Spectral discrimination of breast pathologies in situ using spatial frequency domain imaging

Macroscopic inelastic scattering imaging using a hyperspectral line-scanning system identifies invasive breast cancer in lumpectomy and mastectomy specimens

Significance

Of patients with early-stage breast cancer, 60% to 75% undergo breast-conserving surgery. Of those, 20% or more need a second surgery because of an incomplete tumor resection only discovered days after surgery. An intraoperative imaging technology allowing cancer detection on the margins of breast specimens could reduce re-excision procedure rates and improve patient survival.

Aim

We aimed to develop an experimental protocol using hyperspectral line-scanning Raman spectroscopy to image fresh breast specimens from cancer patients. Our objective was to determine whether macroscopic specimen images could be produced to distinguish invasive breast cancer from normal tissue structures.

Approach

A hyperspectral inelastic scattering imaging instrument was used to interrogate eight specimens from six patients undergoing breast cancer surgery. Machine learning models trained with a different system to distinguish cancer from normal breast structures were used to produce tissue maps with a field-of-view of 1    cm 2 classifying each pixel as either cancer, adipose, or other normal tissues. The predictive model results were compared with spatially correlated histology maps of the specimens.

Results

A total of eight specimens from six patients were imaged. Four of the hyperspectral images were associated with specimens containing cancer cells that were correctly identified by the new ex vivo pathology technique. The images associated with the remaining four specimens had no histologically detectable cancer cells, and this was also correctly predicted by the instrument.

Conclusions

We showed the potential of hyperspectral Raman imaging as an intraoperative breast cancer margin assessment technique that could help surgeons improve cosmesis and reduce the number of repeat procedures in breast cancer surgery.

Evaluating Receptor-Specific Fresh Specimen Staining for Tumor Margin Detection in Clinical Breast Specimens

PURPOSE

Reliable and rapid identification of tumor in the margins of breast specimens during breast-conserving surgery to reduce repeat surgery rates is an active area of investigation. Dual-stain difference imaging (DDSI) is one of many approaches under evaluation for this application. This technique aims to topically apply fluorescent stain pairs (one targeted to a receptor-of-interest and the other a spectrally distinct isotype), image both stains, and compute a normalized difference image between the two channels. Prior evaluation and optimization in a variety of preclinical models produced encouraging diagnostic performance. Herein, we report on a pilot clinical study which evaluated HER2-targeted DDSI on 11 human breast specimens.

PROCEDURES

Gross sections from 11 freshly excised mastectomy specimens were processed using a HER2-receptor-targeted DDSI protocol shortly after resection. After staining with the dual-probe protocol, specimens were imaged on a fluorescence scanner, followed by tissue fixation for hematoxylin and eosin and anti-HER2 immunohistochemical staining. Receiver operator characteristic curves and area under the curve (AUC) analysis were used to assess diagnostic performance of the resulting images. Performance values were also compared to expression level determined from IHC staining.

RESULTS

Eight of the 11 specimens presented with distinguishable invasive ductal carcinoma and/or were not affected by an imaging artifact. In these specimens, the DDSI technique provided an AUC = 0.90 ± 0.07 for tumor-to-adipose tissue and 0.81 ± 0.15 for tumor-to-glandular tissue, which was significantly higher than AUC values recovered from images of the targeted probe alone. DDSI values and diagnostic performance did not correlate with HER2 expression level, and tumors with low HER2 expression often produced high AUC, suggesting that even the low expression levels were enough to help distinguish tumor.

CONCLUSIONS

The results from this preliminary study of rapid receptor-specific staining in human specimens were consistent with prior preclinical results and demonstrated promising diagnostic potential.

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.

In situ Raman spectroscopy and machine learning unveil biomolecular alterations in invasive breast cancer

SIGNIFICANCE

As many as 60% of patients with early stage breast cancer undergo breast-conserving surgery. Of those, 20% to 35% need a second surgery because of incomplete resection of the lesions. A technology allowing in situ detection of cancer could reduce re-excision procedure rates and improve patient survival.

AIM

Raman spectroscopy was used to measure the spectral fingerprint of normal breast and cancer tissue ex-vivo. The aim was to build a machine learning model and to identify the biomolecular bands that allow one to detect invasive breast cancer.

APPROACH

The system was used to interrogate specimens from 20 patients undergoing lumpectomy, mastectomy, or breast reduction surgery. This resulted in 238 ex-vivo measurements spatially registered with standard histology classifying tissue as cancer, normal, or fat. A technique based on support vector machines led to the development of predictive models, and their performance was quantified using a receiver-operating-characteristic analysis.

RESULTS

Raman spectroscopy combined with machine learning detected normal breast from ductal or lobular invasive cancer with a sensitivity of 93% and a specificity of 95%. This was achieved using a model based on only two spectral bands, including the peaks associated with C-C stretching of proteins around 940    cm - 1 and the symmetric ring breathing at 1004    cm - 1 associated with phenylalanine.

CONCLUSIONS

Detection of cancer on the margins of surgically resected breast specimen is feasible with Raman spectroscopy.

Ultracompact Deep Neural Network for Ultrafast Optical Property Extraction in Spatial Frequency Domain Imaging (SFDI)

Spatial frequency domain imaging (SFDI) is a powerful, label-free imaging technique capable of the wide-field quantitative mapping of tissue optical properties and, subsequently, chromophore concentrations. While SFDI hardware acquisition methods have advanced towards video-rate, the inverse problem (i.e., the mapping of acquired diffuse reflectance to optical properties) has remained a bottleneck for real-time data processing and visualization. Deep learning methods are adept at fitting nonlinear patterns, and may be ideal for rapidly solving the SFDI inverse problem. While current deep neural networks (DNN) are growing increasingly larger and more complex (e.g., with millions of parameters or more), our study shows that it can also be beneficial to move in the other direction, i.e., make DNNs that are smaller and simpler. Here, we propose an ultracompact, two-layer, fully connected DNN structure (each layer with four and two neurons, respectively) for ultrafast optical property extractions, which is 30×–600× faster than current methods with a similar or improved accuracy, allowing for an inversion time of 5.5 ms for 696 × 520 pixels. We further demonstrated the proposed inverse model in numerical simulations, and comprehensive phantom characterization, as well as offering in vivo measurements of dynamic physiological processes. We further demonstrated that the computation time could achieve another 200× improvement with a GPU device. This deep learning structure will help to enable fast and accurate real-time SFDI measurements, which are crucial for pre-clinical, clinical, and industrial applications.

Deep learning in macroscopic diffuse optical imaging

SIGNIFICANCE

Biomedical optics system design, image formation, and image analysis have primarily been guided by classical physical modeling and signal processing methodologies. Recently, however, deep learning (DL) has become a major paradigm in computational modeling and has demonstrated utility in numerous scientific domains and various forms of data analysis.

AIM

We aim to comprehensively review the use of DL applied to macroscopic diffuse optical imaging (DOI).

APPROACH

First, we provide a layman introduction to DL. Then, the review summarizes current DL work in some of the most active areas of this field, including optical properties retrieval, fluorescence lifetime imaging, and diffuse optical tomography.

RESULTS

The advantages of using DL for DOI versus conventional inverse solvers cited in the literature reviewed herein are numerous. These include, among others, a decrease in analysis time (often by many orders of magnitude), increased quantitative reconstruction quality, robustness to noise, and the unique capability to learn complex end-to-end relationships.

