Diagnostic potential of multimodal imaging of ovarian tissue using optical coherence tomography and second-harmonic generation microscopy Weston A. Welge Andrew T. DeMarco Jennifer M. Watson Photini S. Rice Jennifer K. Barton Matthew A. Kupinski
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Journal of Medical Imaging 1(2), 025501 (Jul–Sep 2014)
Diagnostic potential of multimodal imaging of ovarian tissue using optical coherence tomography and second-harmonic generation microscopy Weston A. Welge,a,* Andrew T. DeMarco,b Jennifer M. Watson,c Photini S. Rice,c Jennifer K. Barton,a,c and Matthew A. Kupinskia a
University of Arizona, College of Optical Sciences, 1630 East University Boulevard, Tucson, Arizona 85721, United States University of Arizona, Department of Speech, Language, and Hearing Sciences, 1131 East 2nd Street, Tucson, Arizona 85721, United States c University of Arizona, Department of Biomedical Engineering, 1657 East Helen Street, Tucson, Arizona 85721, United States b
Abstract. Ovarian cancer is particularly deadly because it is usually diagnosed after it has metastasized. We have previously identified features of ovarian cancer using optical coherence tomography (OCT) and second-harmonic generation (SHG) microscopy (targeting collagen). OCT provides an image of the ovarian microstructure, while SHG provides a high-resolution map of collagen fiber bundle arrangement. Here, we investigated the diagnostic potential of dual-modality OCT and SHG imaging. We conducted a fully crossed, multireader, multicase study using seven human observers. Each observer classified 44 ex vivo mouse ovaries (16 normal and 28 abnormal) as normal or abnormal from OCT, SHG, and simultaneously viewed, coregistered OCT and SHG images and provided a confidence rating on a six-point scale. We determined the average receiver operating characteristic (ROC) curves, area under the ROC curves (AUC), and other quantitative figures of merit. The results show that OCT has diagnostic potential with an average AUC of 0.91 0.06. The average AUC for SHG was less promising at 0.71 0.13. The average AUC for simultaneous OCT and SHG was not significantly different from OCT alone, possibly due to the limited SHG field of view. The high performance of OCT and coregistered OCT and SHG warrants further investigation. © 2014 Society of Photo-Optical Instrumentation Engineers (SPIE) [DOI: 10.1117/1.JMI.1.2.025501]
Keywords: optical coherence tomography; second-harmonic generation; ovarian cancer; image quality; multimodal imaging. Paper 14031PR received Mar. 25, 2014; revised manuscript received Jun. 10, 2014; accepted for publication Jun. 26, 2014; published online Jul. 18, 2014.
1
Introduction
Ovarian cancer is the deadliest cancer of the female reproductive system. While the prevalence of ovarian cancer is low (the lifetime risk in the United States is ∼1.4%), the majority of patients with the disease will die from it. This is because 61% of diagnoses are made after the disease has metastasized, and the fiveyear survival rate after metastasis is just 27%. By comparison, the survival rates at the localized and regional stages are 92 and 72%, respectively.1 Biopsy during laparoscopy is the gold standard for diagnosis, but the procedure is too invasive to be a routine screening procedure. An effective screening procedure capable of detecting ovarian cancer at an early stage might greatly reduce the mortality rate of this disease. Unfortunately, two of the most common screening methods for ovarian cancer, CA-125 blood test and transvaginal sonography (TVS), have not reduced ovarian cancer mortality compared to routine medical care.2 Research has shown elevated serum levels of CA-125 in women with ovarian cancer;3 however, other factors also affect CA-125 levels, leading to false positives.4 TVS can detect some morphological changes of the ovaries associated with ovarian cancer. The procedure is fast and safe, which enables it to be a common screening procedure. Unfortunately, TVS suffers from moderate sensitivity and a low positive predictive value (PPV) (85.0 and 14.01%, respectively, from a study of annual TVS screening on 25,327
*Address all correspondence to: Weston A. Welge, E-mail: wwelge@email .arizona.edu
Journal of Medical Imaging
women).5 TVS performs especially poorly when the ovaries undergo only minor volume change due to cancer.5 The inability of TVS to resolve cellular or microstructural features in ovarian tissue is an obstacle that may prevent common diagnosis of early-stage ovarian cancer. Optical imaging techniques may be attractive alternatives to TVS due to their superior resolution. Optical imaging has been shown to resolve microstructural or cellular features in ovarian tissue and to obtain biochemical information through the use of specific or nonspecific fluorescent contrast agents, or through interrogating endogenous fluorophores. These capabilities may be used to guide biopsy, the current gold standard for cancer diagnosis, by highlighting suspicious regions. The obvious disadvantage of optical techniques is the requirement of close proximity of the imaging probe to the tissue, thereby making these procedures more invasive than TVS. However, the potential for superior diagnostic accuracy makes optical techniques worthy of investigation as screening procedures. Upon identification of highly effective optical imaging techniques for ovarian cancer diagnosis, our attention can be turned to the necessary engineering to reduce the invasiveness of the imaging system to enable widespread and routine use in the clinic. Several different optical modalities have been used to investigate ovarian tissue, including fluorescence spectroscopy,6 multispectral imaging,7 confocal microlaparoscopy,8 and optical resolution photoacoustic microscopy.9 Two optical techniques that have shown promise in the identification of abnormal 0091-3286/2014/$25.00 © 2014 SPIE
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Welge et al.: Diagnostic potential of multimodal imaging of ovarian tissue using optical coherence tomography. . .
