A method to detect thermal damage in bovine liver utilising diffuse reflectance spectroscopy

Background and aim The lack of a fast and accurate intraoperative tumour margin assessment tool contributes to high positive circumferential resection margin rates for oesophageal and gastric cancers, which is associated with local recurrence and poor long-term survival. Diffuse reflectance spectroscopy (DRS) can provide non-invasive, accurate, and real-time tissue classification based on quantification of light reflectance in tissue, providing a unique optical fingerprint of the tissue. This study aimed to validate the use of DRS for discrimination between normal and cancerous tissues based on ex-vivo gastric and oesophageal cancer resection specimens. Methods Consecutive patients undergoing resections for gastric and oesophageal carcinomas at a tertiary referral centre in London between July 2020 and June 2021 were included. Reflectance data in the 468-720 nm spectral range were recorded on ex-vivo specimens within 15 minutes of surgical specimen excision. Based on video recordings of the data acquisition procedure and probe tip tracking, sampling sites were correlated with histological reports manually labeled as either normal or cancer (Figure 1). Following spectral data normalisation and feature selection, four supervised machine learning approaches were tested for classification using 5-fold cross-validation. Overall classification accuracy and Area Under the Curve (AUC) across machine learning approaches were compared. Results A total of 32 patients (median age 68, 75% male) were included. 11,862 mean spectra (2990 spectra for normal oesophagus, 4628 for normal stomach, 2305 for gastric cancer, and 1939 for oesophageal cancer) were collected. For oesophageal cancer, classification accuracy was 96.22 ± 0.50 and AUC was 99.24 ± 0.19, while for gastric cancer, the accuracy was 93.86 ± 0.66, and AUC 98.50 ± 0.28 for gastric cancer. For both malignancy types, Extreme Gradient Boosting was the best performing classifier. The differences in spectral data between patients receiving neoadjuvant treatment and treatment naïve were non-significant. Conclusion Machine learning can be used to accurately differentiate between normal and cancerous oesophageal and gastric tissues in an ex-vivo setting, based on DRS data. Features used for class discrimination should be investigated to find possible physiological correlates of spectral data differences. Future intraoperative validation is required to assess the utility of this technology for real-time tumour margin mapping and its effect on long-term outcomes.

This study presents the first in vivo real-time tissue characterization during image-guided percutaneous lung biopsies using diffuse reflectance spectroscopy (DRS) sensing at the tip of a biopsy needle with integrated optical fibers. Tissues from 21 consented patients undergoing lung cancer surgery were measured intraoperatively using the fiber-optic platform capable of assessing various physical tissue properties highly correlated to tissue architecture and composition. In addition, the method was tested for clinical use by performing DRS tissue sensing during 11 routine biopsy procedures in patients with suspected lung cancer. We found that water content and scattering amplitude are the primary discriminators for the transition from healthy lung tissue to tumor tissue and that the reliability of these parameters is not affected by the amount of blood at the needle tip. In the 21 patients measured intraoperatively, the water-to-scattering ratio yielded a 56% to 81% contrast difference between tumor and surrounding tissue. Analysis of the 11 image-guided lung biopsy procedures showed that the tissue diagnosis derived from DRS was diagnostically discriminant in each clinical case. DRS tissue sensing integrated into a biopsy needle may be a powerful new tool for biopsy guidance that can be readily used in routine diagnostic lung biopsy procedures. This approach may not only help to increase the successful biopsy yield for histopathologic analysis, but may also allow specific sampling of vital tumor tissue for genetic profiling.