CONCLUSIONS

The heavily validated capability of DL's use across a wide range of complex inverse solving methodologies has enormous potential to bring novel DOI modalities, otherwise deemed impractical for clinical translation, to the patient's bedside.

Birefringent tissue-mimicking phantom for polarization-sensitive optical coherence tomography imaging

SIGNIFICANCE

Tissue birefringence is an important parameter to consider when designing realistic, tissue-mimicking phantoms. Options for suitable birefringent materials that can be used to accurately represent tissue scattering are limited.

AIM

To introduce a method of fabricating birefringent tissue phantoms with a commonly used material-polydimethylsiloxane (PDMS)-for imaging with polarization-sensitive optical coherence tomography (PS-OCT).

APPROACH

Stretch-induced birefringence was characterized in PDMS phantoms made with varying curing ratios, and the resulting phantom birefringence values were compared with those of biological tissues.

RESULTS

We showed that, with induced birefringence levels up to 2.1  ×  10  -  4, PDMS can be used to resemble the birefringence levels in weakly birefringent tissues. We demonstrated the use of PDMS in the development of phantoms to mimic the normal and diseased bladder wall layers, which can be differentiated by their birefringence levels.

CONCLUSIONS

PDMS allows accurate control of tissue scattering and thickness, and it exhibits controllable birefringent properties. The use of PDMS as a birefringent phantom material can be extended to other birefringence imaging systems beyond PS-OCT and to mimic other organs.

Imaging sub-diffuse optical properties of cancerous and normal skin tissue using machine learning-aided spatial frequency domain imaging

SIGNIFICANCE

Sub-diffuse optical properties may serve as useful cancer biomarkers, and wide-field heatmaps of these properties could aid physicians in identifying cancerous tissue. Sub-diffuse spatial frequency domain imaging (sd-SFDI) can reveal such wide-field maps, but the current time cost of experimentally validated methods for rendering these heatmaps precludes this technology from potential real-time applications.

AIM

Our study renders heatmaps of sub-diffuse optical properties from experimental sd-SFDI images in real time and reports these properties for cancerous and normal skin tissue subtypes.

APPROACH

A phase function sampling method was used to simulate sd-SFDI spectra over a wide range of optical properties. A machine learning model trained on these simulations and tested on tissue phantoms was used to render sub-diffuse optical property heatmaps from sd-SFDI images of cancerous and normal skin tissue.

RESULTS

The model accurately rendered heatmaps from experimental sd-SFDI images in real time. In addition, heatmaps of a small number of tissue samples are presented to inform hypotheses on sub-diffuse optical property differences across skin tissue subtypes.

CONCLUSION

These results bring the overall process of sd-SFDI a fundamental step closer to real-time speeds and set a foundation for future real-time medical applications of sd-SFDI such as image guided surgery.

Bioreactor design and validation for manufacturing strategies in tissue engineering

The fields of regenerative medicine and tissue engineering offer new therapeutic options to restore, maintain or improve tissue function following disease or injury. To maximize the biological function of a tissue-engineered clinical product, specific conditions must be maintained within a bioreactor to allow the maturation of the product in preparation for implantation. Specifically, the bioreactor should be designed to mimic the mechanical, electrochemical and biochemical environment that the product will be exposed to in vivo. Real-time monitoring of the functional capacity of tissue-engineered products during manufacturing is a critical component of the quality management process. The present review provides a brief overview of bioreactor engineering considerations. In addition, strategies for bioreactor automation, in-line product monitoring and quality assurance are discussed.

Signal to Noise Ratio as a Cross-Platform Metric for Intraoperative Fluorescence Imaging

Real-time molecular imaging to guide curative cancer surgeries is critical to ensure removal of all tumor cells; however, visualization of microscopic tumor foci remains challenging. Wide variation in both imager instrumentation and molecular labeling agents demands a common metric conveying the ability of a system to identify tumor cells. Microscopic disease, comprised of a small number of tumor cells, has a signal on par with the background, making the use of signal (or tumor) to background ratio inapplicable in this critical regime. Therefore, a metric that incorporates the ability to subtract out background, evaluating the signal itself relative to the sources of uncertainty, or noise is required. Here we introduce the signal to noise ratio (SNR) to characterize the ultimate sensitivity of an imaging system and optimize factors such as pixel size. Variation in the background (noise) is due to electronic sources, optical sources, and spatial sources (heterogeneity in tumor marker expression, fluorophore binding, and diffusion). Here, we investigate the impact of these noise sources and ways to limit its effect on SNR. We use empirical tumor and noise measurements to procedurally generate tumor images and run a Monte Carlo simulation of microscopic disease imaging to optimize parameters such as pixel size.

Modeling and Synthesis of Breast Cancer Optical Property Signatures With Generative Models

Is it possible to find deterministic relationships between optical measurements and pathophysiology in an unsupervised manner and based on data alone? Optical property quantification is a rapidly growing biomedical imaging technique for characterizing biological tissues that shows promise in a range of clinical applications, such as intraoperative breast-conserving surgery margin assessment. However, translating tissue optical properties to clinical pathology information is still a cumbersome problem due to, amongst other things, inter- and intrapatient variability, calibration, and ultimately the nonlinear behavior of light in turbid media. These challenges limit the ability of standard statistical methods to generate a simple model of pathology, requiring more advanced algorithms. We present a data-driven, nonlinear model of breast cancer pathology for real-time margin assessment of resected samples using optical properties derived from spatial frequency domain imaging data. A series of deep neural network models are employed to obtain sets of latent embeddings that relate optical data signatures to the underlying tissue pathology in a tractable manner. These self-explanatory models can translate absorption and scattering properties measured from pathology, while also being able to synthesize new data. The method was tested on a total of 70 resected breast tissue samples containing 137 regions of interest, achieving rapid optical property modeling with errors only limited by current semi-empirical models, allowing for mass sample synthesis and providing a systematic understanding of dataset properties, paving the way for deep automated margin assessment algorithms using structured light imaging or, in principle, any other optical imaging technique seeking modeling. Code is available.

Optical tissue measurements of invasive carcinoma and ductal carcinoma in situ for surgical guidance

Background

Although the incidence of positive resection margins in breast-conserving surgery has decreased, both incomplete resection and unnecessary large resections still occur. This is especially the case in the surgical treatment of ductal carcinoma in situ (DCIS). Diffuse reflectance spectroscopy (DRS), an optical technology based on light tissue interactions, can potentially characterize tissue during surgery thereby guiding the surgeon intraoperatively. DRS has shown to be able to discriminate pure healthy breast tissue from pure invasive carcinoma (IC) but limited research has been done on (1) the actual optical characteristics of DCIS and (2) the ability of DRS to characterize measurements that are a mixture of tissue types.

Methods

In this study, DRS spectra were acquired from 107 breast specimens from 107 patients with proven IC and/or DCIS (1488 measurement locations). With a generalized estimating equation model, the differences between the DRS spectra of locations with DCIS and IC and only healthy tissue were compared to see if there were significant differences between these spectra. Subsequently, different classification models were developed to be able to predict if the DRS spectrum of a measurement location represented a measurement location with “healthy” or “malignant” tissue. In the development and testing of the models, different definitions for “healthy” and “malignant” were used. This allowed varying the level of homogeneity in the train and test data.

Results

It was found that the optical characteristics of IC and DCIS were similar. Regarding the classification of tissue with a mixture of tissue types, it was found that using mixed measurement locations in the development of the classification models did not tremendously improve the accuracy of the classification of other measurement locations with a mixture of tissue types. The evaluated classification models were able to classify measurement locations with > 5% malignant cells with a Matthews correlation coefficient of 0.41 or 0.40. Some models showed better sensitivity whereas others had better specificity.

