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Optics Express

Optics Express

  • Editor: Michael Duncan
  • Vol. 14, Iss. 15 — Jul. 24, 2006
  • pp: 6713–6723
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In-vivo NIR autofluorescence imaging of rat mammary tumors

Laure S. Fournier, Vincenzo Lucidi, Kirill Berejnoi, Theodore Miller, Stavros G. Demos, and Robert C. Brasch  »View Author Affiliations


Optics Express, Vol. 14, Issue 15, pp. 6713-6723 (2006)
http://dx.doi.org/10.1364/OE.14.006713


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Abstract

We investigate in vivo detection of mammary tumors in a rat model using autofluorescence imaging in the red and far-red spectral regions. The objective was to explore this method for non-invasive detection of malignant tumors and correlation between autofluorescence properties of tumors and their pathologic status. Eighteen tumor-bearing rats, bearing eight benign and seventeen malignant tumors were imaged. Autofluorescence images were acquired using spectral windows centered at 700-nm, 750-nm and 800-nm under laser excitation at 632.8-nm and 670-nm. Intensity in the autofluorescence images of malignant tumors under 670-nm excitation was higher than that of the adjacent normal tissue. whereas intensity of benign tumors was lower compared to normal tissue.

© 2006 Optical Society of America

1. Introduction

The use of light for cancer diagnosis dates back to the 1920s, when transillumination was used to investigate breast cancer [4

4. M. Cutler, “Transillumination as an aid in the diagnosis of breast lesions.,” Surg. Gynecol. Obstet. 48, 721–729 (1929).

]. This technique was named diaphanography, but its low sensitivity and specificity limited its clinical usefulness. With progress in photonic technologies, mathematic modeling of light propagation, and increased knowledge of the photophysical properties of tissues, optical imaging (also referred to as optical mammography) has evolved to become a promising imaging tool for oncological applications of the breast [5–7

5. X. Gu, Q. Zhang, M. Bartlett, L. Schutz, L. L. Fajardo, and H. Jiang, “Differentiation of cysts from solid tumors in the breast with diffuse optical tomography,” Acad. Radiol. 11, 53–60 (2004). [CrossRef] [PubMed]

].

The utilization of native optical “signatures” associated with the way tissue components interact with light has also been proven to be a very promising approach for tissue characterization in real time. Changes in the spectroscopic properties of pathological specimens including diverse cancers originating in many organs such as skin, gastrointestinal tract and oral mucosa have stimulated a great deal of interest for its potential application for the detection and treatment of cancer [8

8. G. A. Wagnieres, W. M. Star, and B. C. Wilson, “In vivo fluorescence spectroscopy and imaging for oncological applications,” Photochem. Photobiol. 68, 603–32. (1998). [PubMed]

]. In the case of breast cancer, Raman spectroscopy [9–11

9. C. J. Frank, D. C. Redd, T. S. Gansler, and R. L. McCreery, “Characterization of human breast biopsy specimens with near-IR Raman spectroscopy,” Anal. Chem. 66, 319–26 (1994). [CrossRef] [PubMed]

] and light scattering spectroscopy [12

12. S. G. Demos, H. Savage, A. S. Heerdt, S. Schantz, and R. R. Alfano, “Time Resolved Degree of Polarization for Human Breast Tissue,” Optics Commun. 124, 439 (1996). [CrossRef]

,13

13. D. W. Chicken, A. C. Lee, G. M. Briggs, M. R. S. Keshtgar, K. S. Johnson, D. D. O. Pickard, I. J. Bigio, and S. G. Bown, “Optical biopsy: A novel intraoperative diagnostic tool to determine sentinel lymph node status instantly in breast cancer,” Breast Cancer Res. Treat. 82, S172–S174 (2003).

