Imaging breast cancer morphology using probe-based confocal laser endomicroscopy: towards a real-time intraoperative imaging tool for cavity scanning

Diagnostic accuracy of intraoperative methods for margin assessment in breast cancer surgery: A systematic review & meta-analysis

PURPOSE

There are a wide variety of intraoperative techniques available in breast surgery to achieve low rates for positive margins of excision. The objective of this systematic review was to determine the pooled diagnostic accuracy of intraoperative breast margin assessment techniques that have been evaluated in clinical practice.

METHODS

This study was performed in accordance with PRISMA guidelines. A systematic search of the literature was conducted to identify studies assessing the diagnostic accuracy of intraoperative margin assessment techniques. Only clinical studies with raw diagnostic accuracy data as compared with final permanent section histopathology were included in the meta-analysis. A bivariate model for diagnostic meta-analysis was used to determine overall pooled sensitivity and specificity.

RESULTS

Sixty-one studies were eligible for inclusion in this systematic review and meta-analysis. Cytology demonstrated the best diagnostic accuracy, with pooled sensitivity of 0.92 (95 % CI 0.77-0.98) and a pooled specificity of 0.95 (95 % CI 0.90-0.97). The findings also indicate good diagnostic accuracy for optical spectroscopy, with a pooled sensitivity of 0.86 (95 % CI 0.76-0.93) and a pooled specificity of 0.92 (95 % CI 0.82-0.97).

CONCLUSION

Pooled data indicate that optical spectroscopy, cytology and frozen section have the greatest diagnostic accuracy of currently available intraoperative margin assessment techniques. However, long turnaround time for results and their resource intensive nature has prevented widespread adoption of these methods. The aim of emerging technologies is to compete with the diagnostic accuracy of these established techniques, while improving speed and usability.

Clustering-Guided Twin Contrastive Learning for Endomicroscopy Image Classification

Learning better representations is essential in medical image analysis for computer-aided diagnosis. However, learning discriminative semantic features is a major challenge due to the lack of large-scale well-annotated datasets. Thus, how can we learn a well-structured categorizable embedding space in limited-scale and unlabeled datasets? In this paper, we proposed a novel clustering-guided twin-contrastive learning framework (CTCL) that learns the discriminative representations of probe-based confocal laser endomicroscopy (pCLE) images for gastrointestinal (GI) tumor classification. Compared with traditional contrastive learning, in which only two randomly augmented views of the same instance are considered, the proposed CTCL aligns more semantically related and class-consistent samples by clustering, which improved intra-class tightness and inter-class variability to produce more informative representations. Furthermore, based on the inherent properties of CLE (geometric invariance and intrinsic noise), we proposed to regard CLE images with any angle rotation and CLE images with different noises as the same instance, respectively, for increased variability and diversity of samples. By optimizing CTCL in an end-to-end expectation-maximization framework, comprehensive experimental results demonstrated that CTCL-based visual representations achieved competitive performance on each downstream task as well as more robustness and transferability compared with existing state-of-the-art SSL and supervised methods. Notably, CTCL achieved 75.60%/78.45% and 64.12%/77.37% top-1 accuracy on the linear evaluation protocol and few-shot classification downstream tasks, respectively, which outperformed the previous best results by 1.27%/1.63% and 0.5%/3%, respectively. The proposed method holds great potential to assist pathologists in achieving an automated, fast, and high-precision diagnosis of GI tumors and accurately determining different stages of tumor development based on CLE images.

Breast tissue imaging atlas using ultra-fast confocal microscopy to identify cancer lesions

