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Quantitative imaging analysis to guide biopsy for molecular biomarkers
Author(s): Derek J. Doss; Jon S. Heiselman; Ma Luo; Logan W. Clements; Michael I. Miga; Daniel Brown; Filip Banovac
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Paper Abstract

Although resection and transplantation are primary curative methods of treatment for hepatocellular carcinoma, many patients are not candidates. In these cases, other treatment methods such as selective internal radiation therapy, chemotherapy, or external beam radiation are used. While these treatments are effective, patient-specific customization of treatment could be beneficial. Recent advances in personalized medicine are making this possible, but often there are multiple phenotypes within a proliferating tumor. While not standard, one could envision a serial longitudinal biopsy approach with more phenotypically-targeted therapeutics if one could detect responding and non-responding regions of tumor over time. This work proposes a method to determine active regions of the tumor that differentially respond to treatment to better guide biopsy for longitudinal personalization of treatment. While PET may serve this purpose, it is not easily used for real-time image guidance, is not effective for many types of tumors, and can be confounded by inflammatory responses. In this work, ten total patients with imaging sequences from before and after treatment were retrospectively obtained. Five of these were selected for analysis based on the total liver volume change. A two-phase alignment process comprised of an intensity-based rigid registration followed by a nonrigid refining process driven by bulk deformation of the organ surface was performed. To assess the accuracy of the registration, two metrics were used for preliminary results. The mean closest point surface distance was used to quantify how well the surfaces of the registered livers match and was found to be 2.65±3.54mm. Anatomical features visible in pre- and post-treatment images were also identified. After registration, the mean Euclidean distance between features was found to be 5.22±4.06mm. To assess potential areas of tumor change, the registered tumor pre- and post-treatment were overlaid.

Paper Details

Date Published: 8 March 2019
PDF: 8 pages
Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109512V (8 March 2019); doi: 10.1117/12.2513588
Show Author Affiliations
Derek J. Doss, Vanderbilt Univ. (United States)
Vanderbilt Institute for Surgery and Engineering (United States)
Jon S. Heiselman, Vanderbilt Univ. (United States)
Vanderbilt Institute for Surgery and Engineering (United States)
Ma Luo, Vanderbilt Univ. (United States)
Vanderbilt Institute for Surgery and Engineering (United States)
Logan W. Clements, Vanderbilt Univ. (United States)
Vanderbilt Institute for Surgery and Engineering (United States)
Michael I. Miga, Vanderbilt Univ. (United States)
Vanderbilt Univ. Medical Ctr. (United States)
Vanderbilt Institute for Surgery and Engineering (United States)
Daniel Brown, Vanderbilt Institute for Surgery and Engineering (United States)
Vanderbilt Univ. Medical Ctr. (United States)
Filip Banovac, Vanderbilt Institute for Surgery and Engineering (United States)
Vanderbilt Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 10951:
Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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