Optimization of multimodal spectral imaging for assessment of resection margins during Mohs micrographic surgery for basal cell carcinoma

Intraoperative spectroscopic evaluation of sentinel lymph nodes in breast cancer surgery

BACKGROUND AND OBJECTIVES

Sentinel lymph node (SLN) biopsy is a standard procedure for patients with breast cancer and normal axilla on imaging. Positive SLNs on histological examination can lead to a subsequent surgery for axillary lymph node clearance (ALNC). Here we report a non-destructive technique based on autofluorescence (AF) imaging and Raman spectroscopy for intra-operative assessment of SLNs excised in breast cancer surgery.

METHODS

A microscope integrating AF imaging and Raman spectroscopy modules was built to allow scanning of lymph node biopsy samples. During AF-Raman measurements, AF imaging determined optimal sampling locations for Raman spectroscopy measurements. After optimisation of the AF image analysis and training of classification models based on data from 85 samples, the AF-Raman technique was tested on an independent set of 81 lymph nodes comprising 58 fixed and 23 fresh specimens. The sensitivity and specificity of AF-Raman were calculated using post-operative histology as a standard of reference.

RESULTS

The independent test set contained 66 negative lymph nodes and 15 positive lymph nodes according to the reference standard, collected from 78 patients. For this set of specimens, the area under the receiver operating characteristic (ROC) curve for the AF-Raman technique was 0.93 [0.83-0.98]. AF-Raman was then operated in a regime that maximised detection specificity, producing a 94% detection accuracy: 80% sensitivity and 97% specificity. The main confounders for SLN metastasis were areas rich in histiocytes clusters, for which only few Raman spectra had been included in the training dataset.

DISCUSSION

This preliminary study indicates that with further development and extension of the training dataset by inclusion of additional Raman spectra of histiocytes clusters and capsule, the AF-Raman may become a promising technique for intra-operative assessment of SLNs. Intra-operative detection of positive biopsies could avoid second surgery for axillary clearance.

Fast Raman imaging through the combination of context-aware matrix completion and low spectral resolution

Raman hyperspectral imaging is an effective method for label-free imaging with chemical specificity, yet the weak signals and correspondingly long integration times have hindered its wide adoption as a routine analytical method. Recently, low resolution Raman imaging has been proposed to improve the spectral signal-to-noise ratio, which significantly improves the speed of Raman imaging. In this paper, low resolution Raman spectroscopy is combined with "context-aware" matrix completion, where regions of the sample that are not of interest are skipped, and the regions that are measured are under-sampled, then reconstructed with a low-rank constraint. Both simulations and experiment show that low-resolution Raman boosts the speed and image quality of the computationally-reconstructed Raman images, allowing deeper sub-sampling, reduced exposure time, and an overall >10-fold improvement in imaging speed, without sacrificing chemical specificity or spatial image quality. As the method utilizes traditional point-scan imaging, it retains full confocality and is "backwards-compatible" with pre-existing traditional Raman instruments, broadening the potential scope of Raman imaging applications.

Quantification of Drugs in Brain and Liver Mimetic Tissue Models Using Raman Spectroscopy

Quantitative analysis of drug delivery with in biological systems is an integral challenge in drug development. Analytical techniques are important for assessing both drug target delivery, target action, and drug toxicology. Using mimetic tissue models, we have investigated the efficacy of Raman spectroscopy in quantitative detection of alkyne group and deuterated drugs in rat brain and rat liver tissue models. Lasers with 671 nm and 785 nm wavelengths were assessed for their feasibility in this application due to opposing relative benefits and disadvantages. Thin tissue sections have been tested as a practical means of reducing autofluorescent background by minimizing out-of-focus tissue and therefore maximizing photobleaching rates. Alkyne-tagged drugs were quantitatively measured at 18 ± 5 μg/g drug/tissue mass ratio in rat brain and at 34 ± 6 μg/g in rat liver. Quantification calibration curves were generated for a range of concentrations from 0-500 μg/g. These results show the potential of Raman spectroscopy as a diffraction-limited spatially resolved imaging technique for assessing drug delivery in tissue applications.

