands).


Figure 1
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  		Figure 1.. Schematic diagram of the experimental setup. PC = personal computer; d-fiber = delivery fiber; dc-fiber = delivery and collection fiber; c-fiber = collection fiber.

 
As discussed above, two different white light reflection signals were collected: reflected light collected by the collection fiber (Jc) and reflected light collected by the delivery and collection fiber (Idc). The difference between these signals is called the differential reflectance signal (R = Idc – Jc) and has been investigated in detail previously.141516 This Jc signal, which we have termed diffuse reflectance (but has been referred to by others19 as elastic scattering signal) corresponds to photons multiply (diffusely) scattered in the tissue. The interrogation depth of DRS depends on the optical properties of the tissue under investigation, and is typically of the order of 1 mm.19 We have previously reported that the apparent path length ({tau}) of photons contributing to the differential reflectance signal R is independent of the optical properties of the sample under investigation and is approximately equal to the diameter of the fibers used ({tau} = 0.8 x diameter of the fibers), as long as the fiber diameter is larger than the mean free path of the photons.14 Thus the depth probed by DPS is roughly equal to 1/2 {tau} = 0.4 x diameter of the fibers approximately 160 µm for fiber diameters of 400 µm. Such a tissue depth corresponds to the most superficial layer of the bronchial mucosa.

Spectral Analysis
Autofluorescence and reflectance spectra were analyzed in ranges from 435 to 700 nm and 435 to 900 nm, respectively. Autofluorescence spectra were preprocessed by dividing the spectrum by the maximum intensity of a fluorescence measurement with the probe at a specific distance (approximately 7 mm) from a fluorescence calibration standard (USFS-200; Labsphere; North Sutton, NH), to correct for day-to-day variations in laser output. Diffuse reflectance spectra were preprocessed by dividing the raw spectrum by a reference spectrum measured with the probe subsequently at a specific distance (approximately 7 mm) from a diffuse reflecting white reflectance standard (SRS-99; Labsphere) and from a diffuse reflecting black reflectance standard (SRS-02; Labsphere).

Formula
where J is the raw signal from the collection fiber in contact with the sample, Jwhite is the signal from the collection fiber with the probe tip at the previously mentioned distance from the white reflectance standard, and Jblackis the signal from the collection fiber with the probe tip at the previously mentioned distance from the black reflectance standard. Similarly, the differential path-length spectra are preprocessed as follows14:

Formula
where I is the raw signal from the delivery and collection fiber in contact with the sample, In is the signal from the delivery and collection fiber submersed in a fluid with an appropriate refractive index (for tissue, water would be appropriate), Iwhite is the signal from the delivery and collection fiber with the probe tip at the previously mentioned distance from the white reflectance standard, and Iblackis the signal from the delivery and collection fiber with the probe tip at that same distance from the black reflectance standard.

The variability of all the spectra was decreased by normalization to unit area, and the median intensity of the spectra was used as an additional feature in order to take into account the information related to the intensity of the spectra. To analyze the spectra, principal component analysis was applied. In order to determine the optimal amount of leading principal components used to describe the spectra, we studied the classification accuracy of all spectroscopic modalities, ie, AFS, DRS and DPS, in a range of leading principal components from 1 to 20. As explained below in more detail, the classification accuracy of each spectroscopic modality can be expressed in terms of an area under a receiver operating characteristic (ROC) curve. The largest areas under the ROC curves were found using 15, 20, and 15 leading principal components for AFS, DRS, and DPS, respectively.

