Spatial and Spectral Reconstruction of Breast Lumpectomy Hyperspectral Images.

(1) Background: Hyperspectral imaging has emerged as a promising margin assessment technique for breast-conserving surgery. However, to be implicated intraoperatively, it should be both fast and capable of yielding high-quality images to provide accurate guidance and decision-making throughout the surgery. As there exists a trade-off between image quality and data acquisition time, higher resolution images come at the cost of longer acquisition times and vice versa. (2) Methods: Therefore, in this study, we introduce a deep learning spatial-spectral reconstruction framework to obtain a high-resolution hyperspectral image from a low-resolution hyperspectral image combined with a high-resolution RGB image as input. (3) Results: Using the framework, we demonstrate the ability to perform a fast data acquisition during surgery while maintaining a high image quality, even in complex scenarios where challenges arise, such as blur due to motion artifacts, dead pixels on the camera sensor, noise from the sensor's reduced sensitivity at spectral extremities, and specular reflections caused by smooth surface areas of the tissue. (4) Conclusion: This gives the opportunity to facilitate an accurate margin assessment through intraoperative hyperspectral imaging.

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.

Analytical model for diffuse reflectance in single fiber reflectance spectroscopy.

Cancer progression leads to changing scattering properties of affected tissues. Single fiber reflectance (SFR) spectroscopy detects these changes at small spatial scales, making it a promising tool for early in situ detection. Despite its simplicity and versatility, SFR signal modeling is hugely complicated so that, presently, only approximate models exist. We use a classic approach from geometrical probability to derive accurate analytical expressions for diffuse reflectance in SFR that shows a strong improvement over existing models. We consider the case of limited collection efficiency and the presence of absorption. A Monte Carlo light transport study demonstrates that we adequately describe the contribution of diffuse reflectance to the SFR signal. Additional steps are required to include semi-ballistic, non-diffuse reflectance also present in the SFR measurement.

Toward assessment of resection margins using hyperspectral diffuse reflection imaging (400-1,700 nm) during tongue cancer surgery.

BACKGROUND AND OBJECTIVES: There is a clinical need to assess the resection margins of tongue cancer specimens, intraoperatively. In the current ex vivo study, we evaluated the feasibility of hyperspectral diffuse reflectance imaging (HSI) for distinguishing tumor from the healthy tongue tissue. STUDY DESIGN/MATERIALS AND METHODS: Fresh surgical specimens (n = 14) of squamous cell carcinoma of the tongue were scanned with two hyperspectral cameras that cover the visible and near-infrared spectrum (400-1,700 nm). Each pixel of the hyperspectral image represents a measure of the diffuse optical reflectance. A neural network was used for tissue-type prediction of the hyperspectral images of the visual and near-infrared data sets separately as well as both data sets combined. RESULTS: HSI was able to distinguish tumor from muscle with a good accuracy. The diagnostic performance of both wavelength ranges (sensitivity/specificity of visual and near-infrared were 84%/80% and 77%/77%, respectively) appears to be comparable and there is no additional benefit of combining the two wavelength ranges (sensitivity and specificity were 83%/76%). CONCLUSIONS: HSI has a strong potential for intra-operative assessment of tumor resection margins of squamous cell carcinoma of the tongue. This may optimize surgery, as the entire resection surface can be scanned in a single run and the results can be readily available. Lasers Surg. Med. © 2019 Wiley Periodicals, Inc.

Using Diffuse Reflectance Spectroscopy to Distinguish Tumor Tissue From Fibrosis in Rectal Cancer Patients as a Guide to Surgery.

BACKGROUND AND OBJECTIVES: In patients with rectal cancer who received neoadjuvant (chemo)radiotherapy, fibrosis is induced in and around the tumor area. As tumors and fibrosis have similar visual and tactile feedback, they are hard to distinguish during surgery. To prevent positive resection margins during surgery and spare healthy tissue, it would be of great benefit to have a real-time tissue classification technology that can be used in vivo. STUDY DESIGN/MATERIALS AND METHODS: In this study diffuse reflectance spectroscopy (DRS) was evaluated for real-time tissue classification of tumor and fibrosis. DRS spectra of fibrosis and tumor were obtained on excised rectal specimens. After normalization using the area under the curve, a support vector machine was trained using a 10-fold cross-validation. RESULTS: Using spectra of pure tumor tissue and pure fibrosis tissue, we obtained a mean accuracy of 0.88. This decreased to a mean accuracy of 0.61 when tumor measurements were used in which a layer of healthy tissue, mainly fibrosis, was present between the tumor and the measurement surface. CONCLUSION: It is possible to distinguish pure fibrosis from pure tumor. However, when the measurements on tumor also involve fibrotic tissue, the classification accuracy decreases. Lasers Surg. Med. © 2019 Wiley Periodicals, Inc.

