Visualization and tissue classification of human breast cancer images using ultrahigh-resolution OCT

Clinical evaluation of breast cancer tissue with optical coherence tomography: key findings from a large-scale study

PURPOSE

Breast cancer patients undergoing breast-conserving surgery may require a second operation if positive margins persist but current intraoperative methodologies often lack real-time and comprehensive assessments of tissue margins. This study addresses this critical gap by introducing a novel approach to enhance margin assessment in breast surgery.

METHODS

A total of 252 fresh tissue blocks from 199 patients with different types of breast lesions were scanned with a customized swept-source optical coherence tomography (SS-OCT) system, and the OCT features of normal, benign, and malignant breast tissues, were systematically analyzed.

RESULTS

The qualitative analysis results revealed that adipose tissue has high penetration depth and a typical honeycomb pattern, whereas fibrous tissue has the brightest grayscale values and a bundle-like structure. The lobular area appears as a dark region, and dilated ducts present a distinct tubular structure on B-scan images. Adenosis results in bright areas, fibroadenoma results in typical contour structures, phyllodes tumors present lobular structures, invasive carcinomas present a stellate pattern and low penetration depth, and mucinous carcinoma cancer cells are clearly visible within the low-scattering mucin.

CONCLUSIONS

Importantly, we provide comparative OCT and hematoxylin and eosin (H&E) histology images for less common conditions, such as phyllodes tumors, intraductal papillomas, and mucinous carcinoma. For the first time, we established an 3D OCT-histopathology library with a large field of view and systematically analyzed the multidimensional features. This work strongly supports the feasibility of using OCT technology intraoperatively in surgery. Additionally, the OCT-histopathology library can help pathologists better understand and identify tissue features, thereby enhancing diagnostic efficiency.

Intraoperative evaluation of surgical margins in breast cancer

Achieving clear resection margins at the time of lumpectomy is essential for optimal patient outcomes. Margin status is traditionally determined by pathologic evaluation of the specimen and often is difficult or impossible for the surgeon to definitively know at the time of surgery, resulting in the need for re-operation to obtain clear surgical margins. Numerous techniques have been investigated to enhance the accuracy of intraoperative margin and are reviewed in this manuscript.

Comparing the diagnostic efficacy of optical coherence tomography and frozen section for margin assessment in breast-conserving surgery: a meta-analysis

AIMS

This meta-analysis assessed the relative diagnostic accuracy of optical coherence tomography (OCT) versus frozen section (FS) in evaluating surgical margins during breast-conserving procedures.

METHODS

PubMed and Embase were searched for relevant studies published up to October 2023. The inclusion criteria encompassed studies evaluating the diagnostic accuracy of OCT or FS in patients undergoing breast-conserving surgery. Sensitivity and specificity were analysed using the DerSimonian and Laird method and subsequently transformed through the Freeman-Tukey double inverse sine method.

RESULTS

The meta-analysis encompassed 36 articles, comprising 16 studies on OCT and 20 on FS, involving 10 289 specimens from 8058 patients. The overall sensitivity of OCT was 0.93 (95% CI: 0.90 to 0.96), surpassing that of FS, which was 0.82 (95% CI: 0.71 to 0.92), indicating a significantly higher sensitivity for OCT (p=0.04). Conversely, the overall specificity of OCT was 0.89 (95% CI: 0.83 to 0.94), while FS exhibited a higher specificity at 0.97 (95% CI: 0.95 to 0.99), suggesting a superior specificity for FS (p＜0.01).

CONCLUSIONS

Our meta-analysis reveals that OCT offers superior sensitivity but inferior specificity compared with FS in assessing surgical margins in breast-conserving surgery patients. Further larger well-designed prospective studies are needed, especially those employing a head-to-head comparison design.

PROSPERO REGISTRATION NUMBER

CRD42023483751.

Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine

Personalized medicine transforms healthcare by adapting interventions to individuals' unique genetic, molecular, and clinical profiles. To maximize diagnostic and/or therapeutic efficacy, personalized medicine requires advanced imaging devices and sensors for accurate assessment and monitoring of individual patient conditions or responses to therapeutics. In the field of biomedical optics, short-wave infrared (SWIR) techniques offer an array of capabilities that hold promise to significantly enhance diagnostics, imaging, and therapeutic interventions. SWIR techniques provide in vivo information, which was previously inaccessible, by making use of its capacity to penetrate biological tissues with reduced attenuation and enable researchers and clinicians to delve deeper into anatomical structures, physiological processes, and molecular interactions. Combining SWIR techniques with machine learning (ML), which is a powerful tool for analyzing information, holds the potential to provide unprecedented accuracy for disease detection, precision in treatment guidance, and correlations of complex biological features, opening the way for the data-driven personalized medicine field. Despite numerous biomedical demonstrations that utilize cutting-edge SWIR techniques, the clinical potential of this approach has remained significantly underexplored. This paper demonstrates how the synergy between SWIR imaging and ML is reshaping biomedical research and clinical applications. As the paper showcases the growing significance of SWIR imaging techniques that are empowered by ML, it calls for continued collaboration between researchers, engineers, and clinicians to boost the translation of this technology into clinics, ultimately bridging the gap between cutting-edge technology and its potential for personalized medicine.