High-Throughput Molecular Imaging via Deep-Learning-Enabled Raman Spectroscopy

Raman spectroscopy enables nondestructive, label-free imaging with unprecedented molecular contrast, but is limited by slow data acquisition, largely preventing high-throughput imaging applications. Here, we present a comprehensive framework for higher-throughput molecular imaging via deep-learning-enabled Raman spectroscopy, termed DeepeR, trained on a large data set of hyperspectral Raman images, with over 1.5 million spectra (400 h of acquisition) in total. We first perform denoising and reconstruction of low signal-to-noise ratio Raman molecular signatures via deep learning, with a 10× improvement in the mean-squared error over common Raman filtering methods. Next, we develop a neural network for robust 2-4× spatial super-resolution of hyperspectral Raman images that preserve molecular cellular information. Combining these approaches, we achieve Raman imaging speed-ups of up to 40-90×, enabling good-quality cellular imaging with a high-resolution, high signal-to-noise ratio in under 1 min. We further demonstrate Raman imaging speed-up of 160×, useful for lower resolution imaging applications such as the rapid screening of large areas or for spectral pathology. Finally, transfer learning is applied to extend DeepeR from cell to tissue-scale imaging. DeepeR provides a foundation that will enable a host of higher-throughput Raman spectroscopy and molecular imaging applications across biomedicine.

Intra-operative assessment of sentinel lymph nodes for breast cancer surgery: An update

Lymph node (LN) involvement is the strongest prognostic factor in operable breast cancer (BC). Therefore, accurate assessment of LN status is essential for management of BC patients. The introduction of sentinel LN approach reduced the need for extensive axillary surgery to achieve accurate staging. However, positive sentinel LN as determined on postoperative histological examination often leads to a second axillary operation to ensure an accurate staging and that positive non-sentinel LNs are removed. Although preoperative assessment of LN has improved significantly, its accuracy remains insufficient to avoid further axillary surgery and is not sufficient to predict the status of the LN. Therefore, intraoperative evaluation of the sentinel LN to determine the need for completing lymph node dissection in case of metastasis can provide an important approach to guide BC management decision making. This article reviews the techniques available and under development for intraoperative detection of sentinel LN metastasis in BC surgery. The key features of each technique are described in detail, emphasising the benefits offered by label-free optical techniques: minimal sample preparation, high spatial resolution, and immediate on-site implementation. Optical techniques have the potential to provide a cost-effective and accurate intraoperative platform for the assessment of SLN within the operating theatre.

Selective-sampling Raman imaging techniques for ex vivo assessment of surgical margins in cancer surgery

One of the main challenges in cancer surgery is to ensure the complete excision of the tumour while sparing as much healthy tissue as possible. Histopathology, the gold-standard technique used to assess the surgical margins on the excised tissue, is often impractical for intra-operative use because of the time-consuming tissue cryo-sectioning and staining, and availability of histopathologists to assess stained tissue sections. Raman micro-spectroscopy is a powerful technique that can detect microscopic residual tumours on ex vivo tissue samples with accuracy, based entirely on intrinsic chemical differences. However, raster-scanning Raman micro-spectroscopy is a slow imaging technique that typically requires long data acquisition times wich are impractical for intra-operative use. Selective-sampling Raman imaging overcomes these limitations by using information regarding the spatial properties of the tissue to reduce the number of Raman spectra. This paper reviews the latest advances in selective-sampling Raman techniques and applications, mainly based on multimodal optical imaging. We also highlight the latest results of clinical integration of a prototype device for non-melanoma skin cancer. These promising results indicate the potential impact of Raman spectroscopy for providing fast and objective assessment of surgical margins, helping surgeons ensure the complete removal of tumour cells while sparing as much healthy tissue as possible.

