Use of hyperspectral imaging to distinguish ... - Wiley Online Library

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Use of Hyperspectral Imaging to Distinguish Normal, Precancerous, and Cancerous Cells Anwer M. Siddiqi, MD1 Hui Li, PhD2 Fazlay Faruque, PhD2 Worth Williams, BS2 Kent Lai, PhD3 Michael Hughson, MD1 Steven Bigler, MD1 James Beach, PhD4 William Johnson, PhD5

BACKGROUND. The objective of the current study was to test the hypothesis that the cytologic diagnosis of cancer cells can be enhanced by the technique of hyperspectral imaging (HSI).

METHODS. As a proof of principle, HSI was employed to obtain hyperspectrum from a normal human fibroblast, as well as its telomerase-immortalized and SV40-transformed derivatives. Novel algorithms were developed to differentiate among these cell models based on spectral and spatial differences. Using the same technique with modified algorithms, the authors were able to differentiate among normal and precancerous (low-grade [LG] and high-grade [HG]) cervical cells and squamous cell carcinoma (SCC) on liquid-based Papanicolaou (Pap)

1

Department of Pathology, University of Mississippi Medical Center, Jackson, Mississippi. 2

Geographic Information Systems, University of Mississippi Medical Center, Jackson, Mississippi. 3

The Dr. John T. Macdonald Foundation Center for Medical Genetics, Department of Pediatrics, University of Miami Miller School of Medicine, Miami, Florida.

test slides.

RESULTS. The specificity for identifying normal fibroblast cell type based on spatial and spectral algorithms was 74.2%. The sensitivity for identifying telomeraseimmortalized and SV40-transformed cells was 100% and 90.3%, respectively. The system identified normal cervical cells with a specificity of 95.8%. With regard to LG precancerous cells and HG precancerous cells, the sensitivity was 66.7% and 93.5%, respectively. The sensitivity detected for SCC was 98.6%.

CONCLUSIONS. HSI can be utilized in prescreening liquid-based Pap test slides to

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improve efficiency in Pap test diagnoses with the goal of ultimately reducing the

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mortality from cervical cancer while reducing health care costs. Cancer (Cancer Cytopathol) 2008;114:13–21. Ó 2008 American Cancer Society.

Institute for Technology Development, NASAStennis Space Center, Stennis, Mississippi. Department of Biomedical Statistics, University of Mississippi Medical Center, Jackson, Mississippi.

KEYWORDS: hyperspectral imaging, Papanicolaou test, cervical cancer, cytology, dysplasia, precancerous, computer detection, spectral algorithms, spatial algorithms.

A

Supported by Grant NAS13-03032/USM-MRCSSC09282005-1 (NASA) to Dr. Fazlay Faruque. An abstract of this article was presented as a poster at the 96th Annual Meeting of the United States and Canadian Academy of Pathology, San Diego, California, March 24-30, 2007. Address for reprints: Anwer M. Siddiqi, MD, Department of Pathology, University of Mississippi Medical Center, Jackson, MS 39216-4505; Fax: (601) 9844967; E-mail: [email protected] Received August 7, 2007; revision received September 28, 2007; accepted November 2, 2007.

Ó 2008 American Cancer Society

ccording to the latest statistics of the American Cancer Society, approximately 11,150 new cases of invasive cervical cancer will be diagnosed in the U.S. in 2007, of which 3670 women will die.1 Cervical cancer was once the leading cause of cancer death for female cancer patients, but between 1955 and 1992 the number of patients who died from this disease in the U.S. was reported to have decreased by 74%.1 This significant improvement was the result of the increased use of the Papanicolaou (Pap) test, a screening procedure that helps detect abnormal changes in the cervix before cancer develops. Because this test detects precancerous changes at a curable stage, the death rate from cervical cancer continues to decline by nearly 4% each year.1 However, the accuracy of the Pap test is strongly affected by disease prevalence. Higher disease prevalence is associated with higher estimates of sensitivity and lower estimates of specificity (with a greater effect on specificity).2 Nanda et al reported the mean sensitivity of the Pap test to be 47% (range, 30-

DOI 10.1002/cncr.23286 Published online 22 January 2008 in Wiley InterScience (www.interscience.wiley.com).