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.

Intraoperative Assessment of Breast Cancer Tissues after Breast-Conserving Surgery Based on Mapping the Attenuation Coefficients in 3D Cross-Polarization Optical Coherence Tomography

Intraoperative differentiation of tumorous from non-tumorous tissue can help in the assessment of resection margins in breast cancer and its response to therapy and, potentially, reduce the incidence of tumor recurrence. In this study, the calculation of the attenuation coefficient and its color-coded 2D distribution was performed for different breast cancer subtypes using spectral-domain CP OCT. A total of 68 freshly excised human breast specimens containing tumorous and surrounding non-tumorous tissues after BCS was studied. Immediately after obtaining structural 3D CP OCT images, en face color-coded attenuation coefficient maps were built in co-(Att(co)) and cross-(Att(cross)) polarization channels using a depth-resolved approach to calculating the values in each A-scan. We determined spatially localized signal attenuation in both channels and reported ranges of attenuation coefficients to five selected breast tissue regions (adipose tissue, non-tumorous fibrous connective tissue, hyalinized tumor stroma, low-density tumor cells in the fibrotic tumor stroma and high-density clusters of tumor cells). The Att(cross) coefficient exhibited a stronger gain contrast of studied tissues compared to the Att(co) coefficient (i.e., conventional attenuation coefficient) and, therefore, allowed improved differentiation of all breast tissue types. It has been shown that color-coded attenuation coefficient maps may be used to detect inter- and intra-tumor heterogeneity of various breast cancer subtypes as well as to assess the effectiveness of therapy. For the first time, the optimal threshold values of the attenuation coefficients to differentiate tumorous from non-tumorous breast tissues were determined. Diagnostic testing values for Att(cross) coefficient were higher for differentiation of tumor cell areas and tumor stroma from non-tumorous fibrous connective tissue: diagnostic accuracy was 91-99%, sensitivity-96-98%, and specificity-87-99%. Att(co) coefficient is more suitable for the differentiation of tumor cell areas from adipose tissue: diagnostic accuracy was 83%, sensitivity-84%, and specificity-84%. Therefore, the present study provides a new diagnostic approach to the differentiation of breast cancer tissue types based on the assessment of the attenuation coefficient from real-time CP OCT data and has the potential to be used for further rapid and accurate intraoperative assessment of the resection margins during BCS.

Translational Potential of Fluorescence Polarization for Breast Cancer Cytopathology

Breast cancer is the most common malignancy in women. The standard of care for diagnosis involves invasive core needle biopsy followed by time-consuming histopathological evaluation. A rapid, accurate, and minimally invasive method to diagnose breast cancer would be invaluable. Therefore, this clinical study investigated the fluorescence polarization (Fpol) of the cytological stain methylene blue (MB) for the quantitative detection of breast cancer in fine needle aspiration (FNA) specimens. Cancerous, benign, and normal cells were aspirated from excess breast tissues immediately following surgery. The cells were stained in aqueous MB solution (0.05 mg/mL) and imaged using multimodal confocal microscopy. The system provided MB Fpol and fluorescence emission images of the cells. Results from optical imaging were compared to clinical histopathology. In total, we imaged and analyzed 3808 cells from 44 breast FNAs. Fpol images displayed quantitative contrast between cancerous and noncancerous cells, whereas fluorescence emission images showed the morphological features comparable to cytology. Statistical analysis demonstrated that MB Fpol is significantly higher (p < 0.0001) in malignant vs. benign/normal cells. It also revealed a correlation between MB Fpol values and tumor grade. The results indicate that MB Fpol could provide a reliable, quantitative diagnostic marker for breast cancer at the cellular level.

Defining the relevance of surgical margins. Part two: Strategies to improve prediction of recurrence risk

Due to the complex nature of tumour biology and the integration between host tissues and molecular processes of the tumour cells, a continued reliance on the status of the microscopic cellular margin should not remain our only determinant of the success of a curative-intent surgery for patients with cancer. Based on current evidence, relying on a purely cellular focus to provide a binary indication of treatment success can provide an incomplete interpretation of potential outcome. A more holistic analysis of the cancer margin may be required. If we are to move ahead from our current situation - and allow treatment plans to be more intelligently tailored to meet the requirements of each individual tumour - we need to improve our utilisation of techniques that either improve recognition of residual tumour cells within the surgical field or enable a more comprehensive interrogation of tumour biology that identifies a risk of recurrence. In the second article in this series on defining the relevance of surgical margins, the authors discuss possible alternative strategies for margin assessment and evaluation in the canine and feline cancer patient. These strategies include considering adoption of the residual tumour classification scheme; intra-operative imaging systems including fluorescence-guided surgery, optical coherence tomography and Raman spectroscopy; molecular analysis and whole transcriptome analysis of tissues; and the development of a biologic index (nomogram). These techniques may allow evaluation of individual tumour biology and the status of the resection margin in ways that are different to our current techniques. Ultimately, these techniques seek to better define the risk of tumour recurrence following surgery and provide the surgeon and patient with more confidence in margin assessment.

