Using DRS during breast conserving surgery: identifying robust optical parameters and influence of inter-patient variation

Breast Cancer Diagnosis Using Extended-Wavelength-Diffuse Reflectance Spectroscopy (EW-DRS)-Proof of Concept in Ex Vivo Breast Specimens Using Machine Learning

This study aims to investigate the feasibility of using diffuse reflectance spectroscopy (DRS) to distinguish malignant breast tissue from adjacent healthy tissue, and to evaluate if an extended-wavelength range (450-1550 nm) has an advantage over the standard wavelength range (450-900 nm). Multivariate statistics and machine learning algorithms, either linear discriminant analysis (LDA) or support vector machine (SVM) are used to distinguish the two tissue types in breast specimens (total or partial mastectomy) from 23 female patients with primary breast cancer. EW-DRS has a sensitivity of 94% and specificity of 91% as compared to a sensitivity of 40% and specificity of 71% using the standard wavelength range. The results suggest that DRS can discriminate between malignant and healthy breast tissue, with improved outcomes using an extended wavelength. It is also possible to construct a simple analytical model to improve the diagnostic performance of the DRS technique.

Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging

SIGNIFICANCE

Hyperspectral reflectance imaging can be used in medicine to identify tissue types, such as tumor tissue. Tissue classification algorithms are developed based on, e.g., machine learning or principle component analysis. For the development of these algorithms, data are generally preprocessed to remove variability in data not related to the tissue itself since this will improve the performance of the classification algorithm. In hyperspectral imaging, the measured spectra are also influenced by reflections from the surface (glare) and height variations within and between tissue samples.

AIM

To compare the ability of different preprocessing algorithms to decrease variations in spectra induced by glare and height differences while maintaining contrast based on differences in optical properties between tissue types.

APPROACH

We compare eight preprocessing algorithms commonly used in medical hyperspectral imaging: standard normal variate, multiplicative scatter correction, min-max normalization, mean centering, area under the curve normalization, single wavelength normalization, first derivative, and second derivative. We investigate conservation of contrast stemming from differences in: blood volume fraction, presence of different absorbers, scatter amplitude, and scatter slope-while correcting for glare and height variations. We use a similarity metric, the overlap coefficient, to quantify contrast between spectra. We also investigate the algorithms for clinical datasets from the colon and breast.

CONCLUSIONS

Preprocessing reduces the overlap due to glare and distance variations. In general, the algorithms standard normal variate, min-max, area under the curve, and single wavelength normalization are the most suitable to preprocess data used to develop a classification algorithm for tissue classification. The type of contrast between tissue types determines which of these four algorithms is most suitable.

Tissue-mimicking phantom materials with tunable optical properties suitable for assessment of diffuse reflectance spectroscopy during electrosurgery

Emerging intraoperative tumor margin assessment techniques require the development of more complex and reliable organ phantoms to assess the performance of the technique before its translation into the clinic. In this work, electrically conductive tissue-mimicking materials (TMMs) based on fat, water and agar/gelatin were produced with tunable optical properties. The composition of the phantoms allowed for the assessment of tumor margins using diffuse reflectance spectroscopy, as the fat/water ratio served as a discriminating factor between the healthy and malignant tissue. Moreover, the possibility of using polyvinyl alcohol (PVA) or transglutaminase in combination with fat, water and gelatin for developing TMMs was studied. The diffuse spectral response of the developed phantom materials had a good match with the spectral response of porcine muscle and adipose tissue, as well as in vitro human breast tissue. Using the developed recipe, anatomically relevant heterogeneous breast phantoms representing the optical properties of different layers of the human breast were fabricated using 3D-printed molds. These TMMs can be used for further development of phantoms applicable for simulating the realistic breast conserving surgery workflow in order to evaluate the intraoperative optical-based tumor margin assessment techniques during electrosurgery.