Conclusion

The results suggest that DRS has the potential to detect malignant tissue, including DCIS, in healthy breast tissue and could thus be helpful for surgical guidance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-021-01436-5.

Spatial-Frequency Domain Imaging: An Emerging Depth-Varying and Wide-Field Technique for Optical Property Measurement of Biological Tissues

Measurement of optical properties is critical for understanding light-tissue interaction, properly interpreting measurement data, and gaining better knowledge of tissue physicochemical properties. However, conventional optical measuring techniques are limited in point measurement, which partly hinders the applications on characterizing spatial distribution and inhomogeneity of optical properties of biological tissues. Spatial-frequency domain imaging (SFDI), as an emerging non-contact, depth-varying and wide-field optical imaging technique, is capable of measuring the optical properties in a wide field-of-view on a pixel-by-pixel basis. This review first describes the typical SFDI system and the principle for estimating optical properties using the SFDI technique. Then, the applications of SFDI in the fields of biomedicine, as well as food and agriculture, are reviewed, including burn assessment, skin tissue evaluation, tumor tissue detection, brain tissue monitoring, and quality evaluation of agro-products. Finally, a discussion on the challenges and future perspectives of SFDI for optical property estimation is presented.

Dual-DMD hyperspectral spatial frequency domain imaging (SFDI) using dispersed broadband illumination with a demonstration of blood stain spectral monitoring

Spatial frequency domain imaging (SFDI) is a widefield diffuse optical measurement technique capable of generating 2D maps of sub-surface absorption and scattering in biological tissue. We developed a new hyperspectral SFDI instrument capable of collecting images at wavelengths from the visible to the near infrared. The system utilizes a custom-built monochromator with a digital micromirror device (DMD) that can dynamically select illumination wavelength bands from a broadband quartz tungsten halogen lamp, and a second DMD to provide spatially modulated sample illumination. The system is capable of imaging 10 wavelength bands in approximately 25 seconds. The spectral resolution can be varied from 12 to 30 nm by tuning the input slit width and the output DMD column width. We compared the optical property extraction accuracy between the new device and a commercial SFDI system and found an average error of 23% in absorption and 6% in scattering. The system was highly stable, with less than 5% variation in absorption and less than 0.2% variation in scattering across all wavelengths over two hours. The system was used to monitor hyperspectral changes in the optical absorption and reduced scattering spectra of blood exposed to air over 24 hours. This served as a general demonstration of the utility of this system, and points to a potential application for blood stain age estimation. We noted significant changes in both absorption and reduced scattering spectra over multiple discrete stages of aging. To our knowledge, these are the first measurement of changes in scattering of blood stains. This hyperspectral SFDI system holds promise for a multitude of applications in quantitative tissue and diffuse sample imaging.

Biophotonic technologies for assessment of breast tumor surgical margins-A review

Breast conserving surgery (BCS) offering similar surgical outcomes as mastectomy while retaining breast cosmesis is becoming increasingly popular for the management of early stage breast cancers. However, its association with reoperation rates of 20% to 40% following incomplete tumor removal warrants the need for a fast and accurate intraoperative surgical margin assessment tool that offers cellular, structural and molecular information of the whole specimen surface to a clinically relevant depth. Biophotonic technologies are evolving to qualify as such an intraoperative tool for clinical assessment of breast cancer surgical margins at the microscopic and macroscopic scale. Herein, we review the current research in the application of biophotonic technologies such as photoacoustic imaging, Raman spectroscopy, multimodal multiphoton imaging, diffuse optical imaging and fluorescence imaging using medically approved dyes for breast cancer detection and/or tumor subtype differentiation toward intraoperative assessment of surgical margins in BCS specimens, and possible challenges in their route to clinical translation.

Review of Laser Raman Spectroscopy for Surgical Breast Cancer Detection: Stochastic Backpropagation Neural Networks

Laser Raman spectroscopy (LRS) is a highly specific biomolecular technique which has been shown to have the ability to distinguish malignant and normal breast tissue. This paper discusses significant advancements in the use of LRS in surgical breast cancer diagnosis, with an emphasis on statistical and machine learning strategies employed for precise, transparent and real-time analysis of Raman spectra. When combined with a variety of "machine learning" techniques LRS has been increasingly employed in oncogenic diagnostics. This paper proposes that the majority of these algorithms fail to provide the two most critical pieces of information required by the practicing surgeon: a probability that the classification of a tissue is correct, and, more importantly, the expected error in that probability. Stochastic backpropagation artificial neural networks inherently provide both pieces of information for each and every tissue site examined by LRS. If the networks are trained using both human experts and an unsupervised classification algorithm as gold standards, rapid progress can be made understanding what additional contextual data is needed to improve network classification performance. Our patients expect us to not simply have an opinion about their tumor, but to know how certain we are that we are correct. Stochastic networks can provide that information.

Rapid assessment of breast tumor margins using deep ultraviolet fluorescence scanning microscopy

SIGNIFICANCE

Re-excision rates for women with invasive breast cancer undergoing breast conserving surgery (or lumpectomy) have decreased in the past decade but remain substantial. This is mainly due to the inability to assess the entire surface of an excised lumpectomy specimen efficiently and accurately during surgery.

AIM

The goal of this study was to develop a deep-ultraviolet scanning fluorescence microscope (DUV-FSM) that can be used to accurately and rapidly detect cancer cells on the surface of excised breast tissue.

APPROACH

A DUV-FSM was used to image the surfaces of 47 (31 malignant and 16 normal/benign) fresh breast tissue samples stained in propidium iodide and eosin Y solutions. A set of fluorescence images were obtained from each sample using low magnification (4  ×  ) and fully automated scanning. The images were stitched to form a color image. Three nonmedical evaluators were trained to interpret and assess the fluorescence images. Nuclear-cytoplasm ratio (N/C) was calculated and used for tissue classification.

RESULTS

DUV-FSM images a breast sample with subcellular resolution at a speed of 1.0  min  /  cm2. Fluorescence images show excellent visual contrast in color, tissue texture, cell density, and shape between invasive carcinomas and their normal counterparts. Visual interpretation of fluorescence images by nonmedical evaluators was able to distinguish invasive carcinoma from normal samples with high sensitivity (97.62%) and specificity (92.86%). Using N/C alone was able to differentiate patch-level invasive carcinoma from normal breast tissues with reasonable sensitivity (81.5%) and specificity (78.5%).

CONCLUSIONS

DUV-FSM achieved a good balance between imaging speed and spatial resolution with excellent contrast, which allows either visual or quantitative detection of invasive cancer cells on the surfaces of a breast surgical specimen.

Spatial frequency domain imaging for the longitudinal monitoring of vascularization during mouse femoral graft healing

Allograft is the current gold standard for treating critical-sized bone defects. However, allograft healing is usually compromised partially due to poor host-mediated vascularization. In the efforts towards developing new methods to enhance allograft healing, a non-terminal technique for monitoring the vascularization is needed in pre-clinical mouse models. In this study, we developed a non-invasive instrument based on spatial frequency domain imaging (SFDI) for longitudinal monitoring of the mouse femoral graft healing. SFDI technique provided total hemoglobin concentration (THC) and oxygen saturation (StO2) of the graft and the surrounding soft tissues. SFDI measurements were performed from 1 day before to 44 days after graft transplantation. Autograft, another type of bone graft with higher vascularization potential was also measured as a comparison to allograft. For both grafts, the overall temporal changes of the measured THC agreed with the physiological expectations of vascularization timeline during bone healing. A significantly greater increase in THC was observed in the autograft group compared to the allograft group, which agreed with the expectation that allografts have more compromised vascularization.