Real-time Tracking and Classification of Tumor and Nontumor Tissue in Upper Gastrointestinal Cancers Using Diffuse Reflectance Spectroscopy for Resection Margin Assessment

Importance

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.

Objective

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.

Design, Setting, and Participants

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.

Exposures

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.

Main Outcomes and Measures

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.

Results

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.

Conclusions and Relevance

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.

The Use of Optical Coherence Tomography for Gross Examination and Sampling of Fixed Breast Specimens: A Pilot Study

Thorough gross examination of breast cancer specimens is critical in order to sample relevant portions for subsequent microscopic examination. This task would benefit from an imaging tool which permits targeted and accurate block selection. Optical coherence tomography (OCT) is a non-destructive imaging technique that visualizes tissue architecture and has the potential to be an adjunct at the gross bench. Our objectives were: (1) to familiarize pathologists with the appearance of breast tissue entities on OCT; and (2) to evaluate the yield and quality of OCT images of unprocessed, formalin-fixed breast specimens for the purpose of learning and establishment of an OCT–histopathology library. Methods: Firstly, 175 samples from 40 formalin-fixed, unprocessed breast specimens with residual tissue after final diagnosis were imaged with OCT and then processed into histology slides. Histology findings were correlated with features on OCT. Results: Residual malignancy was seen in 30% of tissue samples. Corresponding OCT images demonstrated that tumor can be differentiated from fibrous stroma, based on features such as irregular boundary, heterogeneous texture and reduced penetration depth. Ductal carcinoma in situ can be subtle, and it is made more recognizable by the presence of comedo necrosis and calcifications. OCT features of benign and malignant breast entities were compiled in a granular but user-friendly reference tool. Conclusion: OCT images of fixed breast tissue were of sufficient quality to reproduce features of breast entities previously described in fresh tissue specimens. Our findings support the use of readily available unprocessed, fixed breast specimens for the establishment of an OCT–histopathology library.

Optical coherence tomography holds promise to transform the diagnostic anatomic pathology gross evaluation process

SIGNIFICANCE

Real-time histology can close a variety of gaps in tissue diagnostics. Currently, gross pathology analysis of excised tissue is dependent upon visual inspection and palpation to identify regions of interest for histopathological processing. Such analysis is limited by the variable correlation between macroscopic and microscopic findings. The current standard of care is costly, burdensome, and inefficient.

AIM

We are the first to address this gap by introducing optical coherence tomography (OCT) to be integrated in real-time during the pathology grossing process.

APPROACH

This is achieved by our high-resolution, ultrahigh-speed, large field-of-view OCT device designed for this clinical application.

RESULTS

We demonstrate the feasibility of imaging tissue sections from multiple human organs (breast, prostate, lung, and pancreas) in a clinical gross pathology setting without interrupting standard workflows.

CONCLUSIONS

OCT-based real-time histology evaluation holds promise for addressing a gap that has been present for >100 years.

Classification of gastric cancerous tissues by a residual network based on optical coherence tomography images

Optical coherence tomography (OCT) is a noninvasive, radiation-free, and high-resolution imaging technology. The intraoperative classification of normal and cancerous tissue is critical for surgeons to guide surgical operations. Accurate classification of gastric cancerous OCT images is beneficial to improve the effect of surgical treatment based on the deep learning method. The OCT system was used to collect images of cancerous tissues removed from patients. An intelligent classification method of gastric cancerous tissues based on the residual network is proposed in this study and optimized with the ResNet18 model. Four residual blocks are used to reset the model structure of ResNet18 and reduce the number of network layers to identify cancerous tissues. The model performance of different residual networks is evaluated by accuracy, precision, recall, specificity, F1 value, ROC curve, and model parameters. The classification accuracies of the proposed method and ResNet18 both reach 99.90%. Also, the model parameters of the proposed method are 44% of ResNet18, which occupies fewer system resources and is more efficient. In this study, the proposed deep learning method was used to automatically recognize OCT images of gastric cancerous tissue. This artificial intelligence method could help promote the clinical application of gastric cancerous tissue classification in the future.

Multi-class classification of breast tissue using optical coherence tomography and attenuation imaging combined via deep learning

We demonstrate a convolutional neural network (CNN) for multi-class breast tissue classification as adipose tissue, benign dense tissue, or malignant tissue, using multi-channel optical coherence tomography (OCT) and attenuation images, and a novel Matthews correlation coefficient (MCC)-based loss function that correlates more strongly with performance metrics than the commonly used cross-entropy loss. We hypothesized that using multi-channel images would increase tumor detection performance compared to using OCT alone. 5,804 images from 29 patients were used to fine-tune a pre-trained ResNet-18 network. Adding attenuation images to OCT images yields statistically significant improvements in several performance metrics, including benign dense tissue sensitivity (68.0% versus 59.6%), malignant tissue positive predictive value (PPV) (79.4% versus 75.5%), and total accuracy (85.4% versus 83.3%), indicating that the additional contrast from attenuation imaging is most beneficial for distinguishing between benign dense tissue and malignant tissue.

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.

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.