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CANCER (CANCER CYTOPATHOLOGY) February 25, 2008 / Volume 114 / Number 1

80%) and the mean specificity as 95% (range, 86100%).3 A multicenter, split-sample study found that the ThinPrep Pap test (Cytyc Corporation, Marlborough, Mass) detected 18% more cases of low-grade (LG) or more serious lesions compared with conventional smears.4 Nevertheless, there is a considerable need to improve the detection capability of this screening procedure to reduce cervical cancer mortality while reducing health care costs. One way is to combine the Pap test with another test that is more sensitive, specific, and objective. Although there are many ways to study cells and their components, the use of light microscopy provides a direct image of the object of interest, and is still the most important tool with which to study cellular structures and detect abnormalities for pathologists. However, light microscopy has its limitations. The interaction of light with an object changes the phase relationship of the light waves in a way that produces complex interference effects. No amount of refinement of the lenses used in a microscope can overcome this limitation imposed by the wave-like nature of light.5 In addition, there is some degree of interobserver subjectivity in evaluating borderline dysplastic cells among pathologists. To improve on these potential pitfalls, we have begun to investigate the technique of hyperspectral imaging (HSI) to study cancer cells in liquid-based Pap test slides. HSI is a technique that combines conventional imaging and spectrophotometry and can be employed to collect optical spectra from separate positions in a 2dimensional spatial array.6 Thus, HSI yields another dimension of data and together the spatial and spectral dimensions can probe more completely for light interactions with pathology. HSI provides a continuous, essentially complete record of the spectral responses of materials over the wavelengths considered. For relatively ‘‘pure’’ materials (eg, individual minerals or, in this case, cellular materials), it is possible to construct a spectral curve from the hyperspectral data that can then be matched with the spectral signatures of individual materials collected from the laboratory or identified earlier and available in data banks or spectral libraries. Specific reflectance peaks and absorption troughs can be read directly from these curves to allow for the precise identification of a material, class, cell, or feature. In a typical HSI setup, a standard epifluorescence microscope is optically coupled with an imaging spectrograph with a wavelength range of 400 to 1000 nanometers (nm), with output recorded through a charge-coupled device camera to a computer.6 By evaluating an expanded spectrum of trans-

mitted light beyond the visual spectrum, one can also obtain additional information to further characterize the cells of interest. In the current study, we used the technique of HSI to characterize cell types with varying degrees of dysplasia, which were grown on glass slides or deposited on TriPath (Burlington, NC) liquid-based Pap test slides. We demonstrated that different cell types exhibited distinctive spectral and spatial features. Therefore, we concluded that HSI could be used to improve the overall efficacy and objectivity of the Pap test.

MATERIALS AND METHODS Cell Samples Fibroblast model analogous to normal, precancerous, and cancerous cells Noncancerous human fibroblasts (GM00054) and their SV40-transformed variant (GM00638A) were purchased from Coriell Cell Repository (Camden, NJ). A telomerase-immortalized variant of GM00054 was constructed by infecting the noncancerous fibroblasts with a Moloney Murine Leukemia Virus (MMLV)based retroviral vector, LZRS, containing the fulllength cDNA for human telomerase reverse transcriptase (hTERT; Geron Corporation, Menlo Park, Calif) using the protocol described previously.7,8 Activation of telomerase in transduced cells by the hTERT gene product was confirmed by a polymerase chain reaction (PCR) enzyme-linked immunoadsorbent assaybased telomere repeat amplification protocol according to the manufacturer’s recommendations (Roche Molecular Biochemicals, Indianapolis, Ind). We have also monitored for any possible abnormal chromosomal changes (eg, polyploidy) by sending telomerase-immortalized samples to the Emory Genetics Laboratory (Atlanta, Ga) for cytogenetic examination once every 10 to 15 passages. Fibroblasts were routinely maintained in Dulbecco modified Eagle media (DMEM) supplemented with 10% fetal bovine serum. Before HSI examination, fibroblasts were grown on coverslips using standard protocols and were stained with hematoxylin and eosin (H & E). The inverted coverslips were then mounted on 25 mm 3 75 mm 3 1.0 mm glass slides. Cervical cells: normal, LG, high-grade, and squamous cell carcinoma Normal, LG, and high-grade (HG) H & E-stained cervical cells deposited on TriPath liquid-based Pap test slides and squamous cell carcinoma (SCC) cells were taken from previously diagnosed specimens obtained from the archives of the Department of Pathology at the University of Mississippi Medical Center (Jackson, Miss). The TriPath slides were studied with

