Intraoperative optical coherence tomography for soft tissue sarcoma differentiation and margin identification.
Intraoperative optical coherence tomography for soft tissue sarcoma differentiation and margin identification.
复制标题
DOI：
10.1002/lsm.22633
复制
发表时间：
2017-03
影响因子：
2.4
通讯作者：
Boppart SA
中科院分区：
文献类型：
--
作者：
Mesa KJ;Selmic LE;Pande P;Monroy GL;Reagan J;Samuelson J;Driskell E;Li J;Marjanovic M;Chaney EJ;Boppart SA
肉瘤是一种罕见但高度侵袭性的肿瘤，手术切除后局部复发率可达50%。因此，临床上迫切需要准确的组织分化和边缘评估，以减少不完全切除和局部复发。本研究的目的是探讨使用光学相干断层扫描（OCT）和一种新的图像纹理为基础的处理算法，以区分肉瘤肌肉和脂肪组织。在本研究中，使用术中OCT对19名猫和犬兽医患者进行肿瘤边缘勾画，以帮助验证肿瘤切除。虽然较低散射的脂肪组织与较高散射的肌肉和肿瘤组织的区分相对简单，但仅基于散射强度和显微结构特征来区分致密的高散射肌肉和肿瘤组织类型更具挑战性。为了以更客观和自动化的方式改善组织类型区分，在应用于OCT图像的自定义算法中实现了三个描述性统计指标，即变异系数、标准差和范围。从肉瘤手术期间取出的19个离体组织标本的38个部位采集了超过22，800张OCT图像。在生成与标准苏木精和伊红染色组织病理学相关的初始OCT图像集之后，随后使用超过760个图像进行自动分析。使用基于纹理的图像处理指标，发现肉瘤、肌肉和脂肪组织的OCT图像彼此之间存在统计学差异（p ≤ 0.001）。这些结果证明了术中OCT与自动组织分化算法沿着作为手术室软组织肉瘤边缘勾画的指导工具的潜力。
Sarcomas are rare but highly aggressive tumors, and local recurrence after surgical excision can occur 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. 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, standard deviation, and range, were implemented in a custom algorithm applied to the OCT images. 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). 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.