New generation ultra-fast fluorescence confocal microscopy (UFCM) allows to image histological architecture of fresh breast tissue and may be used for ex vivo intraoperative analysis for margin status. The criteria to identify breast tumoral and non-tumoral tissues in UFCM images are still objects of investigation. The objective of the study was to create an atlas of ex vivo UFCM images of breast tissues and breast carcinomas based on the first extensive collection of large field-of-view UFCM breast images. One hundred sixty patients who underwent conserving surgery for breast cancer were included. Their fresh surgical specimens were sliced, stained with acridine orange, and imaged at high resolution with large-field-of-view UFCM. The resulting images were digitally false colored to resemble frozen sections. Each UFCM image was correlated with the corresponding definitive histology. Representative images of normal tissue, inflammation, benign lesions, invasive carcinoma (IC), and ductal carcinoma in situ (DCIS) were collected. A total of 320 large-field images were recorded from 58 IC of no special type, 44 invasive lobular carcinomas, 1 invasive mucinous carcinoma, 47 DCIS, 2 lobular carcinomas in situ, and 8 specimens without cancer. Representative images of the main components of the normal breast and the main types of ICs and DCIS were annotated to establish an UFCM atlas. UFCM enables the imaging of the fresh breast tissue sections. Main morphological criteria defined in traditional histopathology such as tissue architecture and cell features can be applied to describe UFCM images content. The generated atlas of the main normal or tumoral tissue features will support the adoption of this optical technology for the intraoperative examination of breast specimens in clinical practice as it can be used to train physicians on UFCM images and develop artificial intelligence algorithms. Further studies are needed to document rare breast lesions.

SENOSI Confocal Microscopy: A New and Innovating Way to Detect Positive Margins in Non-Palpable Breast Cancer?

In Switzerland, breast cancer is the leading cancer among women, with breast-conserving surgery (BCS) being the preferred treatment for small tumors. The margin status post-surgery is a critical predictor of local recurrence. Achieving negative margins remains a challenge, leading to re-excision in 20-30% of cases. Traditional methods like intraoperative examination palpation and radiography have limitations in assessing excised margins. This study introduces the Histolog® Scanner, a confocal microscopy tool, as a potential solution. It provides real-time images of tissue architecture, allowing for rapid and accurate assessment of excised margins. Our research compared the Histolog® Scanner with standard per-operative radiography in patients with non palpable breast cancer. Preliminary results indicate that the Histolog® Scanner offers a reliable and time-efficient method for margin assessment, suggesting its potential for clinical integration.

Context-aware dynamic filtering network for confocal laser endomicroscopy image denoising

Objective.As an emerging diagnosis technology for gastrointestinal diseases, confocal laser endomicroscopy (CLE) is limited by the physical structure of the fiber bundle, leading to the inevitable production of various forms of noise during the imaging process. However, existing denoising methods based on hand-crafted features inefficiently deal with realistic noise in CLE images. To alleviate this challenge, we proposed context-aware kernel estimation and multi-scale dynamic fusion modules to remove realistic noise in CLE images, including multiplicative and additive white noise.Approach.Specifically, a realistic noise statistics model with random noise specific to CLE data is constructed and further used to develop a self-supervised denoised model without the participation of clean images. Secondly, context-aware kernel estimation, which improves the representation of features by similar learnable region weights, addresses the problem of the non-uniform distribution of noises in CLE images and proposes a lightweight denoised model (CLENet). Thirdly, we have developed a multi-scale dynamic fusion module that decouples and recalibrates features, providing a precise and contextually enriched representation of features. Finally, we integrated two developed modules into a U-shaped backbone to build an efficient denoising network named U-CLENet.Main Results.Both proposed methods achieve comparable or better performance with low computational complexity on two gastrointestinal disease CLE image datasets using the same training benchmark.Significance.The proposed approaches improve the visual quality of unclear CLE images for various stages of tumor development, helping to reduce the rate of misdiagnosis in clinical decision-making and achieve computer graphics-assisted diagnosis.

Contrastive Adversarial Learning for Endomicroscopy Imaging Super-Resolution

Endomicroscopy is an emerging imaging modality for real-time optical biopsy. One limitation of existing endomicroscopy based on coherent fibre bundles is that the image resolution is intrinsically limited by the number of fibres that can be practically integrated within the small imaging probe. To improve the image resolution, Super-Resolution (SR) techniques combined with image priors can enhance the clinical utility of endomicroscopy whereas existing SR algorithms suffer from the lack of explicit guidance from ground truth high-resolution (HR) images. In this article, we propose an unsupervised SR pipeline to allow stable offline and kernel-generic learning. Our method takes advantage of both internal statistics and external cross-modality priors. To improve the joint learning process, we present a Sharpness-aware Contrastive Generative Adversarial Network (SCGAN) with two dedicated modules, a sharpness-aware generator and a contrastive-learning discriminator. In the generator, an auxiliary task of sharpness discrimination is formulated to facilitate internal learning by considering the rankings of training instances in various sharpness levels. In the discriminator, we design a contrastive-learning module to mitigate the ill-posed nature of SR tasks via constraints from both positive and negative images. Experiments on multiple datasets demonstrate that SCGAN reduces the performance gap between previous unsupervised approaches and the upper bounds defined in supervised settings by more than 50%, delivering a new state-of-the-art performance score for endomicroscopy super-resolution. Further application on a realistic Voronoi-based pCLE downsampling kernel proves that SCGAN attains PSNR of 35.851 dB, improving 5.23 dB compared with the traditional Delaunay interpolation.