Magnetic resonance imaging system for intraoperative margin assessment for DCIS and invasive breast cancer using the ClearSight™ system in breast-conserving surgery-Results from a postmarketing study

BACKGROUND AND OBJECTIVES

Breast-conserving surgery (BCS) is followed by reoperations in approximately 25%. Reoperations lead to an increased risk of infection and wound healing problems as well as a worse cosmetic outcome. Several technical approaches for an intraoperative margin assessment to decrease the reoperation rate are under evaluation, some of them are still experimental.

METHODS

A prospective single-arm post-marketing study with 60 patients undergoing BCS for ductal carcinoma in situ (DCIS) and invasive breast cancer was conducted. The specimen was intraoperatively examined by the ClearSight™ system, a mobile magnetic resonance imaging system that is based on a diffusion-weighted imaging protocol. However, the results were blinded to the surgeon.

RESULTS

The ClearSight™ system was performed for both ductal and lobular breast cancer and DCIS, with a sensitivity of 0.80 (95% confidence interval [CI]: 0.44-0.96) and a specificity of 0.84 (95% CI 0.72-0.92), with an overall diagnostic accuracy of 80%.

CONCLUSION

Had the ClearSight™ been known to the surgeon intraoperatively, the reoperation rate would have been reduced by 83% for invasive carcinoma, from 10% to 2%, and 50% for DCIS, from 30% to 15% reoperations. A trial designed to examine the impact on reoperation rates is currently ongoing.

Tumor radiosensitization by photobiomodulation

PURPOSE

To investigate the safety of photobiomodulation therapy (PBM) in tumors and its potential as a radiosensitizer when combined with radiotherapy.

METHODS

We have performed in vitro experiments in A431 cells to assess proliferation and cell cycle after PBM, as well as clonogenic assay and H2AX-gamma immunolabeling to quantify double strand breaks after the combination of PBM and radiation. In vivo experiments in xenografts included Kaplan-Meier survival analysis, optical coherence tomography (OCT) and histological analysis.

RESULTS

PBM did not induce proliferation in vitro, but increased the G2/M fraction by 27% 24h after illumination, resulting in an enhancement of 30% in radiation effect in the clonogenic assay. The median survival of the PBM-RT group increased by 4 days and the hazard ratio was 0.417 (CI 95%: 0.173-1.006) when compared to radiation alone. OCT analysis over time demonstrated that PBM increases tumor necrosis due to radiation, and histological analysis showed that illumination increased cell differentiation and angiogenesis, which may play a role in the synergetic effect of PBM and radiation.

CONCLUSION

PBM technique may be one of the most appropriate approaches for radiosensitizing tumors while protecting normal tissue because of its low cost and low training requirements for staff.

Three-dimensional virtual histology in unprocessed resected tissues with photoacoustic remote sensing (PARS) microscopy and optical coherence tomography (OCT)

Histological images are critical in the diagnosis and treatment of cancers. Unfortunately, current methods for capturing these microscopy images require resource intensive tissue preparation that may delay diagnosis for days or weeks. To streamline this process, clinicians are limited to assessing small macroscopically representative subsets of tissues. Here, a combined photoacoustic remote sensing (PARS) microscope and swept source optical coherence tomography system designed to circumvent these diagnostic limitations is presented. The proposed multimodal microscope provides label-free three-dimensional depth resolved virtual histology visualizations, capturing nuclear and extranuclear tissue morphology directly on thick unprocessed specimens. The capabilities of the proposed method are demonstrated directly in unprocessed formalin fixed resected tissues. The first images of nuclear contrast in resected human tissues, and the first three-dimensional visualization of subsurface nuclear morphology in resected Rattus tissues, captured with a non-contact photoacoustic system are presented here. Moreover, the proposed system captures the first co-registered OCT and PARS images enabling direct histological assessment of unprocessed tissues. This work represents a vital step towards the development of a rapid histological imaging modality to circumvent the limitations of current histopathology techniques.

Shot-noise limited, supercontinuum-based optical coherence tomography

We present the first demonstration of shot-noise limited supercontinuum-based spectral domain optical coherence tomography (SD-OCT) with an axial resolution of 5.9 μm at a center wavelength of 1370 nm. Current supercontinuum-based SD-OCT systems cannot be operated in the shot-noise limited detection regime because of severe pulse-to-pulse relative intensity noise of the supercontinuum source. To overcome this disadvantage, we have developed a low-noise supercontinuum source based on an all-normal dispersion (ANDi) fiber, pumped by a femtosecond laser. The noise performance of our 90 MHz ANDi fiber-based supercontinuum source is compared to that of two commercial sources operating at 80 and 320 MHz repetition rate. We show that the low-noise of the ANDi fiber-based supercontinuum source improves the OCT images significantly in terms of both higher contrast, better sensitivity, and improved penetration. From SD-OCT imaging of skin, retina, and multilayer stacks we conclude that supercontinuum-based SD-OCT can enter the domain of shot-noise limited detection.

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.

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.