Optical scatter imaging of resected breast tumor structures matches the patterns of micro-computed tomography

In patients undergoing breast-conserving surgery (BCS), the rate of re-excision procedures to remove residual tumor left behind after initial resection can be high. Projection radiography, and recently, volumetric x-ray imaging are used to assess margin adequacy, but x-ray imaging lacks contrast between healthy, abnormal benign, and malignant fibrous tissues important for surgical decision making. The purpose of this study was to compare micro-CT and optical scatter imagery of surgical breast specimens and to demonstrate enhanced contrast-to intra-tumoral morphologies and tumor boundary features revealed by optical scatter imaging. A total of 57 breast tumor slices from 57 patients were imagedex vivoby spatially co-registered micro-CT and optical scatter scanning. Optical scatter exhibited greater similarity with micro-CT in 89% (51/57) of specimens versus diffuse white light (DWL) luminance using mutual information (mean ± standard deviation of 0.48 ± 0.21 versus 0.24 ± 0.12;p < 0.001) and in 81% (46/57) of specimens using the Sørensen-Dice coefficient (0.48 ± 0.21 versus 0.33 ± 0.18;p < 0.001). The coefficient of variation (CV) quantified the feature content in each image. Optical scatter exhibited the highest CV in every specimen (optical scatter: 0.70 ± 0.17; diffuse luminance: 0.24 ± 01; micro-CT: 0.15 ± 0.03 for micro-CT;p < 0.001). Optical scatter also exhibited the highest contrast ratios across representative tumor boundaries with adjacent healthy/benign fibrous tissues (1.5-3.7 for optical scatter; 1.0-1.1 for diffuse luminance; 1.0-1.1 for micro-CT). The two main findings from this study were: first, optical scatter contrast was in general similar to the radiological view of the tissue relative to DWL imaging; and second, optical scatter revealed additional features associated with fibrous tissue structures of similar radiodensity that may be relevant to diagnosis. The value of micro-CT lies in its rapid three-dimensional scanning of specimen morphology, and combined with optical scatter imaging with sensitivity to fibrous surface tissues, may be an attractive solution for margin assessment during BCS.

Proton density fat fraction of the spinal column: an MRI cadaver study

Background

The increased popularity of minimally invasive spinal surgery calls for a revision of guidance techniques to prevent injuries of nearby neural and vascular structures. Lipid content has previously been proposed as a distinguishing criterion for different bone tissues to provide guidance along the interface of cancellous and cortical bone. This study aims to investigate how fat is distributed throughout the spinal column to confirm or refute the suitability of lipid content for guidance purposes.

Results

Proton density fat fraction (PDFF) was assessed over all vertebral levels for six human cadavers between 53 and 92 years of age, based on fat and water MR images. According to their distance to the vertebra contour, the data points were grouped in five regions of interest (ROIs): cortical bone (−1 mm to 0 mm), pre-cortical zone (PCZ) 1–3 (0–1 mm; 1–2 mm; 2–3 mm), and cancellous bone (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document}≥ 3 mm). For PCZ1 vs. PCZ2, a significant difference in mean PDFF of between −7.59 pp and −4.39 pp on average was found. For cortical bone vs. PCZ1, a significant difference in mean PDFF of between −27.09 pp and −18.96 pp on average was found.

Conclusion

A relationship between distance from the cortical bone boundary and lipid content could be established, paving the way for guidance techniques based on fat fraction detection for spinal surgery.

Intraoperative tumor margin assessment using diffuse reflectance spectroscopy: the effect of electrosurgery on tissue discrimination using ex vivo animal tissue models

Using an intraoperative margin assessment technique during breast-conserving surgery (BCS) helps surgeons to decrease the risk of positive margin occurrence. Diffuse reflectance spectroscopy (DRS) has the potential to discriminate healthy breast tissue from cancerous tissue. We investigated the performance of an electrosurgical knife integrated with a DRS on porcine muscle and adipose tissue. Characterization of the formed debris on the optical fibers after electrosurgery revealed that the contamination is mostly burned tissue. Even with contaminated optical fibers, both tissues could still be discriminated with DRS based on fat/water ratio. Therefore, an electrosurgical knife integrated with DRS may be a promising technology to provide the surgeon with real-time guidance during BCS.