To evaluate the quality of discrimination between the low-grade and high-grade lesions, linear discriminant analysis was applied. Specifically, we used the Karhunen-Loeve linear classifier, also known as the regularized linear discriminant function. For all spectroscopic modalities, the classifier was constructed using the leave-one-person-out approach, for which the classifier is constructed on a training set containing all spectra except the spectra measured in one particular patient. Compared to the leave-one-spectrum-out or leave-one-lesion-out methods, the leave-one-patient-out method improves the independence of the lesion classification since this approach prevents that spectra belonging to the same patient (which might not be considered to be completely independent) will be used both in the training data set (used to construct the classifier) and in the independent test set (used to test the goodness of the classification rule). Using the classifier, we obtain for all spectra a classification label, ie, low grade or high grade, and two posterior class probabilities (probability 1 and probability 2), which correspond to the confidence (from 0 to 1) of the classification rule about the membership of a certain spectrum to the low-grade or high-grade lesion classes. When multiple measurements were made on the same lesion, the final decision of the classifier for this lesion was obtained using the "mean fusion" approach. In the mean fusion approach, the posterior class probabilities obtained for all spectra measured on the same lesion were averaged, and the maximum value of these means of probability 1 and probability 2 were retained for the final decision of the classifier. The advantage of this approach is that the final decision on a lesion measured multiple times is an average between decisions made for each measurement separately.

For all spectroscopic modalities, ie, AFS, DRS and DPS, the sensitivity and the specificity of the classifier were calculated with different threshold values to distinguish the low-grade and high-grade tissue. Based on these calculations for different thresholds, ROC curves are constructed by plotting (1 – specificity) values against corresponding values of sensitivity. We calculate for each spectroscopic modality the mean and the SD of the area under the ROC curve using the leave-one-lesion-out method. Using this method, an area under the ROC curve was calculated 191 times for 190 lesions (each time one lesion was left out), and the 191 ROC curves were used to calculated the mean and the SD of the area under the ROC curve for a given spectroscopic technique.

Results

Optical Spectra
Typical AFS, DRS, and DPS spectra measured in low-grade and high-grade bronchial lesions are illustrated in Figure 2 . The average intensity of the autofluorescence spectra (Fig 2, top, A) is higher in low-grade lesions than in high-grade lesions. For diffuse reflectance spectra (Fig 2, center, B) and differential path length spectra (Fig 2, bottom, C), the decrease in signal intensity observed in the wavelength range of 500 to 600 nm corresponds to the light absorption of blood and was generally higher on high-grade lesions compared to low-grade lesions.


Figure 2
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  		Figure 2.. Illustration of autofluorescence (top, A), diffuse reflectance (center, B), and differential path length (bottom, C) spectra measured in low-grade and high-grade bronchial lesions.

 
Classification Accuracy of Optical Spectroscopy
The ROC curves for the three spectroscopic modalities are presented in Figure 3 . The mean and SD of the areas under the ROC curves, as well as the highest combined sensitivity and specificity for the three spectroscopic modalities, are summarized in Table 1 . It is observed that DRS and DPS have larger areas under the ROC curves than AFS. Similarly, higher specificities and sensitivities were measured for DRS and DPS than for AFS.


Figure 3
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  		Figure 3.. ROC curves for the three different spectroscopic modalities. Black triangle and solid line indicate DPS. White triangle and dotted line indicate DRS. White circle and dotted line indicate AFS.

 

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  		Table 1.. Areas Under the ROC Curves, as Well as the Highest Combined Sensitivity and Specificity for DRS, DPS, AFS Spectroscopy for the Distinction Between High-Grade and Low-Grade Bronchial Lesions

 
Improvement of Autofluorescence Bronchoscopy Specificity
For this analysis, the relevant data set consists only of spectra measured on the 101 lesions found by autofluorescence imaging. For the analysis of this subset of spectra, the classification accuracy of AFS, DPS, and DRS was evaluated using the threshold value with the smallest classification error (corresponding to the highest combined sensitivity and specificity) derived from the ROC curves constructed using the complete data set. For the classification results of the combination of the three spectroscopic techniques, the principle of majority voting was applied (the classification according to the majority of the three spectroscopic classifications determined the final classification). Table 2 summarizes the classification results of each of the three spectroscopic techniques and of their combination for bronchial lesions found by autofluorescence bronchoscopy. It must be noted that all these classification values are not absolute values but rather relative values, since our data set consists of measurements on lesions already classified as abnormal by autofluorescence bronchoscopy. We find that all three spectroscopic techniques improve the positive predictive value of bronchoscopic imaging. This improvement is combined with a high relative negative predictive value, relative sensitivity, and relative specificity. White light reflectance spectroscopy (DRS and DPS) yielded better results than AFS. The best classification results were obtained when the three spectroscopic techniques were combined.