Diagnostic Accuracy of Quantitative Micro-Elastography for Margin Assessment in Breast-Conserving Surgery

Inadequate margins in breast-conserving surgery (BCS) are associated with an increased likelihood of local recurrence of breast cancer. Currently, approximately 20% of BCS patients require repeat surgery due to inadequate margins at the initial operation. Implementation of an accurate, intraoperative margin assessment tool may reduce this re-excision rate. This study determined, for the first time, the diagnostic accuracy of quantitative micro-elastography (QME), an optical coherence tomography (OCT)-based elastography technique that produces images of tissue microscale elasticity, for detecting tumor within 1 mm of the margins of BCS specimens. Simultaneous OCT and QME were performed on the margins of intact, freshly excised specimens from 83 patients undergoing BCS and on dissected specimens from 7 patients undergoing mastectomy. The resulting three-dimensional images (45 × 45 × 1 mm) were coregistered with postoperative histology to determine tissue types present in each scan. Data from 12 BCS patients and the 7 mastectomy patients served to build a set of images for reader training. One hundred and fifty-four subimages (10 × 10 × 1 mm) from the remaining 71 BCS patients were included in a blinded reader study, which resulted in 69.0% sensitivity and 79.0% specificity using OCT images, versus 92.9% sensitivity and 96.4% specificity using elasticity images. The quantitative nature of QME also facilitated development of an automated reader, which resulted in 100.0% sensitivity and 97.7% specificity. These results demonstrate high accuracy of QME for detecting tumor within 1 mm of the margin and the potential for this technique to improve outcomes in BCS. SIGNIFICANCE: An optical imaging technology probes breast tissue elasticity to provide accurate assessment of tumor margin involvement in breast-conserving surgery.

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.

Three-dimensional mapping of the attenuation coefficient in optical coherence tomography to enhance breast tissue microarchitecture contrast

Effective intraoperative tumor margin assessment is needed to reduce re-excision rates in breast-conserving surgery (BCS). Mapping the attenuation coefficient in optical coherence tomography (OCT) throughout a sample to create an image (attenuation imaging) is one promising approach. For the first time, three-dimensional OCT attenuation imaging of human breast tissue microarchitecture using a wide-field (up to ~45 × 45 × 3.5 mm) imaging system is demonstrated. Representative results from three mastectomy and one BCS specimen (from 31 specimens) are presented with co-registered postoperative histology. Attenuation imaging is shown to provide substantially improved contrast over OCT, delineating nuanced features within tumors (including necrosis and variations in tumor cell density and growth patterns) and benign features (such as sclerosing adenosis). Additionally, quantitative micro-elastography (QME) images presented alongside OCT and attenuation images show that these techniques provide complementary contrast, suggesting that multimodal imaging could increase tissue identification accuracy and potentially improve tumor margin assessment.

Fully Automated Postlumpectomy Breast Margin Assessment Utilizing Convolutional Neural Network Based Optical Coherence Tomography Image Classification Method

BACKGROUND

The purpose of this study was to develop a deep learning classification approach to distinguish cancerous from noncancerous regions within optical coherence tomography (OCT) images of breast tissue for potential use in an intraoperative setting for margin assessment.

METHODS

A custom ultrahigh-resolution OCT (UHR-OCT) system with an axial resolution of 2.7 μm and a lateral resolution of 5.5 μm was used in this study. The algorithm used an A-scan-based classification scheme and the convolutional neural network (CNN) was implemented using an 11-layer architecture consisting of serial 3 × 3 convolution kernels. Four tissue types were classified, including adipose, stroma, ductal carcinoma in situ, and invasive ductal carcinoma.

RESULTS

The binary classification of cancer versus noncancer with the proposed CNN achieved 94% accuracy, 96% sensitivity, and 92% specificity. The mean five-fold validation F1 score was highest for invasive ductal carcinoma (mean standard deviation, 0.89 ± 0.09) and adipose (0.79 ± 0.17), followed by stroma (0.74 ± 0.18), and ductal carcinoma in situ (0.65 ± 0.15).

CONCLUSION

It is feasible to use CNN based algorithm to accurately distinguish cancerous regions in OCT images. This fully automated method can overcome limitations of manual interpretation including interobserver variability and speed of interpretation and may enable real-time intraoperative margin assessment.

Characterization of the human myocardium by optical coherence tomography

Imaging of cardiac tissue structure plays a critical role in the treatment and understanding of cardiovascular disease. Optical coherence tomography (OCT) offers the potential to provide valuable, high-resolution imaging of cardiac tissue. However, there is a lack of comprehensive OCT imaging data of the human heart, which could improve identification of structural substrates underlying cardiac abnormalities. The objective of this study was to provide qualitative and quantitative analysis of OCT image features throughout the human heart. Fifty human hearts were acquired, and tissues from all chambers were imaged with OCT. Histology was obtained to verify tissue composition. Statistical differences between OCT image features corresponding to different tissue types and chambers were estimated using analysis of variance. OCT imaging provided features that were able to distinguish structures such as thickened collagen, as well as adipose tissue and fibrotic myocardium. Statistically significant differences were found between atria and ventricles in attenuation coefficient, and between adipose and all other tissue types. This study provides an overview of OCT image features throughout the human heart, which can be used for guiding future applications such as OCT-integrated catheter-based treatments or ex vivo investigation of structural substrates.