] have demonstrated potential for the differentiation of breast tissue components. More recent studies using fresh human tissue specimens containing normal and cancer tissue from various body organs (including breast) have indicated the potential of NIR autofluorescence imaging under long wavelength excitation for cancer detection [14–16

14. S. G. Demos, R. Gandour-Edwards, R. Ramsamooj, and R. DeVere White, “Spectroscopic detection of bladder cancer using near-infrared imaging techniques,” J. Biomed. Opt. 9, 767–71 (2004). [CrossRef] [PubMed]

]. Zhang et al demonstrated that this signal has a lifetime on the order of 1 ns, thus it is due to emission by a tissue chromophore [16

16. G. Zhang, S. G. Demos, and R. R. Alfano, “Far-red and NIR spectral wing emission from tissues under 532-nm and 632-nm photo-excitation,” Lasers Life Sci. 9, 1–16 (1999).

] but, to the best of our knowledge, an exact identification has not yet been made. It has been hypothetized that porphyrins may be the fluorophore giving rise to changes in the NIR autofluorescence intensity in cancer tissue [14

14. S. G. Demos, R. Gandour-Edwards, R. Ramsamooj, and R. DeVere White, “Spectroscopic detection of bladder cancer using near-infrared imaging techniques,” J. Biomed. Opt. 9, 767–71 (2004). [CrossRef] [PubMed]

,15

15. S. G. Demos, R. Gandour-Edwards, R. Ramsamooj, and R. White, “Near-infrared autofluorescence imaging for detection of cancer,” J. Biomed. Opt. 9, 587–92 (2004). [CrossRef] [PubMed]

,17

17. M. Inaguma and K. Hashimoto, “Porphyrin-like fluorescence in oral cancer: In vivo fluorescence spectral characterization of lesions by use of a near-ultraviolet excited autofluorescence diagnosis system and separation of fluorescent extracts by capillary electrophoresis,” Cancer 86, 2201–11. (1999). [CrossRef] [PubMed]

]. This change may be the result of a change in the production of porphyrins in neoplasia due to change in the heme-biosynthetic pathway.

In this study, we investigate NIR autofluorescence imaging for noninvasive detection of subsurface tumors in vivo using a rat model of chemically-induced mammary tumors. This living rodent model was selected because it produces a range of tumors that span the whole pathologic spectrum, such as those observed in human breast masses. A second objective was to evaluate possible correlation of the NIR autofluorescence intensity arising from tumors with their pathologic status.

2. Experimental arrangement

2.1 Tumor model

The study was conducted with the approval of the Committee for Animal Research of the University of California San Francisco, and conformed to the guidelines of the National Institutes of Health for the care and use of laboratory animals.

Tumors were chemically induced in eighteen female Sprague Dawley rats by injecting a single dose of N-ethyl-N-nitrosurea (ENU) intra-peritoneally. Tumors developed in the mammary gland of the rat over a period of 5 to 15 months. Typically, the tumor pathologies mimic the complete range of tumor pathologies encountered in humans, from benign fibroadenomas to highly malignant carcinomas [18

18. G. Stoica and A. Koestner, “Diverse spectrum of tumors in male Sprague-Dawley rats following single high doses of N-ethyl-N-nitrosourea (ENU),” Am. J. Pathol. 116, 319–26 (1984). [PubMed]

]. High doses tend to induce fast-growing malignant tumors, whereas lower doses tend to induce slower-growing benign tumors. To obtain a range of pathologies and tumor grades, different doses of chemical were used, ranging from 90 to 250 mg/kg. Forty tumors in total were imaged and analyzed by pathology, when tumor diameters reached at least 1-2 cm.