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  		Table 2.. Classification Results of AFS, DRS, and DPS and of Their Combination for Bronchial Lesions Found by Autofluorescence Bronchoscopic Imaging (n = 101)

 
Discussion

In this study, we examine the accuracy of various optical techniques for the classification of malignant lesions of the bronchial tree. We observe that AFS, DRS, and DPS allow the distinction of malignant from nonmalignant bronchial mucosa with high accuracy. All three optical spectroscopic modalities, especially when they are combined, increase the positive predictive value of autofluorescence imaging alone for the detection of bronchial tumor.

We find that the diagnostic accuracy of white light reflectance spectroscopy (both DRS and DPS) was superior to AFS. This result suggests that the optical properties of the bronchial mucosa contain the most discriminative information for tissue classification. In this view, the classification accuracy of autofluorescence spectroscopy can be attributed to the fact that autofluorescence spectra depend on the optical properties as well, but that the accuracy of the classification is reduced by the large natural variations in fluorophore concentrations having less relevance for tissue diagnostics. Changes in tissue optical properties are related to changes in both the scattering and absorption properties of tissue. Scattering modifications are related to changes in tissue architecture such as thickening of the epithelial layer and to cellular changes such as an increase of the nucleus chromatin content, variations in the nucleus-cytoplasma ratio, and changes in the intracytosolic content.3 Absorption modifications are mostly related to modifications of the concentration and distribution of light absorbers such as hemoglobin.4 Tumors are known to be commonly more vascularized than normal tissue. Such increased blood content is related to the increased density of microvessels and the blood stasis occurring in tumoral tissue.2021 Unfortunately, no information concerning the physiologic changes in the bronchial cancerous tissue underlying the modifications of the tissue optical properties can be obtained from principal component analysis. However, we recently reported that several physiologic parameters related to tissue microvasculature and blood oxygenation can be extracted from DPS spectra measured on bronchial mucosa.16 We have reported that bronchial tumors are characterized by a higher blood content than normal and metaplastic/mild dysplastic bronchial mucosa. This suggests that the optical variations observed in bronchial tumors with DRS, DPS, and AFS may be attributed largely to absorption of light by blood.

The equally good classification results observed with DRS and DPS suggest that the optical changes present in cancerous tissue are already detectable in the first 150 to 200 µm of the tumor surface. We expect DPS to be superior in classifying superficial lesions such as severe dysplasia and carcinoma in situ, since DPS measures only the superficial layer of tissue, which is where the precancerous changes develop. Unfortunately, no conclusion regarding this issue can be drawn from our data due to the nature of the population of lesions that was studied. During the examination of 191 bronchial lesions, we found only 6 severe dysplasia/CIS lesions, and 4 of these lesions were detected without autofluorescence bronchoscopy, which leaves only 2 CIS lesions in the test set of 101 lesions found by autofluorescence bronchoscopy. Such a low fraction of superficial lesions hampers any comparison between the classification accuracy of DRS and DPS for these epithelial lesions. The exact prevalence of severe dysplasia and CIS in the bronchial tree is unknown. In a review22 of fluorescence bronchoscopy data in a selected population of smokers and former smokers with sputum atypia, prevalence rates of 6% and 1.6% were reported for severe dysplasia and CIS, respectively. Further study of DPS in a selected population of patients with severe dysplasia/CIS should be performed to show the benefit of DPS for the analysis of (pre-)malignant lesions confined to the epithelium.