ovarian tissue, but do not require exogenous fluorophores, are optical coherence tomography (OCT) and second-harmonic generation (SHG) microscopy. OCT generates cross-sectional images similar to TVS, but with micron-scale resolution.10 Near-infrared light penetrates the tissue and some of the light reflects at interfaces of refractive index. By scanning over a region of tissue, OCT generates two- or three-dimensional images of the anatomical structure of the tissue to a depth up to 2 mm. Current commercial OCT systems have lateral and axial resolutions better than 10 μm. This high-resolution capability allows OCT to detect minute changes in cellular layer thickness or other microstructural changes. Three-dimensional volumetric images can be generated in a few seconds. SHG is a form of multiphoton microscopy in which contrast is formed by the nonlinear scattering of incident light by molecules with noncentrosymmetric structure.11 The nonlinear nature of the scattering in SHG results in emission light with double the frequency of the excitation light. Scanning the focused excitation beam across the tissue generates three-dimensional volumetric images. Optical sectioning occurs because the emission intensity falls off with distance from the beam focus to the fourth power. The necessity of high numerical aperture to generate efficient second-harmonic emission yields lateral resolutions 0.9 for OCT, meaning that 90% of the time, a decision made by an observer given the particular distribution of positive and negative cases in this study will be correct. It should be noted, however, that the distribution in this study does not match the actual distribution of ovarian disorders that naturally occur in women. 86,138 women in the United States are currently estimated to have ovarian cancer (∼0.12% of the female population).1 Tubular adenoma is extremely rare in women and the effects of DMBA dosing do not occur naturally. Therefore, the PPV and NPV reported here cannot be generalized to the clinical population.24 A benefit of OCT is that several ovarian features can be analyzed—such as surface smoothness, attenuation, and
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Fig. 4 Fitted average receiver operating characteristic (ROC) curves for each modality. Binormal ROC curves were fit to each observer trial to yield the binormal distribution parameters a and b. These parameters were averaged over all observers to form an average binormal ROC curve for each modality.
Journal of Medical Imaging
OCT
SHG
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Fig. 5 Comparison of average area under the ROC curves (AUCs) for each modality. All average AUCs are significantly greater than chance (p < 0.01). The average AUC for OCT was significantly greater than the average AUC for SHG (p ¼ 0.011). Likewise, the average AUC for OCT+SHG was greater than SHG (p ¼ 0.013) The average AUC for OCT was not found to be significantly different from OCT+SHG.
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texture variability—to make a classification decision. The large field of view is also helpful because the observers could view the entire ovary. Observers reading SHG images correctly classified the tissue better than chance, but the overall performance was poorer than OCT. With SHG alone, readers can be expected to correctly classify abnormal ovaries greater than two-thirds of the time. The sensitivity was greater than the specificity, which suggests that the observers found signs of abnormality to be more noticeable than signs of normality. The overall diagnostic performance of SHG in this study was not shown to be a promising screening technique on its own, though there were some particular limitations to the SHG data we used. There are a few possible explanations for the poor performance of SHG. The most likely shortcoming of the SHG data in this study is the limited field of view. The SHG images were taken over a 400 × 400 μm region centered on the center of the 3 × 3 μm OCT field of view. By limiting the field of view, the abnormal collagen structures of VCD/DMBA effect and carcinoma samples might be difficult to detect, since these structural changes could be most easily noticed when observing the collagen structure over the entire ovary. We envision that with a clinical dual-modality endoscopic OCT-SHG system, the clinician would obtain SHG images at several regions in an ovary guided by the gross OCT data. The smaller amount of SHG data compared to OCT may also limit the performance of SHG in this study. Each OCT volume contained two orders of magnitude more slices in the depth direction than each SHG volume. This difference in data set size was mainly attributable to speed. The OCT system was able to obtain near-isotropic pixel-size image sets usually encompassing the entire lateral extent of the ovary in 30 s, whereas the SHG system required 15 s per image. Therefore, a limited number of slices were obtained at a relatively large slice separation (10 μm) much greater than the image axial resolution (1.2 μm). This shortcoming could be improved with the use of higher incident light power and faster scanning. The low incident power (20 mW) was chosen as a safe power level to mimic the necessarily low power levels required for in vivo imaging. SHG also had lower depth of penetration, with signal loss at ∼100 μm depth, whereas OCT was able to image ∼1 mm deep. We and others have created metrics for SHG imaging of collagen with statistically significant differences between healthy and malignant ovarian tissue. Some examples include Fourier analysis,19,29 gray-level co-occurrence matrices,19 SHG emission intensity and directionality,30 and the distribution of collagen fiber angles relative to the epithelial boundary.29 Therefore, we believe that addressing the SHG shortcomings in this study may improve the observer diagnostic performance of a dual-modality OCT-SHG imaging system. When comparing the imaging modalities, the average AUCs show that OCT is superior to SHG for classifying ovarian tissue as normal or abnormal using nonexpert observers. Interestingly, we also found that observers did not perform significantly differently from OCT when they could simultaneously view SHG images of the same tissue. The average AUC for the multimodal trial should have been higher than OCT alone if the observers came to their decisions primarily based on the OCT data, but were able to strengthen their confidence with the SHG data. This suggests that the extra information provided by the SHG images was either not always used or was possibly misleading. It is possible that some of the dual-modality images Journal of Medical Imaging