Hyperspectral Imaging in Distinguishing Cells/Siddiqi et al.

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their original stains according to the manufacturer’s staining protocol without any destaining. The use of these specimens was approved by the University of Mississippi Medical Center Institutional Review Board (IRB File 2007-0006).

Capturing of Hyperspectra HSI setup Cells were scanned using a Nikon Eclipse 800 upright microscope (Nikon Corporation, Tokyo, Japan) with a 340 objective and 310 eyepiece, equipped with a V100-E hyperspectral camera system (Institute for Technology Development (ITD), Stennis, Miss). Illumination was controlled by a DC-stabilized 100-watt halogen lamp. During HSI recording, lamp voltage was set to a constant 11 volts, and an internal neutral density filter was used to reduce light intensity for periodic monitoring of the scanned cells. Light intensity was fixed to keep the illumination color temperature constant for all data sets. The images obtained were 400 3 800 pixels in a spectral range of 400 to 1000 nm at 2.5-nm wavelength intervals with 600 bands of spatial resolution. Images were processed using HyperVisual Software from ITD. ITD also provided the wavelength calibration files specific for individual cameras. Fibroblasts and cervical cells spectra On each type of fibroblast slide, random clusters of fibroblasts at 2 separate sites were scanned, generating 6 data sets. The cells examined at each site were limited to 1 to 2 cells in thickness. The data sets were obtained under total magnification of 3400 (340 objective and 310 eyepiece). On each liquidbased Pap slide, 2 sets of cells (normal, LG, HG, and SCC) generating 8 sets of data were obtained under a total magnification of 3400. Each scan therefore had a mixture of normal and abnormal cells, and the normal cells served as an internal control. A limited number of LG and HG cells were selected because the purpose of this initial study was to examine the spectral characterization of precancerous and cancerous cells. Calibration spectra Two calibration images were obtained: 1) a dark image with no light to the camera, which was used to remove the system noise; and 2) a reference blank or flat light image, for which an area on the slide was scanned with all layers of glass except the cell structures. The latter was used to remove nonuniformity in the image caused by uneven illumination; periodic scan line striping; and the impact of lamp, medium, and glass on cell reflectance and transmittance.

FIGURE 1. Steps in the development of a unique spectral library. NDNI indicates normalized difference nuclear index; PPI, pixel purity index; MNF, minimum noise fraction.

Processing and Analysis of HSI Data Development of a unique reference library for each cell type A unique reference library was developed for normal, precancerous, and cancerous fibroblasts/cervical cells. The following 3 steps were involved in developing a reference library (Fig. 1). Normalized difference vegetation index. Normalized difference vegetation index (NDVI 9) is a method applied to remotely sensed data to analyze vegetation characteristics based on their reflectance value.

