Use of a multiseparation fiber optic probe for the optical diagnosis of breast cancer

A Comparison of Spectroscopy and Imaging Techniques Utilizing Spectrally Resolved Diffusely Reflected Light for Intraoperative Margin Assessment in Breast-Conserving Surgery: A Systematic Review and Meta-Analysis

Up to 19% of patients require re-excision surgery due to positive margins in breast-conserving surgery (BCS). Intraoperative margin assessment tools (IMAs) that incorporate tissue optical measurements could help reduce re-excision rates. This review focuses on methods that use and assess spectrally resolved diffusely reflected light for breast cancer detection in the intraoperative setting. Following PROSPERO registration (CRD42022356216), an electronic search was performed. The modalities searched for were diffuse reflectance spectroscopy (DRS), multispectral imaging (MSI), hyperspectral imaging (HSI), and spatial frequency domain imaging (SFDI). The inclusion criteria encompassed studies of human in vivo or ex vivo breast tissues, which presented data on accuracy. The exclusion criteria were contrast use, frozen samples, and other imaging adjuncts. 19 studies were selected following PRISMA guidelines. Studies were divided into point-based (spectroscopy) or whole field-of-view (imaging) techniques. A fixed-or random-effects model analysis generated pooled sensitivity/specificity for the different modalities, following heterogeneity calculations using the Q statistic. Overall, imaging-based techniques had better pooled sensitivity/specificity (0.90 (CI 0.76-1.03)/0.92 (CI 0.78-1.06)) compared with probe-based techniques (0.84 (CI 0.78-0.89)/0.85 (CI 0.79-0.91)). The use of spectrally resolved diffusely reflected light is a rapid, non-contact technique that confers accuracy in discriminating between normal and malignant breast tissue, and it constitutes a potential IMA tool.

Spatially resolved diffuse reflectance spectroscopy endoscopic sensing with custom Si photodetectors

Early detection and surveillance of disease progression in epithelial tissue is key to improving long term patient outcomes for colon and esophageal cancers, which account for nearly a quarter of cancer related mortalities worldwide. Spatially resolved diffuse reflectance spectroscopy (SRDRS) is a non-invasive optical technique to sense biological changes at the cellular and sub-cellular level that occur when normal tissue becomes diseased, and has the potential to significantly improve the current standard of care for endoscopic gastrointestinal (GI) screening. Herein the design, fabrication, and characterization of the first custom SRDRS device to enable endoscopic SRDRS GI tissue characterization using a custom silicon (Si) thin film multi-pixel endoscopic optical sensor (MEOS) is described.

Raman spectroscopy as a diagnostic tool for monitoring acute nephritis

Both acute nephritis and chronic nephritis account for substantial morbidity and mortality worldwide, partly due to the lack of reliable tools for detecting disease early and monitoring its progression non-invasively. In this work, Raman spectroscopy coupled with multivariate analysis are employed for the first time to study the accelerated progression of nephritis in anti-GBM mouse model. Preliminary results show up to 98% discriminant accuracy for the severe and midly diseased and the healthy among two strains of mice with different susceptibility to acute glomerulonephritis. This technique has the potential for non-invasive or minimally-invasive early diagnosis, prognosis, and monitoring of renal disease progression.

Early detection and differentiation of venous and arterial occlusion in skin flaps using visible diffuse reflectance spectroscopy and autofluorescence spectroscopy

Our previous preclinical study demonstrated that both visible diffuse reflectance and autofluorescence spectroscopy, each of which yields a different set of physiological information, can predict skin flap viability with high accuracy in a MacFarlane rat dorsal skin flap model. In this report, we further evaluated our technique for the early detection and differentiation of venous occlusion and arterial occlusion in a rat groin flap model. We performed both diffuse reflectance and autofluorescence measurements on the skin flap model and statistically differentiated between flaps with and without occlusions as well as between flaps with venous occlusion and those with arterial occlusion based on these non-invasive optical measurements. Our preliminary results suggested that visible diffuse reflectance and autofluorescence spectroscopy can be potentially used clinically to detect both venous and arterial occlusion and differentiate one from the other accurately at an early time point.

A Quantitative Diffuse Reflectance Imaging (QDRI) System for Comprehensive Surveillance of the Morphological Landscape in Breast Tumor Margins

In an ongoing effort to address the clear clinical unmet needs surrounding breast conserving surgery (BCS), our group has developed a next-generation multiplexed optical-fiber-based tool to assess breast tumor margin status during initial surgeries. Specifically detailed in this work is the performance and clinical validation of a research-grade intra-operative tool for margin assessment based on diffuse optical spectroscopy. Previous work published by our group has illustrated the proof-of-concept generations of this device; here we incorporate a highly optimized quantitative diffuse reflectance imaging (QDRI) system utilizing a wide-field (imaging area = 17cm²) 49-channel multiplexed fiber optic probe, a custom raster-scanning imaging platform, a custom dual-channel white LED source, and an astronomy grade imaging CCD and spectrograph. The system signal to noise ratio (SNR) was found to be greater than 40dB for all channels. Optical property estimation error was found to be less than 10%, on average, over a wide range of absorption (μ_a = 0–8.9cm^-1) and scattering (μ_s’ = 7.0–9.7cm^-1) coefficients. Very low inter-channel and CCD crosstalk was observed (2% max) when used on turbid media (including breast tissue). A raster-scanning mechanism was developed to achieve sub-pixel resolution and was found to be optimally performed at an upsample factor of 8, affording 0.75mm spatially resolved diffuse reflectance images (λ = 450–600nm) of an entire margin (area = 17cm²) in 13.8 minutes (1.23cm²/min). Moreover, controlled pressure application at the probe-tissue interface afforded by the imaging platform reduces repeated scan variability, providing <1% variation across repeated scans of clinical specimens. We demonstrate the clinical utility of this device through a pilot 20-patient study of high-resolution optical parameter maps of the ratio of the β-carotene concentration to the reduced scattering coefficient. An empirical cumulative distribution function (eCDF) analysis is used to reduce optical property maps to quantitative distributions representing the morphological landscape of breast tumor margins. The optimizations presented in this work provide an avenue to rapidly survey large tissue areas on intra-operative time scales with improved sensitivity to regions of focal disease that may otherwise be overlooked.