Towards the use of diffuse reflectance spectroscopy for real-time in vivo detection of breast cancer during surgery.

BACKGROUND: Breast cancer surgeons struggle with differentiating healthy tissue from cancer at the resection margin during surgery. We report on the feasibility of using diffuse reflectance spectroscopy (DRS) for real-time in vivo tissue characterization. METHODS: Evaluating feasibility of the technology requires a setting in which measurements, imaging and pathology have the best possible correlation. For this purpose an optical biopsy needle was used that had integrated optical fibers at the tip of the needle. This approach enabled the best possible correlation between optical measurement volume and tissue histology. With this optical biopsy needle we acquired real-time DRS data of normal tissue and tumor tissue in 27 patients that underwent an ultrasound guided breast biopsy procedure. Five additional patients were measured in continuous mode in which we obtained DRS measurements along the entire biopsy needle trajectory. We developed and compared three different support vector machine based classification models to classify the DRS measurements. RESULTS: With DRS malignant tissue could be discriminated from healthy tissue. The classification model that was based on eight selected wavelengths had the highest accuracy and Matthews Correlation Coefficient (MCC) of 0.93 and 0.87, respectively. In three patients that were measured in continuous mode and had malignant tissue in their biopsy specimen, a clear transition was seen in the classified DRS measurements going from healthy tissue to tumor tissue. This transition was not seen in the other two continuously measured patients that had benign tissue in their biopsy specimen. CONCLUSIONS: It was concluded that DRS is feasible for integration in a surgical tool that could assist the breast surgeon in detecting positive resection margins during breast surgery. Trail registration NIH US National Library of Medicine-clinicaltrails.gov, NCT01730365. Registered: 10/04/2012 https://clinicaltrials.gov/ct2/show/study/NCT01730365.

Nerve detection using optical spectroscopy, an evaluation in four different models: In human and swine, in-vivo, and post mortem.

OBJECTIVE: Identification of peripheral nerve tissue is crucial in both surgery and regional anesthesia. Recently, optical tissue identification methods are presented to facilitate nerve identification in transcutaneous procedures and surgery. Optimization and validation of such techniques require large datasets. The use of alternative models to human in vivo, like human post mortem, or swine may be suitable to test, optimize and validate new optical techniques. However, differences in tissue characteristics and thus optical properties, like oxygen saturation and tissue perfusion are to be expected. This requires a structured comparison between the models. STUDY DESIGN: Comparative observational study. METHODS: Nerve and surrounding tissues in human (in vivo and post mortem) and swine (in vivo and post mortem) were structurally compared macroscopically, histologically, and spectroscopically. Diffuse reflective spectra were acquired (400-1,600 nm) after illumination with a broad band halogen light. An analytical model was used to quantify optical parameters including concentrations of optical absorbers. RESULTS: Several differences were found histologically and in the optical parameters. Histologically nerve and adipose tissue (subcutaneous fat and sliding fat) showed clear similarities between human and swine while human muscle enclosed more adipocytes and endomysial collagen. Optical parameters revealed model dependent differences in concentrations of ß-carotene, water, fat, and oxygen saturation. The similarity between optical parameters is, however, sufficient to yield a strong positive correlation after cross model classification. CONCLUSION: This study shows and discusses similarities and differences in nerve and surrounding tissues between human in vivo and post mortem, and swine in vivo and post mortem; this could support the discussion to use an alternative model to optimize and validate optical techniques for clinical nerve identification. Lasers Surg. Med. 50:253-261, 2018. © 2017 Wiley Periodicals, Inc.

Nerve detection during surgery: optical spectroscopy for peripheral nerve localization.

Precise nerve localization is of major importance in both surgery and regional anesthesia. Optically based techniques can identify tissue through differences in optical properties, like absorption and scattering. The aim of this study was to evaluate the potential of optical spectroscopy (diffuse reflectance spectroscopy) for clinical nerve identification in vivo. Eighteen patients (8 male, 10 female, age 53 ± 13 years) undergoing inguinal lymph node resection or resection or a soft tissue tumor in the groin were included to measure the femoral or sciatic nerve and the surrounding tissues. In vivo optical measurements were performed using Diffuse Reflectance Spectroscopy (400-1600 nm) on nerve, near nerve adipose tissue, muscle, and subcutaneous fat using a needle-shaped probe. Model-based analyses were used to derive verified quantitative parameters as concentrations of optical absorbers and several parameters describing scattering. A total of 628 optical spectra were recorded. Measured spectra reveal noticeable tissue specific characteristics. Optical absorption of water, fat, and oxy- and deoxyhemoglobin was manifested in the measured spectra. The parameters water and fat content showed significant differences (P < 0.005) between nerve and all surrounding tissues. Classification using k-Nearest Neighbor based on the derived parameters revealed a sensitivity of 85% and a specificity of 79%, for identifying nerve from surrounding tissues. Diffuse Reflectance Spectroscopy identifies peripheral nerve bundles. The differences found between tissue groups are assignable to the tissue composition and structure.