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We used a similar concept to identify and extract nuclei from cells using their unique characteristics of light transmission for cell classification, the normalized difference nuclear index (NDNI). 1. Calculation of NDNI to extract the nuclei from the cells of interest: a. Identify the spectral bands with the highest and lowest light transmission. b. Calculate the NDNI based on the above 2 category bands from the following equation: NDNI ¼ ðth tl Þ=ðth þ tl Þ, in which th is the average of the transmission from the highest transmission bands, and tl is the average of the transmission from the lowest transmission bands. 2. Extraction of nuclei images from cell images using NDNI: a. Find the nucleus NDNI threshold for each cell image. b. Use the calculated NDNI to extract nuclei based on the identified NDNI threshold for each cell image. 3. Establishment of a unique spectral library for nuclei of each cell type: a. Use minimum noise fraction (MNF)10 transformation algorithm to transform hyperspectral data of the nuclei into its i) inherent dimensional bands and ii) noise bands. b. Use the pixel purity index (PPI) algorithm11 to calculate PPI values on an MNF-transformed result excluding the noise bands processed in Step 3a. c. Locate and identify the purest pixels as spectral library endmembers (the end members are the signature profiles of nuclei of cell types) by extracting the n-dimensional scatter plots from the MNF result with the high PPI in the nuclei of cells using the n-D Visualizer.

Identification of cells using a unique cell reference library The following algorithm was used to identify cell types after the establishment of the library, and includes 2 phases. Phase I uses only spectral information and phase II adds morphologic spatial information to improve the results of cell identification in the first phase (Fig. 2). Phase I (a-c) a. Normalize the spectrum of the spectral library and cell images to be identified to allow comparison of individual absorption features with the spectral library across different cell images from a common baseline using the method of continuum removal.12

FIGURE 2. Steps to identify different cell types after the establishment of a unique spectral library. RMS indicates root mean square; N/C ratio, nuclear/cytoplasmic ratio.

b. Compare the fit of each spectral band of each cell image with each endmember in the spectral library using the spectral feature fitting algorithm, which is based on the least-squares technique. The output is a ‘‘scale’’ image and root mean square (RMS) error image. The ‘‘scale’’ image output is a measure of feature absorption depth, which is related to the material abundance of interest.13 The RMS error image is an output used in determining a degree of error on the fitting scale. c. A combined match score is calculated for each endmember by calculating the scale/RMS image value ratios. A pixel with a high scale and low RMS indicates a better match with the reference library cell type for ideal cell classification.

Phase II (d, e) d. In the second phase, the nuclear size and the nuclear/cytoplasmic ratio (N/C) for each cell are calculated from a selected spectral band for each cell image. e. Differences in the N/C ratio are then utilized to further distinguish the fibroblast and cervical cell types identified in phase I using the unique spectral signature.

Hyperspectral Imaging in Distinguishing Cells/Siddiqi et al.

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FIGURE 3. Squamous cell carcinoma (SCC) cell identification. f. Due to the overlapping of SCC cells and their distinguishing spectra, the classification of these cells was processed based on spectral information at the pixel level without the use of spatial information (Fig. 3). The center of the nuclei usually has more chromatin material and therefore has less light transmission and a higher NDNI. The pixels in the periphery of the nucleus have the lowest NDNI and our research has shown that classification based on the spectral profile from this region is unreliable compared with the central portion of the nucleus.

Statistical Analysis Sensitivity and specificity tests were used to describe the validity of normal and abnormal classification processes in this research. For fibroblasts, a truepositive result would be abnormal cells (telomeraseimmortalized or SV40-transformed) that the system correctly identified as abnormal (telomerase-immortalized or SV40-transformed) based on preassigned criteria. A true-negative finding would be normal, primary cells that the system correctly identified as normal, primary fibroblasts based on preassigned criteria. For cervical cells, a true-positive finding would be abnormal cells (LG, HG, or SCC) that the system correctly identified as abnormal (LG, HG, or SCC) based on preassigned criteria. A true-negative finding would be normal cells that the system correctly identified as normal based on preassigned criteria.

RESULTS Transformation of Human Fibroblasts Led to Changes in Hyperspectra Figure 4A shows the corrected spectral library plots of the H & E-stained noncancerous fibroblasts, their telomerase-immortalized derivative, and their SV40transformed derivative. The maximum absorption is between 500 and 630 nm. Using this information, we proceeded to examine 308 randomly chosen fibro-