Clinical integration of fast Raman spectroscopy for Mohs micrographic surgery of basal cell carcinoma

We present the first clinical integration of a prototype device based on integrated auto-fluorescence imaging and Raman spectroscopy (Fast Raman device) for intra-operative assessment of surgical margins during Mohs micrographic surgery of basal cell carcinoma (BCC). Fresh skin specimens from 112 patients were used to optimise the tissue pre-processing and the Fast Raman algorithms to enable an analysis of complete Mohs layers within 30 minutes. The optimisation allowed >95% of the resection surface area to be investigated (including the deep and epidermal margins). The Fast Raman device was then used to analyse skin layers excised from the most relevant anatomical sites (nose, temple, eyelid, cheek, forehead, eyebrow and lip) and to detect the three main types of BCC (nodular, superficial and infiltrative). These results suggest that the Fast Raman technique is a promising tool to provide an objective diagnosis "tumour clear yes/no" during Mohs surgery of BCC. This clinical integration study is a key step towards a larger scale diagnosis test accuracy study to reliably determine the sensitivity and specificity in a clinical setting.

Combined total internal reflection AF spectral-imaging and Raman spectroscopy for fast assessment of surgical margins during breast cancer surgery

The standard treatment for breast cancer is surgical removal mainly through breast-conserving surgery (BCS). We developed a new technique based on auto-fluorescence (AF) spectral imaging and Raman spectroscopy for fast intraoperative assessment of excision margins in BCS. A new wide-field AF imaging unit based on total internal reflection (TIR) was combined with a Raman spectroscopy microscope equipped with a 785 nm laser. The wavelength of the AF excitation was optimized to 365 nm in order to maximize the discrimination of adipose tissue. This approach allows for the non-adipose regions of tissue, which are at a higher risk of containing a tumor, to be targeted more efficiently by the Raman spectroscopy measurements. The integrated TIR-AF-Raman was tested on small tissue samples as well as fresh wide local excisions, delivering the analysis of the entire cruciate surface of BCS specimens (5.1 × 7.6 cm²) in less than 45 minutes and also providing information regarding the location of the tumor in the specimen. Full automation of the instrument and selection of a faster translation stage would allow for the measurement of BCS specimens within an intraoperative time scale (20 minutes). This study demonstrates that the TIR-AF Raman microscope represents a feasible step towards the development of a technique for intraoperative assessment of large WLE within intraoperative timescales.

Applications of Spatial Light Modulators in Raman Spectroscopy

Advances in consumer display screen technologies have historically been adapted by researchers across the fields of optics as they can be used as electronically controlled spatial light modulators (SLMs) for a variety of uses. The performance characteristics of such SLM devices based on liquid crystal (LC) and digital micromirror device (DMD) technologies, in particular, has developed to the point where they are compatible with increasingly sensitive instrumental applications, for example, Raman spectroscopy. Spatial light modulators provide additional flexibility, from modulation of the laser excitation (including multiple laser foci patterns), manipulation of microscopic samples (optical trapping), or selection of sampling volume (adaptive optics or spatially offset Raman spectroscopy), to modulation in the spectral domain for high-resolution spectral filtering or multiplexed/compressive fast detection. Here, we introduce the benefits of different SLM devices as a part of Raman instrumentation and provide a variety of recent example applications which have benefited from their incorporation into a Raman system.

Nanoparticles: synthesis and applications

This chapter focuses on the synthesis, functionalization, and applications of metallic, semiconductor, magnetic, and multifunctional nanoparticles. Synthesis methods such as chemical reduction, coprecipitation, seeding, microemulsion, hydrothermal synthesis, and sonoelectrodeposition are outlined. Functionalized nanoparticles are suitable for numerous applications. Several applications of nanoparticles in life sciences and the enviromment are discussed. Finally, some future trends are pointed out.