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.

Optimizing algorithm development for tissue classification in colorectal cancer based on diffuse reflectance spectra

Diffuse reflectance spectroscopy can be used in colorectal cancer surgery for tissue classification. The main challenge in the classification task is to separate healthy colorectal wall from tumor tissue. In this study, four normalization techniques, four feature extraction methods and five classifiers are applied to nine datasets, to obtain the optimal method to separate spectra measured on healthy colorectal wall from spectra measured on tumor tissue. All results are compared to the use of the entire non-normalized spectra. It is found that the most optimal classification approach is to apply a feature extraction method on non-normalized spectra combined with support vector machine or neural network classifier.

Influence of neoadjuvant chemotherapy on diffuse reflectance spectra of tissue in breast surgery specimens

Diffuse reflectance spectroscopy (DRS) can discriminate different tissue types based on optical characteristics. Since this technology has the ability to detect tumor tissue, several groups have proposed to use DRS for margin assessment during breast-conserving surgery for breast cancer. Nowadays, an increasing number of patients with breast cancer are being treated by neoadjuvant chemotherapy. Limited research has been published on the influence of neoadjuvant chemotherapy on the optical characteristics of the tissue. Hence, it is unclear whether margin assessment based on DRS is feasible in this specific group of patients. We investigate whether there is an effect of neoadjuvant chemotherapy on optical measurements of breast tissue. To this end, DRS measurements were performed on 92 ex-vivo breast specimens from 92 patients, treated with neoadjuvant chemotherapy and without neoadjuvant chemotherapy. Generalized estimating equation (GEE) models were generated, comparing the measurements of patients with and without neoadjuvant chemotherapy in datasets of different tissue types using a significance level of 5%. As input for the GEE models, either the intensity at a specific wavelength or a fit parameter, derived from the spectrum, was used. In the evaluation of the intensity, no influence of neoadjuvant chemotherapy was found, since none of the wavelengths were significantly different between the measurements with and the measurements without neoadjuvant chemotherapy in any of the datasets. These results were confirmed by the analysis of the fit parameters, which showed a significant difference for the amount of collagen in only one dataset. All other fit parameters were not significant for any of the datasets. These findings may indicate that assessment of the resection margin with DRS is also feasible in the growing population of breast cancer patients who receive neoadjuvant chemotherapy. However, it is possible that we did not detect neoadjuvant chemotherapy effect in the some of the datasets due to the small number of measurements in those datasets.

Broadband hyperspectral imaging for breast tumor detection using spectral and spatial information

Complete tumor removal during breast-conserving surgery remains challenging due to the lack of optimal intraoperative margin assessment techniques. Here, we use hyperspectral imaging for tumor detection in fresh breast tissue. We evaluated different wavelength ranges and two classification algorithms; a pixel-wise classification algorithm and a convolutional neural network that combines spectral and spatial information. The highest classification performance was obtained using the full wavelength range (450-1650 nm). Adding spatial information mainly improved the differentiation of tissue classes within the malignant and healthy classes. High sensitivity and specificity were accomplished, which offers potential for hyperspectral imaging as a margin assessment technique to improve surgical outcome.

Towards the use of diffuse reflectance spectroscopy for real-time in vivo detection of breast cancer during surgery

Background

Breast cancer surgeons struggle with differentiating healthy tissue from cancer at the resection margin during surgery. We report on the feasibility of using diffuse reflectance spectroscopy (DRS) for real-time in vivo tissue characterization.

Methods

Evaluating feasibility of the technology requires a setting in which measurements, imaging and pathology have the best possible correlation. For this purpose an optical biopsy needle was used that had integrated optical fibers at the tip of the needle. This approach enabled the best possible correlation between optical measurement volume and tissue histology. With this optical biopsy needle we acquired real-time DRS data of normal tissue and tumor tissue in 27 patients that underwent an ultrasound guided breast biopsy procedure. Five additional patients were measured in continuous mode in which we obtained DRS measurements along the entire biopsy needle trajectory. We developed and compared three different support vector machine based classification models to classify the DRS measurements.