FIGURE 4. (A) Corrected spectral library plots of the hematoxylin and eosin-stained noncancerous fibroblasts, their telomerase-immortalized derivative, and their SV40-transformed derivative. nm indicates nanometers. (B) Corrected hyperspectral image of normal fibroblasts on the left and its algorithm-based nuclear classification on the right. (C) Corrected hyperspectral image of precancerous fibroblasts on the left and its algorithm-based nuclear classification on the right. (D) Corrected hyperspectral image of cancerous fibroblasts on the left and its algorithm-based nuclear classification on the right. (Green indicates classified as normal; yellow, classified as precancerous; red, classified as cancerous fibroblast nuclei).

blast cells (120 GM00054 cells, 43 GM00054TM cells, and 145 GM00638A cells) (Table 1). Of the 120 noncancerous GM00054 cells, 89 were classified as noncancerous, whereas 9 were identified as precancerous and 22 were identified as cancerous by HSI (Fig. 4B) (Table 1). This resulted in a specificity of 95.2% for noncancerous fibroblasts (Table 1). Of the 43 telomerase-immortalized GM00054TM cells, all were classified as precancerous by HSI, giving a sensitivity of 100% (Table 1). With regard to the 145 SV40-transformed

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TABLE 1 Algorithm-based Classification Results for Fibroblasts Fibroblasts Algorithm-based classification

GM00054

GM00054TM

GM00638A

No. of cells

Noncancerous Precancerous Cancerous Total

89 9 22 120

0 43 0 43

9 5 131 145

98 57 153 308

GM00054 indicates primary fibroblasts; GM00054TM, telomerase-immortalized derivative of GM00054; GM00638A, SV40-transformed derivative of GM00054.

fibroblasts, 131 were classified as cancerous by HSI, yielding a sensitivity of 90.3% (Table 1).

Cervical Cells with Varying Degree of Dysplasia Demonstrated Different Hyperspectra In an attempt to replicate what we achieved in a well-controlled fibroblast model system in patient samples, we used HSI to examine Pap-stained cervical cells with different degrees of dysplasia deposited on TriPath liquid-based Pap test slides. As shown in Figure 5, the maximum absorption of these cells was between 470 and 690 nm, which is slightly wider than that for the fibroblast cells (Fig. 4). We then examined 124 previously characterized cervical cells (72 normal cells, 6 LG cervical intraepithelial neoplasia [CIN 1], and 46 [CIN 2] and [CIN 3] cells) (Table 2) (Fig. 5B, 5C, and 5D). Of the 72 normal, noncancerous cells, 69 were termed noncancerous by HSI, whereas 1 was identified as LG and 2 were classified as HG (Fig. 5B) (Table 2). These results led to a specificity of 95.8% for normal cells (Table 2). Of the 6 LG cells, only 4 were identified as LG, giving a sensitivity of 66.7% (Table 2). Of a total of 46 HG cells, 43 were identified as HG, resulting in a sensitivity of 93.5% (Table 2). Cervical SCC Hyperspectra In the current study, we also used HSI to differentiate among SCC and other cervical cells. Due to the overlapping of SCC cells, the sensitivity and specificity were based on each individual cellular pixel classification. We examined a total of 51,804 SCC cellular pixels, and through HSI we categorized 51,063 as true SCC cells (Fig. 6) (Table 3). This gave us an overall sensitivity of 98.6%.

DISCUSSION HSI Applications Originally developed for geologic surveys,14,15 HSI has slowly been adopted as an effective and noninva-

FIGURE 5. (A) Corrected spectral library plots of the Papanicolaou-stained normal, low-grade (LG), high-grade (HG), and squamous cell carcinoma (SCC) cells. nm indicates nanometers. (B) Corrected hyperspectral image of LG cervical cells on the left and its algorithm-based nuclear classification on the right. (C) Corrected hyperspectral image of HG (cervical intraepithelial neoplasia [CIN] type 2) cervical cells on the left and its algorithm-based nuclear classification on the right. (D) Corrected hyperspectral image of HG (CIN type 3) cervical cells on the left and its algorithm-based nuclear classification on the right. (Green indicates classified as normal; yellow, classified as LG nuclei; red, classified as HG nuclei).