may appear normal in OCT and abnormal in SHG or vice versa potentially due to the limitations of the SHG images. Again, performance of the multimodality trial might be improved in future studies by collecting high-density SHG images across the entire OCT field of view. A successful candidate for a new ovarian cancer screening procedure must exhibit high sensitivity and specificity for any reader. The observers’ binary classification data for OCT resulted in a sensitivity of 0.94 and specificity of 0.88. A controlled trial in the United Kingdom of >50; 000 women found transvaginal sonography to have a sensitivity of 84.9% and a specificity of 98.2% for the detection of ovarian and tubal cancers.31 An American study of >25; 000 women screened annually with TVS yielded a sensitivity of 85.0% and a specificity of 98.7% (histopathological analysis provided the truth values for both studies).5 These results suggest that TVS can discriminate ovarian cancer from healthy tissue reasonably well, but this does not mean they have high clinical value. The PPVs for TVS were 5.3 (Ref. 31) and 14.01% (Ref. 5) for the U.K. and U.S. trials, respectively. Since the prevalence of ovarian cancer is very low, TVS yields many more false positives than true positives (true positives would equal false positives for PPV ¼ 50%). Because sensitivity and specificity are independent of prevalence, they overestimate the clinical value of a test for diseases with low prevalence in the general population. Our discriminatory OCT results (sensitivity and specificity) are promising and comparable to the performance found in the U.K. and U.S. trials, but a study of the diagnostic performance of OCT and SHG on ovarian cancers with a disease distribution that matches the natural prevalence would provide the most useful information regarding the clinical value of such an imaging system. We could not directly compare OCT and SHG to TVS using a mouse model because the resolution of TVS (∼0.25 mm) is insufficient to resolve key anatomical features of the mouse ovary, such as oocytes or immature follicles.32 An important point of optical imaging techniques is that they can distinguish abnormalities, such as tubular adenoma, which may precede cancer. This means that survival might be improved. As such, OCT imaging for ovarian cancer screening warrants further investigation. A future observer study should be conducted using in vivo images of human ovaries, preferably obtained by an endoscopic imaging system, in order to gain a more accurate measure of the diagnostic accuracy of OCT and SHG as they would be used clinically. Furthermore, future investigations of SHG should cover a larger area of the ovarian tissue. The performance of expert readers could also be evaluated; although given that both OCT and SHG are developmental technologies for this application, few expert readers currently exist. Finally, the distribution of abnormalities should be designed to closely match the distribution seen in the female population.
Acknowledgments The authors thank Elizabeth Krupinski for generously providing space and equipment for the observer trials and for sharing helpful suggestions. Financial support was provided by the National Institutes of Health research grant R01 CA119200, NIH Cardiovascular Biomedical Engineering Training Grant T32 HL007955, and the University of Arizona Cancer Center Support Grant CCSG-CA023074. This paper is based on work presented in a proceedings paper.33
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References 1. N. Howlader et al., Eds., “SEER cancer statistics review, 1975-2010,” 14 June 2013, http://seer.cancer.gov/archive/csr/1975_2010/ (28 January 2014). 2. S. S. Buys et al., “Effect of screening on ovarian cancer mortality: the prostate, lung, colorectal and ovarian (PLCO) cancer screening randomized controlled trial,” JAMA 305(22), 2295–2303 (2011). 3. R. C. Bast, Jr. et al., “New tumor markers: CA125 and beyond,” Int. J. Gynecol. Cancer 15(Suppl 3), 274–281 (2005). 4. C. C. Johnson et al., “The epidemiology of CA-125 in women without evidence of ovarian cancer in the prostate, lung, colorectal and ovarian cancer (PLCO) screening trial,” Gynecol. Oncol. 110(3), 383–389 (2008). 5. J. R. van Nagell, Jr. et al., “Ovarian cancer screening with annual transvaginal sonography: findings of 25,000 women screened,” Cancer 109(9), 1887–1896 (2007). 6. R. George et al., “Clinical research device for ovarian cancer detection by optical spectroscopy in the ultraviolet C-visible,” J. Biomed. Opt. 15(5), 057009 (2010). 7. G. M. van Dam et al., “Intraoperative tumor-specific fluorescence imaging in ovarian cancer by folate receptor-α targeting: first in-human results,” Nat. Med. 17(10), 1315–1319 (2011). 8. A. A. Tanbakuchi et al., “In vivo imaging of ovarian tissue using a novel confocal microlaparoscope,” Am. J. Obstet. Gynecol. 202(1), 90.e1– 90.e9 (2010). 9. T. Wang et al., “Characterization of ovarian tissue based on quantitative analysis of photoacoustic microscopy images,” Biomed. Opt. Express 4(12), 2763–2768 (2013). 10. D. Huang et al., “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991). 11. P. J. Campagnola and L. M. Loew, “Second-harmonic imaging microscopy for visualizing biomolecular arrays in cells, tissues and organisms,” Nat. Biotechnol. 21(11), 1356–1360 (2003). 12. C. Pitris et al., “High-resolution imaging of gynecologic neoplasms using optical coherence tomography,” Obstet. Gynecol. 93(1), 135–139 (1999). 13. Y. Yang et al., “Optical scattering coefficient estimated by optical coherence tomography correlates with collagen content in ovarian tissue,” J. Biomed. Opt. 16(9), 090504 (2011). 