Adaptive Boosting (AdaBoost)-based multiwavelength spatial frequency domain imaging and characterization for ex vivo human colorectal tissue assessment

The current gold standard diagnostic test for colorectal cancer remains histological inspections of endoluminal neoplasia in biopsy specimens. However, biopsy site selection requires visual inspection of the bowel, typically with a white-light endoscope. Therefore, this technique is poorly suited to detect small or innocuous-appearing lesions. We hypothesize that an alternative modality-multiwavelength spatial frequency domain imaging (SFDI)-would be able to differentiate various colorectal neoplasia from normal tissue. In this ex vivo study of human colorectal tissues, we report the optical absorption and scattering signatures of normal, adenomatous polyp and cancer specimens. An abnormal vs. normal adaptive boosting (AdaBoost) classifier is trained to dichotomize tissue based on SFDI imaging characteristics, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.95 is achieved. We conclude that AdaBoost-based multiwavelength SFDI can differentiate abnormal from normal colorectal tissues, potentially improving endoluminal screening of the distal gastrointestinal tract in the future.

JP, Austin W, Tabassum SM, Aguénounon E, Tilbury K, Saager RB, Gioux S, Roblyer D. OpenSFDI: an open-source guide for constructing a spatial frequency domain imaging system

Significance: Spatial frequency domain imaging (SFDI) is a diffuse optical measurement technique that can quantify tissue optical absorption (μ_a) and reduced scattering (<inline-formula>μs'</inline-formula>) on a pixel-by-pixel basis. Measurements of μ_a at different wavelengths enable the extraction of molar concentrations of tissue chromophores over a wide field, providing a noncontact and label-free means to assess tissue viability, oxygenation, microarchitecture, and molecular content. We present here openSFDI: an open-source guide for building a low-cost, small-footprint, three-wavelength SFDI system capable of quantifying μ_a and <inline-formula>μs'</inline-formula> as well as oxyhemoglobin and deoxyhemoglobin concentrations in biological tissue. The companion website provides a complete parts list along with detailed instructions for assembling the openSFDI system.<p> Aim: We describe the design of openSFDI and report on the accuracy and precision of optical property extractions for three different systems fabricated according to the instructions on the openSFDI website.</p> <p> Approach: Accuracy was assessed by measuring nine tissue-simulating optical phantoms with a physiologically relevant range of μ_a and <inline-formula>μs'</inline-formula> with the openSFDI systems and a commercial SFDI device. Precision was assessed by repeatedly measuring the same phantom over 1 h.</p> <p> Results: The openSFDI systems had an error of 0  ±  6  %   in μ_a and -2  ±  3  %   in <inline-formula>μs'</inline-formula>, compared to a commercial SFDI system. Bland-Altman analysis revealed the limits of agreement between the two systems to be   ±  0.004  mm^-  1 for μ_a and -0.06 to 0.1  mm^-  1 for <inline-formula>μs'</inline-formula>. The openSFDI system had low drift with an average standard deviation of 0.0007  mm^-  1 and 0.05  mm^-  1 in μ_a and <inline-formula>μs'</inline-formula>, respectively.</p>,<p> Conclusion: The openSFDI provides a customizable hardware platform for research groups seeking to utilize SFDI for quantitative diffuse optical imaging.</p>

Ultrasound Guided Optoacoustic Tomography in Assessment of Tumor Margins for Lumpectomies

PURPOSE: To determine the accuracy of a handheld ultrasound-guided optoacoustic tomography (US-OT) probe developed for human deep-tissue imaging in ex vivo assessment of tumor margins postlumpectomy. METHODS: A custom-built two-dimensional (2D) US-OT–handheld probe was used to scan 15 lumpectomy breast specimens. Optoacoustic signals acquired at multiple wavelengths between 700 and 1100 nm were reconstructed using model linear algorithm, followed by spectral unmixing for lipid and deoxyhemoglobin (Hb). Distribution maps of lipid and Hb on the anterior, posterior, superior, inferior, medial, and lateral margins of the specimens were inspected for margin involvement, and results were correlated with histopathologic findings. The agreement in tumor margin assessment between US-OT and histopathology was determined using the Bland–Altman plot. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of margin assessment using US-OT were calculated. RESULTS: Ninety margins (6 × 15 specimens) were assessed. The US-OT probe resolved blood vessels and lipid up to a depth of 6 mm. Negative and positive margins were discriminated by marked differences in the distribution patterns of lipid and Hb. US-OT assessments were concordant with histopathologic findings in 87 of 89 margins assessed (one margin was uninterpretable and excluded), with diagnostic accuracy of 97.9% (kappa = 0.79). The sensitivity, specificity, PPV, and NPV were 100% (4/4), 97.6% (83/85), 66.7% (4/6), and 100% (83/83), respectively. CONCLUSION: US-OT was capable of providing distribution maps of lipid and Hb in lumpectomy specimens that predicted tumor margins with high sensitivity and specificity, making it a potential tool for intraoperative tumor margin assessment.

Influence of neoadjuvant chemotherapy on diffuse reflectance spectra of tissue in breast surgery specimens

Diffuse reflectance spectroscopy (DRS) can discriminate different tissue types based on optical characteristics. Since this technology has the ability to detect tumor tissue, several groups have proposed to use DRS for margin assessment during breast-conserving surgery for breast cancer. Nowadays, an increasing number of patients with breast cancer are being treated by neoadjuvant chemotherapy. Limited research has been published on the influence of neoadjuvant chemotherapy on the optical characteristics of the tissue. Hence, it is unclear whether margin assessment based on DRS is feasible in this specific group of patients. We investigate whether there is an effect of neoadjuvant chemotherapy on optical measurements of breast tissue. To this end, DRS measurements were performed on 92 ex-vivo breast specimens from 92 patients, treated with neoadjuvant chemotherapy and without neoadjuvant chemotherapy. Generalized estimating equation (GEE) models were generated, comparing the measurements of patients with and without neoadjuvant chemotherapy in datasets of different tissue types using a significance level of 5%. As input for the GEE models, either the intensity at a specific wavelength or a fit parameter, derived from the spectrum, was used. In the evaluation of the intensity, no influence of neoadjuvant chemotherapy was found, since none of the wavelengths were significantly different between the measurements with and the measurements without neoadjuvant chemotherapy in any of the datasets. These results were confirmed by the analysis of the fit parameters, which showed a significant difference for the amount of collagen in only one dataset. All other fit parameters were not significant for any of the datasets. These findings may indicate that assessment of the resection margin with DRS is also feasible in the growing population of breast cancer patients who receive neoadjuvant chemotherapy. However, it is possible that we did not detect neoadjuvant chemotherapy effect in the some of the datasets due to the small number of measurements in those datasets.