Boosting few-shot confocal endomicroscopy image recognition with feature-level MixSiam

As an emerging early diagnostic technology for gastrointestinal diseases, confocal laser endomicroscopy lacks large-scale perfect annotated data, leading to a major challenge in learning discriminative semantic features. So, how should we learn representations without labels or a few labels? In this paper, we proposed a feature-level MixSiam method based on the traditional Siamese network that learns the discriminative features of probe-based confocal laser endomicroscopy (pCLE) images for gastrointestinal (GI) tumor classification. The proposed method is divided into two stages: self-supervised learning (SSL) and few-shot learning (FS). First, in the self-supervised learning stage, the novel feature-level-based feature mixing approach introduced more task-relevant information via regularization, facilitating the traditional Siamese structure can adapt to the large intra-class variance of the pCLE dataset. Then, in the few-shot learning stage, we adopted the pre-trained model obtained through self-supervised learning as the base learner in the few-shot learning pipeline, enabling the feature extractor to learn richer and more transferable visual representations for rapid generalization to other pCLE classification tasks when labeled data are limited. On two disjoint pCLE gastrointestinal image datasets, the proposed method is evaluated. With the linear evaluation protocol, feature-level MixSiam outperforms the baseline by 6% (Top-1) and the supervised model by 2% (Top1), which demonstrates the effectiveness of the proposed feature-level-based feature mixing method. Furthermore, the proposed method outperforms the previous baseline method for the few-shot classification task, which can help improve the classification of pCLE images lacking large-scale annotated data for different stages of tumor development.

Preliminary study of the safety of acrinol in probe-based confocal laser endomicroscopy during transurethral resection of bladder tumors

We previously reported that probe-based confocal laser endomicroscopy using acrinol can depict cancerous nuclei. The objective of this study was to confirm the safety of acrinol in patients. For all seven patients, '50 ml' of a 0.1% acrinol and '1 ml' of 10% fluorescein in 99 ml of normal saline were introduced into the bladder. The laser probe adhered to the suspicious lesion from the working channel of the cystoscope. The patients underwent mucosal biopsy and transurethral resection after observation. Adverse events were noted during a valuation using common terminology criteria for adverse events version 4.0. Confocal laser endomicroscopy detected the nuclei of cancer cells in all seven patients. No adverse event was observed in any of the seven patients. Confocal laser endomicroscopy using acrinol as a novel dye can help visualize the cancerous nuclei of bladder urothelial carcinoma during cystoscopy without severe adverse events.

Imaging of lumpectomy surface with large field-of-view confocal laser scanning microscope for intraoperative margin assessment - POLARHIS study

INTRODUCTION

Breast-conserving surgery (BCS) in case of breast cancer and/or in-situ-carcinoma lesions (DCIS) intends to completely remove breast cancer while saving healthy tissue as much as possible to achieve better aesthetic and psychological outcomes for the patient. Such modality should result in postoperative tumor-free margins of the surgical resection in order to carry on with the next therapeutical steps of the patient care. However, 10-40% of patients undergo more than one procedure to achieve acceptable cancer-negative margins. A 2nd operation or further operation (re-operation) has physical, psychological, and economic consequences. It also delays the administration of adjuvant therapy, and has been associated with an elevated risk of local and distant disease relapse. In addition, a high re-operation rate can have significant economic effects - both for the service provider and for the payer. A more efficient intraoperative assessment of the margin may address these issues. Recently, a large field-of-view confocal laser scanning microscope designed to allow real-time intraoperative margin assessment has arrived on the market - the Histolog Scanner. In this paper, we present the first evaluation of lumpectomy margins assessment with this new device.

