Kelly J Mesa1,2, Laura E Selmic3, Paritosh Pande1, Guillermo L Monroy1,4, Jennifer Reagan3, Jonathan Samuelson5, Elizabeth Driskell3, Joanne Li1,4, Marina Marjanovic1,4, Eric J Chaney1, Stephen A Boppart1,2,4,6. 1. Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois. 2. Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois. 3. Department of Veterinary Clinical Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois. 4. Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois. 5. Department of Pathobiology, University of Illinois at Urbana-Champaign, Urbana, Illinois. 6. Department of Internal Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois.
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.
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.
Authors: Sarah J Erickson-Bhatt; Ryan M Nolan; Nathan D Shemonski; Steven G Adie; Jeffrey Putney; Donald Darga; Daniel T McCormick; Andrew J Cittadine; Adam M Zysk; Marina Marjanovic; Eric J Chaney; Guillermo L Monroy; Fredrick A South; Kimberly A Cradock; Z George Liu; Magesh Sundaram; Partha S Ray; Stephen A Boppart Journal: Cancer Res Date: 2015-09-15 Impact factor: 12.701
Authors: Holly A Phelps; Charles A Kuntz; Rowan J Milner; Barbara E Powers; Nicholas J Bacon Journal: J Am Vet Med Assoc Date: 2011-07-01 Impact factor: 1.936
Authors: Andras A Lindenmaier; Leigh Conroy; Golnaz Farhat; Ralph S DaCosta; Costel Flueraru; I Alex Vitkin Journal: Opt Lett Date: 2013-04-15 Impact factor: 3.776
Authors: Jianfeng Wang; Yang Xu; Kelly J Mesa; Fredrick A South; Eric J Chaney; Darold R Spillman; Ronit Barkalifa; Marina Marjanovic; P Scott Carney; Anna M Higham; Z George Liu; Stephen A Boppart Journal: Biomed Opt Express Date: 2018-11-28 Impact factor: 3.732
Authors: Laura E Selmic; Jonathan Samuelson; Jennifer K Reagan; Kelly J Mesa; Elizabeth Driskell; Joanne Li; Marina Marjanovic; Stephen A Boppart Journal: Vet Comp Oncol Date: 2018-11-13 Impact factor: 2.613
Authors: Sarah J Erickson-Bhatt; Kelly J Mesa; Marina Marjanovic; Eric J Chaney; Adeel Ahmad; Pin-Chieh Huang; Z George Liu; Kelly Cunningham; Stephen A Boppart Journal: Transl Res Date: 2017-12-08 Impact factor: 7.012
Authors: Christina J Cocca; Laura E Selmic; Jonathan Samuelson; Pin-Chieh Huang; Jianfeng Wang; Stephen A Boppart Journal: Vet Surg Date: 2019-08-07 Impact factor: 1.495
Authors: Carolina Fabelo; Laura E Selmic; Pin-Cheh Huang; Jonathan P Samuelson; Jennifer K Reagan; Alexandra Kalamaras; Vincent Wavreille; Guillermo L Monroy; Marina Marjanovic; Stephen A Boppart Journal: Vet Comp Oncol Date: 2020-07-26 Impact factor: 2.613