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Purpose:Complete tumor removal during cancer surgery remains challenging due to the lack of accurate techniques for intraoperative margin assessment. This study evaluates the use of hyperspectral imaging for margin assessment by reporting its use in fresh human breast specimens.Experimental Design:Hyperspectral data were first acquired on tissue slices from 18 patients after gross sectioning of the resected breast specimen. This dataset, which contained over 22,000 spectra, was well correlated with histopathology and was used to develop a support vector machine classification algorithm and test the classification performance. In addition, we evaluated hyperspectral imaging in clinical practice by imaging the resection surface of six lumpectomy specimens. With the developed classification algorithm, we determined if hyperspectral imaging could detect malignancies in the resection surface.Results:The diagnostic performance of hyperspectral imaging on the tissue slices was high; invasive carcinoma, ductal carcinoma in situ, connective tissue, and adipose tissue were correctly classified as tumor or healthy tissue with accuracies of 93%, 84%, 70%, and 99%, respectively. These accuracies increased with the size of the area, consisting of one tissue type. The entire resection surface was imaged within 10 minutes, and data analysis was performed fast, without the need of an experienced operator. On the resection surface, hyperspectral imaging detected 19 of 20 malignancies that, according to the available histopathology information, were located within 2 mm of the resection surface.Conclusions:These findings show the potential of using hyperspectral imaging for margin assessment during breast-conserving surgery to improve surgical outcome.

The four tissue classes, their average spectra before and after SNV normalization and their main differences.

There is an unmet clinical need for an accurate, rapid and reliable tool for margin assessment during breast-conserving surgeries. Ultrasound offers the potential for a rapid, reproducible, and non-invasive method to assess margins. However, it is challenged by certain drawbacks, including a low signal-to-noise ratio, artifacts, and the need for experience with the acquirement and interpretation of images. A possible solution might be computer-aided ultrasound evaluation. In this study, we have developed new ensemble approaches for automated breast tumor segmentation. The ensemble approaches to predict positive and close margins (distance from tumor to margin ≤ 2.0 mm) in the ultrasound images were based on 8 pre-trained deep neural networks. The best optimum ensemble approach for segmentation attained a median Dice score of 0.88 on our data set. Furthermore, utilizing the segmentation results we were able to achieve a sensitivity of 96% and a specificity of 76% for predicting a close margin when compared to histology results. The promising results demonstrate the capability of AI-based ultrasound imaging as an intraoperative surgical margin assessment tool during breast-conserving surgery.

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.

Complete tumor removal during breast-conserving surgery remains challenging due to the lack of optimal intraoperative margin assessment techniques. Here, we use hyperspectral imaging for tumor detection in fresh breast tissue. We evaluated different wavelength ranges and two classification algorithms; a pixel-wise classification algorithm and a convolutional neural network that combines spectral and spatial information. The highest classification performance was obtained using the full wavelength range (450-1650nm). Adding spatial information mainly improved the differentiation of tissue classes within the malignant and healthy classes. High sensitivity and specificity were accomplished, which offers potential for hyperspectral imaging as a margin assessment technique to improve surgical outcome. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

For the validation of optical diagnostic technologies, experimental results need to be benchmarked against the gold standard. Currently, the gold standard for tissue characterization is assessment of hematoxylin and eosin (H&E)-stained sections by a pathologist. When processing tissue into H&E sections, the shape of the tissue deforms with respect to the initial shape when it was optically measured. We demonstrate the importance of accounting for these tissue deformations when correlating optical measurement with routinely acquired histopathology. We propose a method to register the tissue in the H&E sections to the optical measurements, which corrects for these tissue deformations. We compare the registered H&E sections to H&E sections that were registered with an algorithm that does not account for tissue deformations by evaluating both the shape and the composition of the tissue and using microcomputer tomography data as an independent measure. The proposed method, which did account for tissue deformations, was more accurate than the method that did not account for tissue deformations. These results emphasize the need for a registration method that accounts for tissue deformations, such as the method presented in this study, which can aid in validating optical techniques for clinical use. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

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.

Real-time intra-operative resection margin assessment during breast-conserving surgery (BCS) is important to prevent incomplete tumor removal. Nowadays, in up to 37% of the women undergoing BCS, tumor positive margin are found after surgery. We test the feasibility of hyperspectral imaging to predict these positive margins in fresh lumpectomy specimens, to prevent incomplete tumor removal. Hyperspectral diffuse reflectance images (900-1700 nm) were collected on fresh lumpectomy specimens. These specimens were obtained from women undergoing primary BCS, that did not get neo-adjuvant treatment. To ensure hyperspectral images of the entire resection surface, we treated the specimen as a cube and imaged it from six different sides. Next, a SVM classification algorithm, which we developed and tested with a different dataset, was applied to these hyperspectral images to predict positive margins. Finally, we compared the margin assessment performed with hyperspectral imaging with histopathology, the gold standard for margin assessment. It was found that hyperspectral imaging could be used in the clinical workflow. First, data acquisition of the entire resection side was fast and took only 20 seconds per resection side. Second, with the earlier developed classification algorithm, data analysis could be performed in the operating theater in limited amount of time. Third, with hyperspectral imaging we were able to find 12 out of 13 positive resection sides. The one positive resection side that was missed contained a single malignant pocket smaller than 1 mm2. These preliminary findings make hyperspectral imaging a promising technique for resection margin assessment during BCS.