Integrated Device for in Vivo Fine Needle Aspiration Biopsy and Elastic Scattering Spectroscopy in Preoperative Thyroid Nodules

Thyroid nodules are a frequent clinical finding and the most common endocrine malignancy is thyroid cancer. The standard of care in the management of a patient with a thyroid nodule is to perform a preoperative fine needle aspiration (FNA) biopsy of the suspect nodule under ultrasound imaging guidance. In a significant percentage of the cases, cytological assessment of the biopsy material yields indeterminate results, the consequence of which is diagnostic thyroidectomy. Unfortunately, 75–80% of diagnostic thyroidectomies following indeterminate cytology result in benign designation by post-surgery histopathology, indicating potentially unnecessary surgeries. Clearly, the potential exists for the improvement in patient care and the reduction of overall procedure costs if an improved preoperative diagnostic technique was developed. Elastic scattering spectroscopy (ESS) is an optical biopsy technique that is mediated by optical fiber probes and has been shown to be effective in differentiating benign from malignant thyroid tissue in ex vivo surgical tissue samples. The goal of the current research was to integrate the ESS fiber optic probes into a device that can also collect cells for cytological assessment and, thus, enable concurrent spectroscopic interrogation and biopsy of a suspect nodule with a single needle penetration. The primary challenges to designing the device included miniaturizing the standard ESS fiber optic probe to fit within an FNA needle and maintaining the needle’s aspiration functionality. We demonstrate the value of the fabricated prototype devices by assessing their preliminary performance in an on-going clinical study with >120 patients. The devices have proven to be clinically friendly, collecting both aspirated cells and optical data from the same location in thyroid nodules and with minimal disruption of clinical procedure. In the future, such integrated devices could be used to complement FNA-based cytological results and have the potential to both reduce the number of diagnostic thyroidectomies on benign nodules and improve the surgical approach for patients with thyroid malignancies, thereby, decreasing healthcare costs and improving patient outcomes.

Diffuse reflectance spectroscopy of epithelial tissue with a smart fiber-optic probe

Diffuse reflectance spectroscopy (DRS) with a fiber-optic probe can noninvasively quantify the optical properties of epithelial tissues and has shown the potential as a cost-effective, fast and sensitive tool for diagnosis of early precancerous changes in the cervix and oral cavity. However, current DRS systems are susceptible to several sources of systematic and random errors, such as uncontrolled probe-to-tissue pressure and lack of a real-time calibration that can significantly impair the measurement accuracy, reliability and validity of this technology as well as its clinical utility. In addition, such systems use bulky, high power and expensive optical components which impede their widespread use in low- and middle-income countries (LMICs) where epithelial cancer related death is disproportionately high. In this paper we report a portable, easy-to-use and low cost, yet accurate and reliable DRS device that can aid in the screening and diagnosis of oral and cervical cancer. The device uses an innovative smart fiber-optic probe to eliminate operator bias, state-of-the-art photonics components to reduce size and power consumption, and automated software to reduce the need of operator training. The device showed a mean error of 1.4 ± 0.5% and 6.8 ± 1.7% for extraction of phantom absorption and reduced scattering coefficients, respectively. A clinical study on healthy volunteers indicated that a pressure below 1.0 psi is desired for oral mucosal tissues to minimize the probe effects on tissue physiology and morphology.

Rapid determination of oxygen saturation and vascularity for cancer detection

A rapid heuristic ratiometric analysis for estimating tissue hemoglobin concentration and oxygen saturation from measured tissue diffuse reflectance spectra is presented. The analysis was validated in tissue-mimicking phantoms and applied to clinical measurements in head and neck, cervical and breast tissues. The analysis works in two steps. First, a linear equation that translates the ratio of the diffuse reflectance at 584 nm and 545 nm to estimate the tissue hemoglobin concentration using a Monte Carlo-based lookup table was developed. This equation is independent of tissue scattering and oxygen saturation. Second, the oxygen saturation was estimated using non-linear logistic equations that translate the ratio of the diffuse reflectance spectra at 539 nm to 545 nm into the tissue oxygen saturation. Correlations coefficients of 0.89 (0.86), 0.77 (0.71) and 0.69 (0.43) were obtained for the tissue hemoglobin concentration (oxygen saturation) values extracted using the full spectral Monte Carlo and the ratiometric analysis, for clinical measurements in head and neck, breast and cervical tissues, respectively. The ratiometric analysis was more than 4000 times faster than the inverse Monte Carlo analysis for estimating tissue hemoglobin concentration and oxygen saturation in simulated phantom experiments. In addition, the discriminatory power of the two analyses was similar. These results show the potential of such empirical tools to rapidly estimate tissue hemoglobin in real-time spectral imaging applications.

Advancing optical imaging for breast margin assessment: an analysis of excisional time, cautery, and patent blue dye on underlying sources of contrast

Breast conserving surgery (BCS) is a recommended treatment for breast cancer patients where the goal is to remove the tumor and a surrounding rim of normal tissue. Unfortunately, a high percentage of patients return for additional surgeries to remove all of the cancer. Post-operative pathology is the gold standard for evaluating BCS margins but is limited due to the amount of tissue that can be sampled. Frozen section analysis and touch-preparation cytology have been proposed to address the surgical needs but also have sampling limitations. These issues represent an unmet clinical need for guidance in resecting malignant tissue intra-operatively and for pathological sampling. We have developed a quantitative spectral imaging device to examine margins intra-operatively. The context in which this technology is applied (intra-operative or post-operative setting) is influenced by time after excision and surgical factors including cautery and the presence of patent blue dye (specifically Lymphazurin™, used for sentinel lymph node mapping). Optical endpoints of hemoglobin ([THb]), fat ([β-carotene]), and fibroglandular content via light scattering (<µ_s’>) measurements were quantified from diffuse reflectance spectra of lumpectomy and mastectomy specimens using a Monte Carlo model. A linear longitudinal mixed-effects model was used to fit the optical endpoints for the cautery and kinetics studies. Monte Carlo simulations and tissue mimicking phantoms were used for the patent blue dye experiments. [THb], [β-carotene], and <µ_s’> were affected by <3.3% error with <80 µM of patent blue dye. The percent change in [β-carotene], <µ_s’>, and [β-carotene]/<µ_s’> was <14% in 30 minutes, while percent change in [THb] was >40%. [β-carotene] and [β-carotene]/<µ_s’> were the only parameters not affected by cautery. This work demonstrates the importance of understanding the post-excision kinetics of ex-vivo tissue and the presence of cautery and patent blue dye for breast tumor margin assessment, to accurately interpret data and exploit underling sources of contrast.

