Complementary use of polarization-sensitive and standard OCT metrics for enhanced intraoperative differentiation of breast cancer

Compressed sensing of human breast optical coherence 3-D image volume data using predictive coding

There are clinical needs for optical coherence tomography (OCT) of large areas within a short period of time, such as imaging resected breast tissue for the evaluation of cancer. We report on the use of denoising predictive coding (DN-PC), a novel compressed sensing (CS) algorithm for reconstruction of OCT volumes of human normal breast and breast cancer tissue. The DN-PC algorithm has been rewritten to allow for computational parallelization and efficient memory transfer, resulting in a net reduction of computation time by a factor of 20. We compress image volumes at decreasing A-line sampling rates to evaluate a relation between reconstruction behavior and image features of breast tissue.

Diagnostic accuracy of optical coherence tomography for margin assessment in breast-conserving surgery: A systematic review and meta-analysis

BACKGROUND

Breast cancer is the most common malignant tumor among women, and its incidence is increasing annually. At present, the results of the study on whether optical coherence tomography (OCT) can be used as an intraoperative margin assessment method for breast-conserving surgery (BCS) are inconsistent. We herein conducted this systematic review and meta-analysis to assess the diagnostic value of OCT in BCS.

METHODS

PubMed, Web of Science, Cochrane Library, and Embase were used to search relevant studies published up to September 15, 2022. We used Review Manager 5.4, Meta-Disc 1.4, and STATA 16.0 for statistical analysis.

RESULTS

The results displayed 18 studies with 782 patients included according to the inclusion and exclusion criteria. Meta-analysis showed the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and the area under the curve (AUC) of OCT in the margin assessment of BCS were 0.91 (95% CI 0.88-0.93), 0.88 (95% CI 0.83-0.92), 7.53 (95% CI 5.19-10.93), 0.11(95% CI 0.08-0.14), 70.37 (95% CI 39.78-124.47), and 0.94 (95% CI 0.92-0.96), respectively.

CONCLUSIONS

OCT is a promising technique in intraoperative margin assessment of breast cancer patients.

Development of polarization-sensitive optical coherence tomography imaging platform and metrics to quantify electrostimulation-induced peripheral nerve injury in vivo in a small animal model

Significance

Neuromodulation devices are rapidly evolving for the treatment of neurological diseases and conditions. Injury from implantation or long-term use without obvious functional losses is often only detectable through terminal histology. New technologies are needed that assess the peripheral nervous system (PNS) under normal and diseased or injured conditions.

Aim

We aim to demonstrate an imaging and stimulation platform that can elucidate the biological mechanisms and impacts of neurostimulation in the PNS and apply it to the sciatic nerve to extract imaging metrics indicating electrical overstimulation.

Approach

A sciatic nerve injury model in a 15-rat cohort was observed using a newly developed imaging and stimulation platform that can detect electrical overstimulation effects with polarization-sensitive optical coherence tomography. The sciatic nerve was electrically stimulated using a custom-developed nerve holder with embedded electrodes for 1 h, followed by a 1-h recovery period, delivered at above-threshold Shannon model k -values in experimental groups: sham control (SC, n = 5 , 0.0    mA / 0    Hz ), stimulation level 1 (SL1, n = 5 , 3.4    mA / 50    Hz , and k = 2.57 ), and stimulation level 2 (SL2, n = 5 , 6.8    mA / 100    Hz , and k = 3.17 ).

Results

The stimulation and imaging system successfully captured study data across the cohort. When compared to a SC after a 1-week recovery, the fascicle closest to the stimulation lead showed an average change of + 4 % / - 309 % (SL1/SL2) in phase retardation and - 79 % / - 148 % in optical attenuation relative to SC. Analysis of immunohistochemistry (IHC) shows a + 1 % / - 36 % difference in myelin pixel counts and - 13 % / + 29 % difference in axon pixel counts, and an overall increase in cell nuclei pixel count of + 20 % / + 35 % . These metrics were consistent with IHC and hematoxylin/eosin tissue section analysis.

