Rapid and high-resolution imaging of human liver specimens by full-field optical coherence tomography

Optical coherence tomography combined with convolutional neural networks can differentiate between intrahepatic cholangiocarcinoma and liver parenchyma ex vivo

PURPOSE

Surgical resection with complete tumor excision (R0) provides the best chance of long-term survival for patients with intrahepatic cholangiocarcinoma (iCCA). A non-invasive imaging technology, which could provide quick intraoperative assessment of resection margins, as an adjunct to histological examination, is optical coherence tomography (OCT). In this study, we investigated the ability of OCT combined with convolutional neural networks (CNN), to differentiate iCCA from normal liver parenchyma ex vivo.

METHODS

Consecutive adult patients undergoing elective liver resections for iCCA between June 2020 and April 2021 (n = 11) were included in this study. Areas of interest from resection specimens were scanned ex vivo, before formalin fixation, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined, providing a diagnosis for each scan. An Xception CNN was trained, validated, and tested in matching OCT scans to their corresponding histological diagnoses, through a 5 × 5 stratified cross-validation process.

RESULTS

Twenty-four three-dimensional scans (corresponding to approx. 85,603 individual) from ten patients were included in the analysis. In 5 × 5 cross-validation, the model achieved a mean F1-score, sensitivity, and specificity of 0.94, 0.94, and 0.93, respectively.

CONCLUSION

Optical coherence tomography combined with CNN can differentiate iCCA from liver parenchyma ex vivo. Further studies are necessary to expand on these results and lead to innovative in vivo OCT applications, such as intraoperative or endoscopic scanning.

Optical coherence tomography and convolutional neural networks can differentiate colorectal liver metastases from liver parenchyma ex vivo

PURPOSE

Optical coherence tomography (OCT) is an imaging technology based on low-coherence interferometry, which provides non-invasive, high-resolution cross-sectional images of biological tissues. A potential clinical application is the intraoperative examination of resection margins, as a real-time adjunct to histological examination. In this ex vivo study, we investigated the ability of OCT to differentiate colorectal liver metastases (CRLM) from healthy liver parenchyma, when combined with convolutional neural networks (CNN).

METHODS

Between June and August 2020, consecutive adult patients undergoing elective liver resections for CRLM were included in this study. Fresh resection specimens were scanned ex vivo, before fixation in formalin, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined. A pre-trained CNN (Xception) was used to match OCT scans to their corresponding histological diagnoses. To validate the results, a stratified k-fold cross-validation (CV) was carried out.

RESULTS

A total of 26 scans (containing approx. 26,500 images in total) were obtained from 15 patients. Of these, 13 were of normal liver parenchyma and 13 of CRLM. The CNN distinguished CRLM from healthy liver parenchyma with an F1-score of 0.93 (0.03), and a sensitivity and specificity of 0.94 (0.04) and 0.93 (0.04), respectively.

CONCLUSION

Optical coherence tomography combined with CNN can distinguish between healthy liver and CRLM with great accuracy ex vivo. Further studies are needed to improve upon these results and develop in vivo diagnostic technologies, such as intraoperative scanning of resection margins.

Research progress on the application of optical coherence tomography in the field of oncology

Optical coherence tomography (OCT) is a non-invasive imaging technique which has become the “gold standard” for diagnosis in the field of ophthalmology. However, in contrast to the eye, nontransparent tissues exhibit a high degree of optical scattering and absorption, resulting in a limited OCT imaging depth. And the progress made in the past decade in OCT technology have made it possible to image nontransparent tissues with high spatial resolution at large (up to 2mm) imaging depth. On the one hand, OCT can be used in a rapid, noninvasive way to detect diseased tissues, organs, blood vessels or glands. On the other hand, it can also identify the optical characteristics of suspicious parts in the early stage of the disease, which is of great significance for the early diagnosis of tumor diseases. Furthermore, OCT imaging has been explored for imaging tumor cells and their dynamics, and for the monitoring of tumor responses to treatments. This review summarizes the recent advances in the OCT area, which application in oncological diagnosis and treatment in different types: (1) superficial tumors:OCT could detect microscopic information on the skin’s surface at high resolution and has been demonstrated to help diagnose common skin cancers; (2) gastrointestinal tumors: OCT can be integrated into small probes and catheters to image the structure of the stomach wall, enabling the diagnosis and differentiation of gastrointestinal tumors and inflammation; (3) deep tumors: with the rapid development of OCT imaging technology, it has shown great potential in the diagnosis of deep tumors such in brain tumors, breast cancer, bladder cancer, and lung cancer.