Bimodal spectroscopy for in vivo characterization of hypertrophic skin tissue : pre-clinical experimentation, data selection and classification

This study aims at investigating the efficiency of bimodal spectroscopy in detection of hypertrophic scar tissue on a preclinical model. Fluorescence and Diffuse Reflectance spectra were collected from 55 scars deliberately created on ears of 20 rabbits, amongst which some received tacrolimus injection to provide non-hypertrophic scar tissue. The spectroscopic data measured on hypertrophic and non-hypertrophic scar tissues were used for developing our classification algorithm. Spectral features were extracted from corrected data and analyzed to classify the scar tissues into hypertrophic or non-hypertrophic. The Algorithm was developed using k-NN classifier and validated by comparing to histological classification result with Leave-One-Out cross validation. Bimodal spectroscopy showed promising results in detecting hypertrophic tissue (sensibility 90.5%, specificity 94.4%). The features used for classification were extracted from the autofluorescence spectra collected at 4 CEFS with excitations at 360, 410, and 420 nm. This indicates the hypertrophic process may involve change in concentration of several fluorophores (collagen, elastin and NADH) excited in this range, or modification in volume of explored tissue layers (epidermis and dermis) due to tissue thickening.

Joint derivation method for determining optical properties based on steady-state spatially resolved diffuse reflectance measurement at small source-detector separations and large reduced albedo range: theory and simulation

Accurate determination of the optical properties (the absorption coefficient μ(a) and the reduced scattering coefficient μ(s) (')) of tissues is very important in a variety of diagnostic and therapeutic procedures. Optical diffusion theory is frequently used as the forward model for describing the photon transfer in media with large reduced albedos (a(')) and in large source-detector separations (SDS). Several other methods (PN approximation, hybrid diffusion-P3 approximation) have also been published that describe photon transfer in media with low a(') or small SDSs. We studied the theoretical models for the steady-state spatially resolved diffuse reflectance measurement to accurately determine μ(a) and μ(s) (') at large a(') range but small SDSs. Instead of using a single model, a joint derivation method is proposed. The developed method uses one of the best aforementioned theoretical methods separately in five ranges of a(') determined from several forward models. In the region of small SDSs (the range between 0.4 and 8 mm) and large a(') range (between 0.5 and 0.99), the best theoretical derivation model was determined. The results indicate that the joint derivation method can improve the derivation accuracy and that a(') range can be determined by the steady-state spatially resolved diffuse reflectance measurement.

Performance of a lookup table-based approach for measuring tissue optical properties with diffuse optical spectroscopy

Diffuse optical spectroscopy (DOS) provides a powerful tool for fast and noninvasive disease diagnosis. The ability to leverage DOS to accurately quantify tissue optical parameters hinges on the model used to estimate light-tissue interaction. We describe the accuracy of a lookup table (LUT)-based inverse model for measuring optical properties under different conditions relevant to biological tissue. The LUT is a matrix of reflectance values acquired experimentally from calibration standards of varying scattering and absorption properties. Because it is based on experimental values, the LUT inherently accounts for system response and probe geometry. We tested our approach in tissue phantoms containing multiple absorbers, different sizes of scatterers, and varying oxygen saturation of hemoglobin. The LUT-based model was able to extract scattering and absorption properties under most conditions with errors of less than 5 percent. We demonstrate the validity of the lookup table over a range of source-detector separations from 0.25 to 1.48 mm. Finally, we describe the rapid fabrication of a lookup table using only six calibration standards. This optimized LUT was able to extract scattering and absorption properties with average RMS errors of 2.5 and 4 percent, respectively.

Wide-field spectral imaging of human ovary autofluorescence and oncologic diagnosis via previously collected probe data

With no sufficient screening test for ovarian cancer, a method to evaluate the ovarian disease state quickly and nondestructively is needed. The authors have applied a wide-field spectral imager to freshly resected ovaries of 30 human patients in a study believed to be the first of its magnitude. Endogenous fluorescence was excited with 365-nm light and imaged in eight emission bands collectively covering the 400- to 640-nm range. Linear discriminant analysis was used to classify all image pixels and generate diagnostic maps of the ovaries. Training the classifier with previously collected single-point autofluorescence measurements of a spectroscopic probe enabled this novel classification. The process by which probe-collected spectra were transformed for comparison with imager spectra is described. Sensitivity of 100% and specificity of 51% were obtained in classifying normal and cancerous ovaries using autofluorescence data alone. Specificity increased to 69% when autofluorescence data were divided by green reflectance data to correct for spatial variation in tissue absorption properties. Benign neoplasm ovaries were also found to classify as nonmalignant using the same algorithm. Although applied ex vivo, the method described here appears useful for quick assessment of cancer presence in the human ovary.

First-in-human pilot study of a spatial frequency domain oxygenation imaging system

Oxygenation measurements are widely used in patient care. However, most clinically available instruments currently consist of contact probes that only provide global monitoring of the patient (e.g., pulse oximetry probes) or local monitoring of small areas (e.g., spectroscopy-based probes). Visualization of oxygenation over large areas of tissue, without a priori knowledge of the location of defects, has the potential to improve patient management in many surgical and critical care applications. In this study, we present a clinically compatible multispectral spatial frequency domain imaging (SFDI) system optimized for surgical oxygenation imaging. This system was used to image tissue oxygenation over a large area (16×12 cm) and was validated during preclinical studies by comparing results obtained with an FDA-approved clinical oxygenation probe. Skin flap, bowel, and liver vascular occlusion experiments were performed on Yorkshire pigs and demonstrated that over the course of the experiment, relative changes in oxygen saturation measured using SFDI had an accuracy within 10% of those made using the FDA-approved device. Finally, the new SFDI system was translated to the clinic in a first-in-human pilot study that imaged skin flap oxygenation during reconstructive breast surgery. Overall, this study lays the foundation for clinical translation of endogenous contrast imaging using SFDI.