Raman spectroscopy for cancer detection and cancer surgery guidance: translation to the clinics

Oncological applications of Raman spectroscopy have been contemplated, pursued, and developed at academic level for at least 25 years. Published studies aim to detect pre-malignant lesions, detect cancer in less invasive stages, reduce the number of unnecessary biopsies and guide surgery towards the complete removal of the tumour with adequate tumour resection margins. This review summarizes actual clinical needs in oncology that can be addressed by spontaneous Raman spectroscopy and it provides an overview over the results that have been published between 2007 and 2017. An analysis is made of the current status of translation of these results into clinical practice. Despite many promising results, most of the applications addressed in scientific studies are still far from clinical adoption and commercialization. The main hurdles are identified, which need to be overcome to ensure that in the near future we will see the first Raman spectroscopy-based solutions being used in routine oncologic diagnostic and surgical procedures.

Intra-operative spectroscopic assessment of surgical margins during breast conserving surgery

Background

In over 20% of breast conserving operations, postoperative pathological assessment of the excised tissue reveals positive margins, requiring additional surgery. Current techniques for intra-operative assessment of tumor margins are insufficient in accuracy or resolution to reliably detect small tumors. There is a distinct need for a fast technique to accurately identify tumors smaller than 1 mm² in large tissue surfaces within 30 min.

Methods

Multi-modal spectral histopathology (MSH), a multimodal imaging technique combining tissue auto-fluorescence and Raman spectroscopy was used to detect microscopic residual tumor at the surface of the excised breast tissue. New algorithms were developed to optimally utilize auto-fluorescence images to guide Raman measurements and achieve the required detection accuracy over large tissue surfaces (up to 4 × 6.5 cm²). Algorithms were trained on 91 breast tissue samples from 65 patients.

Results

Independent tests on 121 samples from 107 patients - including 51 fresh, whole excision specimens - detected breast carcinoma on the tissue surface with 95% sensitivity and 82% specificity. One surface of each uncut excision specimen was measured in 12–24 min. The combination of high spatial-resolution auto-fluorescence with specific diagnosis by Raman spectroscopy allows reliable detection even for invasive carcinoma or ductal carcinoma in situ smaller than 1 mm².

Conclusions

This study provides evidence that this multimodal approach could provide an objective tool for intra-operative assessment of breast conserving surgery margins, reducing the risk for unnecessary second operations.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-1002-2) contains supplementary material, which is available to authorized users.

Automated multimodal spectral histopathology for quantitative diagnosis of residual tumour during basal cell carcinoma surgery

Multimodal spectral histopathology (MSH), an optical technique combining tissue auto-fluorescence (AF) imaging and Raman micro-spectroscopy (RMS), was previously proposed for detection of residual basal cell carcinoma (BCC) at the surface of surgically-resected skin tissue. Here we report the development of a fully-automated prototype instrument based on MSH designed to be used in the clinic and operated by a non-specialist spectroscopy user. The algorithms for the AF image processing and Raman spectroscopy classification had been first optimised on a manually-operated laboratory instrument and then validated on the automated prototype using skin samples from independent patients. We present results on a range of skin samples excised during Mohs micrographic surgery, and demonstrate consistent diagnosis obtained in repeat test measurement, in agreement with the reference histopathology diagnosis. We also show that the prototype instrument can be operated by clinical users (a skin surgeon and a core medical trainee, after only 1-8 hours of training) to obtain consistent results in agreement with histopathology. The development of the new automated prototype and demonstration of inter-instrument transferability of the diagnosis models are important steps on the clinical translation path: it allows the testing of the MSH technology in a relevant clinical environment in order to evaluate its performance on a sufficiently large number of patients.

Raman Plus X: Biomedical Applications of Multimodal Raman Spectroscopy

Raman spectroscopy is a label-free method of obtaining detailed chemical information about samples. Its compatibility with living tissue makes it an attractive choice for biomedical analysis, yet its translation from a research tool to a clinical tool has been slow, hampered by fundamental Raman scattering issues such as long integration times and limited penetration depth. In this review we detail the how combining Raman spectroscopy with other techniques yields multimodal instruments that can help to surmount the translational barriers faced by Raman alone. We review Raman combined with several optical and non-optical methods, including fluorescence, elastic scattering, OCT, phase imaging, and mass spectrometry. In each section we highlight the power of each combination along with a brief history and presentation of representative results. Finally, we conclude with a perspective detailing both benefits and challenges for multimodal Raman measurements, and give thoughts on future directions in the field.