Methods and applications of full-field optical coherence tomography: a review

SIGNIFICANCE

Full-field optical coherence tomography (FF-OCT) enables en face views of scattering samples at a given depth with subcellular resolution, similar to biopsy without the need of sample slicing or other complex preparation. This noninvasive, high-resolution, three-dimensional (3D) imaging method has the potential to become a powerful tool in biomedical research, clinical applications, and other microscopic detection.

AIM

Our review provides an overview of the disruptive innovations and key technologies to further improve FF-OCT performance, promoting FF-OCT technology in biomedical and other application scenarios.

APPROACH

A comprehensive review of state-of-the-art accomplishments in OCT has been performed. Methods to improve performance of FF-OCT systems are reviewed, including advanced phase-shift approaches for imaging speed improvement, methods of denoising, artifact reduction, and aberration correction for imaging quality optimization, innovations for imaging flux expansion (field-of-view enlargement and imaging-depth-limit extension), new implementations for multimodality systems, and deep learning enhanced FF-OCT for information mining, etc. Finally, we summarize the application status and prospects of FF-OCT in the fields of biomedicine, materials science, security, and identification.

RESULTS

The most worth-expecting FF-OCT innovations include combining the technique of spatial modulation of optical field and computational optical imaging technology to obtain greater penetration depth, as well as exploiting endogenous contrast for functional imaging, e.g., dynamic FF-OCT, which enables noninvasive visualization of tissue dynamic properties or intracellular motility. Different dynamic imaging algorithms are compared using the same OCT data of the colorectal cancer organoid, which helps to understand the disadvantages and advantages of each. In addition, deep learning enhanced FF-OCT provides more valuable characteristic information, which is of great significance for auxiliary diagnosis and organoid detection.

CONCLUSIONS

FF-OCT has not been completely exploited and has substantial growth potential. By elaborating the key technologies, performance optimization methods, and application status of FF-OCT, we expect to accelerate the development of FF-OCT in both academic and industry fields. This renewed perspective on FF-OCT may also serve as a road map for future development of invasive 3D super-resolution imaging techniques to solve the problems of microscopic visualization detection.

Evolution of the liver biopsy and its future

Liver biopsies are commonly used to evaluate a wide variety of medical disorders, including neoplasms and post-transplant complications. However, its use is being impacted by improved clinical diagnosis of disorders, and non-invasive methods for evaluating liver tissue and as a result the indications of a liver biopsy have undergone major changes in the last decade. The evolution of highly effective treatments for some of the common indications for liver biopsy in the last decade (e.g., viral hepatitis B and C) has led to a decline in the number of liver biopsies in recent years. At the same time, the emergence of better technologies for histologic evaluation, tissue content analysis and genomics are among the many new and exciting developments in the field that hold great promise for the future and are going to shape the indications for a liver biopsy in the future. Recent advances in slide scanners now allow creation of "digital/virtual" slides that have image of the entire tissue section present in a slide [whole slide imaging (WSI)]. WSI can now be done very rapidly and at very high resolution, allowing its use in routine clinical practice. In addition, a variety of technologies have been developed in recent years that use different light sources and/or microscopes allowing visualization of tissues in a completely different way. One such technique that is applicable to liver specimens combines multiphoton microscopy (MPM) with advanced clearing and fluorescent stains known as Clearing Histology with MultiPhoton Microscopy (CHiMP). Although it has not yet been extensively validated, the technique has the potential to decrease inefficiency, reduce artifacts, and increase data while being readily integrable into clinical workflows. Another technology that can provide rapid and in-depth characterization of thousands of molecules in a tissue sample, including liver tissues, is matrix assisted laser desorption/ionization (MALDI) mass spectrometry. MALDI has already been applied in a clinical research setting with promising diagnostic and prognostic capabilities, as well as being able to elucidate mechanisms of liver diseases that may be targeted for the development of new therapies. The logical next step in huge data sets obtained from such advanced analysis of liver tissues is the application of machine learning (ML) algorithms and application of artificial intelligence (AI), for automated generation of diagnoses and prognoses. This review discusses the evolving role of liver biopsies in clinical practice over the decades, and describes newer technologies that are likely to have a significant impact on how they will be used in the future.

Rapid, High-Resolution, Label-Free, and 3-Dimensional Imaging to Differentiate Colorectal Adenomas and Non-Neoplastic Polyps With Micro-Optical Coherence Tomography

“Resect and discard” paradigm is one of the main strategies to deal with colorectal diminutive polyps after optical diagnosis. However, there are risks that unrecognized potentially malignant lesions are discarded without accurate diagnosis. The purpose of this study is to validate the potential of micro-optical coherence tomography (μOCT) to improve the diagnostic accuracy of colorectal lesions and help endoscopists make better clinical decision without additional pathology costs.