Pilot clinical study for quantitative spectral diagnosis of non-melanoma skin cancer

BACKGROUND

Several research groups have demonstrated the non-invasive diagnostic potential of diffuse optical spectroscopy (DOS) and laser-induced fluorescence (LIF) techniques for early cancer detection. By combining both modalities, one can simultaneously measure quantitative parameters related to the morphology, function and biochemical composition of tissue and use them to diagnose malignancy. The objective of this study was to use a quantitative reflectance/fluorescence spectroscopic technique to determine the optical properties of normal skin and non-melanoma skin cancers and the ability to accurately classify them. An additional goal was to determine the ability of the technique to differentiate non-melanoma skin cancers from normal skin.

STUDY DESIGN

The study comprised 48 lesions measured from 40 patients scheduled for a biopsy of suspected non-melanoma skin cancers. White light reflectance and laser-induced fluorescence spectra (wavelength range = 350-700 nm) were collected from each suspected lesion and adjacent clinically normal skin using a custom-built, optical fiber-based clinical instrument. After measurement, the skin sites were biopsied and categorized according to histopathology. Using a quantitative model, we extracted various optical parameters from the measured spectra that could be correlated to the physiological state of tissue.

RESULTS

Scattering from cancerous lesions was significantly lower than normal skin for every lesion group, whereas absorption parameters were significantly higher. Using numerical cut-offs for our optical parameters, our clinical instrument could classify basal cell carcinomas with a sensitivity and specificity of 94% and 89%, respectively. Similarly, the instrument classified actinic keratoses and squamous cell carcinomas with a sensitivity of 100% and specificity of 50%.

CONCLUSION

The measured optical properties and fluorophore contributions of normal skin and non-melanoma skin cancers are significantly different from each other and correlate well with tissue pathology. A diagnostic algorithm that combines these extracted properties holds promise for the potential non-invasive diagnosis of skin cancer.

INTEGRATED OPTICAL TOOLS FOR MINIMALLY INVASIVE DIAGNOSIS AND TREATMENT AT GASTROINTESTINAL ENDOSCOPY

Over the past two decades, the bulk of gastrointestinal (GI) endoscopic procedures has shifted away from diagnostic and therapeutic interventions for symptomatic disease toward cancer prevention in asymptomatic patients. This shift has resulted largely from a decrease in the incidence of peptic ulcer disease in the era of antisecretory medications coupled with emerging evidence for the efficacy of endoscopic detection and eradication of dysplasia, a histopathological biomarker widely accepted as a precursor to cancer. This shift has been accompanied by a drive toward minimally-invasive, in situ optical diagnostic technologies that help assess the mucosa for cellular changes that relate to dysplasia. Two competing but complementary approaches have been pursued. The first approach is based on broad-view targeting of "areas of interest" or "red flags." These broad-view technologies include standard white light endoscopy (WLE), high-definition endoscopy (HD), and "electronic" chromoendoscopy (narrow-band-type imaging). The second approach is based on multiple small area or point-source (meso/micro) measurements, which can be either machine (spectroscopy) or human-interpreted (endomicroscopy, magnification endoscopy), much as histopatholgy slides are. In this paper we present our experience with the development and testing of a set of familiar but "smarter" standard tissue-sampling tools that can be routinely employed during screening/surveillance endoscopy. These tools have been designed to incorporate fiberoptic probes that can mediate spectroscopy or endomicroscopy. We demonstrate the value of such tools by assessing their preliminary performance from several ongoing clinical studies. Our results have shown promise for a new generation of integrated optical tools for a variety of screening/surveillance applications during GI endoscopy. Integrated devices should prove invaluable for dysplasia surveillance strategies that currently result in large numbers of benign biopsies, which are of little clinical consequence, including screening for colorectal polyps and surveillance of "flat" dysplasia such as Barrett's esophagus and chronic colitis due to inflammatory bowel diseases.

Evaluation of a novel noncontact spectrally and spatially resolved reflectance setup with continuously variable source-detector separation using silicone phantoms

We present a new variant of a noncontact, oblique incidence spatially resolved reflectance setup. The continuously variable source detector separation enables adaptation to high and low albedo samples. Absorption (μ(a)) and reduced scattering coefficients (μ(') (s)) are determined in the wavelength range of 400-1000 nm using a lookup table, calculated by a Monte Carlo simulation of the light transport. The method is characterized by an silicone phantom study covering a wide parameter range 0.01 mm(-1) ≤ μ(a) ≤ 2.5 mm(-1) and 0.2 mm(-1) ≤ μ(') (s) ≤ 10 mm(-1), which includes the optical parameters of tissue in the visible and near infrared. The influence of the incident angle and the detection aperture on the simulated remission was examined. Using perpendicular incidence and 90-deg detection aperture in the Monte Carlo simulation in contrast to the experimental situation with 30-deg incidence and 4.6-deg detection aperture is shown to be valid for the parameter range μ(') (s) > 1 mm(-1) and μ(a) < 1.2 mm(-1). A Mie calculation is presented, showing that a decreasing reduced scattering coefficient for increasing absorption can be the consequence of real physics instead of cross talk.

Visible light optical spectroscopy is sensitive to neovascularization in the dysplastic cervix

Neovascularization in cervical intraepithelial neoplasia (CIN) is studied because it is the precursor to the third most common female cancer worldwide. Diffuse reflectance from 450-600 nm was collected from 46 patients (76 sites) undergoing colposcopy at Duke University Medical Center. Total hemoglobin, derived using an inverse Monte Carlo model, significantly increased in CIN 2+ (N=12) versus CIN 1 (N=16) and normal tissues (N=48) combined with P<0.004. Immunohistochemistry using monoclonal anti-CD34 was used to quantify microvessel density to validate the increased hemoglobin content. Biopsies from 51 sites were stained, and up to three hot spots per slide were selected for microvessel quantification by two observers. Similar to the optical study results, microvessel density was significantly increased in CIN 2+ (N=16) versus CIN 1 (N=21) and normal tissue (N=14) combined with P<0.007. Total vessel density, however, was not significantly associated with dysplastic grade. Hence, our quantitative optical spectroscopy system is primarily sensitive to dysplastic neovascularization immediately beneath the basement membrane, with minimal confounding from vascularity inherent in the normal stromal environment. This tool could have potential for in vivo applications in screening for cervical cancer, prognostics, and monitoring of antiangiogenic effects in chemoprevention therapies.