Imaging of subendocardial adipose tissue and fiber orientation distributions in the human left atrium using optical coherence tomography

Background

Optical coherence tomography (OCT) has the potential to provide real‐time imaging guidance for atrial fibrillation ablation, with promising results for lesion monitoring. OCT can also offer high‐resolution imaging of tissue composition, but there is insufficient cardiac OCT data to inform the use of OCT to reveal important tissue architecture of the human left atrium. Thus, the objective of this study was to define OCT imaging data throughout the human left atrium, focusing on the distribution of adipose tissue and fiber orientation as seen from the endocardium.

Methods and Results

Human hearts (n = 7) were acquired for imaging the left atrium with OCT. A spectral‐domain OCT system with 1325 nm center wavelength, 6.5 μm axial resolution, 15 μm lateral resolution, and a maximum imaging depth of 2.51 mm in the air was used. Large‐scale OCT image maps of human left atrial tissue were developed, with adipose thickness and fiber orientation extracted from the imaging data. OCT imaging showed scattered distributions of adipose tissue around the septal and pulmonary vein regions, up to a depth of about 0.43 mm from the endocardial surface. The total volume of adipose tissue detected by OCT over one left atrium ranged from 1.42 to 28.74 mm³. Limited fiber orientation information primarily around the pulmonary veins and the septum could be identified.

Conclusion

OCT imaging could provide adjunctive information on the distribution of subendocardial adipose tissue, particularly around thin areas around the pulmonary veins and septal regions. Variations in OCT‐detected tissue composition could potentially assist ablation guidance.

Evaluation of surgically excised breast tissue microstructure using wide-field optical coherence tomography

BACKGROUND

Currently, positive margins at lumpectomy contribute to health care cost, patient anxiety, and treatment delay. Multiple technology solutions are being explored with the aim of lowering re-excision rates for breast-conserving surgery (BCS). We examined wide-field optical coherence tomography (WF-OCT), an innovative adjunct intraoperative imaging tool for tissue visualization of margins.

METHODS

This IRB-approved pilot study included women with invasive or in situ carcinoma scheduled for primary BCS. Lumpectomy specimens and any final/revised margins were imaged by optical coherence tomography immediately prior to standard histological processing. The optical coherence tomography used provided two-dimensional, cross-sectional, real-time depth visualization of the margin widths around excised specimens. A volume of images was captured for 10 × 10 cm tissue surface at high resolution (sub-30 μm) to a depth of 2 mm. Integrated interpretation was performed incorporating final pathology linked with the optical image data for correlation.

RESULTS

Wide-field optical coherence tomography was performed on 185 tissue samples (50 lumpectomy specimens and 135 additional margin shaves) in 50 subjects. Initial diagnosis was invasive ductal carcinoma (IDC) in 10, ductal carcinoma in situ (DCIS) in 14, IDC/DCIS in 22, invasive lobular carcinoma (ILC) in 2, ILC/DCIS in 1, and sarcoma in 1. Optical coherence tomography was concordant with final pathology in 178/185 tissue samples for overall accuracy of 86% and 96.2% (main specimen alone and main specimen + shave margins). Of seven samples that were discordant, 57% (4/7) were considered close (DCIS < 2 mm from margin) per final pathology.

CONCLUSION

Wide-field optical coherence tomography demonstrated concordance with histology at tissue margins, supporting its potential for use as a real-time adjunct intraoperative imaging tool for margin assessment. Further studies are needed for comprehensive evaluation in the intraoperative setting.

Chip-based frequency comb sources for optical coherence tomography

Optical coherence tomography (OCT) is a powerful interferometric imaging technique widely used in medical fields such as ophthalmology, cardiology and dermatology. Superluminescent diodes (SLDs) are widely used as light sources in OCT. Recently integrated chip-based frequency combs have been demonstrated in numerous platforms and the possibility of using these broadband chip-scale combs for OCT has been raised extensively over the past few years. However, the use of these chip-based frequency combs as light sources for OCT requires bandwidth and power compatibility with current OCT systems and have not been shown to date. Here we generate frequency combs based on chip-scale lithographically-defined microresonators and demonstrate its capability as a novel light source for OCT. The combs are designed with a small spectral line spacing of 0.21 nm which ensure imaging range comparable to commercial system and operated at non-phase locked regime which provide conversion efficiency of 30%. The comb source is shown to be compatible with a standard commercial spectral domain (SD) OCT system and enables imaging of human tissue with image quality comparable to the one achieved with tabletop commercial sources. The comb source also provides a path towards fully integrated OCT systems.

Grading upper tract urothelial carcinoma with the attenuation coefficient of in-vivo optical coherence tomography

INTRODUCTION

With catheter based optical coherence tomography (OCT), high resolution images of the upper urinary tract can be obtained, thereby facilitating the detection of upper tract urothelial carcinomas (UTUC). We hypothesized that the attenuation coefficient of the OCT signal (μOCT ) is related to the histopathologic grade of the tumor.

OBJECTIVES

In this study, we aimed to define the μOCT cut-off for discriminating high grade and low grade papillary UTUC.