Conclusions

The poststimulation changes observed in our study are manifestations of nerve injury and repair, specifically degeneration and angiogenesis. Optical imaging metrics quantify these processes and may help evaluate the safety and efficacy of neuromodulation devices.

Multiscale Label-Free Imaging of Fibrillar Collagen in the Tumor Microenvironment

With recent advances in cancer therapeutics, there is a great need for improved imaging methods for characterizing cancer onset and progression in a quantitative and actionable way. Collagen, the most abundant extracellular matrix protein in the tumor microenvironment (and the body in general), plays a multifaceted role, both hindering and promoting cancer invasion and progression. Collagen deposition can defend the tumor with immunosuppressive effects, while aligned collagen fiber structures can enable tumor cell migration, aiding invasion and metastasis. Given the complex role of collagen fiber organization and topology, imaging has been a tool of choice to characterize these changes on multiple spatial scales, from the organ and tumor scale to cellular and subcellular level. Macroscale density already aids in the detection and diagnosis of solid cancers, but progress is being made to integrate finer microscale features into the process. Here we review imaging modalities ranging from optical methods of second harmonic generation (SHG), polarized light microscopy (PLM), and optical coherence tomography (OCT) to the medical imaging approaches of ultrasound and magnetic resonance imaging (MRI). These methods have enabled scientists and clinicians to better understand the impact collagen structure has on the tumor environment, at both the bulk scale (density) and microscale (fibrillar structure) levels. We focus on imaging methods with the potential to both examine the collagen structure in as natural a state as possible and still be clinically amenable, with an emphasis on label-free strategies, exploiting intrinsic optical properties of collagen fibers.

Bimodal polarization-sensitive imaging and diffuse reflectance spectroscopy facilitated rapid augmented reality for tissue stratification

We reported the development of a bimodal large field of view (~95 cm² ) polarization-sensitive (PS) imaging and point-wise diffuse reflectance (DR) spectroscopy platform synergising the advantages of the two modalities. PS-DR spectroscopy allows simultaneous label-free biochemical and structural analysis of the investigated tissue. For real applications, the PS imaging first grossly stratified tissue into different categories, followed by high information dimension PS-DR spectroscopy ascertaining ambiguous tissue regions identified by the PS imaging. Tested on a tissue phantom consisting of porcine esophagus muscularis propria and submucosa, it has demonstrated the system developed could complete tissue interrogation within 0.6 minutes and with separation accuracy of 95.87%. Further test on tissue phantom consisting of natural and crushed bovine tendon mimicking normal and cancerous breast tissues validated the potential of system developed to effectively and rapidly investigate large tissue area, facilitating augmented tissue boundary demarcation.

Longitudinal investigation of a xenograft tumor zebrafish model using polarization-sensitive optical coherence tomography

Breast cancer is a leading cause of death in female patients worldwide. Further research is needed to get a deeper insight into the mechanisms involved in the development of this devastating disease and to find new therapy strategies. The zebrafish is an established animal model, especially in the field of oncology, which has shown to be a promising candidate for pre-clinical research and precision-based medicine. To investigate cancer growth in vivo in zebrafish, one approach is to explore xenograft tumor models. In this article, we present the investigation of a juvenile xenograft zebrafish model using a Jones matrix optical coherence tomography (JM-OCT) prototype. Immunosuppressed wild-type fish at 1-month post-fertilization were injected with human breast cancer cells and control animals with phosphate buffered saline in the tail musculature. In a longitudinal study, the scatter, polarization, and vasculature changes over time were investigated and quantified in control versus tumor injected animals. A significant decrease in birefringence and an increase in scattering signal was detected in tumor injected zebrafish in comparison to the control once. This work shows the potential of JM-OCT as a non-invasive, label-free, three-dimensional, high-resolution, and tissue-specific imaging tool in pre-clinical cancer research based on juvenile zebrafish models.

