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.

Diagnostic accuracy of autofluorescence-Raman microspectroscopy for surgical margin assessment during Mohs micrographic surgery of basal cell carcinoma.

BACKGROUND: Autofluorescence (AF)-Raman microspectroscopy is a technology that can detect residual basal cell carcinoma (BCC) on the resection margin of fresh, surgically excised tissue specimens. The technology does not require tissue fixation, staining, labelling or sectioning, and provides quantitative diagnosis maps of the surgical margins in 30 min. OBJECTIVES: To determine the accuracy of the AF-Raman instrument in detecting incomplete BCC excisions during Mohs micrographic surgery (MMS), using histology as the reference standard. METHODS: Skin layers from 130 patients undergoing MMS at the Nottingham University Hospitals NHS Trust (September 2022-July 2023) were investigated with the AF-Raman instrument. The layers were measured when fresh, immediately after excision. The AF-Raman results and the intraoperative assessment by Mohs surgeons were compared with a postoperative consensus-derived reference produced by three dermatopathologists. The sensitivity, specificity, and positive and negative predictive values were calculated. The study was registered with ClinicalTrials.gov (NCT03482622). RESULTS: AF-Raman analysis was successfully completed for 125 of 130 layers and, on average, covered 91% of the specimen surface area, with the lowest surface area covered being 87% for the eyelid and the highest being 94% for forehead specimens. The AF-Raman instrument identified positive margins in 24 of 36 BCC-positive cases [67% sensitivity, 95% confidence interval (CI) 49-82] and negative margins in 65 of 89 BCC-negative cases (73% specificity, 95% CI 63-82). Only one of 12 false-negative cases was caused by misclassification by the AF-Raman algorithm. The other 11 false-negatives cases were a result of no valid Raman signal being recorded at the location of the residual BCC due to either occlusion by blood or poor contact between tissue and the cassette window. The intraoperative diagnosis by Mohs surgeons identified positive margins in 31 of 36 BCC-positive cases (86% sensitivity, 95% CI 70-95) and negative margins in 79 of 89 BCC-negative cases (89% specificity, 95% CI 81-95). CONCLUSIONS: The AF-Raman instrument has the potential to provide intraoperative microscopic assessment of surgical margins in BCC surgery. Further improvements are required for tissue processing, to ensure complete coverage of the surgical specimens.

Basal cell carcinoma (BCC) is one of the most common human cancers, occurring mostly on the face and neck. Most BCCs are treated by cutting them out under local anaesthetic. This is routinely done in a hospital by a dermatologist or plastic surgeon. Surgery aims to remove all the cancer leaving the smallest scar possible, but it is often difficult to know how much normal skin to remove. Results from the laboratory often take 1 to 2 weeks to show if the cancer is clear. A technique called 'Mohs' (micrographic surgery) is recommended for these 'high-risk' BCCs. Mohs surgery removes thin layers of skin and investigates them under a microscope while the patient is still in the hospital. This is repeated until all the layers are clear of cancer. Because of the patchy availability of Mohs surgery, many patients with high-risk BCCs are treated by traditional methods that may not be as good as Mohs. We have developed an instrument that scans layers of skin and can quickly detect BCC. The instrument allows surgeons to check each removed skin layer for cancer cells to decide if more layers need to be removed. In this study, the instrument was tested on skin tissue layers from 130 patients who had Mohs surgery at the Nottingham Treatment Centre. The results showed that the instrument can measure skin layers in approximately 30 minutes and identify BCC with a similar accuracy to a Mohs surgeon, but only when the skin layers are prepared properly. With future improvements, the technology might be used to guide Mohs surgery or help surgeons in centres that do not have access to Mohs surgery.

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.

Diagnosis of tumors during tissue-conserving surgery with integrated autofluorescence and Raman scattering microscopy.

Tissue-conserving surgery is used increasingly in cancer treatment. However, one of the main challenges in this type of surgery is the detection of tumor margins. Histopathology based on tissue sectioning and staining has been the gold standard for cancer diagnosis for more than a century. However, its use during tissue-conserving surgery is limited by time-consuming tissue preparation steps (1-2 h) and the diagnostic variability inherent in subjective image interpretation. Here, we demonstrate an integrated optical technique based on tissue autofluorescence imaging (high sensitivity and high speed but low specificity) and Raman scattering (high sensitivity and high specificity but low speed) that can overcome these limitations. Automated segmentation of autofluorescence images was used to select and prioritize the sampling points for Raman spectroscopy, which then was used to establish the diagnosis based on a spectral classification model (100% sensitivity, 92% specificity per spectrum). This automated sampling strategy allowed objective diagnosis of basal cell carcinoma in skin tissue samples excised during Mohs micrographic surgery faster than frozen section histopathology, and one or two orders of magnitude faster than previous techniques based on infrared or Raman microscopy. We also show that this technique can diagnose the presence or absence of tumors in unsectioned tissue layers, thus eliminating the need for tissue sectioning. This study demonstrates the potential of this technique to provide a rapid and objective intraoperative method to spare healthy tissue and reduce unnecessary surgery by determining whether tumor cells have been removed.