METHODS

For this post-hoc analysis, data from OCT imaging of papillary UTUC was obtained from patients during ureterorenoscopy. OCT images and raw data were simultaneously analyzed with in-house developed software. The μOCT determined in papillary UTUCs and corresponding histopathologic grading from either biopsies or radical resection specimens were compared.

RESULTS

Thirty-five papillary UTUC from 35 patients were included. μOCT analysis was feasible in all cases. The median μOCT was 3.3 mm-1 (IQR 2.7-3.7 mm-1 ) for low-grade UTUC and 4.9 mm-1 (IQR 4.3-6.1 mm-1 ) for high-grade UTUC (P = 0.004). ROC analysis yielded a μOCT cut-off value of >4.0 mm-1 (AUC = 0.85, P < 0.001) with a sensitivity of 83% and a specificity of 94% for high-grade papillary UTUC.

CONCLUSIONS

This study proposes a μOCT cut-off of 4.0 mm-1 for quantitative grading of UTUC with ureterorenoscopic OCT imaging. The promising diagnostic accuracy calculations justify further studies to validate the proposed cut-off value. Implementation of the software for the μOCT analysis in OCT systems may allow for μOCT assessment at real time during ureterorenoscopy. Lasers Surg. Med. 51:399-406, 2019. © 2019 Wiley Periodicals, Inc.

OCT-elastography-based optical biopsy for breast cancer delineation and express assessment of morphological/molecular subtypes

Application of compressional optical coherence elastography (OCE) for delineation of tumor and peri-tumoral tissue with simultaneous assessment of morphological/molecular subtypes of breast cancer is reported. The approach is based on the ability of OCE to quantitatively visualize stiffness of studied samples and then to perform a kind of OCE-based biopsy by analyzing elastographic B-scans that have sizes ~several millimeters similarly to bioptates used for "gold-standard" histological examinations. The method relies on identification of several main tissue constituents differing in their stiffness in the OCE scans. Initially the specific stiffness ranges for the analyzed tissue components (adipose tissue, fibrous and hyalinized tumor stroma, lymphocytic infiltrate and agglomerates of tumor cells) are determined via comparison of OCE and morphological/molecular data. Then assessment of non-tumor/tumor regions and tumor subtypes is made based on percentage of pixels with different characteristic stiffness ("stiffness spectrum") in the OCE image, also taking into account spatial localization of different-stiffness regions. Examples of high contrast among benign (or non-invasive) and several subtypes of invasive breast tumors in terms of their stiffness spectra are given.

Automated assessment of breast cancer margin in optical coherence tomography images via pretrained convolutional neural network

The benchmark method for the evaluation of breast cancers involves microscopic testing of a hematoxylin and eosin (H&E)-stained tissue biopsy. Resurgery is required in 20% to 30% of cases because of incomplete excision of malignant tissues. Therefore, a more accurate method is required to detect the cancer margin to avoid the risk of recurrence. In the recent years, convolutional neural networks (CNNs) has achieved excellent performance in the field of medical images diagnosis. It automatically extracts the features from the images and classifies them. In the proposed study, we apply a pretrained Inception-v3 CNN with reverse active learning for the classification of healthy and malignancy breast tissue using optical coherence tomography (OCT) images. This proposed method attained the sensitivity, specificity and accuracy is 90.2%, 91.7% and 90%, respectively, with testing datasets collected from 48 patients (22 normal fibro-adipose tissue and 26 Invasive ductal carcinomas cancerous tissues). The trained network utilizes for the breast cancer margin assessment to predict the tumor with negative margins. Additionally, the network output is correlated with the corresponding histology image. Our results lay the foundation for the future that the proposed method can be used to perform automatic intraoperative identification of breast cancer margins in real-time and to guide core needle biopsies.

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.

Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks

Assessing the surgical margin during breast lumpectomy operations can avoid the need for additional surgery. Optical coherence tomography (OCT) is an imaging technique that has been proven to be efficient for this purpose. However, to avoid overloading the surgeon during the operation, automatic cancer detection at the surface of the removed tissue is needed. This work explores automated margin assessment on a sample of patient data collected at the Pathology Department, Severance Hospital (Seoul, South Korea). Some methods based on the spatial statistics of the images have been developed, but the obtained results are still far from human performance. In this work, we investigate the possibility to use deep neural networks (DNNs) for real time margin assessment, demonstrating performance significantly better than the reported literature and close to the level of a human expert. Since the goal is to detect the presence of cancer, a patch-based classification method is proposed, as it is sufficient for detection, and requires training data that is easier and cheaper to collect than for other approaches such as segmentation. For that purpose, we train a DNN architecture that was proved to be efficient for small images on patches extracted from images containing only cancer or only normal tissue as determined by pathologists in a university hospital. As the number of available images in all such studies is by necessity small relative to other deep network applications such as ImageNet, a good regularization method is needed. In this work, we propose to use a recently introduced function norm regularization that attempts to directly control the function complexity, in contrast to classical approaches such as weight decay and DropOut. As neither the code nor the data of previous results are publicly available, the obtained results are compared with reported results in the literature for a conservative comparison. Moreover, our method is applied to locally collected data on several data configurations. The reported results are the average over the different trials. The experimental results show that the use of DNNs yields significantly better results than other techniques when evaluated in terms of sensitivity, specificity, F1 score, G-mean and Matthews correlation coefficient. Function norm regularization yielded higher and more robust results than competing regularization methods. We have demonstrated a system that shows high promise for (partially) automated margin assessment of human breast tissue, Equal error rate (EER) is reduced from approximately 12% (the lowest reported in the literature) to 5% - a 58% reduction. The method is computationally feasible for intraoperative application (less than 2 s per image) at the only cost of a longer offline training time.