2.2 Optical spectroscopic imaging

A schematic diagram of our experimental setup is shown in Fig. 1. Our custom imaging system incorporated an OPO laser pumped at 532 nm by the second harmonic of an Nd:YAG laser. The temporal width of the laser pulses was ≈5 ns and the spectral linewidth was ≈1 nm. This laser operates at a repetition rate of 20 Hz and provided tunable operation from 660 to 970 nm, permitting coverage of the far red and near infrared spectral region. It also included a Helium-Neon laser emitting at a 632.8-nm wavelength. The laser light was transferred into the enclosed imaging compartment of the system using a fiber bundle. The central portion of the diverging laser beam was used to photo-excite the region of interest in the animal in order to acquire the autofluorescence image. A liquid nitrogen-cooled CCD camera was used to capture the images. A laptop computer remotely operated the system.

Fig. 1. Schematic layout of the key components of the optical imaging system.

Prior to imaging, rats were anesthetized with a 20 mg/kg intra-peritoneal dose of pentobarbital, and shaved of their hair in the zone of the tumor and adjacent normal mammary tissue. Each animal was then placed on a trough so as to have the tumor facing the imaging system. An initial image was acquired under ambient light using the same spectral window used for the autofluorescence images. This image was used as an anatomical reference of the tumor position.

2.3 Image analysis

The exact spatial map of the illumination intensity was acquired by recording the fluorescence image of a high quality sheet of paper. Regions of interest (ROI) were placed on the tumor and the adjacent normal mammary tissue, to measure mean signal intensity, and yield a quantitative measure of tumor-to-normal tissue contrast, using the WinView/32 software (Roper Scientific, Inc., Tucson, AZ). The WinView/32 software allowed drawing of rectangular regions of interest placed in the center of the tumor. The image was composed of 512×512 pixels and its size was 13.5 cm2, yielding a spatial resolution of 0.26 mm.

ROI were placed on tumor and normal tissue on the autofluorescence images, guided by the difference of signal intensities between tumor and normal tissue. In rats with several tumors, the autofluorescence intensity was measured on each tumor. To make sure that the possible inhomogeneity of illumination or the angle of the tissue with the laser illumination or the CCD camera did not influence the measures, a ROI was placed on adjacent normal tissue for each tumor. The tumor-to-normal tissue ratio (T/N) was calculated as the ratio of the average signal intensities of the tumor over the adjacent normal tissue. Analysis of the images was performed prior to knowledge of the pathological results.

2.4 Pathological analysis

At the end of each imaging protocol, animals were sacrificed by a lethal intravenous dose of pentobarbital followed by bilateral thoracotomies. Tumors were resected, fixed in 10 % buffered formalin, processed into paraffin and sectioned. Conventional hematoxylin and eosin staining was performed for microscopic assessments of tumor morphology and tumor grading according to the Scarff-Bloom-Richardson (SBR) method. This method grades malignant tumors according to three characteristics: glandular formation, nuclear pleomorphism, and mitotic activity (scores 3-9 reflecting progressive grades of malignancy) [19

19. R. Scarff and H. Torloni, “Histological typing of breast tumors,” in World Health Organization, (Geneva, 1968), pp. 13–20.

].

2.5 Statistical analysis

Tumor-to-normal tissue ratio values (T/N) were compared between the benign and malignant groups of tumors by performing non-parametric Mann-Whitney tests using the statistical software Statview (SAS Institute Inc., Cary, NC). A P value less than 0.05 was considered statistically significant. The best combination of excitation/detection filters was chosen as that which yielded the most statistically significantly different results between the T/N ratios of tumor and normal soft tissues. Sensitivity, specificity, positive and negative predictive values were calculated using a criteria of T/N < or > 1 to differentiate between benign and malignant tumors. An ANOVA analysis was performed to compare the estimated OI-derived autofluorescence parameter T/N, with the histological tumor SBR score, the ENU injection dose, and the incubation period before the tumors appeared.

