Identifying Intestinal Metaplasia at the Squamocolumnar Junction by Using Optical Coherence Tomography

Background: Optical coherence tomography (OCT) is an optical imaging method that produces high-resolution cross-sectional images of the esophagus. The accuracy of OCT for differentiating tissue types at the squamocolumnar junction (SCJ) has not been established. Objective: The purpose of this study was to identify and validate OCT image criteria for distinguishing metaplastic from nonmetaplastic tissue at the SCJ. Design: A total of 196 biopsy-correlated OCT images of the SCJ were acquired from 113 patients undergoing upper endoscopy. A pathologist blinded to the OCT results reviewed each pathology specimen and determined the presence of the following histopathology: gastric cardia, squamous mucosa, pancreatic metaplasia, and intestinal metaplasia. An algorithm for diagnosing specialized intestinal metaplasia (SIM) was created by reviewing a training set of 40 biopsy-correlated OCT images. Two blinded investigators prospectively tested the algorithm on a validation set of 123 images. Results: OCT images of squamous mucosa were characterized by a layered appearance without epithelial glands; gastric cardia, by vertical pit and gland structure, a well-defined epithelial surface reflectivity, and relatively poor image penetration; and SIM by an irregular architecture and good image penetration. The OCT criteria were 85% sensitive and 95% specific for SIM when applied retrospectively to the training set. When applied to the validation set, the algorithm was 81% sensitive for both OCT readers and 66% and 57% specific for diagnosing SIM. The interobserver agreement was good (κ = 0.53). Conclusions: OCT imaging can identify SIM at the SCJ with an accuracy similar to that of endoscopy

Automated algorithm for differentiation of human breast tissue using low coherence interferometry for fine needle aspiration biopsy guidance

Fine needle aspiration biopsy (FNAB) is a rapid and cost-effective method for obtaining a first-line diagnosis of a palpable mass of the breast. However, because it can be difficult to manually discriminate between adipose tissue and the fibroglandular tissue more likely to harbor disease, this technique is plagued by a high number of nondiagnostic tissue draws. We have developed a portable, low coherence interferometry (LCI) instrument for FNAB guidance to combat this problem. The device contains an optical fiber probe inserted within the bore of the fine gauge needle and is capable of obtaining tissue structural information with a spatial resolution of 10 μm over a depth of approximately 1.0 mm. For such a device to be effective clinically, algorithms that use the LCI data must be developed for classifying different tissue types. We present an automated algorithm for differentiating adipose tissue from fibroglandular human breast tissue based on three parameters computed from the LCI signal (slope, standard deviation, spatial frequency content). A total of 260 breast tissue samples from 58 patients were collected from excised surgical specimens. A training set (N = 72) was used to extract parameters for each tissue type and the parameters were fit to a multivariate normal density. The model was applied to a validation set (N = 86) using likelihood ratios to classify groups. The overall accuracy of the model was 91.9% (84.0 to 96.7) with 98.1% (89.7 to 99.9) sensitivity and 82.4% (65.5 to 93.2) specificity where the numbers in parentheses represent the 95% confidence intervals. These results suggest that LCI can be used to determine tissue type and guide FNAB of the breast.Medical Free Electron Laser program (Grant No. FA9550-04-1-0079)National Institutes of Health (U.S.) (Ruth L. Kirschstein individual fellowship Grant No. 1 F31 EB005141–01A2

Measurement of collagen and smooth muscle cell content in atherosclerotic plaques using polarization-sensitive optical coherence tomography

ObjectivesThe purpose of this study was to investigate the measurement of collagen and smooth muscle cell (SMC) content in atherosclerotic plaques using polarization-sensitive optical coherence tomography (PSOCT).BackgroundA method capable of evaluating plaque collagen content and SMC density can provide a measure of the mechanical fidelity of the fibrous cap and can enable the identification of high-risk lesions. Optical coherence tomography has been demonstrated to provide cross-sectional images of tissue microstructure with a resolution of 10 μm. A recently developed technique, PSOCT measures birefringence, a material property that is elevated in tissues such as collagen and SMCs.MethodsWe acquired PSOCT images of 87 aortic plaques obtained from 20 human cadavers. Spatially averaged PSOCT birefringence, Φ, was measured and compared with plaque collagen and SMC content, quantified morphometrically by picrosirius red and smooth muscle actin staining at the corresponding locations.ResultsThere was a high positive correlation between PSOCT measurements of Φ and total collagen content in all plaques (r = 0.67, p < 0.001) and in fibrous caps of necrotic core fibroatheromas (r = 0.68, p < 0.001). Polarization-sensitive optical coherence tomography measurements of Φ demonstrated a strong positive correlation with thick collagen fiber content (r = 0.76, p < 0.001) and SMC density (r = 0.74, p < 0.01).ConclusionsOur results demonstrate that PSOCT enables the measurement of birefringence in plaques and in fibrous caps of necrotic core fibroatheromas. Given its potential to evaluate collagen content, collagen fiber thickness, and SMC density, we anticipate that PSOCT will significantly improve our ability to evaluate plaque stability in patients