Review: in vivo optical spectral tissue sensing-how to go from research to routine clinical application?

Innovations in optical spectroscopy have helped the technology reach a point where performance previously seen only in laboratory settings can be translated and tested in real-world applications. In the field of oncology, spectral tissue sensing (STS) by means of optical spectroscopy is considered to have major potential for improving diagnostics and optimizing treatment outcome. The concept has been investigated for more than two decades and yet spectral tissue sensing is not commonly employed in routine medical practice. It is therefore important to understand what is needed to translate technological advances and insights generated through basic scientific research in this field into clinical practice. The aim of the discussion presented here is not to provide a comprehensive review of all work published over the last decades but rather to highlight some of the challenges found in literature and encountered by our group in the quest to translate optical technologies into useful clinical tools. Furthermore, an outlook is proposed on how translational researchers could proceed to eventually have STS incorporated in the process of clinical decision-making.

Microscopic analysis of the localization of two chlorin-based photosensitizers in OSC19 tumors in the mouse oral cavity.

BACKGROUND AND OBJECTIVE: The effect of photodynamic therapy (PDT) is dependent on the localization of photosensitizer in the treatment volume at the time of illumination. Investigation of photosensitizer pharmacokinetics in and around the treatment volume aids in determining the optimal drug light interval for PDT. MATERIALS AND METHODS: In this paper we have investigated the distribution of the photosensitizers chlorin e6 and Bremachlorin in the oral squamous cell carcinoma cell-line OSC19-Luc-Gfp in a tongue tumor, tumor boundary, invasive tumor boundary, and normal tongue tissue by the use of confocal microscopy of frozen sections. Tongues were harvested at t = [3, 4.5, 6, 24, 48] hours after injection. RESULTS: Both photosensitizers showed a decreasing fluorescence with increasing incubation time, and at all time points higher fluorescence was measured in tumor boundary than in tumor itself. For short incubation times, a higher fluorescence intensity was observed in the invasive tumor border and normal tissue compared to tumor tissue. Bremachlorin showed a small increase in tumor to normal ratio at 24 and 48 hours incubation time. Ce6 was undetectable at 48 hours. We did not find a correlation between photosensitizer localization and the presence of vasculature. CONCLUSION: The modest tumor/tumor boundary to normal selectivity of between 1.2 and 2.5 exhibited by Bremachlorin 24 and 48 hours after administration may allow selective targeting of tongue tumors. Further studies investigating the relationship between Bremachlorin concentration and therapeutic efficacy PDT with long incubation times are warranted.

The use of optical spectroscopy for in vivo detection of cervical pre-cancer.

In order to investigate the effectiveness of optical spectroscopy for in vivo diagnosis of cervical pre-cancerous conditions, a series of published studies are surveyed. The six optical technologies investigated include fluorescence spectroscopy, reflectance spectroscopy, and their combination using point probe or multispectral imaging approaches. Searching in the well-known databases, the most recent published works were sought out. Various aspects of the studies were evaluated including the details of the technology used, the pathologic threshold for tissue classification and the gold standard, the study population and prevalence of disease in this population, the method of measurement, the number of clinicians involved in the study, the classification and validation algorithms, and the performance in terms of sensitivity, specificity and, when available, the area under the receiver operating characteristic curve. Forty-four studies conducted from 1994 to 2012 were evaluated. The data are gathered in two comprehensive tables, and five illustrations are provided to simplify a comparison between studies from different points of view. There is a broad band of studies from small pilot studies through phase III clinical trials. Among the reviewed articles, only three factors were found to influence the performance of the optical spectroscopy studies. Multispectral approaches show higher specificity than the point probe approaches (p = 0.001). The use of acetic acid before measurement and prevalence of disease among the studied population, also, have an impact on the sensitivity and specificity of the studies (p < 0.05), respectively.

Separating Surface Reflectance from Volume Reflectance in Medical Hyperspectral Imaging.