3. Experimental results

3.2 Tumor characterization

From the forty mammary tumors examined, three were excluded because they were not breast tumors (one was a sarcoma, another was a pilo-sebaceous tumor, and the last an atypical carcinoma). Another twelve (5 benign, 7 malignant) were excluded for two main reasons. A number of animals had patches of fine hair remaining after shaving which we were not able to remove due to the risk of skin irritation. Indeed, during the experiments we noticed that local inflammation also increased the autofluorescence signal and risked yielding false positives. In addition, in five animals a pus-like collection was discovered at dissection near the tumors corresponding to areas of high NIR fluorescent signal in the images. In each of these cases, it was the author’s opinion (after considering the entire set of experimental results and behaviors observed during the experiments) that the signal intensity measured could not be linked “without a doubt” to the signal content of the tumor or normal tissues.

From the twenty five remaining tumors included in this study (in fifteen rats), seventeen were malignant with Scarff-Bloom Richardson scores ranging from 3 to 6. The eight other tumors were benign fibroadenomas. The malignant tumors developed on average 6½ months after ENU administration, while the benign tumors tended to develop more slowly, on average 10 months after exposure. It can be noted that none of the tumors included in this study demonstrated necrosis on pathological examination. All tumors were palpable, as they were situated directly beneath the skin. Tumor sizes were not statistically different between malignant tumors (mean size: 27 ± 9 mm) and benign tumors (mean size: 25 ± 13 mm). The latter however had much more dispersed tumor sizes, with either small or large tumors, but no intermediate size.

3.2 Tumor-to-normal signal intensity ratio (T/N)

The size of the regions of interest varied according to the size of the tumors: mean tumor ROI sizes were 7993 ± 8089 pixels. For normal tissue, region sizes were always approximately the same size, arbitrarily drawn as roughly a 1 cm2 square (≈ 1500 pixels).

Analysis of the experimental results showed that the 670/800 nm combination yielded statistically significant differences between benign and malignant tumor-to normal signal intensity ratios, as well as the best qualitative (visual) contrast between the tumors and the normal tissue. No statistically significant differences were observed under 632.8-nm excitation. The results are summarized in Table 1. The individual results for each tumor obtained under 670-nm excitation and 800-nm detection are presented in Table 2.

Table 1. Tumor-to-normal average signal intensity ratios at different laser excitation and detection spectral bands, for benign and malignant tumors.

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Table 2. Tumor-to-normal autofluorescence intensity ratios measured using laser excitation at 670 nm and detection spectral window of 750±20 nm 800±20 nm.

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Thirteen of the seventeen cancers appeared visually brighter than adjacent normal mammary soft tissue, with higher cancer/normal tissue signal intensity ratios (T/N>1) for the 670/800-nm combination. Though ratios of malignant tumors were only a few percentage points for certain tumors, it is noteworthy that even in these cases, the difference in fluorescence was visible to the eye (for example the second tumor in Fig. 2 with a T/N ratio of 1.01). The four other malignant tumors (numbers 15, 20, 21 and 24) had signals that were inferior to the signal of adjacent normal mammary soft tissues. Fig. 2 demonstrates a typical example showing the image of three malignant tumors within the same rat clearly visible in the autofluorescence image due to their higher intensity compared to than of the adjacent normal tissue. In this image, the section of the animal that was not shaved from its hair appears as the area of highest intensity due to the fact that the rat’s hair is highly emissive in the NIR spectral region. Of the eight benign tumors, six were darker than the adjacent normal tissue (T/N<1), in sharp contrast to the malignant tumors. A typical example is shown in Fig. 3. The other two benign tumors appeared brighter than the normal tissue (numbers 17 and 18).