Sensitivity of spatially resolved reflectance signals to coincident variations in tissue optical properties

Canonical correlation analysis, a multivariate statistical technique, was used to investigate the degree of association between tissue optical properties and spatially resolved reflectance signals. Monte Carlo modeling was employed to simulate signals corresponding to different combinations of optical properties and these data sets were fed as input to statistical analysis. The results show that it is possible to adjust the separation and angular orientation of source and detector fibers such that the effect of a particular optical property will be augmented among coincident variations in other properties. The trends observed exhibit differences when compared with a conventional univariate sensitivity analysis in which only a single property is varied whereas all other parameters of interest are kept constant.

A low-cost, portable, and quantitative spectral imaging system for application to biological tissues

The ability of diffuse reflectance spectroscopy to extract quantitative biological composition of tissues has been used to discern tissue types in both pre-clinical and clinical cancer studies. Typically, diffuse reflectance spectroscopy systems are designed for single-point measurements. Clinically, an imaging system would provide valuable spatial information on tissue composition. While it is feasible to build a multiplexed fiber-optic probe based spectral imaging system, these systems suffer from drawbacks with respect to cost and size. To address these we developed a compact and low cost system using a broadband light source with an 8-slot filter wheel for illumination and silicon photodiodes for detection. The spectral imaging system was tested on a set of tissue mimicking liquid phantoms which yielded an optical property extraction accuracy of 6.40 +/- 7.78% for the absorption coefficient (micro(a)) and 11.37 +/- 19.62% for the wavelength-averaged reduced scattering coefficient (micro(s)').

Design and validation of a clinical instrument for spectral diagnosis of cutaneous malignancy

We report a probe-based portable and clinically compatible instrument for the spectral diagnosis of melanoma and nonmelanoma skin cancers. The instrument combines two modalities--diffuse reflectance and intrinsic fluorescence spectroscopy--to provide complementary information regarding tissue morphology, function, and biochemical composition. The instrument provides a good signal-to-noise ratio for the collected reflectance and laser-induced fluorescence spectra. Validation experiments on tissue phantoms over a physiologically relevant range of albedos (0.35-0.99) demonstrate an accuracy of close to 10% in determining scattering, absorption and fluorescence characteristics. We also demonstrate the ability of our instrument to collect in vivo diffuse reflectance and fluorescence measurements from clinically normal skin, dysplastic nevus, and malignant nonmelanoma skin cancer.

Clinical feasibility of microscopically-guided breast needle biopsy using a fiber-optic probe with computer-aided detection

Needle biopsy of small or nonpalpable breast lesions has a high nondiagnostic sampling rate even when needle position is guided by stereotaxis or ultrasound. We assess the feasibility of using a near-infrared fiber optic probe and computer-aided detection for the microscopic guidance of needle breast biopsy procedures. Specimens from nine consented patients undergoing breast-conserving surgery were assessed intraoperatively using a needle device with an integrated fiber-optic probe capable of assessing two physical tissue properties highly correlated to pathology. Immediately following surgical resection, specimens were probed by inserting the optical biopsy needle device into the tissue, simulating the procedure used to position standard biopsy needles. Needle positions were marked and correlated with histology, which verified measurements obtained from 58 needle positions, including 40 in adipose and 18 in tumor tissue. This study yielded tissue classifications based on measurement of optical refractive index and scattering. Confidence-rating schemes yielded combined sensitivity of 89% (16/18) and specificity of 78% (31/40). Refractive index tests alone identified tumor tissue with a sensitivity of 83% (15/18) and specificity of 75% (30/40). Scattering profiles independently identified tumor tissue with a sensitivity of 61% (11/18) and specificity of 60% (24/40). These results show that a biopsy needle with an integrated fiber optic probe can be used to identify breast tumor tissue for sampling. Integration of this probe into current practices offers the potential to reduce nondiagnostic sampling rates by directly evaluating in situ microscopic tissue properties in real-time, before removal.

A robust Monte Carlo model for the extraction of biological absorption and scattering in vivo

We have a toolbox to quantify tissue optical properties that is composed of specialized fiberoptic probes for UV-visible diffuse reflectance spectroscopy and a fast, scalable inverse Monte Carlo (MC) model. In this paper, we assess the robustness of the toolbox for quantifying physiologically relevant parameters from turbid tissue-like media. In particular, we consider the effects of using different instruments, fiberoptic probes, and instrument-specific settings for a wide range of optical properties. Additionally, we test the quantitative accuracy of the inverse MC model for extracting the biologically relevant parameters of hemoglobin saturation and total hemoglobin concentration. We also test the effect of double-absorber phantoms (hemoglobin and crocin to model the absorption of hemoglobin and beta carotene, respectively, in the breast) for a range of absorption and scattering properties. We include an assessment on which reference phantom serves as the best calibration standard to enable accurate extraction of the absorption and scattering properties of the target sample. We found the best reference-target phantom combinations to be ones with similar scattering levels. The results from these phantom studies provide a set of guidelines for extracting optical parameters from clinical studies.