Bronchoscopy is, along with thoracic imaging, the cornerstone technique for both diagnosis and staging of lung cancer. Autofluorescence bronchoscopy has been reported to have a higher sensitivity than white light bronchoscopy for the detection of endobronchial (pre-)malignant lesions.232425 However, autofluorescence bronchoscopy is characterized by a low specificity with a high rate of false-positive findings, inducing unnecessary biopsies at a greater cost and a longer examination duration.2627 We find that both autofluorescence and white light reflectance spectroscopy increase the positive predictive value of autofluorescence imaging alone for the detection of bronchial tumors. The highest relative positive predictive value was obtained when the three spectroscopic techniques were combined. However, the very small amount of intraepithelial premalignant lesions such as severe dysplasia and carcinoma in situ in our patient population hampered any separate analyses of these kinds of lesions. Therefore, further studies are needed to conclude on the interest of the combined use of optical spectroscopy (especially DPS) and autofluorescence bronchoscopy for the detection of premalignant lesions of the bronchial tree.

In our study, the initial classification accuracy of the spectroscopic techniques was evaluated using areas under ROC curves. In this procedure, the sensitivity and the specificity of the classifier were calculated with different threshold values to distinguish the low-grade and high-grade tissues. Based on these calculations for different thresholds, ROC curves are constructed by plotting (1 – specificity) values against corresponding values of sensitivity. For the classification analysis of the subset of bronchial lesions detected using autofluorescence bronchoscopy, we chose to use threshold values giving the highest combined sensitivity and specificity. However, it must be noted that the choice of the classification threshold is flexible and can be adapted to the clinical aims. For example, if it is clinically more desirable to have a higher sensitivity at the cost of a lower specificity, the threshold value for the classifier can be adjusted to accommodate this desire.

Conclusion

Optical spectroscopy, especially white light reflectance spectroscopy, allows the real-time, noninvasive distinction of malignant from nonmalignant bronchial mucosa with high accuracy. Combination of optical spectroscopy with autofluorescence bronchoscopy was shown to improve the specificity of autofluorescence bronchoscopy alone for the detection of bronchial tumors. Therefore, these noninvasive techniques may be well implemented in the panel of diagnostic techniques currently used for the detection of endobronchial cancer.

Footnotes

Abbreviations: AFS = autofluorescence spectroscopy; CIS = carcinoma in situ; DPS = differential path length spectroscopy; DRS = diffuse reflectance spectroscopy; ROC = receiver operator characteristic

This project was funded by the Dutch Technology Foundation.

Received for publication August 29, 2005. Accepted for publication October 3, 2005.