Hyperspectral imaging has shown great promise for diagnostic applications, particularly in cancer surgery. However, non-bulk tissue-related spectral variations complicate the data analysis. Common techniques, such as standard normal variate normalization, often lead to a loss of amplitude and scattering information. This study investigates a novel approach to address these spectral variations in hyperspectral images of optical phantoms and excised human breast tissue. Our method separates surface and volume reflectance, hypothesizing that spectral variability arises from significant variations in surface reflectance across pixels. An illumination setup was developed to measure samples with a hyperspectral camera from different axial positions but with identical zenith angles. This configuration, combined with a novel data analysis approach, allows for the estimation and separation of surface reflectance for each direction and volume reflectance across all directions. Validated with optical phantoms, our method achieved an 83% reduction in spectral variability. Its functionality was further demonstrated in excised human breast tissue. Our method effectively addresses variations caused by surface reflectance or glare while conserving surface reflectance information, which may enhance sample analysis and evaluation. It benefits samples with unknown refractive index spectra and can be easily adapted and applied across a wide range of fields where hyperspectral imaging is used.

Toward the use of diffuse reflection spectroscopy for intra-operative tissue discrimination during sarcoma surgery.

Significance: Accurately distinguishing tumor tissue from normal tissue is crucial to achieve complete resections during soft tissue sarcoma (STS) surgery while preserving critical structures. Incomplete tumor resections are associated with an increased risk of local recurrence and worse patient prognosis. Aim: We evaluate the performance of diffuse reflectance spectroscopy (DRS) to distinguish tumor tissue from healthy tissue in STSs. Approach: DRS spectra were acquired from different tissue types on multiple locations in 20 freshly excised sarcoma specimens. A k-nearest neighbors classification model was trained to predict the tissue types of the measured locations, using binary and multiclass approaches. Results: Tumor tissue could be distinguished from healthy tissue with a classification accuracy of 0.90, sensitivity of 0.88, and specificity of 0.93 when well-differentiated liposarcomas were included. Excluding this subtype, the classification performance increased to an accuracy of 0.93, sensitivity of 0.94, and specificity of 0.93. The developed model showed a consistent performance over different histological subtypes and tumor locations. Conclusions: Automatic tissue discrimination using DRS enables real-time intra-operative guidance, contributing to more accurate STS resections.

Margin assessment during breast conserving surgery using diffuse reflectance spectroscopy.

Significance: During breast-conserving surgeries, it is essential to evaluate the resection margins (edges of breast specimen) to determine whether the tumor has been removed completely. In current surgical practice, there are no methods available to aid in accurate real-time margin evaluation. Aim: In this study, we investigated the diagnostic accuracy of diffuse reflectance spectroscopy (DRS) combined with tissue classification models in discriminating tumorous tissue from healthy tissue up to 2 mm in depth on the actual resection margin of in vivo breast tissue. Approach: We collected an extensive dataset of DRS measurements on ex vivo breast tissue and in vivo breast tissue, which we used to develop different classification models for tissue classification. Next, these models were used in vivo to evaluate the performance of DRS for tissue discrimination during breast conserving surgery. We investigated which training strategy yielded optimum results for the classification model with the highest performance. Results: We achieved a Matthews correlation coefficient of 0.76, a sensitivity of 96.7% (95% CI 95.6% to 98.2%), a specificity of 90.6% (95% CI 86.3% to 97.9%) and an area under the curve of 0.98 by training the optimum model on a combination of ex vivo and in vivo DRS data. Conclusions: DRS allows real-time margin assessment with a high sensitivity and specificity during breast-conserving surgeries.

Light fractionated ALA-PDT enhances therapeutic efficacy in vitro; the influence of PpIX concentration and illumination parameters.

Light fractionation, with a long dark interval, significantly increases the response to ALA-PDT in pre-clinical models and in non-melanoma skin cancer. We investigated if this increase in efficacy can be replicated in PAM 212 cells in vitro. The results show a significant decrease in cell survival after light fractionation which is dependent on the PpIX concentration and light dose of the first light fraction. This study supports the hypothesis that an underlying cellular mechanism is involved in the response to light fractionation in which a first light fraction leads to sub-lethally damaged cells that are sensitised to a second light fraction 2 hours later. The current study reveals the in vitro circumstances under which we can investigate the cellular pathways involved.

Localization of liposomal mTHPC formulations within normal epithelium, dysplastic tissue, and carcinoma of oral epithelium in the 4NQO-carcinogenesis rat model.

