Three-dimensional computational analysis of optical coherence tomography images for the detection of soft tissue sarcomas

Abstract. We present a three-dimensional (3-D) computational method to detect soft tissue sarcomas with the goal of automatic surgical margin assessment based on optical coherence tomography (OCT) images. Three parameters are investigated and quantified from OCT images as the indicators for the tissue diagnosis including the signal attenuation (A-line slope), the standard deviation of the signal fluctuations (speckles), and the exponential decay coefficient of its spatial frequency spectrum. The detection of soft tissue sarcomas relies on the combination of these three parameters, which are related to the optical attenuation characteristics and the structural features of the tissue. Pilot experiments were performed on ex vivo human tissue samples with homogeneous pieces (both normal and abnormal) and tumor margins. Our results demonstrate the feasibility of this computational method in the differentiation of soft tissue sarcomas from normal tissues. The features of A-line-based detection and 3-D quantitative analysis yield promise for a computer-aided technique capable of accurately and automatically identifying resection margins of soft tissue sarcomas during surgical treatment.

[1]  Hsiang-Chieh Lee,et al.  Effective indicators for diagnosis of oral cancer using optical coherence tomography. , 2008, Optics express.

[2]  K. Larin,et al.  Phase-sensitive swept source optical coherence tomography for imaging and quantifying of microbubbles in clear and scattering media , 2009 .

[3]  L. Ellis,et al.  Influence of surgical margins on outcome in patients with preoperatively irradiated extremity soft tissue sarcomas , 1994, Cancer.

[4]  R. Pollock,et al.  Current concepts in multimodality therapy for retroperitoneal sarcoma , 2007, Expert review of anticancer therapy.

[5]  R A McLaughlin,et al.  Ultrathin side-viewing needle probe for optical coherence tomography. , 2011, Optics letters.

[6]  D. Winchester,et al.  The National Cancer Data Base report on soft tissue sarcoma , 1996, Cancer.

[7]  G. Ripandelli,et al.  Optical coherence tomography. , 1998, Seminars in ophthalmology.

[8]  Floredes M. Menodiado,et al.  Noncontact measurement of elasticity for the detection of soft-tissue tumors using phase-sensitive optical coherence tomography combined with a focused air-puff system. , 2012, Optics letters.

[9]  D. Sampson,et al.  Parametric imaging of cancer with optical coherence tomography. , 2010, Journal of biomedical optics.

[10]  Freddy T. Nguyen,et al.  Optical coherence tomography: a review of clinical development from bench to bedside. , 2007, Journal of biomedical optics.

[11]  James G. Fujimoto,et al.  Optical coherence tomography: high-resolution imaging in nontransparent tissue , 1999 .

[12]  Steven L. Jacques,et al.  Mapping Tissue Optical Attenuation to Identify Cancer Using Optical Coherence Tomography , 2009, MICCAI.

[13]  S. Boppart,et al.  Optical micro-scale mapping of dynamic biomechanical tissue properties. , 2008, Optics express.

[14]  R. Jain,et al.  Cancer imaging by optical coherence tomography: preclinical progress and clinical potential , 2012, Nature Reviews Cancer.

[15]  J. Fujimoto,et al.  Optical coherence tomography: an emerging technology for biomedical imaging and optical biopsy. , 2000, Neoplasia.

[16]  Floredes M. Menodiado,et al.  Estimation of shear wave velocity in gelatin phantoms utilizing PhS-SSOCT , 2012 .

[17]  M. Pierce,et al.  Polarization-sensitive optical coherence tomography of invasive basal cell carcinoma. , 2004, Journal of biomedical optics.

[18]  David D Sampson,et al.  Ultrathin fiber probes with extended depth of focus for optical coherence tomography. , 2012, Optics letters.

[19]  Freddy T. Nguyen,et al.  Optical Biopsy of Lymph Node Morphology using Optical Coherence Tomography , 2005, Technology in cancer research & treatment.

[20]  B Sundell,et al.  Soft-tissue sarcomas. , 1979, British medical journal.

[21]  Valery V. Tuchin,et al.  Optical Clearing of Tissues and Blood , 2005 .

[22]  D. Sampson,et al.  Imaging of human lymph nodes using optical coherence tomography: potential for staging cancer. , 2010, Cancer research.

[23]  H. Lynch,et al.  Psychologic Aspects of Cancer Genetic Testing: A Research Update for Clinicians , 1997 .

[24]  Laura W Bancroft,et al.  Imaging of fatty tumors: distinction of lipoma and well-differentiated liposarcoma. , 2002, Radiology.

[25]  Tianheng Wang,et al.  Optical scattering coefficient estimated by optical coherence tomography correlates with collagen content in ovarian tissue. , 2011, Journal of biomedical optics.

[26]  S. Boppart,et al.  Optical Coherence Tomography: Feasibility for Basic Research and Image-guided Surgery of Breast Cancer , 2004, Breast Cancer Research and Treatment.

[27]  Kirill V Larin,et al.  Revealing retroperitoneal liposarcoma morphology using optical coherence tomography. , 2011, Journal of biomedical optics.

[28]  S. Boppart,et al.  Computational methods for analysis of human breast tumor tissue in optical coherence tomography images. , 2006, Journal of biomedical optics.