Fig. 2. (a) Light scattering image under ambient light and (b) corresponding autofluorescence image under 670-nm excitation showing a rat containing malignant tumors in supine position (tumors #2 and 3). The spectral window for image formation was at 800-nm in both images. The hair represents the very bright regions visible on the fluorescence image outside the area of interest. On the fluorescence image (b), the tumor (indicated by the black arrow), spontaneously emits more fluorescence than adjacent normal mammary tissue (white arrow).
Fig. 3. (a) Light scattering image under ambient light and (b) corresponding autofluorescence image under 670-nm excitation and detection at 800-nm showing a rat containing a benign tumor in supine position (tumor #7). In the fluorescence image (b), the tumor (white arrow) is on the contrary to Fig. 2, much less fluorescent than the adjacent normal mammary tissue (black arrow). This tumor was shown to be a benign fibroadenoma on pathology. The signal intensity ratio T/N was in this case 0.22. Note the visibility of a blood vessel, absorbing light (black arrowheads), outlined by the brighter normal tissue. Small variation in the autofluorescence intensity in the normal tissue area is attributed to the presence of various organs of the animal located below the surface that exhibit different autofluorescence intensities under the deeply propagating excitation light.

Figure 4 displays the T/N signal intensity ratios of benign and malignant tumors as a function of their size. The intensity ratios of the malignant tumors have a signal that seems independent of size. Indeed, whether small or large, they appear to be more fluorescent than normal mammary tissue. The signal intensity of the four biggest benign tumors, on the other hand, seem to be negatively correlated to size. No definite conclusion can be made, however, due to the small number of benign tumors.

Fig. 4. Tumor-to-normal signal intensity ratio plotted according to size for benign (엯) and malignant (Δ) tumors.

3.3 Statistical analysis

The experimental results in this study of twenty-five induced mammary tumors in rodents demonstrate that optical imaging using the near-infrared native fluorescence was able to non-invasively detect and image most of the malignant tumors. Furthermore, the statistical analysis suggests that this method may help differentiate benign from malignant lesions. More specifically, the mean values under 670 nm excitation of the signal intensity ratios of tumor-to-normal-mammary-tissue (T/N) were 0.83 for the benign tumors, and 1.14 for the malignant tumors for images acquired using the 800±20 nm filter (detection spectral range). These values differed significantly (P=0.009). The mean values under 670 nm excitation and 750±20 nm detection were 0.82 for the benign tumors, and 1.12 for the malignant tumors with a statistically borderline value of P=0.10. Under 632.8 nm excitation, no difference was observed between the normal and tumor (benign or malignant) tissue in the NIR autofluorescence images.

An ANOVA showed no correlation between the T/N ratio and ENU dose or incubation period (respectively P=0.8, 0.5 and 0.5).The analyses failed to show statistically significant levels between the T/N ratios and the SBR tumor scores, whether the analysis included the benign tumors (SBR scored at 3) or not (respectively P=0.16, and 0.48).

To evaluate the possibility that NIR autofluorescence imaging may differentiate benign from malignant tumor, we considered that tumors with higher autofluorescence than adjacent normal tissue (T/N>1) were malignant, and tumors with lower autofluorescence than adjacent normal tissue (T/N<1) were benign. A statistical analysis with these parameters under 670-nm excitation and 800-nm detection yielded a sensitivity of 76 %, a specificity of 75 %, a positive predictive value of 87 %, and a negative predictive value of 60 % (Table 3).

Table 3. Diagnostic value of NIR autofluorescence imaging under 670-nm excitation and 800-nm detection for the differentiation of benign and malignant breast tumors.

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TN is true negatives, TP true positives, FN false negatives, FP false positives, Se sensitivity, Sp specificity, PPV positive predictive value and NPV negative predictive value.

4. Discussion

The quantification of the autofluorescence intensity of the lesions was performed using the tumor-to-normal tissue signal intensity ratio for two reasons. First, ratios allowed for comparison among different animals/tumors. The autofluorescence intensity of the same tissue component varies between different animals (or humans) which necessitates some kind of normalization procedure through comparison between tissue components of the same subject [15

15. S. G. Demos, R. Gandour-Edwards, R. Ramsamooj, and R. White, “Near-infrared autofluorescence imaging for detection of cancer,” J. Biomed. Opt. 9, 587–92 (2004). [CrossRef] [PubMed]

]. Secondly, this T/N ratio closely parallels the qualitative image contrast relied upon routinely by diagnostic imagers for tumor assessments in a clinical context.