References

  1. Wagnières, GA, Star, WM, Wilson, BC (1998) In vivo fluorescence spectroscopy and imaging for oncological applications. Photochem Photobiol ,603-632
  2. Bartlett, M, Huang, G, Larcom, L, et al Measurement of particle size distribution in mammalian cells in vitro by use of polarized light spectroscopy. Appl Optics 2004;43,1296-1307
  3. Beauvoit, B, Chance, B Time-resolved spectroscopy of mitochondria, cells and tissue under normal and pathological conditions. Mol Cell Biochem 1998;184,445-455[CrossRef][Medline]
  4. Sokolov, K, Follen, M, Richards-Kortum, R Optical spectroscopy for detection of neoplasia. Cur Opin Chemical Biol 2002;6,651-658[CrossRef]
  5. Mirabal, YN, Chang, SK, Atkinson, EN, et al Reflectance spectroscopy for in vivo detection of cervical precancer. J Biomed Opt 2002;7,587-594[Medline]
  6. Nordstrom, RJ, Burke, L, Nilodd, JM, et al Identification of cervical intraepithelial neoplasia (CIN) using UV-excited fluorescence and diffuse-reflectance spectroscopy. Lasers Surg Med 2001;28,118-127[CrossRef]
  7. Georgakoudi, I, Sheets, EE, Müller, MG, et al Trimodal spectroscopy for the detection and characterization of cervical precancers in vivo. Am J Obstet Gynecol 2002;186,374-382[CrossRef][Medline]
  8. Breslin, TM, Xu, F, Palmer, GM, et al Autofluorescence and diffuse reflectance properties of malignant and benign breast tissues. Ann Surg Oncol 2004;11,65-70[Abstract/Free Full Text]
  9. Utzinger, U, Brewer, M, Silva, E, et al Reflectance spectroscopy for in vivo characterization of ovarian tissue. Lasers Surg Med 2001;28,56-66[Medline]
  10. Wallace, MB, Perelman, LT, Backman, V, et al Endoscopic detection of dysplasia in patients with Barrett’s esophagus using light-scattering spectroscopy. Gastroenterology 2000;119,677-682[CrossRef][ISI][Medline]
  11. Müller, MG, Valdez, TA, Georgakoudi, I, et al Spectroscopic detection and evaluation of morphologic and biochemical changes in early human oral carcinoma. Cancer 2003;97,1681-1692[CrossRef][Medline]
  12. Hung, J, Lam, S, LeRiche, JC, et al Autofluorescence of normal and malignant bronchial tissue. Lasers Surg Med 1991;11,99-105[ISI][Medline]
  13. Kusunoki, Y, Imamura, F, Uda, H, et al Early detection of lung cancer with laser-induced fluorescence endoscopy and spectrofluorometry. Chest 2000;118,1776-1782[Abstract/Free Full Text]
  14. Amelink, A, Sterenborg, HJCM Measurement of the local optical properties of turbid media by differential path-length spectroscopy. Appl Optics 2004;43,3048-3054
  15. Amelink, A, Sterenborg, HJCM, Bard, MPL, et al In vivo measurement of the local optical properties of tissue by use of differential path-length spectroscopy. Opt Lett 2004;29,1087-1089[CrossRef][Medline]
  16. Bard, MPL, Amelink, A, Noordhoek-Hegt, V, et al Measurement of hypoxia-related parameters in bronchial mucosa by use of optical spectroscopy. Am J Respir Crit Care Med 2005;171,1171-1184
  17. Hirsch, FR, Franklin, WA, Gazdar, AF, et al Early detection of lung cancer: clinical perspectives of recent advances in biology and radiology. Clin Cancer Res 2001;7,5-22[Abstract/Free Full Text]
  18. Brambilla, E, Travis, WD, Colby, TV, et al The new World Health Association classification of lung cancer. Eur Respir J 2001;18,1059-1068[Abstract/Free Full Text]
  19. Bigio, IJ, Bown, SG, Brigs, G, et al Diagnosis of breast cancer using elastic-scattering spectroscopy: preliminary clinical results. J Biomed Opt 2000;5,221-228[CrossRef][Medline]
  20. Jain, RK Determinant of tumor blood flow: a review. Cancer Res 1988;48,2641-2658[Abstract/Free Full Text]
  21. Shijubo, N, Kojima, H, Nagata, M, et al Tumor angiogenesis of non-small cell lung cancer. Microsc Res Tech 2003;60,186-198[CrossRef][ISI][Medline]
  22. Lam, S, MacAulay, C, LeRiche, JC, et al Detection and localization of early lung cancer by fluorescence bronchoscopy. Cancer 2000;89,2468-2473[CrossRef][ISI][Medline]
  23. Hirsch, FR, Prindiville, SA, Miller, YE, et al Fluorescence versus white-light bronchoscopy for detection of preneoplastic lesions: a randomized study. J Natl Cancer Inst 2001;93,1385-1391[Abstract/Free Full Text]
  24. Kennedy, TC, Hirsch, FR, Miller, YE, et al A randomized study of fluorescence bronchoscopy versus white-light bronchoscopy for early detection of lung cancer in high risk patients. Lung Cancer 2000;29,244-245
  25. Lam, S, Kennedy, T, Unger, M, et al Localization of bronchial intraepithelial neoplastic lesions by fluorescence bronchoscopy. Chest 1998;113,696-702[Abstract/Free Full Text]
  26. Sutedja, TG, Venmans, BJ, Smit, EF, et al Fluorescence bronchoscopy for early detection of lung cancer: a clinical perspective. Lung Cancer 2001;34,157-168[CrossRef][ISI][Medline]
  27. Banerjee, AK, Rabbitts, PH, George, J Lung cancer. 3: Fluorescence bronchoscopy; clinical dilemmas and research opportunities. Thorax 2003;58,266-271[Abstract/Free Full Text]




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