Liver tissue classification of en face images by fractal dimension-based support vector machine

Full-field optical coherence tomography (FF-OCT) has been reported with its label-free subcellular imaging performance. To realize quantitive cancer detection, the support vector machine model of classifying normal and cancerous human liver tissue is proposed with en face tomographic images. Twenty samples (10 normal and 10 cancerous) were operated from humans and composed of 285 en face tomographic images. Six histogram features and one proposed fractal dimension parameter that reveal the refractive index inhomogeneities of tissue were extracted and made up the training set. The other different 16 samples (8 normal and 8 cancerous) were imaged (190 images) and employed as the test set with the same features. First, a subcellular-resolution tomographic image library for four histopathological areas in liver tissue was established. Second, the area under the receiver operating characteristics of 0.9378, 0.9858, 0.9391, 0.9517 for prediction of the cancerous hepatic cell, central vein, fibrosis, and portal vein were measured with the test set. The results indicate that the proposed classifier from FF-OCT images shows promise as a label-free assessment of quantified tumor detection, suggesting the fractal dimension-based classifier could aid clinicians in detecting tumor boundaries for resection in surgery in the future.

[Optical coherence tomography of bladder resection specimen]

INTRODUCTION

The diagnosis of bladder urothelial tumors is based on bladder resection and histological analysis of the specimen. The time to obtain the results of the histological analysis increases the treatment delay. Furthermore, the lack of muscle on the specimen forces the surgeon to practice on other procedure. Full field optical coherence tomography (FFOCT) is a recent imaging technique to analyze tissue. The aim of our study was to evaluate the feasibility and diagnostic accuracy of FFOCT to detect muscle and tumor in bladder resection specimen.

PATIENTS AND METHODS

We analyzed with the FFOCT technique bladder resection specimen of 24 consecutives patients. Three readers did the blind analyze of the images, looking for the presence of muscle and tumor on each specimen. Their results were compared with histological analysis to calculate diagnostic accuracy for each reader.

RESULTS

Mean sensibilities for the detection of muscle and tumor were respectively 75% and 81%. Mean specificities for the detection of muscle and tumor were respectively 78.3% and 55.3%.

CONCLUSIONS

Our results suggest that the FFOCT is feasible to analyze bladder resection specimen. Sensibilities and specificities calculated are encouraging for the detection of muscle and tumor. The accuracy of this detection and early-staging tool should be validated by larger studies.

LEVEL OF EVIDENCE

3.

The clinical usefulness of optical coherence tomography during cancer interventions

INTRODUCTION

Tumor detection and visualization plays a key role in the clinical workflow of a patient with suspected cancer, both in the diagnosis and treatment. Several optical imaging techniques have been evaluated for guidance during oncological interventions. Optical coherence tomography (OCT) is a technique which has been widely evaluated during the past decades. This review aims to determine the clinical usefulness of OCT during cancer interventions focussing on qualitative features, quantitative features and the diagnostic value of OCT.

METHODS

A systematic literature search was performed for articles published before May 2018 using OCT in the field of surgical oncology. Based on these articles, an overview of the clinical usefulness of OCT was provided per tumor type.

RESULTS

A total of 785 articles were revealed by our search, of which a total of 136 original articles were available for analysis, which formed the basis of this review. OCT is currently utilised for both preoperative diagnosis and intraoperative detection of skin, oral, lung, breast, hepatobiliary, gastrointestinal, urological, and gynaecological malignancies. It showed promising results in tumor detection on a microscopic level, especially using higher resolution imaging techniques, such as high-definition OCT and full-field OCT.

CONCLUSION

In the near future, OCT could be used as an additional tool during bronchoscopic or endoscopic interventions and could also be implemented in margin assessment during (laparoscopic) cancer surgery if a laparoscopic or handheld OCT device will be further developed to make routine clinical use possible.

Optical coherence tomography assessment of gingival epithelium inflammatory status in periodontal - Systemic affected patients

INTRODUCTION

Optical coherence tomography (OCT) is an imaging tool used in various medical fields (ophthalmology, dermatology), which allows the observation of morphological particularities on the surface of tissues or internal constructive details of about 2-3mms in depth. In periodontology, it has been used as an experimental tool for periodontal pocket analysis (depth, calculus deposits) but not for the assessment of periodontal inflammation in the gingival tissues, which has been the subject of our in-vitro study.

MATERIAL AND METHOD

Gingival samples were collected from three types of patients: patients with periodontal disease; patients with periodontal disease and a systemic comorbidity; periodontal and systemic healthy patients. The samples were scanned with an OCT light beam, resulting two-dimensional images of the gingival tissue (full thickness epithelium and partial connective tissue). The images were assessed using dedicated software, which allowed the quantification of pixels on a given segment in the epithelium. The average pixel densities were then calculated for each patient group and statistically analyzed.