Optical Coherence Tomography: A Novel Imaging Method for Post-lumpectomy Breast Margin Assessment-A Multi-reader Study

RATIONALE AND OBJECTIVES

This study aimed to assess whether different breast cancer subspecialty physicians can be trained to distinguish non-suspicious from suspicious areas of post-lumpectomy specimen margin in patients with breast cancer using optical coherence tomography (OCT) images (a near-infrared based imaging technique) with final histology as the reference standard.

MATERIALS AND METHODS

This institutional review board-exempt, Health Insurance Portability and Accountability Act-compliant study was performed on 63 surgically excised breast specimens from 35 female patients, creating a 90-case atlas containing both non-suspicious and suspicious areas for cancer. OCT images of the specimens were performed, providing 6.5-15 µm resolution with tissue visualization 1-2 mm subsurface. From the 90-case atlas, 40 cases were chosen for training and 40 were randomly selected for reader assessment. Three breast imaging radiologists, two pathologists, two breast surgeons, and one non-clinical reader were trained and assessed for ability to distinguish non-suspicious from suspicious findings blinded to clinical data and corresponding histology slides. Duration of training and assessment, sensitivity, specificity, positive predictive value, negative predictive value, and the area under the curve for each reader were calculated as well as averages by subspecialty.

RESULTS

The average training time was 3.4 hours (standard deviation, 1.2). The average assessment time was 1.9 hours (standard deviation, 0.7). The overall average reader sensitivity, specificity, and accuracy for detecting suspicious findings with histologic confirmation of cancer at the surgical margin for all eight readers were 80%, 87%, and 87%, respectively. Radiologists demonstrated the highest average among the disciplines, 85%, 93%, and 94%, followed by pathologists, 79%, 90%, and 84%, and surgeons, 76%, 84%, and 82% respectively.

CONCLUSIONS

With relatively short training (3.4 hours), readers from different medical specialties were able to distinguish suspicious from non-suspicious OCT imaging findings in ex vivo breast tissue as confirmed by histology. These results support the potential of OCT as a real-time intraoperative tool for post-lumpectomy specimen margin assessment.

Wide-field quantitative micro-elastography of human breast tissue

Currently, 20-30% of patients undergoing breast-conserving surgery require a second surgery due to insufficient surgical margins in the initial procedure. We have developed a wide-field quantitative micro-elastography system for the assessment of tumor margins. In this technique, we map tissue elasticity over a field-of-view of ~46 × 46 mm. We performed wide-field quantitative micro-elastography on thirteen specimens of freshly excised tissue acquired from patients undergoing a mastectomy. We present wide-field optical coherence tomography (OCT) images, qualitative (strain) micro-elastograms and quantitative (elasticity) micro-elastograms, acquired in 10 minutes. We demonstrate that wide-field quantitative micro-elastography can extend the range of tumors visible using OCT-based elastography by providing contrast not present in either OCT or qualitative micro-elastography and, in addition, can reduce imaging artifacts caused by a lack of contact between tissue and the imaging window. Also, we describe how the combined evaluation of OCT, qualitative micro-elastograms and quantitative micro-elastograms can improve the visualization of tumor.

Ex vivo feasibility study of endoscopic intraductal laser ablation of the breast

OBJECTIVE

To determine the feasibility and safety of breast endoscopic thulium laser ablation for treatment of intraductal neoplasia.

STUDY DESIGN

Ductoscopy is a minimally invasive endoscopic approach of the milk ducts of the breast via the nipple. Besides diagnosis in women with pathologic nipple discharge (PND), it allows non-invasive removal of intraductal lesions with a stalk like papillomas. Removal, however, is often incomplete and flat lesions cannot be targeted. We therefore developed laser ductoscopy.

METHODS

Dosimetry of laser ductoscopy was assessed in thirteen mastectomy specimens, applying power settings of 1-5 W with 100-1000 ms pulsed exposure to a 375-μm outer diameter thulium fiber laser. Subsequently histology was obtained from the breast tissue that was treated with the Thulium laser.

RESULTS

Endoscopic view was maintained during ductoscopic laser ablation at 1-3 W. Increasing power to 4-5 W caused impaired vision due to shrinkage of the main duct around the ductoscope tip. Histology revealed localized ablation of the duct wall.

CONCLUSION

We show for the first time that laser ductoscopy is technically feasible. The Thulium laser enables a superficial intraductal ablation and is a useful tool for intraductal interventions. An in vivo prospective study is needed to further demonstrate its potential. Lasers Surg. Med. 50:137-142, 2018. © 2017 Wiley Periodicals, Inc.