Diffuse reflectance spectroscopy is a widely used technique for medical applications that may analyze the optical characteristics of biological tissues. By using diffuse reflectance spectroscopy, different tissue types can be distinguished based on specific changes on reflected light spectrum that are a result of differences on a molecular level between compared tissues. Identification of the structural features of tissue can be performed by applying diffuse reflectance spectroscopy, and the spectra obtained from this technique could provide important diagnostic information about the tissue morphology and physiology. Moreover, different tissue types can be classified using diffuse reflectance spectroscopy, during surgery on the basis of their optical properties that are related to the tissue morphology and constituents. In recent years, several research groups have been shown the feasibility of diffuse reflectance spectroscopy in discriminating benign and malignant tissue, and thus making it a good competitor for margin assessment. Therefore, the diffuse reflectance spectroscopy has the possibility to become an important optical means for disease diagnosis, treatment and prognosis monitoring. This review represents a summary of the literature on diffuse reflectance spectroscopy and its important clinical applications.

BackgroundBreast 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.MethodsEvaluating 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.ResultsWith 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.ConclusionsIt 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

The aim of this work is to assess the performance of multimodal spectroscopic approach combined with single core optical fiber for detection of bladder cancer during surgery in vivo. Multimodal approach combines diffuse reflectance spectroscopy (DRS), fluorescence spectroscopy in the visible (405 nm excitation) and near-infrared (NIR) (690 nm excitation) ranges, and high-wavenumber Raman spectroscopy. All four spectroscopic methods were combined in a single setup. For 21 patients with suspected bladder cancer or during control cystoscopy optical spectra of bladder cancer, healthy bladder wall tissue and/or scars were measured. Classification of cancerous and healthy bladder tissue was performed using machine learning methods. Statistically significant differences in relative total haemoglobin content, oxygenation, scattering, and visible fluorescence intensity were found between tumor and normal tissues. The combination of DRS and visible fluorescence spectroscopy allowed detecting cancerous tissue with sensitivity and specificity of 78% and 91%, respectively. The addition of features extracted from NIR fluorescence and Raman spectra did not improve the quality of classification. This study demonstrates that multimodal spectroscopic approach allows increasing sensitivity and specificity of bladder cancer detection in vivo. The developed approach does not require special probes and can be used with single-core optical fibers applied for laser surgery.

Improved identification of peripheral lung tumors by using diffuse reflectance and fluorescence spectroscopy

A multimodal spectroscopy system was developed to improve breast cancer diagnosis non-invasively. It includes three modules: diffuse reflectance spectroscopy (DRS), ultrasonography and low intensity focused ultrasound (LIFU). An IRB-approved clinical trial is in progress. Currently 33 patients were enrolled with informed consent. DRS signals were analyzed using wavelet technique before needle biopsy was performed for tissue diagnosis. Clinical results showed that LIFU stimulated transitory high-frequency fluctuation in non-cancer tissue, but not in malignant tissue. The average ratio of the variances during LIFU vs. baseline is 2.95 in non-cancer tissue, compared to 1.18 in cancer. This difference is significant (p=0.0007), indicating that high-frequency fluctuation was amplified in non-cancer tissue during LIFU. Current clinical results demonstrate the effectiveness of this promising technique in characterizing cancer vs. non-cancer tissues. Combining this system with breast ultrasound has the potential to increase the specificity of sonographic breast cancer detection, and to reduce unnecessary invasive procedures.

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. 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. 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. 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.

.Significance: Current methods for analyzing pathological muscle tissue are time consuming and rarely quantitative, and they involve invasive biopsies. Faster and less invasive diagnosis of muscle disease may be achievable using marker-free in vivo optical sensing methods.Aim: It was speculated that changes in the biochemical composition and structure of muscle associated with pathology could be measured quantitatively using visible wavelength optical spectroscopy techniques enabling automated classification.Approach: A fiber-optic autofluorescence (AF) and diffuse reflectance (DR) spectroscopy device was manufactured. The device and data processing techniques based on principal component analysis were validated using in situ measurements on healthy skeletal and cardiac muscle. These methods were then applied to two mouse models of genetic muscle disease: a type 1 neurofibromatosis (NF1) limb-mesenchyme knockout () and a muscular dystrophy mouse (mdx).Results: Healthy skeletal and cardiac muscle specimens were separable using AF and DR with receiver operator curve areas (ROC-AUC) of . AF and DR analyses showed optically separable changes in quadriceps muscle (ROC-AUC >0.97) with no differences detected in the heart (ROC-AUC <0.67), which does not undergo gene deletion in this model. Changes in AF spectra in mdx muscle were seen between the 3 week and 10 week time points (ROC-AUC = 0.96) and were not seen in the wild-type controls (ROC-AUC = 0.58).Conclusion: These findings support the utility of in vivo fiber-optic AF and DR spectroscopy for the assessment of muscle tissue. This report highlights that there is considerable scope to develop this marker-free optical technology for preclinical muscle research and for diagnostic assessment of clinical myopathies and dystrophies.