Quantifying skin photodamage with spatial frequency domain imaging: statistical results

We investigated the change in optical properties and vascular parameters to characterize skin tissue from mild photodamage to actinic keratosis (AK) with comparison to a published photodamage scale. Multi-wavelength spatial frequency domain imaging (SFDI) measurements were performed on the dorsal forearms of 55 adult subjects with various amounts of photodamage. Dermatologists rated the levels of photodamage based upon the photographs in blinded fashion to allow comparison with SFDI data. For characterization of statistical data, we used artificial neural networks. Our results indicate that optical and vascular parameters can be used to quantify photodamage and can discriminate between the stages as low, medium, and high grades, with the best performance of ∼70%, ∼76% and 80% for characterization of low- medium- and high-grade lesions, respectively. Ultimately, clinicians can use this noninvasive approach for risk assessment and frequent monitoring of high-risk populations.

Structured light imaging for breast-conserving surgery, part II: texture analysis and classification

Subdiffuse spatial frequency domain imaging (sd-SFDI) data of 42 freshly excised, bread-loafed tumor resections from breast-conserving surgery (BCS) were evaluated using texture analysis and a machine learning framework for tissue classification. Resections contained 56 regions of interest (RoIs) determined by expert histopathological analysis. RoIs were coregistered with sd-SFDI data and sampled into ∼4  ×  4  mm2 subimage samples of confirmed and homogeneous histological categories. Sd-SFDI reflectance textures were analyzed using gray-level co-occurrence matrix pixel statistics, image primitives, and power spectral density curve parameters. Texture metrics exhibited statistical significance (p-value  <  0.05) between three benign and three malignant tissue subtypes. Pairs of benign and malignant subtypes underwent texture-based, binary classification with correlation-based feature selection. Classification performance was evaluated using fivefold cross-validation and feature grid searching. Classification using subdiffuse, monochromatic reflectance (illumination spatial frequency of fx  =  1.37  mm  −  1, optical wavelength of λ  =  490  nm) achieved accuracies ranging from 0.55 (95% CI: 0.41 to 0.69) to 0.95 (95% CI: 0.90 to 1.00) depending on the benign–malignant diagnosis pair. Texture analysis of sd-SFDI data maintains the spatial context within images, is free of light transport model assumptions, and may provide an alternative, computationally efficient approach for wide field-of-view (cm2) BCS tumor margin assessment relative to pixel-based optical scatter or color properties alone.

Structured light imaging for breast-conserving surgery, part I: optical scatter and color analysis

Structured light imaging (SLI) with high spatial frequency (HSF) illumination provides a method to amplify native tissue scatter contrast and better differentiate superficial tissues. This was investigated for margin analysis in breast-conserving surgery (BCS) and imaging gross clinical tissues from 70 BCS patients, and the SLI distinguishability was examined for six malignancy subtypes relative to three benign/normal breast tissue subtypes. Optical scattering images recovered were analyzed with five different color space representations of multispectral demodulated reflectance. Excluding rare combinations of invasive lobular carcinoma and fibrocystic disease, SLI was able to classify all subtypes of breast malignancy from surrounding benign tissues (p-value  <  0.05) based on scatter and color parameters. For color analysis, HSF illumination of the sample generated more statistically significant discrimination than regular uniform illumination. Pathological information about lesion subtype from a presurgical biopsy can inform the search for malignancy on the surfaces of specimens during BCS, motivating the focus on pairwise classification analysis. This SLI modality is of particular interest for its potential to differentiate tissue classes across a wide field-of-view (∼100  cm2) and for its ability to acquire images of macroscopic tissues rapidly but with microscopic-level sensitivity to structural and morphological tissue constituents.

Spatial frequency domain imaging in 2019: principles, applications, and perspectives

Spatial frequency domain imaging (SFDI) has witnessed very rapid growth over the last decade, owing to its unique capabilities for imaging optical properties and chromophores over a large field-of-view and in a rapid manner. We provide a comprehensive review of the principles of this imaging method as of 2019, review the modeling of light propagation in this domain, describe acquisition methods, provide an understanding of the various implementations and their practical limitations, and finally review applications that have been published in the literature. Importantly, we also introduce a group effort by several key actors in the field for the dissemination of SFDI, including publications, advice in hardware and implementations, and processing code, all freely available online.

Spatial frequency domain imaging in 2019: principles, applications, and perspectives

Spatial frequency domain imaging (SFDI) has witnessed very rapid growth over the last decade, owing to its unique capabilities for imaging optical properties and chromophores over a large field-of-view and in a rapid manner. We provide a comprehensive review of the principles of this imaging method as of 2019, review the modeling of light propagation in this domain, describe acquisition methods, provide an understanding of the various implementations and their practical limitations, and finally review applications that have been published in the literature. Importantly, we also introduce a group effort by several key actors in the field for the dissemination of SFDI, including publications, advice in hardware and implementations, and processing code, all freely available online.

Single snapshot imaging of optical properties using a single-pixel camera: a simulation study

We present the effects of using a single-pixel camera approach to extract optical properties with the single-snapshot spatial frequency-domain imaging method. We acquired images of a human hand for spatial frequencies ranging from 0.1 to 0.4  mm  -  1 with increasing compression ratios using adaptive basis scan wavelet prediction strategy. In summary, our findings indicate that the extracted optical properties remained usable up to 99% of compression rate at a spatial frequency of 0.2  mm  -  1 with errors of 5% in reduced scattering and 10% in absorption.

Machine learning approach for rapid and accurate estimation of optical properties using spatial frequency domain imaging

Fast estimation of optical properties from reflectance measurements at two spatial frequencies could pave way for real-time, wide-field and quantitative mapping of vital signs of tissues. We present a machine learning-based approach for estimating optical properties in the spatial frequency domain, where a random forest regression algorithm is trained over data obtained from Monte-Carlo photon transport simulations. The algorithm learns the nonlinear mapping between diffuse reflectance at two spatial frequencies, and the absorption and reduced scattering coefficient of the tissue under consideration. Using this method, absorption and reduced scattering properties could be obtained over a 1 megapixel image in 450 ms with errors as low as 0.556% in absorption and 0.126% in reduced scattering.

Deep learning model for ultrafast multifrequency optical property extractions for spatial frequency domain imaging

Spatial frequency domain imaging (SFDI) is emerging as an important new method in biomedical imaging due to its ability to provide label-free, wide-field tissue optical property maps. Most prior SFDI studies have utilized two spatial frequencies (2-f_x) for optical property extractions. The use of more than two frequencies (multi-f_x) can vastly improve the accuracy and reduce uncertainties in optical property estimates for some tissue types, but it has been limited in practice due to the slow speed of available inversion algorithms. We present a deep learning solution that eliminates this bottleneck by solving the multi-f_x inverse problem 300× to 100,000× faster, with equivalent or improved accuracy compared to competing methods. The proposed deep learning inverse model will help to enable real-time and highly accurate tissue measurements with SFDI.

Low-cost compact multispectral spatial frequency domain imaging prototype for tissue characterization

We present a low-cost, compact, and multispectral spatial frequency domain imaging prototype. Illumination components, including 9 LEDs (660 nm - 950 nm) placed on a custom-designed printed circuit board, linear and rotational motors, a printed sinusoidal pattern, and collimation and projection optics as well as the detection components are incorporated in a compact custom-designed 3D-printed probe. Reconstruction of absorption and reduced scattering coefficients is evaluated via imaging tissue mimicking phantoms and potentials of the probe for biological tissue imaging are evaluated via imaging human ovarian tissue ex vivo.