The analysis of the experimental results summarized in Table 1 reveal a trend that may help explain the observed behaviors based on tissue optics concepts. Given that the tumors are located below the surface and that the excitation light must penetrate through the skin, the 670 nm excitation will more efficiently reach and penetrate into the tumor due to lower absorption and scattering compared to 632.8 nm excitation. Absorption by blood in the vascular network of the tumor may be the main mechanism providing for a difference in the excitation conditions under 632.8 and 670 nm illumination (the molar extinction coefficient of hemoglobin at 633 nm is twice that at 670 nm). Both malignant and benign tumors exhibit high vascularity. This lower absorption at 670 nm may be essential in providing more efficient excitation of the tumor to increase the relative intensity detected signal by the imaging system (compared to signal arising from the skin and non-tumor components of the animal). Similarly, the longer emission wavelength (at 800±20) will be more efficient in reaching the detection system due to lower scattering by the tissue (compared to the shorter imaging wavelength at 750±20 nm) thus providing a better image contrast and lower P value as indicated from the statistical analysis of our experimental results.

5. Conclusion

Near-infrared fluorescence imaging revealed statistically significant differences of an inherent property (NIR autofluorescence) among normal tissue, benign fibroadenomas and malignant adenocarcinomas in a rat model of breast tumors. This study suggests that NIR autofluorescence spectroscopy may help devise methods for in vivo characterization of breast tumors as well as improve specificity of lesion assessment compared to existing modalities. Currently, x-ray mammography and ultrasound offer have good sensitivity, but low specificity [3

3. T. M. Kolb, J. Lichy, and J. H. Newhouse, “Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations,” Radiology 225, 165–75 (2002). [CrossRef] [PubMed]

] (only 20-25 % of lesions undergoing biopsy are ultimately identified as cancer). This work also demonstrated that NIR autofluorescence imaging can detect and image subsurface lesions when differences in the emission intensity between normal and tumor components exist.

Acknowledgments

References and links

1.

National Cancer Institute, “Cancernet resource” (National Cancer Institute, 2003), http://cis.nci.nih.gov/fact/5_6.htm.

2.

National Cancer Institute, “Surveillance, Epidemiology, and End Results: Estimated new cancer cases and deaths for 2004” (National Cancer Institute, 2004), http://seer.cancer.gov/cgi-bin/csr/1975_2001/search.pl#results.

3.

T. M. Kolb, J. Lichy, and J. H. Newhouse, “Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations,” Radiology 225, 165–75 (2002). [CrossRef] [PubMed]

4.

M. Cutler, “Transillumination as an aid in the diagnosis of breast lesions.,” Surg. Gynecol. Obstet. 48, 721–729 (1929).

5.

X. Gu, Q. Zhang, M. Bartlett, L. Schutz, L. L. Fajardo, and H. Jiang, “Differentiation of cysts from solid tumors in the breast with diffuse optical tomography,” Acad. Radiol. 11, 53–60 (2004). [CrossRef] [PubMed]

6.

D. B. Jakubowski, A. E. Cerussi, F. Bevilacqua, N. Shah, D. Hsiang, J. Butler, and B. J. Tromberg, “Monitoring neoadjuvant chemotherapy in breast cancer using quantitative diffuse optical spectroscopy: a case study,” J. Biomed. Opt. 9, 230–8 (2004). [CrossRef] [PubMed]

7.

V. Ntziachristos, A. G. Yodh, M. Schnall, and B. Chance, “Concurrent MRI and diffuse optical tomography of breast after indocyanine green enhancement,” Proc. Natl. Acad. Sci. U. S. A. 97, 2767–72. (2000). [CrossRef] [PubMed]

8.