Aim Gastrointestinal (GI) cancers account for 26% of global cancer incidence with prevalence projected to rise exponentially due to the ageing population and lifestyle choices. Surgical resection is the mainstay of treatment to remove the cancer in its entirety to achieve an R0 resection. Positive margins, when cancerous tissue has been left in situ, is associated with increased morbidity and mortality. Current margin assessment involves histopathological analysis, after resection of the specimen. Diffuse Reflectance Spectroscopy (DRS) and Hyperspectral Imaging (HSI) are novel imaging techniques that have the potential to provide real-time assessment of cancer margins intra-operatively to reduce the incidence of positive resection margins and improve patient outcomes. The aim of this review is to assess the current state of evidence for the use of novel imaging techniques in GI cancer margin assessment. Method A literature review was conducted of studies using DRS and HSI in GI cancers in adult patients, published from inception to October 2020. Results A total of 15 studies were analysed, nine of which used DRS and six used HSI and the majority of studies were performed ex-vivo. Current image acquisition techniques and processing algorithms vary greatly. The sensitivity and specificity of DRS ranged from 0.90-0.98 and 0.88-0.95 respectively and for HSI 0.63-0.98 and 0.69-0.98, respectively across five types of GI cancers. Conclusions DRS and HSI are novel imaging techniques, currently in their infancy but the outlook is promising. With further research focused on standardising methodology and in-vivo settings, DRS and HSI could transform intra-operative margin assessment in GI cancers.

Cancers of the upper gastrointestinal tract remain a major contributor to the global cancer burden. The accurate mapping of tumor margins is of particular importance for curative cancer resection and improvement in overall survival. Current mapping techniques preclude a full resection margin assessment in real time. To evaluate whether diffuse reflectance spectroscopy (DRS) on gastric and esophageal cancer specimens can differentiate tissue types and provide real-time feedback to the operator. This was a prospective ex vivo validation study. Patients undergoing esophageal or gastric cancer resection were prospectively recruited into the study between July 2020 and July 2021 at Hammersmith Hospital in London, United Kingdom. Tissue specimens were included for patients undergoing elective surgery for either esophageal carcinoma (adenocarcinoma or squamous cell carcinoma) or gastric adenocarcinoma. A handheld DRS probe and tracking system was used on freshly resected ex vivo tissue to obtain spectral data. Binary classification, following histopathological validation, was performed using 4 supervised machine learning classifiers. Data were divided into training and testing sets using a stratified 5-fold cross-validation method. Machine learning classifiers were evaluated in terms of sensitivity, specificity, overall accuracy, and the area under the curve. Of 34 included patients, 22 (65%) were male, and the median (range) age was 68 (35-89) years. A total of 14 097 mean spectra for normal and cancerous tissue were collected. For normal vs cancer tissue, the machine learning classifier achieved a mean (SD) overall diagnostic accuracy of 93.86% (0.66) for stomach tissue and 96.22% (0.50) for esophageal tissue and achieved a mean (SD) sensitivity and specificity of 91.31% (1.5) and 95.13% (0.8), respectively, for stomach tissue and of 94.60% (0.9) and 97.28% (0.6) for esophagus tissue. Real-time tissue tracking and classification was achieved and presented live on screen. This study provides ex vivo validation of the DRS technology for real-time differentiation of gastric and esophageal cancer from healthy tissue using machine learning with high accuracy. As such, it is a step toward the development of a real-time in vivo tumor mapping tool for esophageal and gastric cancers that can aid decision-making of resection margins intraoperatively.