MATERIALS AND METHODS

40 consecutive patients undergoing BCS with invasive and/or DCIS were included. The whole surface of the surgical specimens was imaged right after the operation using the Histolog Scanner (HLS). The assessment of all the specimen margins was performed intraoperatively according to the standard-of-care of the center which consists of combined ultrasound (IOUS) and/or conventional specimen radiography (CSR), and gross surgical inspection. Margin assessment on HLS images was blindly performed after the surgery by 5 surgeons and one pathologist. The capabilities to correctly determine margin status in HLS images was compared to the final histopathological assessment. Furthermore, the potential reduction of positive-margin and re-operation rates by utilization of the HLS were extrapolated.

RESULTS

The study population included 7/40 patients with DCIS (17.5%), 17/40 patients with DCIS and invasive ductal cancer (IDC NST) (42.5%), 10/40 patients with IDC NST (25%), 4/40 with invasive lobular cancer (ILC) (10%), and 1/40 patients with a mix of IDC NST, DCIS, and ILC. Clinical routine resulted in 13 patients with positive margins identified by final histopathological assessment, resulting in 12 re-operations (30% re-operation rate). Amongst these 12 patients, 10 had DCIS components involved in their margin, confirming the importance of improving the detection accuracy of this specific lesion. Surgeons, who were given a short familiarization on HLS images, and a pathologist were able to detect positive margins in 4/12 and 7/12 patients (33% and 58%), respectively, that were missed by the intraoperative standard of care. In addition, a retrospective analysis of the HLS images revealed that cancer lesions can be identified in 9/12 (75%) patients with positive margins.

CONCLUSION

The present study presents that breast cancer can be detected by surgeons and pathologists in HLS images of lumpectomy margins leading to a potential reduction of 30% and 75% of the re-operations. The Histolog Scanner is easily inserted into the clinical workflow and has the potential to improve the intraoperative standard-of-care for the assessment of breast conserving treatments. In addition, it has the potential to increase oncological safety and cosmetics by avoiding subsequent resections and can also have a significant positive economic effect for service providers and cost bearers. The data presented in this study will have to be further confirmed in a prospective phase-III-trial.

Development, Implementation and Application of Confocal Laser Endomicroscopy in Brain, Head and Neck Surgery—A Review

When we talk about visualization methods in surgery, it is important to mention that the diagnosis of tumors and how we define tumor borders intraoperatively in a correct way are two main things that would not be possible to achieve without this grand variety of visualization methods we have at our disposal nowadays. In addition, histopathology also plays a very important role, and its importance cannot be neglected either. Some biopsy specimens, e.g., frozen sections, are examined by a histopathologist and lead to tumor diagnosis and the definition of its borders. Furthermore, surgical resection is a very important point when it comes to prognosis and life survival. Confocal laser endomicroscopy (CLE) is an imaging technique that provides microscopic information on the tissue in real time. CLE of disorders, such as head, neck and brain tumors, has only recently been suggested to contribute to both immediate tumor characterization and detection. It can be used as an additional tool for surgical biopsies during biopsy or surgical procedures and for inspection of resection margins during surgery. In this review, we analyze the development, implementation, advantages and disadvantages as well as the future directions of this technique in neurosurgical and otorhinolaryngological disciplines.

Targeted detection of cancer at the cellular level during biopsy by near-infrared confocal laser endomicroscopy

Suspicious nodules detected by radiography are often investigated by biopsy, but the diagnostic yield of biopsies of small nodules is poor. Here we report a method-NIR-nCLE-to detect cancer at the cellular level in real-time during biopsy. This technology integrates a cancer-targeted near-infrared (NIR) tracer with a needle-based confocal laser endomicroscopy (nCLE) system modified to detect NIR signal. We develop and test NIR-nCLE in preclinical models of pulmonary nodule biopsy including human specimens. We find that the technology has the resolution to identify a single cancer cell among normal fibroblast cells when co-cultured at a ratio of 1:1000, and can detect cancer cells in human tumors less than 2 cm in diameter. The NIR-nCLE technology rapidly delivers images that permit accurate discrimination between tumor and normal tissue by non-experts. This proof-of-concept study analyzes pulmonary nodules as a test case, but the results may be generalizable to other malignancies.

Innovations for the future of breast surgery

BACKGROUND

Future innovations in science and technology with an impact on multimodal breast cancer management from a surgical perspective are discussed in this narrative review. The work was undertaken in response to the Commission on the Future of Surgery project initiated by the Royal College of Surgeons of England.

