Intraoperative optical coherence tomography for soft tissue sarcoma differentiation and margin identification

Abstract

Background and objective: Sarcomas are rare but highly aggressive tumors, and local recurrence after surgical excision can occur in up to 50% cases. Therefore, there is a strong clinical need for accurate tissue differentiation and margin assessment to reduce incomplete resection and local recurrence. The purpose of this study was to investigate the use of optical coherence tomography (OCT) and a novel image texture-based processing algorithm to differentiate sarcoma from muscle and adipose tissue.

Study design and methods: In this study, tumor margin delineation in 19 feline and canine veterinary patients was achieved with intraoperative OCT to help validate tumor resection. While differentiation of lower-scattering adipose tissue from higher-scattering muscle and tumor tissue was relatively straightforward, it was more challenging to distinguish between dense highly scattering muscle and tumor tissue types based on scattering intensity and microstructural features alone. To improve tissue-type differentiation in a more objective and automated manner, three descriptive statistical metrics, namely the coefficient of variation (CV), standard deviation (STD), and Range, were implemented in a custom algorithm applied to the OCT images.

Results: Over 22,800 OCT images were collected intraoperatively from over 38 sites on 19 ex vivo tissue specimens removed during sarcoma surgeries. Following the generation of an initial set of OCT images correlated with standard hematoxylin and eosin-stained histopathology, over 760 images were subsequently used for automated analysis. Using texture-based image processing metrics, OCT images of sarcoma, muscle, and adipose tissue were all found to be statistically different from one another (P ≤ 0.001).

Conclusion: These results demonstrate the potential of using intraoperative OCT, along with an automated tissue differentiation algorithm, as a guidance tool for soft tissue sarcoma margin delineation in the operating room. Lasers Surg. Med. 49:240-248, 2017. © 2017 Wiley Periodicals, Inc.

Keywords: OCT; cancer; computer-aided detection; image processing; imaging; surgery; surgical margins.

Fig. 1

Image processing flowchart. Out of 600 images acquired from each imaged site, 10 were selected, ensuring that there was an adequate separation between the chosen images. Segmentation masks were then generated by choosing areas of interest, avoiding areas of saturation and background noise. An active contour was then applied to every mask. A sliding window (yellow box) was applied over every image to determine the statistical metrics of Coefficient of Variation, Standard Deviation, and Range. The results were then multiplied with each mask and overlaid with the original frame. Statistical comparisons between tissue types were made between these resulting B-scan images.

Fig. 2

Physical (histology) and textural (OCT) properties of (A) sarcoma, (B) muscle, and (C) adipose tissue with corresponding OCT images (D-F, respectively). The top row shows H&E-stained digitized histological images of the resected tissue, and the corresponding OCT images are shown in the bottom row. The dashed red boxes in the OCT images correspond to the dimensions of the histology images. The black scale bars across the bottom of each image in (A-C) represent 500 μm, and the scale bars in (D-F) represent 1 mm.

Fig. 3

Box plots of the means of Standard Deviation comparing (top) all the adipose and sarcoma B-scans, and (bottom) all the adipose and muscle B-scans.

Fig. 4

OCT and corresponding histopathology from an excised specimen from the dorsal thorax of a domestic long hair cat. Specimen dimensions were 9.5 cm × 6.5 cm × 5 cm. Three OCT images and corresponding histology are shown for the three inked areas on the specimen. (A) Sarcoma, (B) Muscle, (C) Muscle (m) and Adipose (a). The scale bars for the OCT images represent 1 mm and the scale bars for the histology represent 10 mm.

Fig. 5

Image processing sequence for the descriptive Coefficient of Variation (CV) statistical metric. (A) Original OCT image of sarcoma, (B) corresponding mask, and (C) calculated CV. (D) Raw OCT image of muscle, (E) corresponding mask, and (F) calculated CV. Scale bar represents 1 mm and the color bar ranges from 0.1 to 1.1 in steps of 0.1.

Fig. 6

Box plot of the means of Coefficient of Variation for all muscle and sarcoma B-scans.

Fig. 7

Box plot of the means of Standard Deviation for all muscle and sarcoma B-scans.

Fig. 8

Box plot of the means of Range for all muscle and sarcoma B-scans.