14. J. M. Watson et al., “In vivo time-serial multi-modality optical imaging in a mouse model of ovarian tumorigenesis,” Cancer Biol. Ther. 15(1), 42–60 (2014). 15. L. P. Hariri et al., “Simultaneous optical coherence tomography and laser induced fluorescence imaging in rat model of ovarian carcinogenesis,” Cancer Biol. Ther. 10(5), 438–447 (2010). 16. L. P. Hariri et al., “Laparoscopic optical coherence tomography imaging of human ovarian cancer,” Gynecol. Oncol. 114(2), 188–194 (2009). 17. R. J. Kurman and I. Shih, “Pathogenesis of ovarian cancer. Lessons from morphology and molecular biology and their clinical implications,” Int. J. Gynecol. Pathol. 27(2), 151–160 (2008). 18. N. D. Kirkpatrick, M. A. Brewer, and U. Utzinger, “Endogenous optical biomarkers of ovarian cancer evaluated with multiphoton microscopy,” Cancer Epidemiol. Biomarkers Prev. 16(10), 2048 (2007). 19. J. M. Watson et al., “Analysis of second-harmonic-generation microscopy in a mouse model of ovarian carcinoma,” J. Biomed. Opt. 17(7), 076002 (2012). 20. H. H. Barrett and K. J. Myers, “Image quality,” Chapter 14 in Foundations of Image Science, Wiley-Interscience, Hoboken, NJ (2004). 21. H. Gray, Anatomy of the Human Body, Lea & Febiger, Philadelphia (1918). 22. C. E. Metz, “Some practical issues of experimental design and data analysis in radiological ROC studies,” Invest. Radiol. 24(3), 234–245 (1989). 23. L. D. Brown, T. T. Cai, and A. DasGupta, “Interval estimation for a binomial proportion,” Stat. Sci. 16(2), 101–117 (2001). 24. C. D. Brown and H. T. Davis, “Receiver operating characteristics curves and related decision measures: a tutorial,” Chemometr. Intell. Lab. 80(1), 24–38 (2006). 25. J. A. Hanley, “The robustness of the ‘binormal’ assumptions used in fitting ROC curves,” Med. Decis. Making 8(3), 197–203 (1988). 26. B. D. Gallas et al., “A framework for random-effects ROC analysis: biases with the bootstrap and other variance estimators,” Commun. Stat. A-Theory 38(15), 2586–2603 (2009).
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27. S. L. Hillis, K. S. Berbaum, and C. E. Metz, “Recent developments in the Dorfman-Berbaum-Metz procedure for multireader ROC study analysis,” Acad. Radiol. 15(5), 647–661 (2008). 28. C. E. Metz, “Receiver operating characteristic analysis: a tool for the quantitative evaluation of observer performance and imaging systems,” J. Am. Coll. Radiol. 3(6), 413–422 (2006). 29. J. Adur et al., “Recognition of serous ovarian tumors in human samples by multimodal nonlinear optical microscopy,” J. Biomed. Opt. 16(9), 096017 (2011). 30. O. Nadiarnykh et al., “Alterations of the extracellular matrix in ovarian cancer studied by second harmonic generation imaging microscopy,” BMC Cancer 10, 94 (2010). 31. U. Menon et al., “Sensitivity and specificity of multimodal and ultrasound screening for ovarian cancer, and stage distribution of detected cancers: results of the prevalence screen of the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS),” Lancet Oncol. 10(4), 327– 340 (2009). 32. J. Griffin et al., “Comparative analysis of follicle morphology and oocyte diameter in four mammalian species (mouse, hamster, pig, and human),” J. Exp. Clin. Assist. Reprod. 3, 2 (2006). 33. W. A. Welge et al., “Objective assessment of multimodality optical coherence tomography and second-harmonic generation image quality of ex vivo mouse ovaries using human observers,” Proc. SPIE 8936, 893609 (2014). Weston A. Welge is a doctoral student at the College of Optical Sciences at the University of Arizona. He received his BS in electrical and computer engineering and BA in history from the University of Colorado at Boulder and MS in optical sciences from the University of Arizona. His research focuses on the development of miniature optical endoscopes and novel applications of optical coherence tomography for early cancer detection. Andrew T. DeMarco holds a bachelor’s degree in linguistics and a master’s degree in communication sciences and disorders from Temple University. He holds a certificate of clinical competence from the American Speech-Language Association. He is currently a doctoral candidate in the Department of Speech, Language, and Hearing Sciences at the University of Arizona, where he uses neuroimaging to understand the organization of language in the brain and how that architecture breaks down in aphasia after stroke. Jennifer M. Watson has bachelor’s degrees in mechanical engineering and health sciences, and a PhD in biomedical engineering with a minor in entrepreneurship. She completed her dissertation research on evaluation of ovarian tumor development using the optical imaging modalities multiphoton microscopy and optical coherence tomography. In her current position at Tech Launch Arizona, she works with researchers across campus to assess the commercial potential of technologies, create development and commercialization plans and access key resources. Photini S. Rice holds an associate of applied science degree in medical technology and is ASCP and HEW certified. She worked in the clinical laboratory setting for 10 years and consulted for 3 years at a physician’s office laboratory while working in a university research laboratory. She has spent the last 16 years working in cardiovascular and cancer imaging research at the University of Arizona. Jennifer K. Barton is currently a professor of biomedical engineering, electrical and computer engineering, and optical sciences at the University of Arizona. She has served as the assistant director of the BIO5 Institute and interim vice president for research. She is a fellow of SPIE and the American Institute for Medical and Biological Engineering. Her work on development of miniature multimodality optical endoscopes and light-tissue interaction has led to over 90 peer-reviewed journal papers. Matthew A. Kupinski is an associate professor in the College of Optical Sciences and the Department of Medical Imaging at the University of Arizona. He obtained his PhD from the University of Chicago in 2000. His work focuses on using objective, taskbased measures of image quality to aid in the design of imaging systems.