In 10 to 40% of breast cancer patients treated with breast conserving surgery, ductal carcinoma in situ (DCIS) or invasive carcinoma (IC) is present at the margin of the specimen. Diffuse Reflectance Spectroscopy (DRS) could potentially help the surgeon during surgery in avoiding these tumor positive resection margins. Here we assess the feasibility of DRS for discriminating healthy breast tissue from tumor. For this purpose, DRS measurements (400-1600nm) were obtained in 71 breast specimens. A custom-made grid was placed on top of a slice of the resection specimen to accurately correlate the DRS measurements with histopathology. To assess DRS for the discrimination between healthy and tumor tissue types, a support vector machine based classification algorithm was developed. A total of 1081 locations were included, of which 236 locations were considered homogenous (> 95% fat, connective tissue, IC, or DCIS) and 845 locations consisted of a mixture of tissue types. The spectra of pure DCIS locations were similar to the spectra of pure IC, suggesting that these tissue types could not be separated based on their spectra. In a first analysis it was found that locations of pure fat, pure connective, and pure tumor tissue (both DCIS and IC) could be discriminated with accuracies of 0.95, 0.95, and 0.79 respectively. Classification of mixture locations was more challenging. The accuracy for tumor detection depends on the amount of tumor present in the measurement volume. These results indicate that DRS can potentially be used for the detection of tumor at the resection margin.

Hyperspectral imaging is a promising technique for resection margin assessment during cancer surgery. Thereby, only a specific amount of the tissue below the resection surface, the clinically defined margin width, should be assessed. Since the imaging depth of hyperspectral imaging varies with wavelength and tissue composition, this can have consequences for the clinical use of hyperspectral imaging as margin assessment technique. In this study, a method was developed that allows for hyperspectral analysis of resection margins in breast cancer. This method uses the spectral slope of the diffuse reflectance spectrum at wavelength regions where the imaging depth in tumor and healthy tissue is equal. Thereby, tumor can be discriminated from healthy breast tissue while imaging up to a similar depth as the required tumor-free margin width of 2 mm. Applying this method to hyperspectral images acquired during surgery would allow for robust margin assessment of resected specimens. In this paper, we focused on breast cancer, but the same approach can be applied to develop a method for other types of cancer.

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.

Purpose: Complete tumor removal during cancer surgery remains challenging due to the lack of accurate techniques for intraoperative margin assessment. This study evaluates the use of hyperspectral imaging for margin assessment by reporting its use in fresh human breast specimens. Experimental Design: Hyperspectral data were first acquired on tissue slices from 18 patients after gross sectioning of the resected breast specimen. This dataset, which contained over 22,000 spectra, was well correlated with histopathology and was used to develop a support vector machine classification algorithm and test the classification performance. In addition, we evaluated hyperspectral imaging in clinical practice by imaging the resection surface of six lumpectomy specimens. With the developed classification algorithm, we determined if hyperspectral imaging could detect malignancies in the resection surface. Results: The diagnostic performance of hyperspectral imaging on the tissue slices was high; invasive carcinoma, ductal carcinoma in situ, connective tissue, and adipose tissue were correctly classified as tumor or healthy tissue with accuracies of 93%, 84%, 70%, and 99%, respectively. These accuracies increased with the size of the area, consisting of one tissue type. The entire resection surface was imaged within 10 minutes, and data analysis was performed fast, without the need of an experienced operator. On the resection surface, hyperspectral imaging detected 19 of 20 malignancies that, according to the available histopathology information, were located within 2 mm of the resection surface. Conclusions: These findings show the potential of using hyperspectral imaging for margin assessment during breast-conserving surgery to improve surgical outcome.

