Broadband hyperspectral imaging for breast tumor detection using spectral and spatial information

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 Agreemen

Method for coregistration of optical measurements of breast tissue with histopathology : the importance of accounting for tissue deformations

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

Clinical characteristics of subsequent histologically confirmed meningiomas in long-term childhood cancer survivors:A Dutch LATER study

Background: Meningiomas are the most frequent brain tumours occurring after pediatric cranial radiotherapy (CrRT). Data on course of disease, to inform clinical management of meningiomas, are sparse. This study reports the clinical characteristics of histologically confirmed meningiomas in childhood cancer survivors (CCS) in the Netherlands.& nbsp; Methods: In total, 6015 CCS from the Dutch Long-Term Effects After Childhood Cancer (LATER) cohort were eligible, including 1551 with prior CrRT. These CCS were diagnosed with cancer ag

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-makin

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-makin

Imaging depth variations in hyperspectral imaging: Development of a method to detect tumor up to the required tumor-free margin width

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

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

Spectral sensing for tissue diagnosis during lung biopsy procedures: The importance of an adequate internal reference and real-time feedback

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

Hyperspectral imaging for resection margin assessment during cancer surgery

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 breastconserving surgery to improve surgical outcome