METHODS

Expert opinion was sought around themes of surgical de-escalation, reduction in treatment morbidities, and improving the accuracy of breast-conserving surgery in terms of margin status. There was emphasis on how the primacy of surgical excision in an era of oncoplastic and reconstructive surgery is increasingly being challenged, with more effective systemic therapies that target residual disease burden, and permit response-adapted approaches to both breast and axillary surgery.

RESULTS

Technologies for intraoperative margin assessment can potentially half re-excision rates after breast-conserving surgery, and sentinel lymph node biopsy will become a therapeutic procedure for many patients with node-positive disease treated either with surgery or chemotherapy as the primary modality. Genomic profiling of tumours can aid in the selection of patients for neoadjuvant and adjuvant therapies as well as prevention strategies. Molecular subtypes are predictive of response to induction therapies and reductive approaches to surgery in the breast or axilla.

CONCLUSION

Treatments are increasingly being tailored and based on improved understanding of tumour biology and relevant biomarkers to determine absolute benefit and permit delivery of cost-effective healthcare. Patient involvement is crucial for breast cancer studies to ensure relevance and outcome measures that are objective, meaningful, and patient-centred.

Deep Graph-Based Multimodal Feature Embedding for Endomicroscopy Image Retrieval

Representation learning is a critical task for medical image analysis in computer-aided diagnosis. However, it is challenging to learn discriminative features due to the limited size of the data set and the lack of labels. In this article, we propose a deep graph-based multimodal feature embedding (DGMFE) framework for medical image retrieval with application to breast tissue classification by learning discriminative features of probe-based confocal laser endomicroscopy (pCLE). We first build a multimodality graph model based on the visual similarity between pCLE data and reference histology images. The latent similar pCLE-histology pairs are extracted by walking with the cyclic path on the graph while the dissimilar pairs are extracted based on the geodesic distance. Given the similar and dissimilar pairs, the latent feature space is discovered by reconstructing the similarity between pCLE and histology images via deep Siamese neural networks. The proposed method is evaluated on a clinical database with 700 pCLE mosaics. The accuracy of image retrieval demonstrates that DGMFE can outperform previous works on feature learning. Especially, the top-1 accuracy in an eight-class retrieval task is 0.739, thus demonstrating a 10% improvement compared to the state-of-the-art method.

The role of Micro-CT in imaging breast cancer specimens

PURPOSE

The goal of breast cancer surgery is to remove all of the cancer with a minimum of normal tissue, but absence of full 3-dimensional information on the specimen makes this difficult to achieve.

METHOD

Micro-CT is a high resolution, X-ray, 3D imaging method, widely used in industry but rarely in medicine.

RESULTS

We imaged and analyzed 173 partial mastectomies (129 ductal carcinomas, 14 lobular carcinomas, 28 DCIS). Imaging was simple and rapid. The size and shape of the cancers seen on Micro-CT closely matched the size and shape of the cancers seen at specimen dissection. Micro-CT images of multicentric/multifocal cancers revealed multiple non-contiguous masses. Micro-CT revealed cancer touching the specimen edge for 93% of the 114 cases judged margin positive by the pathologist, and 28 of the cases not seen as margin positive on pathological analysis; cancer occupied 1.55% of surface area when both the pathologist and Micro-CT suggested cancer at the edge, but only 0.45% of surface area for the "Micro-CT-Only-Positive Cases". Thus, Micro-CT detects cancers that touch a very small region of the specimen surface, which is likely to be missed on sectioning.

CONCLUSIONS

Micro-CT provides full 3D images of breast cancer specimens, allowing one to identify, in minutes rather than hours, while the patient is in OR, margin-positive cancers together with information on where the cancer touches the edge, in a fashion more accurate than possible from the histology slides alone.

Automatic motion compensation for structured illumination endomicroscopy using a flexible fiber bundle

SIGNIFICANCE

Confocal laser scanning enables optical sectioning in clinical fiber bundle endomicroscopes, but lower-cost, simplified endomicroscopes use widefield incoherent illumination instead. Optical sectioning can be introduced in these simple systems using structured illumination microscopy (SIM), a multiframe digital subtraction process. However, SIM results in artifacts when the probe is in motion, making the technique difficult to use in vivo and preventing the use of mosaicking to synthesize a larger effective field of view (FOV).

