Exploratory evaluation of spinal cord stimulation with dynamic pulse patterns: a promising approach to improve stimulation sensation, coverage of pain areas, and expected pain relief.

Background: The societal burden of chronic pain and the contribution-in-part to the opioid crisis, is a strong motivation to improve and expand non-addictive treatments, including spinal cord stimulation (SCS). For several decades standard SCS has consisted in delivery of tonic pulses with static parameter settings in frequency, pulse width, and amplitude. These static parameters have limited ability to personalize the quality of paresthesia, the dermatomal coverage, and thus may affect SCS efficacy. Further, static settings may contribute to the build-up of tolerance or loss of efficacy of the therapy over time in some patients. Methods: We conducted an acute exploratory study to evaluate the effects of SCS using time-dynamic pulses as compared to time-static (conventional tonic) stimulation pulses, with the hypotheses that dynamic pulse SCS may enable beneficial tailoring of the sensation and the patient's expectation for better pain relief with SCS. During a single clinic visit, consented subjects undergoing a standard SCS trial had their implanted leads temporarily connected to an investigational external stimulator capable of delivering time-static and six categories of time-dynamic pulse sequences, each characterized by continuously varying a stimulation parameter. Study subjects provided several assessments while blinded to the stimulation pattern, including: drawing of paresthesia maps, descriptions of sensation, and ratings for comfort and helpfulness to pain relief. Results: Even without optimization of the field location, a majority of subjects rated sensations from dynamic stimulation as better or equal to that of static stimulation for comfortableness and for helpfulness to pain relief. The initial data showed a gender and/or pain dermatomal location related preference to a stimulation pattern. In particular, female subjects and subjects with pain at higher dermatomes tended to rank the sensation from dynamic stimulation better. Dynamic stimulation produced greater pain coverage without optimization; in 70% (9/13) of subjects, maximal pain coverage was achieved with a dynamic stimulation pattern. There was also greater variety in the words used by patients to describe stimulation sensation in the free text and free form verbal descriptions associated with dynamic stimulation. Conclusions: With the same electrode configuration and comparable parameter settings, acute SCS using dynamic pulses produced more positive ratings, expanded paresthesia coverage, and greater variation in sensation as compared to SCS using static pulses, suggesting that dynamic stimulation has the potential to improve capabilities of SCS for the treatment of chronic pain. Further study is warranted. Trial Registration: This study was registered at ClinicalTrials.gov under ID NCT02988713, November 2016 (URL: https://clinicaltrials.gov/ct2/show/NCT02988713).

Time-dynamic pulse modulation of spinal cord stimulation reduces mechanical hypersensitivity and spontaneous pain in rats.

Enhancing the efficacy of spinal cord stimulation (SCS) is needed to alleviate the burden of chronic pain and dependence on opioids. Present SCS therapies are characterized by the delivery of constant stimulation in the form of trains of tonic pulses (TPs). We tested the hypothesis that modulated SCS using novel time-dynamic pulses (TDPs) leads to improved analgesia and compared the effects of SCS using conventional TPs and a collection of TDPs in a rat model of neuropathic pain according to a longitudinal, double-blind, and crossover design. We tested the effects of the following SCS patterns on paw withdrawal threshold and resting state EEG theta power as a biomarker of spontaneous pain: Tonic (conventional), amplitude modulation, pulse width modulation, sinusoidal rate modulation, and stochastic rate modulation. Results demonstrated that under the parameter settings tested in this study, all tested patterns except pulse width modulation, significantly reversed mechanical hypersensitivity, with stochastic rate modulation achieving the highest efficacy, followed by the sinusoidal rate modulation. The anti-nociceptive effects of sinusoidal rate modulation on EEG outlasted SCS duration on the behavioral and EEG levels. These results suggest that TDP modulation may improve clinical outcomes by reducing pain intensity and possibly improving the sensory experience.

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.

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.

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.

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.

Feasibility of near-infrared diffuse optical spectroscopy on patients undergoing imageguided core-needle biopsy.

We describe a side-firing fiber optic sensor based on near-infrared spectroscopy for guiding core needle biopsy diagnosis of breast cancer. The sensor is composed of three side firing optical fibers (two source fibers and one detection fiber), providing two source-detector separations. The entire assembly is inserted into a core biopsy needle, allowing for sampling to occur at the biopsy site. A multi-wavelength frequency-domain near-infrared instrument is used to collect diffuse reflectance in the breast tissue through an aperture on the biopsy needle before the tissue is removed for histology. Preliminary in vivo measurements performed on 10 normal or benign breast tissues from 5 women undergoing stereo- or ultrasound-guided core needle biopsy show the ability of the system to determine tissue optical properties and constituent concentrations, which are correlated with breast tissue composition derived from histopathology.