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Journal of Medical Imaging 1(2), 025501 (Jul–Sep 2014)
Diagnostic potential of multimodal imaging of ovarian tissue using optical coherence tomography and second-harmonic generation microscopy Weston A. Welge,a,* Andrew T. DeMarco,b Jennifer M. Watson,c Photini S. Rice,c Jennifer K. Barton,a,c and Matthew A. Kupinskia a
University of Arizona, College of Optical Sciences, 1630 East University Boulevard, Tucson, Arizona 85721, United States University of Arizona, Department of Speech, Language, and Hearing Sciences, 1131 East 2nd Street, Tucson, Arizona 85721, United States c University of Arizona, Department of Biomedical Engineering, 1657 East Helen Street, Tucson, Arizona 85721, United States b
Abstract. Ovarian cancer is particularly deadly because it is usually diagnosed after it has metastasized. We have previously identified features of ovarian cancer using optical coherence tomography (OCT) and second-harmonic generation (SHG) microscopy (targeting collagen). OCT provides an image of the ovarian microstructure, while SHG provides a high-resolution map of collagen fiber bundle arrangement. Here, we investigated the diagnostic potential of dual-modality OCT and SHG imaging. We conducted a fully crossed, multireader, multicase study using seven human observers. Each observer classified 44 ex vivo mouse ovaries (16 normal and 28 abnormal) as normal or abnormal from OCT, SHG, and simultaneously viewed, coregistered OCT and SHG images and provided a confidence rating on a six-point scale. We determined the average receiver operating characteristic (ROC) curves, area under the ROC curves (AUC), and other quantitative figures of merit. The results show that OCT has diagnostic potential with an average AUC of 0.91 0.06. The average AUC for SHG was less promising at 0.71 0.13. The average AUC for simultaneous OCT and SHG was not significantly different from OCT alone, possibly due to the limited SHG field of view. The high performance of OCT and coregistered OCT and SHG warrants further investigation. © 2014 Society of Photo-Optical Instrumentation Engineers (SPIE) [DOI: 10.1117/1.JMI.1.2.025501]
Keywords: optical coherence tomography; second-harmonic generation; ovarian cancer; image quality; multimodal imaging. Paper 14031PR received Mar. 25, 2014; revised manuscript received Jun. 10, 2014; accepted for publication Jun. 26, 2014; published online Jul. 18, 2014.
1
Introduction
Ovarian cancer is the deadliest cancer of the female reproductive system. While the prevalence of ovarian cancer is low (the lifetime risk in the United States is ∼1.4%), the majority of patients with the disease will die from it. This is because 61% of diagnoses are made after the disease has metastasized, and the fiveyear survival rate after metastasis is just 27%. By comparison, the survival rates at the localized and regional stages are 92 and 72%, respectively.1 Biopsy during laparoscopy is the gold standard for diagnosis, but the procedure is too invasive to be a routine screening procedure. An effective screening procedure capable of detecting ovarian cancer at an early stage might greatly reduce the mortality rate of this disease. Unfortunately, two of the most common screening methods for ovarian cancer, CA-125 blood test and transvaginal sonography (TVS), have not reduced ovarian cancer mortality compared to routine medical care.2 Research has shown elevated serum levels of CA-125 in women with ovarian cancer;3 however, other factors also affect CA-125 levels, leading to false positives.4 TVS can detect some morphological changes of the ovaries associated with ovarian cancer. The procedure is fast and safe, which enables it to be a common screening procedure. Unfortunately, TVS suffers from moderate sensitivity and a low positive predictive value (PPV) (85.0 and 14.01%, respectively, from a study of annual TVS screening on 25,327
*Address all correspondence to: Weston A. Welge, E-mail: wwelge@email .arizona.edu
Journal of Medical Imaging
women).5 TVS performs especially poorly when the ovaries undergo only minor volume change due to cancer.5 The inability of TVS to resolve cellular or microstructural features in ovarian tissue is an obstacle that may prevent common diagnosis of early-stage ovarian cancer. Optical imaging techniques may be attractive alternatives to TVS due to their superior resolution. Optical imaging has been shown to resolve microstructural or cellular features in ovarian tissue and to obtain biochemical information through the use of specific or nonspecific fluorescent contrast agents, or through interrogating endogenous fluorophores. These capabilities may be used to guide biopsy, the current gold standard for cancer diagnosis, by highlighting suspicious regions. The obvious disadvantage of optical techniques is the requirement of close proximity of the imaging probe to the tissue, thereby making these procedures more invasive than TVS. However, the potential for superior diagnostic accuracy makes optical techniques worthy of investigation as screening procedures. Upon identification of highly effective optical imaging techniques for ovarian cancer diagnosis, our attention can be turned to the necessary engineering to reduce the invasiveness of the imaging system to enable widespread and routine use in the clinic. Several different optical modalities have been used to investigate ovarian tissue, including fluorescence spectroscopy,6 multispectral imaging,7 confocal microlaparoscopy,8 and optical resolution photoacoustic microscopy.9 Two optical techniques that have shown promise in the identification of abnormal 0091-3286/2014/$25.00 © 2014 SPIE
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Welge et al.: Diagnostic potential of multimodal imaging of ovarian tissue using optical coherence tomography. . .