Worldwide, up to 40% of the breast conserving surgeries require additional operations due to positive resection margins. We propose to reduce this percentage by using hyperspectral imaging for resection margin assessment during surgery. Spectral hypercubes were collected from 26 freshly excised breast specimens with a pushbroom camera (900-1700nm). Computer simulations of the penetration depth in breast tissue suggest a strong variation in sampling depth (~0.5-10 mm) over this wavelength range. This was confirmed with a breast tissue mimicking phantom study. Smaller penetration depths are observed in wavelength regions with high water and/or fat absorption. Consequently, tissue classification based on spectral analysis over the whole wavelength range becomes complicated. This is especially a problem in highly inhomogeneous human tissue. We developed a method, called derivative imaging, which allows accurate tissue analysis, without the impediment of dissimilar sampling volumes. A few assumptions were made based on previous research. First, the spectra acquired with our camera from breast tissue are mainly shaped by fat and water absorption. Second, tumor tissue contains less fat and more water than healthy tissue. Third, scattering slopes of different tissue types are assumed to be alike. In derivative imaging, the derivatives are calculated of wavelengths a few nanometers apart; ensuring similar penetration depths. The wavelength choice determines the accuracy of the method and the resolution. Preliminary results on 3 breast specimens indicate a classification accuracy of 93% when using wavelength regions characterized by water and fat absorption. The sampling depths at these regions are 1mm and 5mm.

Purpose To facilitate effective and efficient training in skills laboratory, objective metrics can be used. Forces exerted on the tissues can be a measure of safe tissue manipulation. To provide feedback during training, expert threshold levels need to be determined. The purpose of this study was to define the magnitude and the direction of navigation forces used during arthroscopic inspection of the wrist. Methods We developed a set-up to mount a cadaver wrist to a 3D force platform that allowed measurement of the forces exerted on the wrist. Six experts in wrist arthroscopy performed two tasks: (1) Introduction of the camera and visualization of the hook. (2) Navigation through the wrist with visualization of five anatomic structures. The magnitude (Fabs) and direction of force were recorded, with the direction defined as α being the angle in the vertical plane and β being the angle in the horizontal plane. The 10th–90th percentile of the data were used to set threshold levels for training. Results The results show distinct force patterns for each of the anatomic landmarks. Median Fabs of the navigation task is 3.8 N (1.8–7.3), α is 3.60 (−54–44) and β is 260 (0–72). Conclusion Unique expert data on navigation forces during wrist arthroscopy were determined. The defined maximum allowable navigation force of 7.3 N (90th percentile) can be used in providing feedback on performance during skills training. The clinical value is that this study contributes to objective assessment of skills levels.

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. (C) 2016 Optical Society of America

Objectives: Difficulties in obtaining a representative tissue sample are a major obstacle in timely selecting the optimal treatment for patients with lung cancer or other malignancies. Having a modality to provide needle guidance and confirm the biopsy site selection could be of great clinical benefit, especially when small masses are targeted. The objective of this study was to evaluate whether diffuse reflectance spectroscopy (DRS) at the tip of a core biopsy needle can be used for biopsy site confirmation in real time, thereby enabling optimized biopsy acquisition and improving diagnostic capability. Materials and methods: We included a total of 23 patients undergoing a routine computed tomography (CT) guided transthoracic needle biopsy of a lesion suspected for lung cancer or metastatic disease. DRS measurements were acquired during needle insertion and clinically relevant parameters were extracted from the spectral data along the needle paths. Histopathology results were compared with the DRS data at the final measurement position. Results: Analysis of the collective data acquired from all enrolled subjects showed significant differences (p < 0.01) for blood content, stO2, water content, and scattering amplitude. The identified spectral contrast matched the final pathology in 20 out of 22 clinical cases that could be used for analysis, which corresponds with an overall diagnostic performance of 91%. Three cases underlined the importance of adequate reference measurements and the need for real time diagnostic feedback. Continuous real time DRS measurements performed during a biopsy procedure in one patient provided clear information with respect to the variation in tissue and allowed identification of the tumour boundary. Conclusions: The presented technology creates a basis for the design and clinical implementation of integrated fibre-optic tools for a variety of minimal invasive applications.

There is a strong need to develop clinical instruments that can perform rapid tissue assessment at the tip of smart clinical instruments for a variety of oncological applications. This study presents the first in vivo real-time tissue characterization during 24 liver biopsy procedures using diffuse reflectance (DR) spectroscopy at the tip of a core biopsy needle with integrated optical fibers. DR measurements were performed along each needle path, followed by biopsy of the target lesion using the same needle. Interventional imaging was coregistered with the DR spectra. Pathology results were compared with the DR spectroscopy data at the final measurement position. Bile was the primary discriminator between normal liver tissue and tumor tissue. Relative differences in bile content matched with the tissue diagnosis based on histopathological analysis in all 24 clinical cases. Continuous DR measurements during needle insertion in three patients showed that the method can also be applied for biopsy guidance or tumor recognition during surgery. This study provides an important validation step for DR spectroscopy-based tissue characterization in the liver. Given the feasibility of the outlined approach, it is also conceivable to make integrated fiber-optic tools for other clinical procedures that rely on accurate instrument positioning.