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.

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.

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

We explore the effects of the illumination and collection geometry on optical spectroscopic diagnosis of breast cancer. Fluorescence and diffuse reflectance spectroscopy in the UV-visible spectral range are made with a multiseparation probe at three illumination-collection separations of 735, 980, and 1225 microm, respectively, from 13 malignant and 34 nonmalignant breast tissues. Statistical analysis is carried out on two types of data inputs: (1) the fluorescence and diffuse reflectance spectra recorded at each of the three illumination-collection separations and (2) the integrated fluorescence (at each excitation wavelength) or diffuse reflectance over the entire spectrum at all three illumination-collection separations. The results show that using the integrated fluorescence intensities recorded at a single excitation wavelength at all three illumination-collection separations can discriminate malignant from nonmalignant breast tissues with similar classification accuracy to that using spectral data measured at several excitation wavelengths with a single illumination-collection separation. These findings have significant implications with respect to the design of an optical system for breast cancer diagnosis. Examining the intensity attenuation at a single wavelength rather than spectral intensities at multiple wavelengths can significantly reduce the measurement and data processing time in a clinical setting as well as the cost and complexity of the optical system.

Investigation of fiber-optic probe designs for optical spectroscopic diagnosis of epithelial pre-cancers.

BACKGROUND AND OBJECTIVES: The first objective of this study was to evaluate the performance of fluorescence spectroscopy for diagnosing pre-cancers in stratified squamous epithelial tissues in vivo using two different probe geometries with (1) overlapping versus (2) non-overlapping illumination and collection areas on the tissue surface. Probe (1) and probe (2) are preferentially sensitive to the fluorescence originating from the tissue surface and sub-surface tissue depths, respectively. The second objective was to design a novel, angled illumination fiber-optic probe to maximally exploit the depth-dependent fluorescence properties of epithelial tissues. STUDY DESIGN/MATERIALS AND METHODS: In the first study, spectra were measured from epithelial pre-cancers and normal tissues in the hamster cheek pouch and analyzed with a non-parametric classification algorithm. In the second study, Monte Carlo modeling was used to simulate fluorescence measurements from an epithelial tissue model with the angled illumination probe. RESULTS: An unbiased classification algorithm based on spectra measured with probes (1) and (2), classified pre-cancerous and normal tissues with 78 and 94% accuracy, respectively. The angled illumination probe design provides the capability to detect fluorescence from a wide range of tissue depths in an epithelial tissue model. CONCLUSIONS: The first study demonstrates that fluorescence originating from sub-surface tissue depths (probe (2)) is more diagnostic than fluorescence originating from the tissue surface (probe (1)) in the hamster cheek pouch model. However in general, it is difficult to know a priori the optimal probe geometry for pre-cancer detection in a particular epithelial tissue model. The angled illumination probe provides the capability to measure tissue fluorescence selectively from different depths within epithelial tissues, thus obviating the need to select a single optimal probe design for the fluorescence-based diagnosis of epithelial pre-cancers.

Autofluorescence and diffuse reflectance properties of malignant and benign breast tissues.

BACKGROUND: Fluorescence spectroscopy is an evolving technology that can rapidly differentiate between benign and malignant tissues. These differences are thought to be due to endogenous fluorophores, including nicotinamide adenine dinucleotide, flavin adenine dinucleotide, and tryptophan, and absorbers such as beta-carotene and hemoglobin. We hypothesized that a statistically significant difference would be demonstrated between benign and malignant breast tissues on the basis of their unique fluorescence and reflectance properties. METHODS: Optical measurements were performed on 56 samples of tumor or benign breast tissue. Autofluorescence spectra were measured at excitation wavelengths ranging from 300 to 460 nm, and diffuse reflectance was measured between 300 and 600 nm. Principal component analysis to dimensionally reduce the spectral data and a Wilcoxon ranked sum test were used to determine which wavelengths showed statistically significant differences. A support vector machine algorithm compared classification results with the histological diagnosis (gold standard). RESULTS: Several excitation wavelengths and diffuse reflectance spectra showed significant differences between tumor and benign tissues. By using the support vector machine algorithm to incorporate relevant spectral differences, a sensitivity of 70.0% and specificity of 91.7% were achieved. CONCLUSIONS: A statistically significant difference was demonstrated in the diffuse reflectance and fluorescence emission spectra of benign and malignant breast tissue. These differences could be exploited in the development of adjuncts to diagnostic and surgical procedures.

Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer (March 2003).

Nonmalignant (n = 36) and malignant (n = 20) tissue samples were obtained from breast cancer and breast reduction surgeries. These tissues were characterized using multiple excitation wavelength fluorescence spectroscopy and diffuse reflectance spectroscopy in the ultraviolet-visible wavelength range, immediately after excision. Spectra were then analyzed using principal component analysis (PCA) as a data reduction technique. PCA was performed on each fluorescence spectrum, as well as on the diffuse reflectance spectrum individually, to establish a set of principal components for each spectrum. A Wilcoxon rank-sum test was used to determine which principal components show statistically significant differences between malignant and nonmalignant tissues. Finally, a support vector machine (SVM) algorithm was utilized to classify the samples based on the diagnostically useful principal components. Cross-validation of this nonparametric algorithm was carried out to determine its classification accuracy in an unbiased manner. Multiexcitation fluorescence spectroscopy was successful in discriminating malignant and nonmalignant tissues, with a sensitivity and specificity of 70% and 92%, respectively. The sensitivity (30%) and specificity (78%) of diffuse reflectance spectroscopy alone was significantly lower. Combining fluorescence and diffuse reflectance spectra did not improve the classification accuracy of an algorithm based on fluorescence spectra alone. The fluorescence excitation-emission wavelengths identified as being diagnostic from the PCA-SVM algorithm suggest that the important fluorophores for breast cancer diagnosis are most likely tryptophan, NAD(P)H and flavoproteins.

Experimental validation of Monte Carlo modeling of fluorescence in tissues in the UV-visible spectrum.

The goal of the work is to experimentally verify Monte Carlo modeling of fluorescence and diffuse reflectance measurements in turbid, tissue phantom models. In particular, two series of simulations and experiments, in which one optical parameter (absorption or scattering coefficient) is varied while the other is fixed, are carried out to assess the effect of the absorption coefficient (mu(a)) and scattering coefficient (mu(s)) on the fluorescence and diffuse reflectance measured from a turbid medium. Moreover, simulations and experiments are carried out for several fiber optic probe geometries that are designed to sample small tissue volumes. Additionally, a group of conversion expressions are derived to convert the optical properties and fluorescence quantum yield measured from tissue phantoms for use in Monte Carlo simulations. The conversions account for the differences between the definitions of the absorption coefficient and fluorescence quantum yield of fluorophores in a tissue phantom model and those in a Monte Carlo simulation. The results indicate that there is good agreement between the simulated and experimentally measured results in most cases. This dataset can serve as a systematic validation of Monte Carlo modeling of fluorescent light propagation in tissues. The simulations are carried out for a wide range of absorption and scattering coefficients as well as ratios of scattering coefficient to absorption coefficient, and thus would be applicable to tissue optical properties over a wide wavelength range (UV-visible/near infrared). The fiber optic probe geometries that are modeled in this study include those commonly used for measuring fluorescence from tissues in practice.

Effect of fiber optic probe geometry on depth-resolved fluorescence measurements from epithelial tissues: a Monte Carlo simulation.

Developing fiber optic probe geometries to selectively measure fluorescence spectra from different sublayers within human epithelial tissues will potentially improve the endogenous fluorescence contrast between neoplastic and nonneoplastic tissues. In this study, two basic fiber optic probe geometries, which are called the variable aperture (VA) and multidistance (MD) approaches, are compared for depth-resolved fluorescence measurements from human cervical epithelial tissues. The VA probe has completely overlapping illumination and collection areas with variable diameters, while the MD probe employs separate illumination and collection fibers with a fixed separation between them. Monte Carlo simulation results show that the total fluorescence detected is significantly higher for the VA probe geometry, while the probing depth is significantly greater for the MD probe geometry. An important observation is that the VA probe is more sensitive to the epithelial layer, while the MD probe is more sensitive to the stromal layer. The effect of other factors, including numerical aperture (NA) and tissue optical properties on the fluorescence measurements with VA and MD probe geometries, are also evaluated. The total fluorescence detected with both probe geometries significantly increases when the fiber NA is changed from 0.22 to 0.37. The sensitivity to different sublayers is found to be strongly dependent on the tissue optical properties. The simulation results are used to design a simple fiber optic probe that combines both the VA and MD geometries to enable fluorescence measurements from the different sublayers within human epithelial tissues.