Rapid and accurate peripheral nerve imaging by multipoint Raman spectroscopy

Raman spectroscopy allows label-free, minimally invasive, and accurate detection of peripheral nerves. However, the conventional Raman imaging technique is time-consuming when measuring a large area of a sample. Establishing a method for rapidly acquiring spatial distribution of a bundle of peripheral nerve fibers is an essential step for Raman spectroscopy towards application in clinical surgery. Here we present a multipoint Raman spectroscopic technique for rapid peripheral nerve imaging. In only 5 seconds, spectra at 32 points situated on ex vivo rat peripheral nerve bundles and adjoining connective tissues were acquired. Principal component regression and discriminant analysis of spectra revealed that the sensitivity, specificity and accuracy for nerve detection were 85.8%, 96.0%, and 90.8%, respectively. Of 158 peripheral nerves, 152 (96.2%) showed ratio of the number of nerve-positive prediction points to the total measurement points being 0.4 or larger, whereas 119 (99.2%) connective tissues among 120 showed ratio smaller than 0.4. Based on the ratio and a bright-field image of the sample, accurate visualization of peripheral nerves was implemented. The results indicated that the multipoint Raman spectroscopic technique is capable of rapid and accurate peripheral nerve imaging.

Implementation of fluorescence confocal mosaicking microscopy by "early adopter" Mohs surgeons and dermatologists: recent progress

Confocal mosaicking microscopy (CMM) enables rapid imaging of large areas of fresh tissue ex vivo without the processing that is necessary for conventional histology. When performed in fluorescence mode using acridine orange (nuclear specific dye), it enhances nuclei-to-dermis contrast that enables detection of all types of basal cell carcinomas (BCCs), including micronodular and thin strands of infiltrative types. So far, this technique has been mostly validated in research settings for the detection of residual BCC tumor margins with high sensitivity of 89% to 96% and specificity of 99% to 89%. Recently, CMM has advanced to implementation and testing in clinical settings by “early adopter” Mohs surgeons, as an adjunct to frozen section during Mohs surgery. We summarize the development of CMM guided imaging of ex vivo skin tissues from bench to bedside. We also present its current state of application in routine clinical workflow not only for the assessment of residual BCC margins in the Mohs surgical setting but also for some melanocytic lesions and other skin conditions in clinical dermatology settings. Last, we also discuss the potential limitations of this technology as well as future developments. As this technology advances further, it may serve as an adjunct to standard histology and enable rapid surgical pathology of skin cancers at the bedside.

Tissue diagnosis using power-sharing multifocal Raman micro-spectroscopy and auto-fluorescence imaging

We describe a multifocal Raman micro-spectroscopy detection method based on a digital micromirror device, which allows for simultaneous "power-sharing" acquisition of Raman spectra from ad hoc sampling points. As the locations of the points can be rapidly updated in real-time via software control of a liquid-crystal spatial light modulator (LC-SLM), this technique is compatible with automated adaptive- and selective-sampling Raman spectroscopy techniques, the latter of which has previously been demonstrated for fast diagnosis of skin cancer tissue resections. We describe the performance of this instrument and show examples of multiplexed measurements on a range of test samples. Following this, we show the feasibility of reducing measurement time for power-shared multifocal Raman measurements combined with confocal auto-fluorescence imaging to provide guided diagnosis of tumours in human skin samples.

Development and validation of Raman spectroscopic classification models to discriminate tongue squamous cell carcinoma from non-tumorous tissue

BACKGROUND

Currently, up to 85% of the oral resection specimens have inadequate resection margins, of which the majority is located in the deeper soft tissue layers. The prognosis of patients with oral cavity squamous cell carcinoma (OCSCC) of the tongue is negatively affected by these inadequate surgical resections. Raman spectroscopy, an optical technique, can potentially be used for intra-operative evaluation of resection margins.