G. A. Wagnieres, W. M. Star, and B. C. Wilson, “In vivo fluorescence spectroscopy and imaging for oncological applications,” Photochem. Photobiol. 68, 603–32. (1998). [PubMed]

9.

C. J. Frank, D. C. Redd, T. S. Gansler, and R. L. McCreery, “Characterization of human breast biopsy specimens with near-IR Raman spectroscopy,” Anal. Chem. 66, 319–26 (1994). [CrossRef] [PubMed]

10.

A. S. Haka, K. E. Shafer-Peltier, M. Fitzmaurice, J. Crowe, R. R. Dasari, and M. S. Feld, “Identifying microcalcifications in benign and malignant breast lesions by probing differences in their chemical composition using Raman spectroscopy,” Cancer Res. 62, 5375–80 (2002). [PubMed]

11.

R. Manoharan, K. Shafer, L. Perelman, J. Wu, K. Chen, G. Deinum, M. Fitzmaurice, J. Myles, J. Crowe, R. R. Dasari, and M. S. Feld, “Raman spectroscopy and fluorescence photon migration for breast cancer diagnosis and imaging,” Photochem. Photobiol. 67, 15–22 (1998). [CrossRef] [PubMed]

12.

S. G. Demos, H. Savage, A. S. Heerdt, S. Schantz, and R. R. Alfano, “Time Resolved Degree of Polarization for Human Breast Tissue,” Optics Commun. 124, 439 (1996). [CrossRef]

13.

D. W. Chicken, A. C. Lee, G. M. Briggs, M. R. S. Keshtgar, K. S. Johnson, D. D. O. Pickard, I. J. Bigio, and S. G. Bown, “Optical biopsy: A novel intraoperative diagnostic tool to determine sentinel lymph node status instantly in breast cancer,” Breast Cancer Res. Treat. 82, S172–S174 (2003).

14.

S. G. Demos, R. Gandour-Edwards, R. Ramsamooj, and R. DeVere White, “Spectroscopic detection of bladder cancer using near-infrared imaging techniques,” J. Biomed. Opt. 9, 767–71 (2004). [CrossRef] [PubMed]

15.

S. G. Demos, R. Gandour-Edwards, R. Ramsamooj, and R. White, “Near-infrared autofluorescence imaging for detection of cancer,” J. Biomed. Opt. 9, 587–92 (2004). [CrossRef] [PubMed]

16.

G. Zhang, S. G. Demos, and R. R. Alfano, “Far-red and NIR spectral wing emission from tissues under 532-nm and 632-nm photo-excitation,” Lasers Life Sci. 9, 1–16 (1999).

17.

M. Inaguma and K. Hashimoto, “Porphyrin-like fluorescence in oral cancer: In vivo fluorescence spectral characterization of lesions by use of a near-ultraviolet excited autofluorescence diagnosis system and separation of fluorescent extracts by capillary electrophoresis,” Cancer 86, 2201–11. (1999). [CrossRef] [PubMed]

18.

G. Stoica and A. Koestner, “Diverse spectrum of tumors in male Sprague-Dawley rats following single high doses of N-ethyl-N-nitrosourea (ENU),” Am. J. Pathol. 116, 319–26 (1984). [PubMed]

19.

R. Scarff and H. Torloni, “Histological typing of breast tumors,” in World Health Organization, (Geneva, 1968), pp. 13–20.

20.

M. M. el-Sharabasy, A. M. el-Waseef, M. M. Hafez, and S. A. Salim, “Porphyrin metabolism in some malignant diseases,” Br. J. Cancer 65, 409–12. (1992). [CrossRef] [PubMed]

21.

L. Rasetti, G. F. Rubino, L. Tettinatti, and G. W. Drago, “Porphyrin porphobilinogen and amino ketone levels in tumor tissue,” Panminerva Med. 7, 105–110 (1965).

22.

B. Zawirska, “Comparative porphyrin content in tumors with contiguous non-neoplastic tissues,” Neoplasma 26, 223–9. (1979). [PubMed]

23.