Quantitative physiology of the precancerous cervix in vivo through optical spectroscopy

Cervical cancer is the second most common female cancer worldwide. The ability to quantify physiological and morphological changes in the cervix is not only useful in the diagnosis of cervical precancers but also important in aiding the design of cost-effective detection systems for use in developing countries that lack well-established screening and diagnostic programs. We assessed the capability of a diffuse reflectance spectroscopy technique to identify contrasts in optical biomarkers that vary with different grades of cervical intraepithelial neoplasia (CIN) from normal cervical tissues. The technology consists of an optical probe and an instrument (with broadband light source, dispersive element, and detector), and a Monte Carlo algorithm to extract optical biomarker contributions including total hemoglobin (Hb) concentration, Hb saturation, and reduced scattering coefficient from the measured spectra. Among 38 patients and 89 sites examined, 46 squamous normal sites, 18 CIN 1, and 15 CIN 2(+) sites were included in the analysis. Total Hb was statistically higher in CIN 2(+) (18.3 +/- 3.6 microM, mean +/- SE) compared with normal (9.58 +/- 1.91 microM) and CIN 1 (12.8 +/- 2.6 microM), whereas scattering was significantly reduced in CIN 1 (8.3 +/- 0.8 cm(-1)) and CIN 2(+) (8.6 +/- 1.0 cm(-1)) compared with normal (10.2 +/- 1.1 cm(-1)). Hemoglobin saturation was not significantly altered in CIN 2(+) compared with normal and CIN 1. The difference in total Hb is likely because of stromal angiogenesis, whereas decreased scattering can be attributed to breakdown of collagen network in the cervical stroma.

Automated algorithm for breast tissue differentiation in optical coherence tomography

An automated algorithm for differentiating breast tissue types based on optical coherence tomography (OCT) data is presented. Eight parameters are derived from the OCT reflectivity profiles and their means and covariance matrices are calculated for each tissue type from a training set (48 samples) selected based on histological examination. A quadratic discrimination score is then used to assess the samples from a validation set. The algorithm results for a set of 89 breast tissue samples were correlated with the histological findings, yielding specificity and sensitivity of 0.88. If further perfected to work in real time and yield even higher sensitivity and specificity, this algorithm would be a valuable tool for biopsy guidance and could significantly increase procedure reliability by reducing both the number of nondiagnostic aspirates and the number of false negatives.

Fluorescence spectroscopy: an adjunct diagnostic tool to image-guided core needle biopsy of the breast

We explored the use of a fiber-optic probe for in vivo fluorescence spectroscopy of breast tissues during percutaneous image-guided breast biopsy. A total of 121 biopsy samples with accompanying histological diagnosis were obtained clinically and investigated in this study. The tissue spectra were analyzed using partial least-squares analysis and represented using a set of principal components (PCs) with dramatically reduced data dimension. For nonmalignant tissue samples, a set of PCs that account for the largest amount of variance in the spectra displayed correlation with the percent tissue composition. For all tissue samples, a set of PCs was identified using a Wilcoxon rank-sum test as showing statistically significant differences between: 1) malignant and fibrous/benign; 2) malignant and adipose; and 3) malignant and nonmalignant breast samples. These PCs were used to distinguish malignant from other nonmalignant tissue types using a binary classification scheme based on both linear and nonlinear support vector machine (SVM) and logistic regression (LR). For the sample set investigated in this study, the SVM classifier provided a cross-validated sensitivity and specificity of up to 81% and 87%, respectively, for discrimination between malignant and fibrous/benign samples, and up to 81% and 81%, respectively, for discriminating between malignant and adipose samples. Classification based on LR was used to generate receiver operator curves with an area under the curve (AUC) of 0.87 for discriminating malignant versus fibrous/benign tissues, and an AUC of 0.84 for discriminating malignant from adipose tissue samples. This study demonstrates the feasibility of performing fluorescence spectroscopy during clinical core needle breast biopsy, and the potential of this technique for identifying breast malignancy in vivo.

A strategy for quantitative spectral imaging of tissue absorption and scattering using light emitting diodes and photodiodes

A diffuse reflectance spectroscopy system was modified as a step towards miniaturization and spectral imaging of tissue absorption and scattering. The modified system uses a tunable source and an optical fiber for illumination and a photodiode in contact with tissue for detection. Compared to the previous system, it is smaller, less costly, and has comparable performance in extracting optical properties in tissue phantoms. Wavelength reduction simulations show the feasibility of replacing the source with LEDs to further decrease system size and cost. Simulated crosstalk analysis indicates that this evolving system can be multiplexed for spectral imaging in the future.

Electromagnetic spectroscopy of normal breast tissue specimens obtained from reduction surgeries: comparison of optical and microwave properties

Techniques utilizing electromagnetic energy at microwave and optical frequencies have been shown to be promising for breast cancer detection and diagnosis. Since different biophysical mechanisms are exploited at these frequencies to discriminate between healthy and diseased tissue, combining these two modalities may result in a more powerful approach for breast cancer detection and diagnosis. Toward this end, we performed microwave dielectric spectroscopy and optical diffuse reflectance spectroscopy measurements at the same sites on freshly excised normal breast tissues obtained from reduction surgeries at the University of Wisconsin Hospital, using microwave and optical probes with very similar sensing volumes. We found that the microwave dielectric constant and effective conductivity are correlated with tissue composition across the entire measurement frequency range (|r| approximately 0.5-0.6, p<0.01) and that the optical absorption coefficient at 460 nm and optical scattering coefficient are correlated with tissue composition (|r| approximately 0.4-0.6, p<0.02). Finally, we found that the optical absorption coefficient at 460 nm is correlated with the microwave dielectric constant and effective conductivity (r=-0.55, p<0.01). Our results suggest that combining optical and microwave modalities for analyzing breast tissue samples may serve as a crosscheck and provide complementary information about tissue composition.

Cost-effective diffuse reflectance spectroscopy device for quantifying tissue absorption and scattering in vivo

A hybrid optical device that uses a multimode fiber coupled to a tunable light source for illumination and a 2.4-mm photodiode for detection in contact with the tissue surface is developed as a first step toward our goal of developing a cost-effective, miniature spectral imaging device to map tissue optical properties in vivo. This device coupled with an inverse Monte Carlo model of reflectance is demonstrated to accurately quantify tissue absorption and scattering in tissue-like turbid synthetic phantoms with a wide range of optical properties. The overall errors for quantifying the absorption and scattering coefficients are 6.0+/-5.6 and 6.1+/-4.7%, respectively. Compared with fiber-based detection, having the detector right at the tissue surface can significantly improve light collection efficiency, thus reducing the requirement for sophisticated detectors with high sensitivity, and this design can be easily expanded into a quantitative spectral imaging system for mapping tissue optical properties in vivo.