RESULTS

The resulted pixel densities were highest for the control group samples, while the lowest pixel densities were found in samples originating from periodontal patients with diabetes mellitus. For the other possible periodontal comorbidity, chronic hepatitis C, image assessment also exhibited lower pixel densities than those of the periodontal group, suggesting that this condition could also have an added effect on the tissular changes induced by periodontal disease.

CONCLUSION

OCT has proven that in an in-vitro environment it can be a useful tool for the assessment of periodontal inflammation in gingival samples of periodontal patients. In terms of inflammatory tissular changes observed by OCT analysis, chronic hepatitis C could be regarded as possible periodontal disease's comorbidity.

Improving lateral resolution and image quality of optical coherence tomography by the multi-frame superresolution technique for 3D tissue imaging

The multi-frame superresolution technique is introduced to significantly improve the lateral resolution and image quality of spectral domain optical coherence tomography (SD-OCT). Using several sets of low resolution C-scan 3D images with lateral sub-spot-spacing shifts on different sets, the multi-frame superresolution processing of these sets at each depth layer reconstructs a higher resolution and quality lateral image. Layer by layer processing yields an overall high lateral resolution and quality 3D image. In theory, the superresolution processing including deconvolution can solve the diffraction limit, lateral scan density and background noise problems together. In experiment, the improved lateral resolution by ~3 times reaching 7.81 µm and 2.19 µm using sample arm optics of 0.015 and 0.05 numerical aperture respectively as well as doubling the image quality has been confirmed by imaging a known resolution test target. Improved lateral resolution on in vitro skin C-scan images has been demonstrated. For in vivo 3D SD-OCT imaging of human skin, fingerprint and retina layer, we used the multi-modal volume registration method to effectively estimate the lateral image shifts among different C-scans due to random minor unintended live body motion. Further processing of these images generated high lateral resolution 3D images as well as high quality B-scan images of these in vivo tissues.

Differences between time domain and Fourier domain optical coherence tomography in imaging tissues

It has been numerously demonstrated that both time domain and Fourier domain optical coherence tomography (OCT) can generate high-resolution depth-resolved images of living tissues and cells. In this work, we compare the common points and differences between two methods when the continuous and random properties of live tissue are taken into account. It is found that when relationships that exist between the scattered light and tissue structures are taken into account, spectral interference measurements in Fourier domain OCT (FDOCT) is more advantageous than interference fringe envelope measurements in time domain OCT (TDOCT) in the cases where continuous property of tissue is taken into account. It is also demonstrated that when random property of tissue is taken into account FDOCT measures the Fourier transform of the spatial correlation function of the refractive index and speckle phenomena will limit the effective limiting imaging resolution in both TDOCT and FDOCT. Finally, the effective limiting resolution of both TDOCT and FDOCT are given which can be used to estimate the effective limiting resolution in various practical applications.

Identification of liver metastases with probe-based confocal laser endomicroscopy at two excitation wavelengths

BACKGROUND

Metastasis of colorectal cancer to the liver is the most common indication for hepatic resection in a western population. Incomplete excision of malignancy due to residual microscopic disease normally results in worse patient outcome. Therefore, a method aiding in the real time discrimination of normal and malignant tissue on a microscopic level would be of benefit.

MATERIAL AND METHODS

The ability of fluorescent probe-based confocal laser endomicroscopy (pCLE) to identify normal and malignant liver tissue was evaluated in an orthotopic murine model of colorectal cancer liver metastasis (CRLM). To maximise information yield, two clinical fluorophores, fluorescein and indocyanine green (ICG) were injected and imaged in a dual wavelength approach (488 and 660 nm, respectively). Visual tissue characteristics on pCLE examination were compared with histological features. Fluorescence intensity in both tissues was statistically analysed to elucidate if this can be used to differentiate between normal and malignant tissue.

RESULTS

Fluorescein (488 nm) enabled good visualisation of normal and CRLM tissue, whereas ICG (660 nm) visualisation was limited to normal liver tissue only. Fluorescence intensity in areas of CRLM was typically 53-100% lower than normal hepatic parenchyma. Using general linear mixed modelling and receiver operating characteristic analysis, high fluorescence intensity was found to be statistically more likely in normal hepatic tissue.

CONCLUSION

Real time discrimination between normal liver parenchyma and metastatic tissue with pCLE examination of fluorescein and ICG is feasible. Employing two (rather than a single) fluorophores allows a combination of qualitative and quantitative characteristics to be used to distinguish between hepatic parenchyma and CRLM. Lasers Surg. Med. 49:280-292, 2017. © 2016 Wiley Periodicals, Inc.