OBJECTIVE

To develop in vitro Raman spectroscopy-based tissue classification models that discriminate OCSCC of the tongue from (subepithelial) non-tumorous tissue.

MATERIALS AND METHODS

Tissue classification models were developed using Principal Components Analysis (PCA) followed by (hierarchical) Linear Discriminant Analysis ((h)LDA). The models were based on a training set of 720 histopathologically annotated Raman spectra, obtained from 25 tongue samples (11 OCSCC and 14 normal) of 10 patients, and were validated by means of an independent validation set of 367 spectra, obtained from 19 tongue samples (6 OCSCC and 13 normal) of 11 patients.

RESULTS

A PCA-LDA tissue classification model 'tumor' versus 'non-tumorous tissue' (i.e. surface squamous epithelium, connective tissue, muscle, adipose tissue, gland and nerve) showed an accuracy of 86% (sensitivity: 100%, specificity: 66%). A two-step PCA-hLDA tissue classification model 'tumor' versus 'non-tumorous tissue' showed an accuracy of 91% (sensitivity: 100%, specificity: 78%).

CONCLUSION

An accurate PCA-hLDA Raman spectroscopy-based tissue classification model for discrimination between OCSCC and (especially the subepithelial) non-tumorous tongue tissue was developed and validated. This model with high sensitivity and specificity may prove to be very helpful to detect tumor in the resection margins.

Reliability of diagnosis from Mohs slides: interpersonal and intrapersonal agreement on basal cell carcinoma presence and histological subtype

BACKGROUND

The success of Mohs micrographic surgery (MMS) depends partly on the correct diagnosis of slides.

OBJECTIVES

To determine reliability of diagnosis from Mohs slides.

METHODS

This was a prospective study evaluating the reliability of diagnosis from Mohs slides of basal cell carcinoma (BCC) presence, BCC location on the slide and BCC subtype among six raters who independently assessed 50 Mohs slides twice with a 2-month interval. Slides were randomly selected whereby difficult-to-diagnose slides were oversampled. For each slide, a reference diagnosis was established by an expert panel. Cohen's kappa (κ) was calculated to determine levels of agreement interpersonally (rater vs. reference diagnosis) and intrapersonally (rater at T1 vs. T2). Multivariable logistic regression was used to determine independent risk factors for slides with interpersonal discordant diagnosis. The variables studied were BCC presence, whether a slide was scored as easy or difficult to diagnose, review duration of the 50 slides, profession and years of experience in diagnosis from Mohs slides.

RESULTS

Interpersonal and intrapersonal agreement were substantial on BCC presence (κ = 0·66 and 0·68) and moderate on BCC subtype (κ = 0·45 and 0·55). Slides that were scored as difficult to diagnose were an independent risk factor for interpersonal discordant diagnosis on BCC presence (odds ratio 3·54, 95% confidence interval 1·81-6·84).

CONCLUSIONS

Reliability of diagnosis from Mohs slides was substantial on BCC presence and moderate on BCC subtype. For slides that are scored difficult to diagnose, a second opinion is recommended to prevent misinterpretation and thereby recurrence of skin cancer.

Advances in the management of basal cell carcinoma

Basal cell carcinoma (BCC), a malignant neoplasm derived from non-keratinizing cells that originate in the basal layer of the epidermis, is the most common cancer in humans. Several factors such as anatomic location, histologic features, primary or recurrent tumors, and patient characteristics influence the choice of treatment modality for BCC. Mohs micrographic surgery (MMS) facilitates optimal margin control and conservation of normal tissue for the management of BCC; however, other treatment modalities may also be implemented in the correct clinical scenario. Other treatment modalities that will be reviewed include simple excision, electrodesiccation and curettage, cryotherapy, topical immunotherapy and chemotherapy, photodynamic therapy, and radiation therapy. In addition, targeted molecular therapeutic options for the treatment of advanced or metastatic BCC will be discussed in this informal review based on recent literature obtained by using PubMed with relevant search terms.