Review of methods for intraoperative margin detection for breast conserving surgery

Breast conserving surgery (BCS) is an effective treatment for early-stage cancers as long as the margins of the resected tissue are free of disease according to consensus guidelines for patient management. However, 15% to 35% of patients undergo a second surgery since malignant cells are found close to or at the margins of the original resection specimen. This review highlights imaging approaches being investigated to reduce the rate of positive margins, and they are reviewed with the assumption that a new system would need high sensitivity near 95% and specificity near 85%. The problem appears to be twofold. The first is for complete, fast surface scanning for cellular, structural, and/or molecular features of cancer, in a lumpectomy volume, which is variable in size, but can be large, irregular, and amorphous. A second is for full, volumetric imaging of the specimen at high spatial resolution, to better guide internal radiologic decision-making about the spiculations and duct tracks, which may inform that surfaces are involved. These two demands are not easily solved by a single tool. Optical methods that scan large surfaces quickly are needed with cellular/molecular sensitivity to solve the first problem, but volumetric imaging with high spatial resolution for soft tissues is largely outside of the optical realm and requires x-ray, micro-CT, or magnetic resonance imaging if they can be achieved efficiently. In summary, it appears that a combination of systems into hybrid platforms may be the optimal solution for these two very different problems. This concept must be cost-effective, image specimens within minutes and be coupled to decision-making tools that help a surgeon without adding to the procedure. The potential for optical systems to be involved in this problem is emerging and clinical trials are underway in several of these technologies to see if they could reduce positive margin rates in BCS.

Light scattering measured with spatial frequency domain imaging can predict stromal versus epithelial proportions in surgically resected breast tissue

This study aims to determine if light scatter parameters measured with spatial frequency domain imaging (SFDI) can accurately predict stromal, epithelial, and adipose fractions in freshly resected, unstained human breast specimens. An explicit model was developed to predict stromal, epithelial, and adipose fractions as a function of light scattering parameters, which was validated against a quantitative analysis of digitized histology slides for N  =  31 specimens using leave-one-out cross-fold validation. Specimen mean stromal, epithelial, and adipose volume fractions predicted from light scattering parameters strongly correlated with those calculated from digitized histology slides (r  =  0.90, 0.77, and 0.91, respectively, p-value <1  ×  10  -  6). Additionally, the ratio of predicted epithelium to stroma classified malignant specimens with a sensitivity and specificity of 90% and 81%, respectively, and also classified all pixels in malignant lesions with 63% and 79%, at a threshold of 1. All specimens and pixels were classified as malignant, benign, or fat with 84% and 75% accuracy, respectively. These findings demonstrate how light scattering parameters acquired with SFDI can be used to accurately predict and spatially map stromal, epithelial, and adipose proportions in fresh unstained, human breast tissue, and suggest that these estimations could provide diagnostic value.

Review of structured light in diffuse optical imaging

Diffuse optical imaging probes deep living tissue enabling structural, functional, metabolic, and molecular imaging. Recently, due to the availability of spatial light modulators, wide-field quantitative diffuse optical techniques have been implemented, which benefit greatly from structured light methodologies. Such implementations facilitate the quantification and characterization of depth-resolved optical and physiological properties of thick and deep tissue at fast acquisition speeds. We summarize the current state of work and applications in the three main techniques leveraging structured light: spatial frequency-domain imaging, optical tomography, and single-pixel imaging. The theory, measurement, and analysis of spatial frequency-domain imaging are described. Then, advanced theories, processing, and imaging systems are summarized. Preclinical and clinical applications on physiological measurements for guidance and diagnosis are summarized. General theory and method development of tomographic approaches as well as applications including fluorescence molecular tomography are introduced. Lastly, recent developments of single-pixel imaging methodologies and applications are reviewed.

On-Site Validation of a Microwave Breast Imaging System, before First Patient Study

This paper presents the Wavelia microwave breast imaging system that has been recently installed at the Galway University Hospital, Ireland, for a first-in-human pilot clinical test. Microwave breast imaging has been extensively investigated over the last two decades as an alternative imaging modality that could potentially bring complementary information to state-of-the-art modalities such as X-ray mammography. Following an overview of the main working principles of this technology, the Wavelia imaging system architecture is presented, as are the radar signal processing algorithms that are used in forming the microwave images in which small tumors could be detectable for disease diagnosis. The methodology and specific quality metrics that have been developed to properly evaluate and validate the performance of the imaging system using complex breast phantoms that are scanned at controlled measurement conditions are also presented in the paper. Indicative results from the application of this methodology to the on-site validation of the imaging system after its installation at the hospital for pilot clinical testing are thoroughly presented and discussed. Given that the imaging system is still at the prototype level of development, a rigorous quality assessment and system validation at nominal operating conditions is very important in order to ensure high-quality clinical data collection.

Intra-operative spectroscopic assessment of surgical margins during breast conserving surgery

Background

In over 20% of breast conserving operations, postoperative pathological assessment of the excised tissue reveals positive margins, requiring additional surgery. Current techniques for intra-operative assessment of tumor margins are insufficient in accuracy or resolution to reliably detect small tumors. There is a distinct need for a fast technique to accurately identify tumors smaller than 1 mm² in large tissue surfaces within 30 min.

Methods

Multi-modal spectral histopathology (MSH), a multimodal imaging technique combining tissue auto-fluorescence and Raman spectroscopy was used to detect microscopic residual tumor at the surface of the excised breast tissue. New algorithms were developed to optimally utilize auto-fluorescence images to guide Raman measurements and achieve the required detection accuracy over large tissue surfaces (up to 4 × 6.5 cm²). Algorithms were trained on 91 breast tissue samples from 65 patients.

Results

Independent tests on 121 samples from 107 patients - including 51 fresh, whole excision specimens - detected breast carcinoma on the tissue surface with 95% sensitivity and 82% specificity. One surface of each uncut excision specimen was measured in 12–24 min. The combination of high spatial-resolution auto-fluorescence with specific diagnosis by Raman spectroscopy allows reliable detection even for invasive carcinoma or ductal carcinoma in situ smaller than 1 mm².

Conclusions

This study provides evidence that this multimodal approach could provide an objective tool for intra-operative assessment of breast conserving surgery margins, reducing the risk for unnecessary second operations.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-1002-2) contains supplementary material, which is available to authorized users.

Quantitative diagnosis of tissue microstructure with wide-field high spatial frequency domain imaging

Non-contact and minimally invasive endoscopic optical imaging is an invaluable diagnostic tool for tissue examination and cancer screening. The point sampling techniques with high sensitivity to the tissue microenvironment are time consuming and often not affordable in clinics. There is a major clinical need for a large field-of-view (FOV) rapid screening method to highlight subtle tissue microstructural alterations. To address this unmet need, we have developed High Spatial Frequency Domain Imaging (HSFDI)-a non-contact imaging modality that spatially maps the tissue microscopic scattering structures over a large field of view (>1cm2). Based on an analytical reflectance model of sub-diffusive light from forward-peaked highly scattering media, HSFDI quantifies the spatially-resolved parameters of the light scattering phase function (i.e., the backscattering probability and the light spreading length) from the reflectance of structured light modulated at high spatial frequencies. Enhanced signal to noise ratio (SNR) is achieved at even ultra-high modulation frequencies with single snapshot multiple frequency demodulation (SSMD). The variations in tissue microstructures, including the strength of the background (pudding) refractive index fluctuation and the prominent scattering structure (plum) morphology, can then be inferred. After validation with optical phantoms, measurements of fresh ex vivo tissue samples revealed significant contrast and differentiation of the phase function parameters between different types and disease states (normal, inflammatory, and cancerous) of tissue whereas tissue absorption and reduced scattering coefficients only show modest changes. HSFDI may provide wide-field images of microscopic structural biomarkers unobtainable with either diffuse light imaging or point-based optical sampling. Potential clinical applications include the rapid screening of excised tissue and the noninvasive examination of suspicious lesions during operation.