ovarian tissue, but do not require exogenous fluorophores, are optical coherence tomography (OCT) and second-harmonic generation (SHG) microscopy. OCT generates cross-sectional images similar to TVS, but with micron-scale resolution.10 Near-infrared light penetrates the tissue and some of the light reflects at interfaces of refractive index. By scanning over a region of tissue, OCT generates two- or three-dimensional images of the anatomical structure of the tissue to a depth up to 2 mm. Current commercial OCT systems have lateral and axial resolutions better than 10 μm. This high-resolution capability allows OCT to detect minute changes in cellular layer thickness or other microstructural changes. Three-dimensional volumetric images can be generated in a few seconds. SHG is a form of multiphoton microscopy in which contrast is formed by the nonlinear scattering of incident light by molecules with noncentrosymmetric structure.11 The nonlinear nature of the scattering in SHG results in emission light with double the frequency of the excitation light. Scanning the focused excitation beam across the tissue generates three-dimensional volumetric images. Optical sectioning occurs because the emission intensity falls off with distance from the beam focus to the fourth power. The necessity of high numerical aperture to generate efficient second-harmonic emission yields lateral resolutions 0.9 for OCT, meaning that 90% of the time, a decision made by an observer given the particular distribution of positive and negative cases in this study will be correct. It should be noted, however, that the distribution in this study does not match the actual distribution of ovarian disorders that naturally occur in women. 86,138 women in the United States are currently estimated to have ovarian cancer (∼0.12% of the female population).1 Tubular adenoma is extremely rare in women and the effects of DMBA dosing do not occur naturally. Therefore, the PPV and NPV reported here cannot be generalized to the clinical population.24 A benefit of OCT is that several ovarian features can be analyzed—such as surface smoothness, attenuation, and
1
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TPF (sensitivity)
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OCT+SHG OCT SHG
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Fig. 4 Fitted average receiver operating characteristic (ROC) curves for each modality. Binormal ROC curves were fit to each observer trial to yield the binormal distribution parameters a and b. These parameters were averaged over all observers to form an average binormal ROC curve for each modality.
Journal of Medical Imaging
OCT
SHG
OCT+SHG
Fig. 5 Comparison of average area under the ROC curves (AUCs) for each modality. All average AUCs are significantly greater than chance (p < 0.01). The average AUC for OCT was significantly greater than the average AUC for SHG (p ¼ 0.011). Likewise, the average AUC for OCT+SHG was greater than SHG (p ¼ 0.013) The average AUC for OCT was not found to be significantly different from OCT+SHG.
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texture variability—to make a classification decision. The large field of view is also helpful because the observers could view the entire ovary. Observers reading SHG images correctly classified the tissue better than chance, but the overall performance was poorer than OCT. With SHG alone, readers can be expected to correctly classify abnormal ovaries greater than two-thirds of the time. The sensitivity was greater than the specificity, which suggests that the observers found signs of abnormality to be more noticeable than signs of normality. The overall diagnostic performance of SHG in this study was not shown to be a promising screening technique on its own, though there were some particular limitations to the SHG data we used. There are a few possible explanations for the poor performance of SHG. The most likely shortcoming of the SHG data in this study is the limited field of view. The SHG images were taken over a 400 × 400 μm region centered on the center of the 3 × 3 μm OCT field of view. By limiting the field of view, the abnormal collagen structures of VCD/DMBA effect and carcinoma samples might be difficult to detect, since these structural changes could be most easily noticed when observing the collagen structure over the entire ovary. We envision that with a clinical dual-modality endoscopic OCT-SHG system, the clinician would obtain SHG images at several regions in an ovary guided by the gross OCT data. The smaller amount of SHG data compared to OCT may also limit the performance of SHG in this study. Each OCT volume contained two orders of magnitude more slices in the depth direction than each SHG volume. This difference in data set size was mainly attributable to speed. The OCT system was able to obtain near-isotropic pixel-size image sets usually encompassing the entire lateral extent of the ovary in 30 s, whereas the SHG system required 15 s per image. Therefore, a limited number of slices were obtained at a relatively large slice separation (10 μm) much greater than the image axial resolution (1.2 μm). This shortcoming could be improved with the use of higher incident light power and faster scanning. The low incident power (20 mW) was chosen as a safe power level to mimic the necessarily low power levels required for in vivo imaging. SHG also had lower depth of penetration, with signal loss at ∼100 μm depth, whereas OCT was able to image ∼1 mm deep. We and others have created metrics for SHG imaging of collagen with statistically significant differences between healthy and malignant ovarian tissue. Some examples include Fourier analysis,19,29 gray-level co-occurrence matrices,19 SHG emission intensity and directionality,30 and the distribution of collagen fiber angles relative to the epithelial boundary.29 Therefore, we believe that addressing the SHG shortcomings in this study may improve the observer diagnostic performance of a dual-modality OCT-SHG imaging system. When comparing the imaging modalities, the average AUCs show that OCT is superior to SHG for classifying ovarian tissue as normal or abnormal using nonexpert observers. Interestingly, we also found that observers did not perform significantly differently from OCT when they could simultaneously view SHG images of the same tissue. The average AUC for the multimodal trial should have been higher than OCT alone if the observers came to their decisions primarily based on the OCT data, but were able to strengthen their confidence with the SHG data. This suggests that the extra information provided by the SHG images was either not always used or was possibly misleading. It is possible that some of the dual-modality images Journal of Medical Imaging