En-face polarization-sensitive optical coherence tomography to characterize early-stage esophageal cancer and determine tumor margin

Current imaging tools are insufficiently sensitive to the early diagnosis of esophageal squamous cell carcinoma (ESCC). The application of polarization-sensitive optical coherence tomography (PS-OCT) to detect tumor-stroma interaction is an interesting issue in cancer diagnosis. In this translational study, we found that en-face PS-OCT effectively characterizes protruding, flat, and depressive type ESCC regardless of animal or human specimens. In addition, the tumor contour and margin could also be drawn and determined on a broad en-face view. The determined tumor margin could be in the proximity of 2 mm to the actual tumor margin, which was proved directly using histology.

Classifying breast cancer in ultrahigh-resolution optical coherence tomography images using convolutional neural networks

Optical coherence tomography (OCT) is being investigated in breast cancer diagnostics as a real-time histology evaluation tool. We present a customized deep convolutional neural network (CNN) for classification of breast tissues in OCT B-scans. Images of human breast samples from mastectomies and breast reductions were acquired using a custom ultrahigh-resolution OCT system with 2.72 µm axial resolution and 5.52 µm lateral resolution. The network achieved 96.7% accuracy, 92% sensitivity, and 99.7% specificity on a dataset of 23 patients. The usage of deep learning will be important for the practical integration of OCT into clinical practice.

In vivo evaluation of endometrium through dual-modality intrauterine endoscopy

Female infertilities are highly associated with poor endometrial receptivity. A receptive endometrium is generally characterized by the normal uterine cavity, intact endometrial surface, appropriate endometrial thickness, and echo pattern. Acquiring comprehensive structural information is the prerequisite of endometrium assessment, which is beyond the ability of any single-modality imaging method. In this paper, we introduce a custom-made intrauterine dual-modality (OCT/ultrasound) endoscopic imaging system and achieve in vivo imaging of rabbit uteri, for the first time to our knowledge. The endometrial features of the injured uteri in both ultrasonic and OCT images are consistent with their corresponding pathology. The quantified parameters, including uterine thickness and endometrial surface roughness, show the correlation with the endometrial injury degree but with poor performance for injury classification. The combination of these parameters was proved to assess the degrees of endometrial injury more accurately. Our work shows the potential of the dual-modality system to be translated into a clinical tool, providing multiple quantitative imaging information and helping evaluate the endometrial receptivity and diagnose infertility.

Rapid non-destructive volumetric tumor yield assessment in fresh lung core needle biopsies using polarization sensitive optical coherence tomography

Adequate tumor yield in core-needle biopsy (CNB) specimens is essential in lung cancer for accurate histological diagnosis, molecular testing for therapeutic decision-making, and tumor biobanking for research. Insufficient tumor sampling in CNB is common, primarily due to inadvertent sampling of tumor-associated fibrosis or atelectatic lung, leading to repeat procedures and delayed diagnosis. Currently, there is no method for rapid, non-destructive intraprocedural assessment of CNBs. Polarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution, volumetric imaging technique that has the potential to meet this clinical need. PS-OCT detects endogenous tissue properties, including birefringence from collagen, and degree of polarization uniformity (DOPU) indicative of tissue depolarization. Here, PS-OCT birefringence and DOPU measurements were used to quantify the amount of tumor, fibrosis, and normal lung parenchyma in 42 fresh, intact lung CNB specimens. PS-OCT results were compared to and validated against matched histology in a blinded assessment. Linear regression analysis showed strong correlations between PS-OCT and matched histology for quantification of tumors, fibrosis, and normal lung parenchyma in CNBs. PS-OCT distinguished CNBs with low tumor content from those with higher tumor content with high sensitivity and specificity. This study demonstrates the potential of PS-OCT as a method for rapid, non-destructive, label-free intra-procedural tumor yield assessment.