Glial tumors grow diffusely in the brain. Survival is correlated to the extent of tumor removal, but tumor borders are often invisible. Resection beyond the borders as defined by conventional methods may further improve prognosis. In this proof-of-concept study, we evaluate diffuse reflectance spectroscopy (DRS) for discrimination between glial tumors and normal brain ex vivo. DRS spectra and histology were acquired from 22 tumor samples and nine brain tissue samples retrieved from 30 patients. The content of biological chromophores and scattering features were estimated by fitting a model derived from diffusion theory to the DRS spectra. DRS parameters differed significantly between tumor and normal brain tissue. Classification using random forest yielded a sensitivity and specificity for the detection of low-grade gliomas of 82.0% and 82.7%, respectively, and the area under curve (AUC) was 0.91. Applied in a hand-held probe or biopsy needle, DRS has the potential to provide intra-operative tissue analysis.

Diffuse reflectance spectroscopy (DRS) is a powerful tool for quantifying optical and physiological tissue properties such as hemoglobin oxygen saturation and vascularity. DRS is increasingly used clinically for distinguishing cancerous lesions from normal tissue. However, its widespread clinical acceptance is still limited due to uncontrolled probe–tissue interface pressure that influences reproducibility and introduces operator-dependent results. In this clinical study, we assessed and validated a pressure-sensing and automatic self-calibration DRS in patients with suspected head and neck squamous cell carcinoma (HNSCC). The clinical study enrolled nineteen patients undergoing HNSCC surgical biopsy procedures. Patients consented to evaluation of this improved DRS system during surgery. For each patient, we obtained 10 repeated measurements on one tumor site and one distant normal location. Using a Monte Carlo-based model, we extracted the hemoglobin saturation data along with total hemoglobin content and scattering properties. A total of twelve cancer tissue samples from HNSCC patients and fourteen normal tissues were analyzed. A linear mixed effects model tested for significance between repeated measurements and compared tumor versus normal tissue. These results demonstrate that cancerous tissues have a significantly lower hemoglobin saturation compared to normal controls (p < 0.001), which may be reflective of tumor hypoxia. In addition, there were minimal changes over time upon probe placement and repeated measurement, indicating that the pressure-induced changes were minimal and repeated measurements did not differ significantly from the initial value. This study demonstrates the feasibility of conducting optical spectroscopy measurements on intact lesions prior to removal during HNSCC procedures, and established that this probe provides diagnostically-relevant physiologic information that may impact further treatment.

Using an intraoperative margin assessment technique during breast-conserving surgery (BCS) helps surgeons to decrease the risk of positive margin occurrence. Diffuse reflectance spectroscopy (DRS) has the potential to discriminate healthy breast tissue from cancerous tissue. We investigated the performance of an electrosurgical knife integrated with a DRS on porcine muscle and adipose tissue. Characterization of the formed debris on the optical fibers after electrosurgery revealed that the contamination is mostly burned tissue. Even with contaminated optical fibers, both tissues could still be discriminated with DRS based on fat/water ratio. Therefore, an electrosurgical knife integrated with DRS may be a promising technology to provide the surgeon with real-time guidance during BCS.

Diffuse reflectance spectroscopy (DRS) has been extensively applied for the characterization of biological tissue, especially for dysplasia and cancer detection, by determination of the tissue optical properties. A major challenge in performing routine clinical diagnosis lies in the extraction of the relevant parameters, especially at high absorption levels typically observed in cancerous tissue. Here, we present a new least-squares support vector machine (LS-SVM) based regression algorithm for rapid and accurate determination of the absorption and scattering properties. Using physical tissue models, we demonstrate that the proposed method can be implemented more than two orders of magnitude faster than the state-of-the-art approaches while providing better prediction accuracy. Our results show that the proposed regression method has great potential for clinical applications including in tissue scanners for cancer margin assessment, where rapid quantification of optical properties is critical to the performance.