may appear normal in OCT and abnormal in SHG or vice versa potentially due to the limitations of the SHG images. Again, performance of the multimodality trial might be improved in future studies by collecting high-density SHG images across the entire OCT field of view. A successful candidate for a new ovarian cancer screening procedure must exhibit high sensitivity and specificity for any reader. The observers’ binary classification data for OCT resulted in a sensitivity of 0.94 and specificity of 0.88. A controlled trial in the United Kingdom of >50; 000 women found transvaginal sonography to have a sensitivity of 84.9% and a specificity of 98.2% for the detection of ovarian and tubal cancers.31 An American study of >25; 000 women screened annually with TVS yielded a sensitivity of 85.0% and a specificity of 98.7% (histopathological analysis provided the truth values for both studies).5 These results suggest that TVS can discriminate ovarian cancer from healthy tissue reasonably well, but this does not mean they have high clinical value. The PPVs for TVS were 5.3 (Ref. 31) and 14.01% (Ref. 5) for the U.K. and U.S. trials, respectively. Since the prevalence of ovarian cancer is very low, TVS yields many more false positives than true positives (true positives would equal false positives for PPV ¼ 50%). Because sensitivity and specificity are independent of prevalence, they overestimate the clinical value of a test for diseases with low prevalence in the general population. Our discriminatory OCT results (sensitivity and specificity) are promising and comparable to the performance found in the U.K. and U.S. trials, but a study of the diagnostic performance of OCT and SHG on ovarian cancers with a disease distribution that matches the natural prevalence would provide the most useful information regarding the clinical value of such an imaging system. We could not directly compare OCT and SHG to TVS using a mouse model because the resolution of TVS (∼0.25 mm) is insufficient to resolve key anatomical features of the mouse ovary, such as oocytes or immature follicles.32 An important point of optical imaging techniques is that they can distinguish abnormalities, such as tubular adenoma, which may precede cancer. This means that survival might be improved. As such, OCT imaging for ovarian cancer screening warrants further investigation. A future observer study should be conducted using in vivo images of human ovaries, preferably obtained by an endoscopic imaging system, in order to gain a more accurate measure of the diagnostic accuracy of OCT and SHG as they would be used clinically. Furthermore, future investigations of SHG should cover a larger area of the ovarian tissue. The performance of expert readers could also be evaluated; although given that both OCT and SHG are developmental technologies for this application, few expert readers currently exist. Finally, the distribution of abnormalities should be designed to closely match the distribution seen in the female population.
Acknowledgments The authors thank Elizabeth Krupinski for generously providing space and equipment for the observer trials and for sharing helpful suggestions. Financial support was provided by the National Institutes of Health research grant R01 CA119200, NIH Cardiovascular Biomedical Engineering Training Grant T32 HL007955, and the University of Arizona Cancer Center Support Grant CCSG-CA023074. This paper is based on work presented in a proceedings paper.33
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References 1. N. Howlader et al., Eds., “SEER cancer statistics review, 1975-2010,” 14 June 2013, http://seer.cancer.gov/archive/csr/1975_2010/ (28 January 2014). 2. S. S. Buys et al., “Effect of screening on ovarian cancer mortality: the prostate, lung, colorectal and ovarian (PLCO) cancer screening randomized controlled trial,” JAMA 305(22), 2295–2303 (2011). 3. R. C. Bast, Jr. et al., “New tumor markers: CA125 and beyond,” Int. J. Gynecol. Cancer 15(Suppl 3), 274–281 (2005). 4. C. C. Johnson et al., “The epidemiology of CA-125 in women without evidence of ovarian cancer in the prostate, lung, colorectal and ovarian cancer (PLCO) screening trial,” Gynecol. Oncol. 110(3), 383–389 (2008). 5. J. R. van Nagell, Jr. et al., “Ovarian cancer screening with annual transvaginal sonography: findings of 25,000 women screened,” Cancer 109(9), 1887–1896 (2007). 6. R. George et al., “Clinical research device for ovarian cancer detection by optical spectroscopy in the ultraviolet C-visible,” J. Biomed. Opt. 15(5), 057009 (2010). 7. G. M. van Dam et al., “Intraoperative tumor-specific fluorescence imaging in ovarian cancer by folate receptor-α targeting: first in-human results,” Nat. Med. 17(10), 1315–1319 (2011). 8. A. A. Tanbakuchi et al., “In vivo imaging of ovarian tissue using a novel confocal microlaparoscope,” Am. J. Obstet. Gynecol. 202(1), 90.e1– 90.e9 (2010). 9. T. Wang et al., “Characterization of ovarian tissue based on quantitative analysis of photoacoustic microscopy images,” Biomed. Opt. Express 4(12), 2763–2768 (2013). 10. D. Huang et al., “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991). 11. P. J. Campagnola and L. M. Loew, “Second-harmonic imaging microscopy for visualizing biomolecular arrays in cells, tissues and organisms,” Nat. Biotechnol. 21(11), 1356–1360 (2003). 12. C. Pitris et al., “High-resolution imaging of gynecologic neoplasms using optical coherence tomography,” Obstet. Gynecol. 93(1), 135–139 (1999). 13. Y. Yang et al., “Optical scattering coefficient estimated by optical coherence tomography correlates with collagen content in ovarian tissue,” J. Biomed. Opt. 16(9), 090504 (2011). 