sive tool in the clinical diagnosis of melanoma, the study of oxygen saturation levels in different tissues, and cancer detection.16-22 Although a few recent studies have demonstrated the use of HSI in cancer diagnosis in vivo,19-22 to our knowledge this technique has never been utilized in the cytopathologic examination of cervical cells. Therefore, the major objective of the current study was to test whether the cytologic diagnosis of precancerous and cancerous cervical cells can be enhanced by HSI technology. During the early phase of this project, different types of sample preparations were tested, including

Hyperspectral Imaging in Distinguishing Cells/Siddiqi et al. TABLE 2 Algorithm-based Classification Results for Cervical Cells

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TABLE 3 Algorithm-based Classification Results for Cervical SCC Cells Test results

Test results Algorithm-based classification

Normal

LG

HG

No. of cells

Normal LG HG Total

69 0 3

1 4 0

2 2 43

72 6 46 124

SCC cells

Normal

LG

HG

SCC

Total

683

0

58

51,063

51,804

LG indicates low-grade precancerous cells; HG, high-grade precancerous cells; SCC, squamous cell carcinoma.

LG indicates low-grade precancerous cells; HG, high-grade precancerous cells.

FIGURE 6. Algorithm-based nuclear extraction and classification of cervi-

derived from each cell (400-1000 nm) that can be used for cell characterization. Analogous to gene expression profiles, discriminating spectral information could be used as a spectral fingerprint for cell identification. As shown in Table 1 and Figure 5, the fibroblast models demonstrated that different cell types can be successfully distinguished in vitro based on spatial and spectral features with a high degree of sensitivity and specificity. The high sensitivity and specificity also demonstrated the effectiveness of the algorithms developed for the current study. The algorithms that we have developed can also take advantage of morphologic spatial features to help distinguish cells when spectral differences are indistinct.

cal squamous cell carcinoma (SCC) cell nuclei. Green indicates classified as normal; yellow, classified as low-grade nuclei; red, classified as high-grade/ SCC nuclei.

slides stained with H & E or Pap stain and slides with unstained cells, both with and without coverslips. The best results were achieved using H & Estained or Pap-stained cells mounted on glass slides with coverslips.

Fibroblast Model Analogous to Normal, Precancerous, and Cancerous Cells To test our hypothesis, we developed a model using the primary fibroblast cell strain (GM00054), its telomerase-immortalized derivative (GM00054TM), and its SV40-transformed derivative (GM00638A). These 3 cell strains were chosen because they were analogous to normal, precancerous, and cancerous cells, respectively. The advantage of using fibroblast cell culture is to ensure a uniform cell population with the same characteristics throughout. Two variants of the GM00054 cell strain were used to ensure that any change in the spectral profile was due to the experimentally induced cell transformation, and not the differences in genetic background. The advantage of HSI compared with plain histocytomorphologic evaluation is the vast amount of spectral information

Cervical Cells: Normal, LG, HG, and SCC Using the fibroblast model as an example, HSI technology was then used to identify normal, dysplastic, and SCC cervical cells on TriPath liquid-based Pap test slides. Similar to the fibroblast model, reference libraries of normal, LG, HG, and SCC cervical cells were developed. Essentially, the same algorithms with minor modifications with regard to utilizing spatial features (both nuclear size and the N/C ratio, whereas only the N/C ratio was used in fibroblast models) were employed to identify the different precancerous grades among the cervical cells. Cancerous cells were identified using only their spectral features because of their unique spectral profile. The spectral differences between the benign and malignant cell nuclei could result from the quantity and organization of chromatin.23,24 H & E and Pap stains help to highlight these chromatin differences, but because these stains were specifically designed for visual evaluation (400-700 nm), their spectral output range is relatively narrow. In the future, the use of stains with spectral output beyond the visual spectrum, infrared, and ultraviolet range may enhance the efficacy of HSI. In other words, the differentiation among some cancers may require the exhibition of a spectral signature outside the visual spectrum, which may not be appreciated when using H & E

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and Pap stains because they are designed for the visual spectrum.