Model based and empirical spectral analysis for the diagnosis of breast cancer

We explored the use of both empirical (Partial Least Squares, PLS) and Monte Carlo model based approaches for the analysis of fluorescence and diffuse reflectance spectra measured ex vivo from freshly excised breast tissues and for the diagnosis of breast cancer. Features extracted using both approaches, i.e. principal components (PCs) obtained from empirical analysis or tissue properties obtained from model based analysis, displayed statistically significant difference between malignant and non-malignant tissues, and can be used to discriminate breast malignancy with comparable sensitivity and specificity of up to 90%. The PC scores of a subset of PCs also displayed significant correlation with the tissue properties extracted from the model based analysis, suggesting both approaches likely probe the same sources of contrast in the tissue spectra that discriminate between malignant and non-malignant breast tissues but in different ways.

Comparison of autofluorescence, diffuse reflectance, and Raman spectroscopy for breast tissue discrimination

For a given diagnostic problem, important considerations are the relative performances of the various optical biopsy techniques. A comparative evaluation of fluorescence, diffuse reflectance, combined fluorescence and diffuse reflectance, and Raman spectroscopy in discriminating different histopathologic categories of human breast tissues is reported. Optical spectra were acquired ex vivo from a total of 74 breast tissue samples belonging to 4 distinct histopathologic categories: invasive ductal carcinoma (IDC), ductal carcinoma in situ (DCIS), fibroadenoma (FA), and normal breast tissue. A probability-based multivariate statistical algorithm capable of direct multiclass classification was developed to analyze the diagnostic content of the spectra measured from the same set of breast tissue sites with these different techniques. The algorithm uses the theory of nonlinear maximum representation and discrimination feature for feature extraction, and the theory of sparse multinomial logistic regression for classification. The results reveal that the performance of Raman spectroscopy is superior to that of all others in classifying the breast tissues into respective histopathologic categories. The best classification accuracy was observed to be approximately 99%, 94%, 98%, and 100% for IDC, DCIS, FA, and normal breast tissues, respectively, on the basis of leave-one-sample-out cross-validation, with an overall accuracy of approximately 99%.

Combined Monte Carlo and finite-difference time-domain modeling for biophotonic analysis: implications on reflectance-based diagnosis of epithelial precancer

Monte Carlo (MC) modeling of photon transport in tissues is generally performed using simplified functions that only approximate the angular scattering properties of tissue constituents. However, such approximations may not be sufficient for fully characterizing tissue scatterers such as cells. Finite-difference time-domain (FDTD) modeling provides a flexible approach to compute realistic tissue phase functions that describe probability of scattering at different angles. We describe a computational framework that combines MC and FDTD modeling, and allows random sampling of scattering directions from FDTD phase functions. We carry out simulations to assess the influence of incorporating realistic FDTD phase functions on modeling spectroscopic reflectance signals obtained from normal and precancerous epithelial tissues. Simulations employ various fiber optic probe designs to analyze the sensitivity of different probe geometries to FDTD-generated phase functions. Combined MC/FDTD modeling results indicate that the form of the phase function used is an important factor in determining the reflectance profile of tissues, and detected reflectance intensity can change up to approximately 30% when a realistic FDTD phase function is used instead of an approximating function. The results presented need to be taken into account when developing photon propagation models or implementing inverse algorithms to extract optical properties from measurements.

Diagnosis of breast cancer using fluorescence and diffuse reflectance spectroscopy: a Monte-Carlo-model-based approach

We explore the use of Monte-Carlo-model-based approaches for the analysis of fluorescence and diffuse reflectance spectra measured ex vivo from breast tissues. These models are used to extract the absorption, scattering, and fluorescence properties of malignant and nonmalignant tissues and to diagnose breast cancer based on these intrinsic tissue properties. Absorption and scattering properties, including beta-carotene concentration, total hemoglobin concentration, hemoglobin saturation, and the mean reduced scattering coefficient are derived from diffuse reflectance spectra using a previously developed Monte Carlo model of diffuse reflectance. A Monte Carlo model of fluorescence described in an earlier manuscript was employed to retrieve the intrinsic fluorescence spectra. The intrinsic fluorescence spectra were decomposed into several contributing components, which we attribute to endogenous fluorophores that may present in breast tissues including collagen, NADH, and retinol/vitamin A. The model-based approaches removes any dependency on the instrument and probe geometry. The relative fluorescence contributions of individual fluorescing components, as well as beta-carotene concentration, hemoglobin saturation, and the mean reduced scattering coefficient display statistically significant differences between malignant and adipose breast tissues. The hemoglobin saturation and the reduced scattering coefficient display statistically significant differences between malignant and fibrous/benign breast tissues. A linear support vector machine classification using (1) fluorescence properties alone, (2) absorption and scattering properties alone, and (3) the combination of all tissue properties achieves comparable classification accuracies of 81 to 84% in sensitivity and 75 to 89% in specificity for discriminating malignant from nonmalignant breast tissues, suggesting each set of tissue properties are diagnostically useful for the discrimination of breast malignancy.

Automated algorithm for differentiation of human breast tissue using low coherence interferometry for fine needle aspiration biopsy guidance

Fine needle aspiration biopsy (FNAB) is a rapid and cost-effective method for obtaining a first-line diagnosis of a palpable mass of the breast. However, because it can be difficult to manually discriminate between adipose tissue and the fibroglandular tissue more likely to harbor disease, this technique is plagued by a high number of nondiagnostic tissue draws. We have developed a portable, low coherence interferometry (LCI) instrument for FNAB guidance to combat this problem. The device contains an optical fiber probe inserted within the bore of the fine gauge needle and is capable of obtaining tissue structural information with a spatial resolution of 10 mum over a depth of approximately 1.0 mm. For such a device to be effective clinically, algorithms that use the LCI data must be developed for classifying different tissue types. We present an automated algorithm for differentiating adipose tissue from fibroglandular human breast tissue based on three parameters computed from the LCI signal (slope, standard deviation, spatial frequency content). A total of 260 breast tissue samples from 58 patients were collected from excised surgical specimens. A training set (N=72) was used to extract parameters for each tissue type and the parameters were fit to a multivariate normal density. The model was applied to a validation set (N=86) using likelihood ratios to classify groups. The overall accuracy of the model was 91.9% (84.0 to 96.7) with 98.1% (89.7 to 99.9) sensitivity and 82.4% (65.5 to 93.2) specificity where the numbers in parentheses represent the 95% confidence intervals. These results suggest that LCI can be used to determine tissue type and guide FNAB of the breast.

