Elastic scattering spectroscopy for early detection of breast cancer: partially supervised Bayesian image classification of scanned sentinel lymph nodes.

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.

Scanning elastic scattering spectroscopy detects metastatic breast cancer in sentinel lymph nodes.

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 20x20 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.