Spectral Signature-Based Identification In the current study, the telomerase-transformed cell strain (GM00054TM) and the SCC cells were found to have a distinct spectrum and our software-based classification system was able to identify these cell types based on the spectral signature alone with sensitivities of 100% and 98.6%, respectively. These results were highly encouraging because they demonstrated that one can potentially identify abnormal cells rapidly based on their unique spectral signatures alone, even when they are present in limited numbers in different preparations.

versus colposcopy without the use of human papillomavirus testing, resulting in the containment of health care costs.

Conclusions The HSI technique is promising as an ancillary method for the evaluation of cervical cytologic preparations and, when used in conjunction with slide scanners, can assist in the automated detection of precancerous and cancerous cells. As such, this strategy has the potential to reduce cervical cancer mortality while saving health care dollars.

REFERENCES 1.

Spectral and Spatial Feature-based Identification Despite the combined use of spectral and spatial features, the LG cells yielded a much lower sensitivity. The spectral signature for LG cells does not appear to be unique; however, based on such a small sample size (n 5 6), we cannot conclude that for certain and this issue will be addressed in future studies. Conversely, the LG cells are easier to detect on microscopy and it is the oversight of HG cells that is of the utmost concern for the examiner. The sensitivity of HSI to detect HG and SCC cells was found to be consistently high and will be particularly useful for the automated detection of these cells. Other Potential HSI Applications In addition to differentiating among LG, HG, and SCC cells, HSI can also help resolve the dilemma surrounding atypical squamous cells of undetermined significance (ASC-US). The management of women with diagnoses of nondiagnostic squamous atypia (ASC-US) has posed a greater challenge.25 The distinction between reactive and dysplastic cells based on unique spectral signatures can be rapid and costeffective. We believe the use of spectral signatures to classify cells as reactive versus dysplastic would essentially minimize observer bias. The cost-effectiveness of this new technology relies on the fact that there will be less labor, fewer consumables, and less time involved when it is compared with the conventional special and immunohistochemical stains used in the diagnosis of cancer. Furthermore, we will not be required to obtain full spectral imaging of the cells in question once we have established the spectral fingerprints of different cancer cell types. Instead, we will be classifying cells and/or tumor sections with specific wavelengths using relatively inexpensive multispectral imagers. This can improve patient triage for routine follow-up

American Cancer Society. A detailed guide for cervical cancer. Available at URL: http://www.cancer.org/docroot/ CRI/content/CRI_2_4_1X_What_are_the_key_statistics_for_ cervical_cancer_8.asp. Accessed on December 13, 2007. 2. Agency for Health Care Policy and Research. Evaluation of cervical cytology. Summary, evidence report/technology assessment. No. 5. Rockville, MD: Agency for Health Care Policy and Research; 1999. Available at URL: http:// www.ahrq.gov/clinic/epcsums/cervsumm.htm. Accessed on December 13, 2007. 3. Nanda K, McCrory D, Myers E, et al. Accuracy of the Papanicolaou test in screening for and follow-up of cervical cytologic abnormalities: a systematic review. Ann Intern Med. 2000;132:810–819. 4. Lee KR, Ashfaq R, Birdsong GG, Corkill ME, McIntosh KM, Inhorn SL. Comparison of conventional Papanicolaou smears and a fluid-based, thin-layer system for cervical cancer screening. Obstet Gynecol. 1997;90:278–284. 5. Aebi U, Bienz K, Brack C, Bullock G, Duerrenberger M, Engel A, et al. Introduction to practical light and electron microscopy. M. E. Muller Institute for Structural Biology, Basel, Switzerland. Available at URL: http://www.mih.unibas. ch/Booklet/Lecture/Chapter1/Chapter1.html. Accessed on December 12, 2007. 6. Schultz RA, Nielsen T, Zavaleta JR, Ruch R, Wyatt R, Garner HR. Hyperspectral imaging: a novel approach for microscopic analysis. Cytometry. 2001;43:239–247. 7. Arbiser JL. The generation and characterization of a cell line derived from a sporadic renal angiomyolipoma. Am J Pathol. 2001;159:483–491. 8. Yang J, Chang E, Cherry AM, et al. Human endothelial cell life extension by telomerase expression. J Biol Chem. 1999;274:26141–26148. 9. Tucker CJ, Newcomb WW, Los SO, Prince SD. Mean and inter-year variation of growing-season normalized difference vegetation index for the Sahel 1981-1989. Int J Remote Sensing. 1991;12:1133–1135. 10. Boardman JW, Kruse FA. Automated spectral analysis: a geological example using AVIRIS data, North Grapevine Mountains, Nevada. In: Environmental Research Institute of Michigan, (Ed.) Proceedings of the 10th Thematic Conference on Geological Remote Sensing, (Ed.) Proc. 10th Thematic Conference on Geological Remote Sensing, San Antonio, TX, 1994:I-407–I-418. 11. Boardman JW. Analysis, understanding and visualization of hyperspectral data as convex sets in n-space. In: Proceedings of the International SPIE Symposium on Imaging Spectrometry, SPIE Vol. 2480, Orlando, FL: 1995:23–36.