Feasibility of dual-contrast fluorescence imaging of pathological breast tissues

The combination of intravital dye, methylene blue (MB), with molecular cancer marker, pH low insertion peptide (pHLIP) conjugated with fluorescent Alexa532 (Alexa532-pHLIP), was evaluated for enhancing contrast of pathological breast tissue ex vivo. Fresh, thick breast specimens were stained sequentially with Alexa532-pHLIP and aqueous MB and imaged using dual-channel fluorescence microscopy. MB and Alexa532-pHLIP accumulated in the nuclei and cytoplasm of cancer cells, respectively. MB also stained nuclei of normal cells. Some Alexa532-pHLIP fluorescence emission was detected from connective tissue and benign cell membranes. Overall, Alexa532-pHLIP showed high affinity to cancer, while MB highlighted tissue morphology. The results indicate that MB and Alexa532-pHLIP provide complementary information and show promise for the detection of breast cancer.

Compressive sensing for polarization sensitive optical coherence tomography

In this report, we report on the implementation of compressive sensing (CS) and sparse sampling in polarization sensitive optical coherence tomography (PS-OCT) to reduce the number of B-scans (frames consisting of an array of A-scans, where each represents a single depth profile of reflections) required for effective volumetric (3D dataset composed of an array of B-scans) PS-OCT measurements (i.e. OCT intensity, and phase retardation) reconstruction. Sparse sampling of PS-OCT is achieved through randomization of step sizes along the slow-axis of PS-OCT imaging, covering the same spatial ranges as those with equal slow-axis step sizes, but with a reduced number of B-scans. Tested on missing B-scan rates of 25%, 50% and 75%, we found CS could reconstruct reasonably good (as evidenced by a correlation coefficient >0.6) PS-OCT measurements with a maximum reduced B-scan rate of 50%, thereby accelerating and doubling the rate of volumetric PS-OCT measurements.

Synthetic polarization-sensitive optical coherence tomography by deep learning

Polarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization sensitivity in an OCT system, however, requires additional hardware and complexity. We developed a deep-learning method to synthesize PS-OCT images by training a generative adversarial network (GAN) on OCT intensity and PS-OCT images. The synthesis accuracy was first evaluated by the structural similarity index (SSIM) between the synthetic and real PS-OCT images. Furthermore, the effectiveness of the computational PS-OCT images was validated by separately training two image classifiers using the real and synthetic PS-OCT images for cancer/normal classification. The similar classification results of the two trained classifiers demonstrate that the predicted PS-OCT images can be potentially used interchangeably in cancer diagnosis applications. In addition, we applied the trained GAN models on OCT images collected from a separate OCT imaging system, and the synthetic PS-OCT images correlate well with the real PS-OCT image collected from the same sample sites using the PS-OCT imaging system. This computational PS-OCT imaging method has the potential to reduce the cost, complexity, and need for hardware-based PS-OCT imaging systems.

Histopathological correlations of bulk tissue polarimetric images: Case study

Polarimetric imaging and image analysis have gained increased interest in soft tissue analysis at the cellular level. However, polarimetric imaging has widely been tested on thin tissue sections to provide reliable information correlated with histopathological findings. Polarimetric bulk tissue analysis always offered an overall assessment of various tissue optical properties for diagnosis. In this study, the histopathological correlation of bulk tissue polarimetry images for soft tissues is discussed. The first-hand information on the use of bulk tissue Mueller polarimetry and image analysis as an alternative to tissue histopathology is presented for surgically extracted colon and breast tissues.