Quantitative multispectral ex vivo optical evaluation of human ovarian tissue using spatial frequency domain imaging

About 85-90% of all ovarian cancers are carcinomas; these manifest clinically as mass-forming epithelial proliferations involving the ovary. In this study, a visible light spatial frequency domain imaging (SFDI) system was used for multispectral ex vivo imaging and quantitative evaluation of freshly excised benign and malignant human ovarian tissues. A total of 14 ovaries from 11 patients undergoing oophorectomy were investigated. Using a logistic regression model with seven significant spectral and spatial features extracted from SFDI images, a sensitivity of 94.06% and specificity of 93.53% were achieved for prediction of histologically confirmed invasive carcinoma.

Calibration and analysis of a multimodal micro-CT and structured light imaging system for the evaluation of excised breast tissue

A multimodal micro-computed tomography (CT) and multi-spectral structured light imaging (SLI) system is introduced and systematically analyzed to test its feasibility to aid in margin delineation during breast conserving surgery (BCS). Phantom analysis of the micro-CT yielded a signal-to-noise ratio of 34, a contrast of 1.64, and a minimum detectable resolution of 240 μm for a 1.2 min scan. The SLI system, spanning wavelengths 490 nm to 800 nm and spatial frequencies up to 1.37 [Formula: see text], was evaluated with aqueous tissue simulating phantoms having variations in particle size distribution, scatter density, and blood volume fraction. The reduced scattering coefficient, [Formula: see text] and phase function parameter, γ, were accurately recovered over all wavelengths independent of blood volume fractions from 0% to 4%, assuming a flat sample geometry perpendicular to the imaging plane. The resolution of the optical system was tested with a step phantom, from which the modulation transfer function was calculated yielding a maximum resolution of 3.78 cycles per mm. The three dimensional spatial co-registration between the CT and optical imaging space was tested and shown to be accurate within 0.7 mm. A freshly resected breast specimen, with lobular carcinoma, fibrocystic disease, and adipose, was imaged with the system. The micro-CT provided visualization of the tumor mass and its spiculations, and SLI yielded superficial quantification of light scattering parameters for the malignant and benign tissue types. These results appear to be the first demonstration of SLI combined with standard medical tomography for imaging excised tumor specimens. While further investigations are needed to determine and test the spectral, spatial, and CT features required to classify tissue, this study demonstrates the ability of multimodal CT/SLI to quantify, visualize, and spatially navigate breast tumor specimens, which could potentially aid in the assessment of tumor margin status during BCS.

Nondestructive determination of optical properties of a pear using spatial frequency domain imaging combined with phase-measuring profilometry

Spatial frequency domain imaging (SFDI), as a rapid, noncontact, and scan-free method, can realize wide-field, quantitative optical property mapping and tomographic imaging for a biological sample. Phase-measuring profilometry (PMP) is a surface profile characterization method. Since the projection of structured light onto an object is the basis for PMP and SFDI, the SFDI system is capable of performing both techniques. In this work, we present the results of a feasibility study with the developed SFDI system to realize acquisition of the optical property information and the surface profile information. The surface profile information was used to correct the absorption (μ_a) maps and reduced scattering (μs') maps. The evaluation of correction effect of the PMP and the calibration and calculation of detection accuracy of the SFDI system were realized by using a series of self-made hemispheric and homogeneous solid phantoms covering a wide range of absorption and reduced scattering coefficients. The results show that the μ_a and μs' maps become more uniform after using profilometry correction. The maximum relative errors of the system after profilometry correction and calibration were 8.74% for μ_a and 4.97% for μs' at the wavelength of 527 nm, respectively. A case study was carried out on a pear to verify the application prospect of the method in the field of agricultural products quality inspection. Results indicate that μ_a and μs' maps of a pear after profilometry correction and calibration were more uniform and more comparable with the reported values.

Detection of Head and Neck Cancer in Surgical Specimens Using Quantitative Hyperspectral Imaging

Purpose: This study intends to investigate the feasibility of using hyperspectral imaging (HSI) to detect and delineate cancers in fresh, surgical specimens of patients with head and neck cancers.Experimental Design: A clinical study was conducted in order to collect and image fresh, surgical specimens from patients (N = 36) with head and neck cancers undergoing surgical resection. A set of machine-learning tools were developed to quantify hyperspectral images of the resected tissue in order to detect and delineate cancerous regions which were validated by histopathologic diagnosis. More than two million reflectance spectral signatures were obtained by HSI and analyzed using machine-learning methods. The detection results of HSI were compared with autofluorescence imaging and fluorescence imaging of two vital-dyes of the same specimens.Results: Quantitative HSI differentiated cancerous tissue from normal tissue in ex vivo surgical specimens with a sensitivity and specificity of 91% and 91%, respectively, and which was more accurate than autofluorescence imaging (P < 0.05) or fluorescence imaging of 2-NBDG (P < 0.05) and proflavine (P < 0.05). The proposed quantification tools also generated cancer probability maps with the tumor border demarcated and which could provide real-time guidance for surgeons regarding optimal tumor resection.Conclusions: This study highlights the feasibility of using quantitative HSI as a diagnostic tool to delineate the cancer boundaries in surgical specimens, and which could be translated into the clinic application with the hope of improving clinical outcomes in the future. Clin Cancer Res; 23(18); 5426-36. ©2017 AACR.

Plum pudding random medium model of biological tissue toward remote microscopy from spectroscopic light scattering

Biological tissue has a complex structure and exhibits rich spectroscopic behavior. There has been no tissue model until now that has been able to account for the observed spectroscopy of tissue light scattering and its anisotropy. Here we present, for the first time, a plum pudding random medium (PPRM) model for biological tissue which succinctly describes tissue as a superposition of distinctive scattering structures (plum) embedded inside a fractal continuous medium of background refractive index fluctuation (pudding). PPRM faithfully reproduces the wavelength dependence of tissue light scattering and attributes the "anomalous" trend in the anisotropy to the plum and the powerlaw dependence of the reduced scattering coefficient to the fractal scattering pudding. Most importantly, PPRM opens up a novel venue of quantifying the tissue architecture and microscopic structures on average from macroscopic probing of the bulk with scattered light alone without tissue excision. We demonstrate this potential by visualizing the fine microscopic structural alterations in breast tissue (adipose, glandular, fibrocystic, fibroadenoma, and ductal carcinoma) deduced from noncontact spectroscopic measurement.

Use of a handheld terahertz pulsed imaging device to differentiate benign and malignant breast tissue

Since nearly 20% of breast-conserving surgeries (BCS) require re-operation, there is a clear need for developing new techniques to more accurately assess tumor resection margins intraoperatively. This study evaluates the diagnostic accuracy of a handheld terahertz pulsed imaging (TPI) system to discriminate benign from malignant breast tissue ex vivo. Forty six freshly excised breast cancer samples were scanned with a TPI handheld probe system, and histology was obtained for comparison. The image pixels on TPI were classified using (1) parameters in combination with support vector machine (SVM) and (2) Gaussian wavelet deconvolution in combination with Bayesian classification. The results were an accuracy, sensitivity, specificity of 75%, 86%, 66% for method 1, and 69%, 87%, 54% for method 2 respectively. This demonstrates the probe can discriminate invasive breast cancer from benign breast tissue with an encouraging degree of accuracy, warranting further study.