Hyperspectral Imaging in Distinguishing Cells/Siddiqi et al. 12. Clark RN, Roush TL. Reflectance spectroscopy: quantitative analysis techniques for remote sensing application. J Geophys Res. 1984;89:6329–6340. 13. Clark RN, Gallagher AJ, Swayze GA. Material absorption band depth mapping of imaging spectrometer data using a complete band shape least-squares fit with library reference spectra. Proc. 2nd Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop, Jet Propulsion Laboratory Publication 90–54. 1990:176–186. 14. Sotin C, Jaumann R, Buratti BJ, et al. Release of volatiles from a possible cryovolcano from near-infrared imaging of Titan. Nature. 2005;435:786–789. 15. Harris AT. Spectral mapping tools from the earth sciences applied to spectral microscopy data. Cytometry A. 2006;69: 872–879. 16. Zavattini G, Vecchi S, Mitchell G, et al. A hyperspectral fluorescence system for 3D in vivo optical imaging. Phys Med Biol. 2006;51:2029–2043. Epub 2006 Apr 3. 17. Khoobehi B, Beach JM, Kawano H. Hyperspectral imaging for measurement of oxygen saturation in the optic nerve head. Invest Ophthalmol Vis Sci. 2004;45:1464–1475. 18. Beach JM, Lanoue M, Brabham K, Khoobehi B. Portable hyperspectral imager for assessment of skin disorders: preliminary measurements. Proc SPIE. Available at URL: http:// spie.org/x648.xml?product_id=590938. 2005;5686:111–118.

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19. Dicker DT, Lerner J, Van Belle P, et al. Differentiation of normal skin and melanoma using high resolution hyperspectral imaging. Cancer Biol Ther. 2006;5:1033–1038. Epub 2006 Aug 9. 20. Martin ME, Wabuyele MB, Chen K, et al. Development of an advanced hyperspectral imaging (HSI) system with applications for cancer detection. Ann Biomed Eng. 2006;34: 1061–1068. Epub 2006 May 9. 21. Manyak MJ, Javitt M, Kang PS, Kreuger WR, Storm ES. The evolution of imaging in advanced prostate cancer. Urol Clin North Am. 2006;33:133–146. 22. Ferris DG, Lawhead RA, Dickman ED, et al. Multimodal hyperspectral imaging for the noninvasive diagnosis of cervical neoplasia. J Low Genit Tract Dis. 2001;5:65–72. 23. Einstein AJ, Wu HS, Sanchez M, Gil J. Fractal characterization of chromatin appearance for diagnosis in breast cytology. J Pathol. 1998;185:366–381. 24. Einstein AJ, Wu HS, Gil J. Self-affinity and lacunarity of chromatin texture in benign and malignant breast epithelial cell nuclei. Phys Rev Lett. 1998;80:397–400. 25. Hughes SA, Sun D, Gibson C, et al. Managing atypical squamous cells of undetermined significance (ASCUS): human papillomavirus testing, ASCUS subtyping, or follow-up cytology? Am J Obstet Gynecol. 2002;186:396– 403.