MAP segmentation in Bayesian hidden Markov models: a case study.

We consider the problem of estimating the maximum posterior probability (MAP) state sequence for a finite state and finite emission alphabet hidden Markov model (HMM) in the Bayesian setup, where both emission and transition matrices have Dirichlet priors. We study a training set consisting of thousands of protein alignment pairs. The training data is used to set the prior hyperparameters for Bayesian MAP segmentation. Since the Viterbi algorithm is not applicable any more, there is no simple procedure to find the MAP path, and several iterative algorithms are considered and compared. The main goal of the paper is to test the Bayesian setup against the frequentist one, where the parameters of HMM are estimated using the training data.

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.

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.

Towards intra-operative diagnosis of tumours during breast conserving surgery by selective-sampling Raman micro-spectroscopy.

Breast-conserving surgery (BCS) is increasingly employed for the treatment of early stage breast cancer. One of the key challenges in BCS is to ensure complete removal of the tumour while conserving as much healthy tissue as possible. In this study we have investigated the potential of Raman micro-spectroscopy (RMS) for automated intra-operative evaluation of tumour excision. First, a multivariate classification model based on Raman spectra of normal and malignant breast tissue samples was built and achieved diagnosis of mammary ductal carcinoma (DC) with 95.6% sensitivity and 96.2% specificity (5-fold cross-validation). The tumour regions were discriminated from the healthy tissue structures based on increased concentration of nucleic acids and reduced concentration of collagen and fat. The multivariate classification model was then applied to sections from fresh tissue of new patients to produce diagnosis images for DC. The diagnosis images obtained by raster scanning RMS were in agreement with the conventional histopathology diagnosis but were limited to long data acquisition times (typically 10,000 spectra mm(-2), which is equivalent to ~5 h mm(-2)). Selective-sampling based on integrated auto-fluorescence imaging and Raman spectroscopy was used to reduce the number of Raman spectra to ~20 spectra mm(-2), which is equivalent to an acquisition time of ~15 min for 5 × 5 mm(2) tissue samples. This study suggests that selective-sampling Raman microscopy has the potential to provide a rapid and objective intra-operative method to detect mammary carcinoma in tissue and assess resection margins.

Development of Raman microspectroscopy for automated detection and imaging of basal cell carcinoma.

We investigate the potential of Raman microspectroscopy (RMS) for automated evaluation of excised skin tissue during Mohs micrographic surgery (MMS). The main aim is to develop an automated method for imaging and diagnosis of basal cell carcinoma (BCC) regions. Selected Raman bands responsible for the largest spectral differences between BCC and normal skin regions and linear discriminant analysis (LDA) are used to build a multivariate supervised classification model. The model is based on 329 Raman spectra measured on skin tissue obtained from 20 patients. BCC is discriminated from healthy tissue with 90+/-9% sensitivity and 85+/-9% specificity in a 70% to 30% split cross-validation algorithm. This multivariate model is then applied on tissue sections from new patients to image tumor regions. The RMS images show excellent correlation with the gold standard of histopathology sections, BCC being detected in all positive sections. We demonstrate the potential of RMS as an automated objective method for tumor evaluation during MMS. The replacement of current histopathology during MMS by a "generalization" of the proposed technique may improve the feasibility and efficacy of MMS, leading to a wider use according to clinical need.

Robust acoustic object detection.

We consider a novel approach to the problem of detecting phonological objects like phonemes, syllables, or words, directly from the speech signal. We begin by defining local features in the time-frequency plane with built in robustness to intensity variations and time warping. Global templates of phonological objects correspond to the coincidence in time and frequency of patterns of the local features. These global templates are constructed by using the statistics of the local features in a principled way. The templates have clear phonetic interpretability, are easily adaptable, have built in invariances, and display considerable robustness in the face of additive noise and clutter from competing speakers. We provide a detailed evaluation of the performance of some diphone detectors and a word detector based on this approach. We also perform some phonetic classification experiments based on the edge-based features suggested here.