AIM

We report and validate an automatic motion compensation technique to overcome motion artifacts and allow generation of mosaics in SIM endomicroscopy.

APPROACH

Motion compensation is achieved using image registration and real-time pattern orientation correction via a digital micromirror device. We quantify the similarity of moving probe reconstructions to those acquired with a stationary probe using the relative mean of the absolute differences (MAD). We further demonstrate mosaicking with a moving probe in mechanical and freehand operation.

RESULTS

Reconstructed SIM images show an improvement in the MAD from 0.85 to 0.13 for lens paper and from 0.27 to 0.12 for bovine tissue. Mosaics also show vastly reduced artifacts.

CONCLUSION

The reduction in motion artifacts in individual SIM reconstructions leads to mosaics that more faithfully represent the morphology of tissue, giving clinicians a larger effective FOV than the probe itself can provide.

Comparative analysis of confocal microscopy on fresh breast core needle biopsies and conventional histology

Background

Evaluation of core needle biopsies (CNB) is a standard procedure for the diagnosis of breast cancer. However, tissue processing and image preparation is a time- consuming procedure and instant on-site availability of high-quality images could substantially improve the efficacy of the diagnostic procedure. Conventional microscopic methods, such as frozen section analysis (FSA) for detection of malignant cells still have clear disadvantages. In the present study, we tested a confocal microscopy scanner on fresh tissue from CNB with intention to develop an alternative device to FSA in clinical practice.

Patients and methods

In 24 patients with suspicious breast lesions standard of care image-guided biopsies were performed. Confocal images have been obtained using the Histolog™ Scanner and evaluated by two independent pathologists. Hematoxylin-Eosin (H&E) histological sections of the biopsies were routinely processed in a blinded fashion with respect to the confocal images.

Results

In total 42 confocal images were generated from 24 biopsy specimens, and available for analysis within a few minutes of taking the biopsy. This resulted in 2 × 42 = 84 pathologic evaluations. In four cases, a pathologic diagnosis was not possible with confocal microscopy. An exact correlation based on the B-classification was reached in 41 out of 80 examinations and in another 35 cases in a broader sense of correspondence definition (i.e. malignant vs. benign).

Conclusions

As a reliable on-site method, the Histolog™ Scanner provides a visualization of cellular details equivalent to the H&E standards, permitting rapid and accurate diagnosis of malignant and benign breast lesions. Furthermore, this device offers great potential for immediate margin analysis of specimen in breast conserving therapy.

En-face optical coherence tomography/fluorescence endomicroscopy for minimally invasive imaging using a robotic scanner

We report a compact rigid instrument capable of delivering en-face optical coherence tomography (OCT) images alongside (epi)-fluorescence endomicroscopy (FEM) images by means of a robotic scanning device. Two working imaging channels are included: one for a one-dimensional scanning, forward-viewing OCT probe and another for a fiber bundle used for the FEM system. The robotic scanning system provides the second axis of scanning for the OCT channel while allowing the field of view (FoV) of the FEM channel to be increased by mosaicking. The OCT channel has resolutions of 25  /  60  μm (axial/lateral) and can provide en-face images with an FoV of 1.6  ×  2.7  mm2. The FEM channel has a lateral resolution of better than 8  μm and can generate an FoV of 0.53  ×  3.25  mm2 through mosaicking. The reproducibility of the scanning was determined using phantoms to be better than the lateral resolution of the OCT channel. Combined OCT and FEM imaging were validated with ex-vivo ovine and porcine tissues, with the instrument mounted on an arm to ensure constant contact of the probe with the tissue. The OCT imaging system alone was validated for in-vivo human dermal imaging with the handheld instrument. In both cases, the instrument was capable of resolving fine features such as the sweat glands in human dermal tissue and the alveoli in porcine lung tissue.