Results

With DRS malignant tissue could be discriminated from healthy tissue. The classification model that was based on eight selected wavelengths had the highest accuracy and Matthews Correlation Coefficient (MCC) of 0.93 and 0.87, respectively. In three patients that were measured in continuous mode and had malignant tissue in their biopsy specimen, a clear transition was seen in the classified DRS measurements going from healthy tissue to tumor tissue. This transition was not seen in the other two continuously measured patients that had benign tissue in their biopsy specimen.

Conclusions

It was concluded that DRS is feasible for integration in a surgical tool that could assist the breast surgeon in detecting positive resection margins during breast surgery.

Trail registration NIH US National Library of Medicine–clinicaltrails.gov, NCT01730365. Registered: 10/04/2012 https://clinicaltrials.gov/ct2/show/study/NCT01730365

In vivo nerve identification in head and neck surgery using diffuse reflectance spectroscopy

Background

Careful identification of nerves during head and neck surgery is essential to prevent nerve damage. Currently, nerves are identified based on anatomy and appearance, optionally combined with electromyography (EMG). In challenging cases, nerve damage is reported in up to 50%. Recently, optical techniques, like diffuse reflectance spectroscopy (DRS) and fluorescence spectroscopy (FS) show potential to improve nerve identification.

Methods

212 intra‐operative DRS/FS measurements were performed. Small nerve branches (1–3 mm), on near‐nerve adipose tissue, muscle and subcutaneous fat were measured during 11 surgical procedures. Tissue identification was based on quantified concentrations of optical absorbers and scattering parameters.

Results

Clinically comprehensive parameters showed significant differences (<0.05) between the tissues. Classification using k‐Nearest Neighbor resulted in 100% sensitivity and a specificity of 83% (accuracy 91%), for the identification of nerve against surrounding tissues.

Conclusions

DRS/FS is a potentially useful intraoperative tool for identification of nerves from adjacent tissues.

Level of Evidence

Observational proof of principle study.

Diffuse reflectance spectroscopy of human liver tumor specimens - towards a tissue differentiating optical biopsy needle using light emitting diodes

Significant numbers of liver biopsies fail to yield representative tissue samples. This study was conducted to evaluate the ability of LED-based diffuse reflectance spectroscopy to discriminate tumors from liver parenchyma. Ex vivo spectra were acquired from malignant lesions and liver parenchyma of 32 patients who underwent liver resection using a white light source and several LEDs. Integrated spectra of two combined LEDs with emission peaks at 470 nm and 515 nm were classified with 98.4% sensitivity and 99.2% specificity. The promising results could yield to a simple handheld and cost-efficient tool for real-time tissue differentiation implemented in a biopsy needle.

Automated detection of breast cancer in resected specimens with fluorescence lifetime imaging

Re-excision rates for breast cancer lumpectomy procedures are currently nearly 25% due to surgeons relying on inaccurate or incomplete methods of evaluating specimen margins. The objective of this study was to determine if cancer could be automatically detected in breast specimens from mastectomy and lumpectomy procedures by a classification algorithm that incorporated parameters derived from fluorescence lifetime imaging (FLIm). This study generated a database of co-registered histologic sections and FLIm data from breast cancer specimens (N  =  20) and a support vector machine (SVM) classification algorithm able to automatically detect cancerous, fibrous, and adipose breast tissue. Classification accuracies were greater than 97% for automated detection of cancerous, fibrous, and adipose tissue from breast cancer specimens. The classification worked equally well for specimens scanned by hand or with a mechanical stage, demonstrating that the system could be used during surgery or on excised specimens. The ability of this technique to simply discriminate between cancerous and normal breast tissue, in particular to distinguish fibrous breast tissue from tumor, which is notoriously challenging for optical techniques, leads to the conclusion that FLIm has great potential to assess breast cancer margins. Identification of positive margins before waiting for complete histologic analysis could significantly reduce breast cancer re-excision rates.