Differentiation of breast tissue types for surgical margin assessment using machine learning and polarization-sensitive optical coherence tomography

We report an automated differentiation model for classifying malignant tumor, fibro-adipose, and stroma in human breast tissues based on polarization-sensitive optical coherence tomography (PS-OCT). A total of 720 PS-OCT images from 72 sites of 41 patients with H&E histology-confirmed diagnoses as the gold standard were employed in this study. The differentiation model is trained by the features extracted from both one standard OCT-based metric (i.e., intensity) and four PS-OCT-based metrics (i.e., phase difference between two channels (PD), phase retardation (PR), local phase retardation (LPR), and degree of polarization uniformity (DOPU)). Further optimized by forward searching and validated by leave-one-site-out-cross-validation (LOSOCV) method, the best feature subset was acquired with the highest overall accuracy of 93.5% for the model. Furthermore, to show the superiority of our differentiation model based on PS-OCT images over standard OCT images, the best model trained by intensity-only features (usually obtained by standard OCT systems) was also obtained with an overall accuracy of 82.9%, demonstrating the significance of the polarization information in breast tissue differentiation. The high performance of our differentiation model suggests the potential of using PS-OCT for intraoperative human breast tissue differentiation during the surgical resection of breast cancer.

Computational adaptive optics for polarization-sensitive optical coherence tomography

Defocus aberration in optical systems, including optical coherence tomography (OCT) systems employing Gaussian illumination, gives rise to the well-known compromise between transverse resolution and depth-of-field. This results in blurry images when out-of-focus, whilst other low-order aberrations (e.g., astigmatism, coma, etc.) present in both the OCT system and biological samples further reduce image resolution and contrast. Computational adaptive optics (CAO) is a computed optical interferometric imaging technique that modifies the phase of the OCT data in the spatial frequency domain to correct optical aberrations and provide improvement of the image quality throughout the three-dimensional (3D) volume. In this Letter, we report the first implementation of CAO for polarization-sensitive OCT to correct defocus and other low-order aberrations, providing enhanced polarization-sensitive imaging contrast (i.e., intensity and phase retardation) on a 3D OCT phantom, molded plastics, ex vivo chicken breast tissue, and ex vivo human breast cancer tissue.

Diagnostic Accuracy of Cross-Polarization OCT and OCT-Elastography for Differentiation of Breast Cancer Subtypes: Comparative Study

The possibility to assess molecular-biological and morphological features of particular breast cancer types can improve the precision of resection margin detection and enable accurate determining of the tumor aggressiveness, which is important for treatment selection. To enable reliable differentiation of breast-cancer subtypes and evaluation of resection margin, without performing conventional histological procedures, here we apply cross-polarization optical coherence tomography (CP-OCT) and compare it with a novel variant of compressional optical coherence elastography (C-OCE) in terms of the diagnostic accuracy (Ac) with histological verification. The study used 70 excised breast cancer specimens with different morphological structure and molecular status (Luminal A, Luminal B, Her2/Neo+, non-luminal and triple-negative cancer). Our first aim was to formulate convenient criteria of visual assessment of CP-OCT and C-OCE images intended (i) to differentiate tumorous and non-tumorous tissues and (ii) to enable more precise differentiation among different malignant states. We identified such criteria based on the presence of heterogeneities and characteristics of signal attenuation in CP-OCT images, as well as the presence of inclusions/mosaic structures combined with visually feasible assessment of several stiffness grades in C-OCE images. Secondly, we performed a blinded reader study of the Ac of C-OCE versus CP-OCT, for delineation of tumorous versus non-tumorous tissues followed by identification of breast cancer subtypes. For tumor detection, C-OCE showed higher specificity than CP-OCT (97.5% versus 93.3%) and higher Ac (96.0 versus 92.4%). For the first time, the Ac of C-OCE and CP-OCT were evaluated for differentiation between non-invasive and invasive breast cancer (90.4% and 82.5%, respectively). Furthermore, for invasive cancers, the difference between invasive but low-aggressive and highly-aggressive subtypes can be detected. For differentiation between non-tumorous tissue and low-aggressive breast-cancer subtypes, Ac was 95.7% for C-OCE and 88.1% for CP-OCT. For differentiation between non-tumorous tissue and highly-aggressive breast cancers, Ac was found to be 98.3% for C-OCE and 97.2% for CP-OCT. In all cases C-OCE showed better diagnostic parameters independently of the tumor type. These findings confirm the high potential of OCT-based examinations for rapid and accurate diagnostics during breast conservation surgery.