14. J. M. Watson et al., “In vivo time-serial multi-modality optical imaging in a mouse model of ovarian tumorigenesis,” Cancer Biol. Ther. 15(1), 42–60 (2014). 15. L. P. Hariri et al., “Simultaneous optical coherence tomography and laser induced fluorescence imaging in rat model of ovarian carcinogenesis,” Cancer Biol. Ther. 10(5), 438–447 (2010). 16. L. P. Hariri et al., “Laparoscopic optical coherence tomography imaging of human ovarian cancer,” Gynecol. Oncol. 114(2), 188–194 (2009). 17. R. J. Kurman and I. Shih, “Pathogenesis of ovarian cancer. Lessons from morphology and molecular biology and their clinical implications,” Int. J. Gynecol. Pathol. 27(2), 151–160 (2008). 18. N. D. Kirkpatrick, M. A. Brewer, and U. Utzinger, “Endogenous optical biomarkers of ovarian cancer evaluated with multiphoton microscopy,” Cancer Epidemiol. Biomarkers Prev. 16(10), 2048 (2007). 19. J. M. Watson et al., “Analysis of second-harmonic-generation microscopy in a mouse model of ovarian carcinoma,” J. Biomed. Opt. 17(7), 076002 (2012). 20. H. H. Barrett and K. J. Myers, “Image quality,” Chapter 14 in Foundations of Image Science, Wiley-Interscience, Hoboken, NJ (2004). 21. H. Gray, Anatomy of the Human Body, Lea & Febiger, Philadelphia (1918). 22. C. E. Metz, “Some practical issues of experimental design and data analysis in radiological ROC studies,” Invest. Radiol. 24(3), 234–245 (1989). 23. L. D. Brown, T. T. Cai, and A. DasGupta, “Interval estimation for a binomial proportion,” Stat. Sci. 16(2), 101–117 (2001). 24. C. D. Brown and H. T. Davis, “Receiver operating characteristics curves and related decision measures: a tutorial,” Chemometr. Intell. Lab. 80(1), 24–38 (2006). 25. J. A. Hanley, “The robustness of the ‘binormal’ assumptions used in fitting ROC curves,” Med. Decis. Making 8(3), 197–203 (1988). 26. B. D. Gallas et al., “A framework for random-effects ROC analysis: biases with the bootstrap and other variance estimators,” Commun. Stat. A-Theory 38(15), 2586–2603 (2009).
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27. S. L. Hillis, K. S. Berbaum, and C. E. Metz, “Recent developments in the Dorfman-Berbaum-Metz procedure for multireader ROC study analysis,” Acad. Radiol. 15(5), 647–661 (2008). 28. C. E. Metz, “Receiver operating characteristic analysis: a tool for the quantitative evaluation of observer performance and imaging systems,” J. Am. Coll. Radiol. 3(6), 413–422 (2006). 29. J. Adur et al., “Recognition of serous ovarian tumors in human samples by multimodal nonlinear optical microscopy,” J. Biomed. Opt. 16(9), 096017 (2011). 30. O. Nadiarnykh et al., “Alterations of the extracellular matrix in ovarian cancer studied by second harmonic generation imaging microscopy,” BMC Cancer 10, 94 (2010). 31. U. Menon et al., “Sensitivity and specificity of multimodal and ultrasound screening for ovarian cancer, and stage distribution of detected cancers: results of the prevalence screen of the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS),” Lancet Oncol. 10(4), 327– 340 (2009). 32. J. Griffin et al., “Comparative analysis of follicle morphology and oocyte diameter in four mammalian species (mouse, hamster, pig, and human),” J. Exp. Clin. Assist. Reprod. 3, 2 (2006). 33. W. A. Welge et al., “Objective assessment of multimodality optical coherence tomography and second-harmonic generation image quality of ex vivo mouse ovaries using human observers,” Proc. SPIE 8936, 893609 (2014). Weston A. Welge is a doctoral student at the College of Optical Sciences at the University of Arizona. He received his BS in electrical and computer engineering and BA in history from the University of Colorado at Boulder and MS in optical sciences from the University of Arizona. His research focuses on the development of miniature optical endoscopes and novel applications of optical coherence tomography for early cancer detection. Andrew T. DeMarco holds a bachelor’s degree in linguistics and a master’s degree in communication sciences and disorders from Temple University. He holds a certificate of clinical competence from the American Speech-Language Association. He is currently a doctoral candidate in the Department of Speech, Language, and Hearing Sciences at the University of Arizona, where he uses neuroimaging to understand the organization of language in the brain and how that architecture breaks down in aphasia after stroke. Jennifer M. Watson has bachelor’s degrees in mechanical engineering and health sciences, and a PhD in biomedical engineering with a minor in entrepreneurship. She completed her dissertation research on evaluation of ovarian tumor development using the optical imaging modalities multiphoton microscopy and optical coherence tomography. In her current position at Tech Launch Arizona, she works with researchers across campus to assess the commercial potential of technologies, create development and commercialization plans and access key resources. Photini S. Rice holds an associate of applied science degree in medical technology and is ASCP and HEW certified. She worked in the clinical laboratory setting for 10 years and consulted for 3 years at a physician’s office laboratory while working in a university research laboratory. She has spent the last 16 years working in cardiovascular and cancer imaging research at the University of Arizona. Jennifer K. Barton is currently a professor of biomedical engineering, electrical and computer engineering, and optical sciences at the University of Arizona. She has served as the assistant director of the BIO5 Institute and interim vice president for research. She is a fellow of SPIE and the American Institute for Medical and Biological Engineering. Her work on development of miniature multimodality optical endoscopes and light-tissue interaction has led to over 90 peer-reviewed journal papers. Matthew A. Kupinski is an associate professor in the College of Optical Sciences and the Department of Medical Imaging at the University of Arizona. He obtained his PhD from the University of Chicago in 2000. His work focuses on using objective, taskbased measures of image quality to aid in the design of imaging systems.
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