Sentinel lymph node biopsy is a standard diagnosis procedure to determine whether breast cancer has spread to the lymph glands in the armpit (the axillary nodes). The metastatic status of the sentinel node (the first node in the axillary chain that drains the affected breast) is the determining factor in surgery between conservative lumpectomy and more radical mastectomy including axillary node excision. The traditional assessment of the node requires sample preparation and pathologist interpretation. An automated elastic scattering spectroscopy (ESS) scanning device was constructed to take measurements from the entire cut surface of the excised sentinel node and to produce ESS images for cancer diagnosis. Here, we report on a partially supervised image classification scheme employing a Bayesian multivariate, finite mixture model with a Markov random field (MRF) spatial prior. A reduced dimensional space was applied to represent the scanning data of the node by a statistical image, in which normal, metastatic, and nonnodal-tissue pixels are identified. Our results show that our model enables rapid imaging of lymph nodes. It can be used to recognize nonnodal areas automatically at the same time as diagnosing sentinel node metastases with sensitivity and specificity of 85% and 94%, respectively. ESS images can help surgeons by providing a reliable and rapid intraoperative determination of sentinel nodal metastases in breast cancer.
A novel method for rapidly detecting metastatic breast cancer within excised sentinel lymph node(s) of the axilla is presented. Elastic scattering spectroscopy (ESS) is a point-contact technique that collects broadband optical spectra sensitive to absorption and scattering within the tissue. A statistical discrimination algorithm was generated from a training set of nearly 3000 clinical spectra and used to test clinical spectra collected from an independent set of nodes. Freshly excised nodes were bivalved and mounted under a fiber-optic plate. Stepper motors raster-scanned a fiber-optic probe over the plate to interrogate the node's cut surface, creating a 20×20 grid of spectra. These spectra were analyzed to create a map of cancer risk across the node surface. Rules were developed to convert these maps to a prediction for the presence of cancer in the node. Using these analyses, a leave-one-out cross-validation to optimize discrimination parameters on 128 scanned nodes gave a sensitivity of 69% for detection of clinically relevant metastases (71% for macrometastases) and a specificity of 96%, comparable to literature results for touch imprint cytology, a standard technique for intraoperative diagnosis. ESS has the advantage of not requiring a pathologist to review the tissue sample.
Sentinel node biopsy is the new standard for lymphatic staging of breast carcinoma. Intraoperative detection of sentinel node metastases avoids a second operation for those patients with metastatic lymph nodes. Elastic scattering spectroscopy is an optical technique which is sensitive to cellular and subcellular changes occurring in malignancy. We analyzed 2078 ESS spectra from 324 axillary sentinel nodes from patients with breast carcinoma. ESS was able to detect metastatic lymph nodes with an overall sensitivity of 60% and specificity of 94%, which is comparable to existing pathological techniques. Nodes completely replaced with metastatic tumour were detected with 100% sensitivity, suggesting that further improvement in sensitivity is likely with more intensive optical sampling of the nodes.
The ability to provide the best treatment for breast cancer depends on establishing whether or not the cancer has spread to the lymph nodes under the arm. Conventional assessment requires tissue removal, preparation, and expert microscopic interpretation. In this study, elastic scattering spectroscopy (ESS) is used to interrogate excised nodes with pulsed broadband illumination and collection of the backscattered light. Multiple spectra are taken from 139 excised nodes (53 containing cancer) in 68 patients, and spectral analysis is performed using a combination of principal component analysis and linear discriminant analysis to correlate the spectra with conventional histology. The data are divided into training and test sets. In test sets containing spectra from only normal nodes and nodes with complete replacement by cancer, ESS detects the spectra from cancerous nodes with 84% sensitivity and 91% specificity (per-spectrum analysis). In test sets that included normal nodes and nodes with partial as well as complete replacement by cancer, ESS detects the nodes with cancer with an average sensitivity of 75% and specificity of 89% (per-node analysis). These results are comparable to those from conventional touch imprint cytology and frozen section histology, but do not require an expert pathologist for interpretation. With automation of the technique, results could be made available almost instantaneously. ESS is a promising technique for the rapid, accurate, and straightforward detection of metastases in excised sentinel lymph nodes.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.