Rapid evaporative ionisation mass spectrometry of electrosurgical vapours for the identification of breast pathology: towards an intelligent knife for breast cancer surgery

BACKGROUND

Re-operation for positive resection margins following breast-conserving surgery occurs frequently (average = 20-25%), is cost-inefficient, and leads to physical and psychological morbidity. Current margin assessment techniques are slow and labour intensive. Rapid evaporative ionisation mass spectrometry (REIMS) rapidly identifies dissected tissues by determination of tissue structural lipid profiles through on-line chemical analysis of electrosurgical aerosol toward real-time margin assessment.

METHODS

Electrosurgical aerosol produced from ex-vivo and in-vivo breast samples was aspirated into a mass spectrometer (MS) using a monopolar hand-piece. Tissue identification results obtained by multivariate statistical analysis of MS data were validated by histopathology. Ex-vivo classification models were constructed from a mass spectral database of normal and tumour breast samples. Univariate and tandem MS analysis of significant peaks was conducted to identify biochemical differences between normal and cancerous tissues. An ex-vivo classification model was used in combination with bespoke recognition software, as an intelligent knife (iKnife), to predict the diagnosis for an ex-vivo validation set. Intraoperative REIMS data were acquired during breast surgery and time-synchronized to operative videos.

RESULTS

A classification model using histologically validated spectral data acquired from 932 sampling points in normal tissue and 226 in tumour tissue provided 93.4% sensitivity and 94.9% specificity. Tandem MS identified 63 phospholipids and 6 triglyceride species responsible for 24 spectral differences between tissue types. iKnife recognition accuracy with 260 newly acquired fresh and frozen breast tissue specimens (normal n = 161, tumour n = 99) provided sensitivity of 90.9% and specificity of 98.8%. The ex-vivo and intra-operative method produced visually comparable high intensity spectra. iKnife interpretation of intra-operative electrosurgical vapours, including data acquisition and analysis was possible within a mean of 1.80 seconds (SD ±0.40).

CONCLUSIONS

The REIMS method has been optimised for real-time iKnife analysis of heterogeneous breast tissues based on subtle changes in lipid metabolism, and the results suggest spectral analysis is both accurate and rapid. Proof-of-concept data demonstrate the iKnife method is capable of online intraoperative data collection and analysis. Further validation studies are required to determine the accuracy of intra-operative REIMS for oncological margin assessment.

Compressed single pixel imaging in the spatial frequency domain

We have developed compressed sensing single pixel spatial frequency domain imaging (cs-SFDI) to characterize tissue optical properties over a wide field of view ( 35 ?? mm × 35 ?? mm ) using multiple near-infrared (NIR) wavelengths simultaneously. Our approach takes advantage of the relatively sparse spatial content required for mapping tissue optical properties at length scales comparable to the transport scattering length in tissue ( l tr ? 1 ?? mm ) and the high bandwidth available for spectral encoding using a single-element detector. cs-SFDI recovered absorption ( ? a ) and reduced scattering ( ? s ? ) coefficients of a tissue phantom at three NIR wavelengths (660, 850, and 940 nm) within 7.6% and 4.3% of absolute values determined using camera-based SFDI, respectively. These results suggest that cs-SFDI can be developed as a multi- and hyperspectral imaging modality for quantitative, dynamic imaging of tissue optical and physiological properties.

Noncontact and Wide-Field Characterization of the Absorption and Scattering Properties of Apple Fruit Using Spatial-Frequency Domain Imaging

Spatial-frequency domain imaging (SFDI), as a noncontact, low-cost and wide-field optical imaging technique, offers great potential for agro-product safety and quality assessment through optical absorption (μ_a) and scattering (μ) property measurements. In this study, a laboratory-based SFDI system was constructed and developed for optical property measurement of fruits and vegetables. The system utilized a digital light projector to generate structured, periodic light patterns and illuminate test samples. The diffuse reflected light was captured by a charge coupled device (CCD) camera with the resolution of 1280 × 960 pixels. Three wavelengths (460, 527, and 630 nm) were selected for image acquisition using bandpass filters in the system. The μ_a and μ were calculated in a region of interest (ROI, 200 × 300 pixels) via nonlinear least-square fitting. Performance of the system was demonstrated through optical property measurement of ‘Redstar’ apples. Results showed that the system was able to acquire spatial-frequency domain images for demodulation and calculation of the μ_a and μ. The calculated μ_a of apple tissue experiencing internal browning (IB) were much higher than healthy apple tissue, indicating that the SFDI technique had potential for IB tissue characterization.

Using DRS during breast conserving surgery: identifying robust optical parameters and influence of inter-patient variation

Successful breast conserving surgery consists of complete removal of the tumor while sparing healthy surrounding tissue. Despite currently available imaging and margin assessment tools, recognizing tumor tissue at a resection margin during surgery is challenging. Diffuse reflectance spectroscopy (DRS), which uses light for tissue characterization, can potentially guide surgeons to prevent tumor positive margins. However, inter-patient variation and changes in tissue physiology occurring during the resection might hamper this light-based technology. Here we investigate how inter-patient variation and tissue status (in vivo vs ex vivo) affect the performance of the DRS optical parameters. In vivo and ex vivo measurements of 45 breast cancer patients were obtained and quantified with an analytical model to acquire the optical parameters. The optical parameter representing the ratio between fat and water provided the best discrimination between normal and tumor tissue, with an area under the receiver operating characteristic curve of 0.94. There was no substantial influence of other patient factors such as menopausal status on optical measurements. Contrary to expectations, normalization of the optical parameters did not improve the discriminative power. Furthermore, measurements taken in vivo were not significantly different from the measurements taken ex vivo. These findings indicate that DRS is a robust technology for the detection of tumor tissue during breast conserving surgery.

Characterizing optical properties and spatial heterogeneity of human ovarian tissue using spatial frequency domain imaging

A spatial frequency domain imaging (SFDI) system was developed for characterizing ex vivo human ovarian tissue using wide-field absorption and scattering properties and their spatial heterogeneities. Based on the observed differences between absorption and scattering images of different ovarian tissue groups, six parameters were quantitatively extracted. These are the mean absorption and scattering, spatial heterogeneities of both absorption and scattering maps measured by a standard deviation, and a fitting error of a Gaussian model fitted to normalized mean Radon transform of the absorption and scattering maps. A logistic regression model was used for classification of malignant and normal ovarian tissues. A sensitivity of 95%, specificity of 100%, and area under the curve of 0.98 were obtained using six parameters extracted from the SFDI images. The preliminary results demonstrate the diagnostic potential of the SFDI method for quantitative characterization of wide-field optical properties and the spatial distribution heterogeneity of human ovarian tissue. SFDI could be an extremely robust and valuable tool for evaluation of the ovary and detection of neoplastic changes of ovarian cancer.

Detecting structural information of scatterers using spatial frequency domain imaging

We demonstrate optical phantom experiments on the phase function parameter γ using spatial frequency domain imaging. The incorporation of two different types of scattering particles allows for control of the optical phantoms’ microscopic scattering properties. By laterally structuring areas with either TiO2 or Al2O3 scattering particles, we were able to obtain almost pure subdiffusive scattering contrast in a single optical phantom. Optical parameter mapping was then achieved using an analytical radiative transfer model revealing the microscopic structural contrast on a macroscopic field of view. As part of our study, we explain several correction and referencing techniques for high spatial frequency analysis and experimentally study the sampling depth of the subdiffusive parameter γ.