Diagnosis of breast cancer using diffuse reflectance spectroscopy: Comparison of a Monte Carlo versus partial least squares analysis based feature extraction technique

BACKGROUND AND OBJECTIVE

We explored the use of diffuse reflectance spectroscopy in the ultraviolet-visible (UV-VIS) spectrum for the diagnosis of breast cancer. A physical model (Monte Carlo inverse model) and an empirical model (partial least squares analysis) based approach, were compared for extracting diagnostic features from the diffuse reflectance spectra.

STUDY DESIGN/METHODS

The physical model and the empirical model were employed to extract features from diffuse reflectance spectra measured from freshly excised breast tissues. A subset of extracted features obtained using each method showed statistically significant differences between malignant and non-malignant breast tissues. These features were separately input to a support vector machine (SVM) algorithm to classify each tissue sample as malignant or non-malignant.

RESULTS AND CONCLUSIONS

The features extracted from the Monte Carlo based analysis were hemoglobin saturation, total hemoglobin concentration, beta-carotene concentration and the mean (wavelength averaged) reduced scattering coefficient. Beta-carotene concentration was positively correlated and the mean reduced scattering coefficient was negatively correlated with percent adipose tissue content in normal breast tissues. In addition, there was a statistically significant decrease in the beta-carotene concentration and hemoglobin saturation, and a statistically significant increase in the mean reduced scattering coefficient in malignant tissues compared to non-malignant tissues. The features extracted from the partial least squares analysis were a set of principal components. A subset of principal components showed that the diffuse reflectance spectra of malignant breast tissues displayed an increased intensity over wavelength range of 440-510 nm and a decreased intensity over wavelength range of 510-600 nm, relative to that of non-malignant breast tissues. The diagnostic performance of the classification algorithms based on both feature extraction techniques yielded similar sensitivities and specificities of approximately 80% for discriminating between malignant and non-malignant breast tissues. While both methods yielded similar classification accuracies, the model based approach provided insight into the physiological and structural features that discriminate between malignant and non-malignant breast tissues.

Spatially resolved reflectance spectroscopy for diagnosis of cervical precancer: Monte Carlo modeling and comparison to clinical measurements

We present Monte Carlo modeling studies to provide a quantitative understanding of contrast observed in spatially resolved reflectance spectra of normal and highly dysplastic cervical tissue. Simulations have been carried out to analyze the sensitivity of spectral measurements to a range of changes in epithelial and stromal optical properties that are reported to occur as dysplasia develops and to predict reflectance spectra of normal and highly dysplastic tissue at six different source-detector separations. Simulation results provide important insights into specific contributions of different optical parameters to the overall spectral response. Predictions from simulations agree well with in vivo measurements from cervical tissue and successfully describe spectral differences observed in reflectance measurements from normal and precancerous tissue sites. Penetration depth statistics of photons detected at the six source-detector separations are also presented to reveal the sampling depth profile of the fiber-optic probe geometry simulated. The modeling studies presented provide a framework to meaningfully interpret optical signals obtained from epithelial tissues and to optimize design of optical sensors for in vivo reflectance measurements for precancer detection. Results from this study can facilitate development of analytical photon propagation models that enable inverse estimation of diagnostically relevant optical parameters from in vivo reflectance measurements.

Model-based analysis of clinical fluorescence spectroscopy for in vivo detection of cervical intraepithelial dysplasia

We present a mathematical model to calculate the relative concentration of light scatterers, light absorbers, and fluorophores in the epithelium and stroma. This mathematical description is iteratively fit to the fluorescence spectra measured in vivo, yielding relative concentrations of each molecule. The mathematical model is applied to a total of 493 fluorescence measurements of normal and dysplastic cervical tissue acquired in vivo from 292 patients. The estimated parameters are compared with histopathologic diagnosis to evaluate their diagnostic potential. The mathematical model is validated using fluorescence spectra simulated with known sets of optical parameters. Subsequent application of the mathematical model to in vivo fluorescence measurements from cervical tissue yields fits that accurately describe measured data. The optical parameters estimated from 493 fluorescence measurements show an increase in epithelial flavin adenine dinucleotide (FAD) fluorescence, a decrease in epithelial keratin fluorescence, an increase in epithelial light scattering, a decrease in stromal collagen fluorescence, and an increase in stromal hemoglobin light absorption in dysplastic tissue compared to normal tissue. These changes likely reflect an increase in the metabolic activity and loss of differentiation of epithelial dysplastic cells, and stromal angiogenesis associated with dysplasia. The model presented here provides a tool to analyze clinical fluorescence spectra yielding quantitative information about molecular changes related to dysplastic transformation.

Monte Carlo-based inverse model for calculating tissue optical properties. Part II: Application to breast cancer diagnosis

The Monte Carlo-based inverse model of diffuse reflectance described in part I of this pair of companion papers was applied to the diffuse reflectance spectra of a set of 17 malignant and 24 normal-benign ex vivo human breast tissue samples. This model allows extraction of physically meaningful tissue parameters, which include the concentration of absorbers and the size and density of scatterers present in tissue. It was assumed that intrinsic absorption could be attributed to oxygenated and deoxygenated hemoglobin and beta-carotene, that scattering could be modeled by spheres of a uniform size distribution, and that the refractive indices of the spheres and the surrounding medium are known. The tissue diffuse reflectance spectra were evaluated over a wavelength range of 400-600 nm. The extracted parameters that showed the statistically most significant differences between malignant and nonmalignant breast tissues were hemoglobin saturation and the mean reduced scattering coefficient. Malignant tissues showed decreased hemoglobin saturation and an increased mean reduced scattering coefficient compared with nonmalignant tissues. A support vector machine classification algorithm was then used to classify a sample as malignant or nonmalignant based on these two extracted parameters and produced a cross-validated sensitivity and specificity of 82% and 92%, respectively.