BACKGROUND AND OBJECTIVE: Foslip and Fospeg are liposomal formulations of the photosensitizer mTHPC (Foscan), which is used for photodynamic therapy (PDT) of malignancies. Literature suggests that liposomal mTHPC formulations have better properties and increased tumor uptake compared to Foscan. To investigate this, we used the 4NQO-induced carcinogen model to compare the localization of the different mTHPC formulations within normal, precancerous, and cancerous tissue. In contrast to xenograft models, the 4NQO model closely mimics the carcinogenesis of human oral dysplasia. MATERIALS AND METHODS: Fifty-four rats drank water with the carcinogen 4NQO. When oral examination revealed tumor, the rats received 0.15 mg/kg mTHPC (Foscan, Foslip, or Fospeg). At 2, 4, 8, 24, 48, or 96 hours after injection the rats were sacrificed. Oral tissue was sectioned for HE slides and for fluorescence confocal microscopy. The HE slides were scored on the severity of dysplasia by the epithelial atypia index (EAI). The calibrated fluorescence intensity per formulation or time point was correlated to EAI. RESULTS: Fospeg showed higher mTHPC fluorescence in normal and tumor tissue compared to both Foscan and Foslip. Significant differences in fluorescence between tumor and normal tissue were found for all formulations. However, at 4, 8, and 24 hours only Fospeg showed a significant difference. The Pearson's correlation between EAI and mTHPC fluorescence proved weak for all formulations. CONCLUSION: In our induced carcinogenesis model, Fospeg exhibited a tendency for higher fluorescence in normal and tumor tissue compared to Foslip and Foscan. In contrast to Foscan and Foslip, Fospeg showed significantly higher fluorescence in tumor versus normal tissue at earlier time points, suggesting a possible clinical benefit compared to Foscan. Low correlation between grade of dysplasia and mTHPC fluorescence was found.

Diffuse reflection spectroscopy at the fingertip: design and performance of a compact side-firing probe for tissue discrimination during colorectal cancer surgery.

Optical technologies are widely used for tissue sensing purposes. However, maneuvering conventional probe designs with flat-tipped fibers in narrow spaces can be challenging, for instance during pelvic colorectal cancer surgery. In this study, a compact side-firing fiber probe was developed for tissue discrimination during colorectal cancer surgery using diffuse reflectance spectroscopy. The optical behavior was compared to flat-tipped fibers using both Monte Carlo simulations and experimental phantom measurements. The tissue classification performance was examined using freshly excised colorectal cancer specimens. Using the developed probe and classification algorithm, an accuracy of 0.92 was achieved for discriminating tumor tissue from healthy tissue.

Tissue Classification of Breast Cancer by Hyperspectral Unmixing.

(1) Background: Assessing the resection margins during breast-conserving surgery is an important clinical need to minimize the risk of recurrent breast cancer. However, currently there is no technique that can provide real-time feedback to aid surgeons in the margin assessment. Hyperspectral imaging has the potential to overcome this problem. To classify resection margins with this technique, a tissue discrimination model should be developed, which requires a dataset with accurate ground-truth labels. However, establishing such a dataset for resection specimens is difficult. (2) Methods: In this study, we therefore propose a novel approach based on hyperspectral unmixing to determine which pixels within hyperspectral images should be assigned to the ground-truth labels from histopathology. Subsequently, we use this hyperspectral-unmixing-based approach to develop a tissue discrimination model on the presence of tumor tissue within the resection margins of ex vivo breast lumpectomy specimens. (3) Results: In total, 372 measured locations were included on the lumpectomy resection surface of 189 patients. We achieved a sensitivity of 0.94, specificity of 0.85, accuracy of 0.87, Matthew's correlation coefficient of 0.71, and area under the curve of 0.92. (4) Conclusion: Using this hyperspectral-unmixing-based approach, we demonstrated that the measured locations with hyperspectral imaging on the resection surface of lumpectomy specimens could be classified with excellent performance.

Diffuse reflectance spectroscopy for accurate margin assessment in breast-conserving surgeries: importance of an optimal number of fibers.

During breast-conserving surgeries, it remains challenging to accomplish adequate surgical margins. We investigated different numbers of fibers for fiber-optic diffuse reflectance spectroscopy to differentiate tumorous breast tissue from healthy tissue ex vivo up to 2 mm from the margin. Using a machine-learning classification model, the optimal performance was obtained using at least three emitting fibers (Matthew's correlation coefficient (MCC) of 0.73), which was significantly higher compared to the performance of using a single-emitting fiber (MCC of 0.48). The percentage of correctly classified tumor locations varied from 75% to 100% depending on the tumor percentage, the tumor-margin distance and the number of fibers.