2D Au-Coated Resonant MEMS Scanner for NIR Fluorescence Intraoperative Confocal Microscope

The electrostatic MEMS scanner plays an important role in the miniaturization of the microscopic imaging system. We have developed a new two-dimensional (2D) parametrically-resonant MEMS scanner with patterned Au coating (>90% reflectivity at an NIR 785-nm wavelength), for a near-infrared (NIR) fluorescence intraoperative confocal microscopic imaging system with a compact form factor. A silicon-on-insulator (SOI)-wafer based dicing-free microfabrication process has been developed for mass-production with high yield. Based on an in-plane comb-drive configuration, the resonant MEMS scanner performs 2D Lissajous pattern scanning with a large mechanical scanning angle (MSA, ±4°) on each axis at low driving voltage (36 V). A large field-of-view (FOV) has been achieved by using a post-objective scanning architecture of the confocal microscope. We have integrated the new MEMS scanner into a custom-made NIR fluorescence intraoperative confocal microscope with an outer diameter of 5.5 mm at its distal-end. Axial scanning has been achieved by using a piezoelectric actuator-based driving mechanism. We have successfully demonstrated ex vivo 2D imaging on human tissue specimens with up to five frames/s. The 2D resonant MEMS scanner can potentially be utilized for many applications, including multiphoton microendoscopy and wide-field endoscopy.

Transfer Recurrent Feature Learning for Endomicroscopy Image Recognition

Probe-based confocal laser endomicroscopy (pCLE) is an emerging tool for epithelial cancer diagnosis, which enables in-vivo microscopic imaging during endoscopic procedures and facilitates the development of automatic recognition algorithms to identify the status of tissues. In this paper, we propose a transfer recurrent feature learning framework for classification tasks on pCLE videos. At the first stage, the discriminative feature of single pCLE frame is learned via generative adversarial networks based on both pCLE and histology modalities. At the second stage, we use recurrent neural networks to handle the varying length and irregular shape of pCLE mosaics taking the frame-based features as input. The experiments on real pCLE data sets demonstrate that our approach outperforms, with statistical significance, state-of-the-art approaches. A binary classification accuracy of 84.1% has been achieved.

Fiber bundle shifting endomicroscopy for high-resolution imaging

Flexible endomicroscopes commonly use coherent fiber bundles with high core densities to facilitate high-resolution in vivo imaging during endoscopic and minimally-invasive procedures. However, under-sampling due to the inter-core spacing limits the spatial resolution, making it difficult to resolve smaller cellular features. Here, we report a compact and rapid piezoelectric transducer (PZT) based bundle-shifting endomicroscopy system in which a super-resolution (SR) image is restored from multiple pixelation-limited images by computational means. A miniaturized PZT tube actuates the fiber bundle behind a GRIN micro-lens and a Delaunay triangulation based algorithm reconstructs an enhanced SR image. To enable real-time cellular-level imaging, imaging is performed using a line-scan confocal laser endomicroscope system with a raw frame rate of 120 fps, delivering up to 2 times spatial resolution improvement for a field of view of 350 µm at a net frame rate of 30 fps. The resolution enhancement is confirmed using resolution phantoms and ex vivo fluorescence endomicroscopy imaging of human breast specimens is demonstrated.

Intraoperative Robotic-Assisted Large-Area High-Speed Microscopic Imaging and Intervention

OBJECTIVE

Probe-based confocal endomicroscopy is an emerging high-magnification optical imaging technique that provides in vivo and in situ cellular-level imaging for real-time assessment of tissue pathology. Endomicroscopy could potentially be used for intraoperative surgical guidance, but it is challenging to assess a surgical site using individual microscopic images due to the limited field-of-view and difficulties associated with manually manipulating the probe.

METHODS

In this paper, a novel robotic device for large-area endomicroscopy imaging is proposed, demonstrating a rapid, but highly accurate, scanning mechanism with image-based motion control, which is able to generate histology-like endomicroscopy mosaics. The device also includes, for the first time in robotic-assisted endomicroscopy, the capability to ablate tissue without the need for an additional tool.

RESULTS

The device achieves preprogrammed trajectories with positioning accuracy of less than 30 [Formula: see text], while the image-based approach demonstrated that it can suppress random motion disturbances up to [Formula: see text]. Mosaics are presented from a range of ex vivo human and animal tissues, over areas of more than [Formula: see text], scanned in approximate [Formula: see text].

CONCLUSION

This paper demonstrates the potential of the proposed instrument to generate large-area, high-resolution microscopic images for intraoperative tissue identification and margin assessment.

SIGNIFICANCE

This approach presents an important alternative to current histology techniques, significantly reducing the tissue assessment time, while simultaneously providing the capability to mark and ablate suspicious areas intraoperatively.

