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

Abstract

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.

Figure 1

Schematic of ESS scanning device (not to scale). The bivalved node is mounted on a mobile scanning stage. It is coupled to a fiber-optic plate above it using a drop of saline or sterile water and gentle but firm pressure. The fiber-optic plate is coupled to a fixed fiber-optic probe with microscope immersion oil. Stepper motors move the scanning stage in an x-y raster pattern to create a 20×20 grid of spectra at 0.5-mm intervals.

Figure 2

Distribution of canonical scores generated from the point-measurement training set. We show the scores generated using an LDA from 20 PCs.

Figure 3

ROC curve from leave-one-out cross-validation point measurement from the point-measurement ESS training set. These results are on a per-spectrum basis.

Figure 4

A sample scanned node containing metastatic deposits. (a) The false-color map was constructed using point-wise analyses of spectra by a 20-PC LDA algorithm. Red shows regions of metastases; blue normal tissue. (b) Binary map on a cutoff of 2.0—pink identifies cancer; black normal tissue. (c) Photograph of cut surface of node. The two regions show (1) normal tissue and (2) a central region of normal tissue surrounded by a halo of metastases.

Figure 5

Sample scanned node showing a (∼2 mm) metastatic deposit (confirmed on histology). (a) Scanned false-color image using 20 PCs. (b) Binary map based on a cutoff of 2.0—pink identifies regions of cancer; black denotes normal tissue or background. (c) Photograph of cut surface of node. This deposit was not detected with touch imprint cytology—possibly because the metastatis was below the cut surface of the node. However, this is an isolated case, and one should not draw too many conclusions from it.

Figure 6

Monte Carlo simulation of photon transport for a homogeneous tissue-like material. Photons were injected at z=0, −200<x<200 and collected at z=0, 200<x<400; these plots show binned voxel visualisations of scattering events. (a) μ_a=10 cm⁻¹, ${\mu }_s^{\prime }=20{\mathrm{cm}}^{-1}$ (reasonable values for tissue at 400 nm, where absorption is highest). (b) μ_a=0.1 cm⁻¹, ${\mu }_s^{\prime }=10{\mathrm{cm}}^{-1}$ (reasonable values for tissue at 650 nm, where absorption is lower).