T. Theodossiou and A. J. MacRobert, “Comparison of the photodynamic effect of exogenous photoprotoporphyrin and protoporphyrin IX on PAM 212 murine keratocytes.,” Photochem. Photobiol. 76, 530–537 (2002). [CrossRef] [PubMed]

24.

D. M. Harris and J. Werkhaven, “Endogenous porphyrin fluorescence in tumors,” Lasers Surg. Med. 7, 467–72. (1987). [CrossRef] [PubMed]

25.

H. Daldrup, D. M. Shames, M. Wendland, Y. Okuhata, T. M. Link, W. Rosenau, Y. Lu, and R. C. Brasch, “Correlation of dynamic contrast-enhanced MR imaging with histologic tumor grade: comparison of macromolecular and small-molecular contrast media,” AJR Am. J. Roentgenol. 171, 941–9. (1998). [PubMed]

26.

M. Y. Su, Z. Wang, P. M. Carpenter, X. Lao, A. Muhler, and O. Nalcioglu, “Characterization of N-ethyl-N-nitrosourea-induced malignant and benign breast tumors in rats by using three MR contrast agents,” J. Magn. Reson. 9, 177–86 (1999). [CrossRef]

OCIS Codes
(000.1430) General : Biology and medicine
(110.3080) Imaging systems : Infrared imaging
(170.1610) Medical optics and biotechnology : Clinical applications
(170.3880) Medical optics and biotechnology : Medical and biological imaging
(170.4580) Medical optics and biotechnology : Optical diagnostics for medicine

ToC Category:
Medical Optics and Biotechnology

History
Original Manuscript: March 9, 2006
Revised Manuscript: June 21, 2006
Manuscript Accepted: June 22, 2006
Published: July 24, 2006

Virtual Issues
Vol. 1, Iss. 8 Virtual Journal for Biomedical Optics

Citation
Laure S. Fournier, Vincenzo Lucidi, Kirill Berejnoi, Theodore Miller, Stavros G. Demos, and Robert C. Brasch, "In-vivo NIR autofluorescence imaging of rat mammary tumors," Opt. Express 14, 6713-6723 (2006)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-14-15-6713


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References

  1. National Cancer Institute, "Cancernet resource" (National Cancer Institute, 2003), http://cis.nci.nih.gov/fact/5_6.htm.
  2. National Cancer Institute, "Surveillance, Epidemiology, and End Results: Estimated new cancer cases and deaths for 2004" (National Cancer Institute, 2004), http://seer.cancer.gov/cgi-bin/csr/1975_2001/search.pl#results
  3. T. M. Kolb, J. Lichy and J. H. Newhouse, "Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations," Radiology 225, 165-75 (2002). [CrossRef] [PubMed]
  4. M. Cutler, "Transillumination as an aid in the diagnosis of breast lesions.," Surg. Gynecol. Obstet. 48,721-729 (1929).
  5. X. Gu, Q. Zhang, M. Bartlett, L. Schutz, L. L. Fajardo and H. Jiang, "Differentiation of cysts from solid tumors in the breast with diffuse optical tomography," Acad. Radiol. 11,53-60 (2004). [CrossRef] [PubMed]
  6. D. B. Jakubowski, A. E. Cerussi, F. Bevilacqua, N. Shah, D. Hsiang, J. Butler and B. J. Tromberg, "Monitoring neoadjuvant chemotherapy in breast cancer using quantitative diffuse optical spectroscopy: a case study," J. Biomed. Opt. 9,230-8 (2004). [CrossRef] [PubMed]
  7. V. Ntziachristos, A. G. Yodh, M. Schnall and B. Chance, "Concurrent MRI and diffuse optical tomography of breast after indocyanine green enhancement," Proc. Natl. Acad. Sci. U. S. A. 97,2767-72. (2000). [CrossRef] [PubMed]
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