Rapid evaporative ionisation mass spectrometry of electrosurgical vapours for the identification of breast pathology: towards an intelligent knife for breast cancer surgery

BACKGROUND

Re-operation for positive resection margins following breast-conserving surgery occurs frequently (average = 20-25%), is cost-inefficient, and leads to physical and psychological morbidity. Current margin assessment techniques are slow and labour intensive. Rapid evaporative ionisation mass spectrometry (REIMS) rapidly identifies dissected tissues by determination of tissue structural lipid profiles through on-line chemical analysis of electrosurgical aerosol toward real-time margin assessment.

METHODS

Electrosurgical aerosol produced from ex-vivo and in-vivo breast samples was aspirated into a mass spectrometer (MS) using a monopolar hand-piece. Tissue identification results obtained by multivariate statistical analysis of MS data were validated by histopathology. Ex-vivo classification models were constructed from a mass spectral database of normal and tumour breast samples. Univariate and tandem MS analysis of significant peaks was conducted to identify biochemical differences between normal and cancerous tissues. An ex-vivo classification model was used in combination with bespoke recognition software, as an intelligent knife (iKnife), to predict the diagnosis for an ex-vivo validation set. Intraoperative REIMS data were acquired during breast surgery and time-synchronized to operative videos.

RESULTS

A classification model using histologically validated spectral data acquired from 932 sampling points in normal tissue and 226 in tumour tissue provided 93.4% sensitivity and 94.9% specificity. Tandem MS identified 63 phospholipids and 6 triglyceride species responsible for 24 spectral differences between tissue types. iKnife recognition accuracy with 260 newly acquired fresh and frozen breast tissue specimens (normal n = 161, tumour n = 99) provided sensitivity of 90.9% and specificity of 98.8%. The ex-vivo and intra-operative method produced visually comparable high intensity spectra. iKnife interpretation of intra-operative electrosurgical vapours, including data acquisition and analysis was possible within a mean of 1.80 seconds (SD ±0.40).

CONCLUSIONS

The REIMS method has been optimised for real-time iKnife analysis of heterogeneous breast tissues based on subtle changes in lipid metabolism, and the results suggest spectral analysis is both accurate and rapid. Proof-of-concept data demonstrate the iKnife method is capable of online intraoperative data collection and analysis. Further validation studies are required to determine the accuracy of intra-operative REIMS for oncological margin assessment.

Intraoperative Margin Management in Breast-Conserving Surgery: A Systematic Review of the Literature

BACKGROUND

Breast surgeons have a wide variety of intraoperative techniques available to help achieve low rates for positive margins of excision, with variable levels of evidence.

METHODS

A systematic review of the medical literature from 1995 to July 2016 was conducted, with 434 abstracts identified and evaluated. The analysis included 106 papers focused on intraoperative management of breast cancer margins and contained actionable data.

RESULTS

Ultrasound-guided lumpectomy for palpable tumors, as an alternative to palpation guidance, can lower positive margin rates, but the effect when used as an alternative to wire localization (WL) for nonpalpable tumors is less certain. Localization techniques such as radioactive seed localization and radioguided occult lesion localization were found potentially to lower positive margin rates as alternatives to WL depending on baseline positive margin rates. Intraoperative pathologic methods including gross histology, frozen section analysis, and imprint cytology all have the potential to lower the rates of positive margins. Cavity-shave margins and the Marginprobe device both lower rates of positive margins, with some potential for negative cosmetic effects. Specimen radiography and multiple miscellaneous techniques did not affect positive margin rates or provided too little evidence for formation of a conclusion.

CONCLUSIONS

A systematic review of the literature showed evidence that several intraoperative techniques and actions can lower the rates of positive margins. These results are presented together with graded recommendations.

Ex Vivo (Fluorescence) Confocal Microscopy in Surgical Pathology: State of the Art

First developed in 1957, confocal microscopy is a powerful imaging tool that can be used to obtain near real-time reflected light images of untreated human tissue with nearly histologic resolution. Besides its research applications, in the last decades, confocal microscopy technology has been proposed as a useful device to improve clinical diagnosis, especially in ophthalmology, dermatology, and endomicroscopy settings, thanks to advances in instrument development. Compared with the wider use of the in vivo tissue assessment, ex vivo applications of confocal microscopy are not fully explored. A comprehensive review of the current literature was performed here, focusing on the reliable applications of ex vivo confocal microscopy in surgical pathology and on some potential evolutions of this new technique from pathologists' viewpoint.