Automated full-field polarization-sensitive optical coherence tomography diagnostic systems for breast cancer

Intraoperative delineation of breast cancer is a major challenge. An effective breast tissue screening technique may reduce the risk of re-excision during surgery by specifically identifying positive margins. In this study, a high-resolution automated full-field polarization-sensitive optical coherence tomography (FF-PS-OCT) system was developed to classify healthy and malignant human breast tissue from quantitative phase retardation information of the tissues in ex vivo. Twelve breast tissue samples [four healthy, eight malignant (cancerous)] were imaged with the FF-PS-OCT system and the different phase features were extracted from the acquired OCT images (106), based on the differences in the optical signatures of the healthy and malignant tissues. A linear support vector model classifier was trained using 75 images, with a sensitivity of 92.10% and specificity of 89.18% was achieved. Thirty-one images were used to test the model, with a sensitivity of 90.90% and specificity of 85.0% achieved.

Detection of Oral Dysplastic and Early Cancerous Lesions by Polarization-Sensitive Optical Coherence Tomography

Detection of oral dysplastic and early-stage cancerous lesions is difficult with the current tools. Half of oral cancers are diagnosed in a late stage. Detection of early stromal change to predict malignant transformation is a new direction in the diagnosis of early-stage oral cancer. The application of new optical tools to image stroma in vivo is under investigation, and polarization-sensitive optical coherence tomography (PS-OCT) is potentially one of those tools. This is a preliminary study to sequentially image oral stromal changes from normal, hyperplasia, and dysplasia to early-stage cancer by PS-OCT in vivo. We used 4-Nitroquinoline-1-oxide drinking water to induce dysplasia and early-stage oral cancer in 19 K14-EGFP-miR-211-GFP transgenic mice. A total of 8 normal, 12 hyperplastic, 11 dysplastic, and 4 early-stage cancerous lesions were enrolled. A new analytic process of PS-OCT imaging was proposed, called an en-face birefringence map. From the birefringence map, the sensitivity, specificity, positive predictive value, and negative predictive values to detect dysplasia and early-stage cancer were 100.00%, 95.00%, 93.75%, and 100.00%, respectively, and the kappa value of these images between two investigators was 0.942. The mean size of malignant lesions detected in this study is 1.66 ± 0.93 mm. This pilot animal study validates the use of PS-OCT to detect small and early-stage oral malignancy with high accuracy and consistency.

Evaluating optical coherence tomography for surgical margin assessment of canine mammary tumours

Optical coherence tomography (OCT) uses near-infrared light waves to generate real-time, high-resolution images on the microscopic scale similar to low power histopathology. Previous studies have demonstrated the use of OCT for real-time surgical margin assessment for human breast cancer. The use of OCT for canine mammary tumours (CMT) could allow intra-operative visualisation of residual tumour at the surgical margins. The purpose of this study was to assess OCT imaging for the detection of incomplete tumour resection following CMT surgery. We hypothesized that the OCT images would have comparable features to histopathological images of tissues at the surgical margins of CMT resections along with a high sensitivity of OCT detection of incomplete surgical excision of CMT. Thirty surgical specimens were obtained from nineteen client-owned dogs undergoing surgical resection of CMT. OCT image appearance and characteristics of adipose tissue, skin, mammary tissue and mammary tumour at the surgical margins were distinct and different. The OCT images of normal and abnormal tissues at the surgical margins were utilized to develop a dataset of OCT images for observer evaluation. The sensitivity and specificity for ex vivo images were 83.3% and 82.0% (observer 1) and 70.0% and 67.9% (observer 2). The sensitivity and specificity for in vivo images were 70.0% and 89.3% (observer 1) and 76.7% and 67.9% (observer 2). These results indicate a potential use of OCT for surgical margin assessment for CMT to optimize surgical intervention and clinical outcomes. Improved training and experience of observers may improve sensitivity and specificity.