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8 March 2019Quantitative imaging analysis to guide biopsy for molecular biomarkers
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Derek J. Doss,1,2 Jon S. Heiselman,1,2 Ma Luo,1,2 Logan W. Clements,1,2 Michael I. Miga,1,3,2 Daniel Brown,2,3 Filip Banovac2,3

1Vanderbilt Univ. (United States)
2Vanderbilt Institute for Surgery and Engineering (United States)
3Vanderbilt Univ. Medical Ctr. (United States)
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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.
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Derek J. Doss,Jon S. Heiselman,Ma Luo,Logan W. Clements,Michael I. Miga,Daniel Brown, andFilip Banovac"Quantitative imaging analysis to guide biopsy for molecular biomarkers", Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109512V (8 March 2019);https://doi.org/10.1117/12.2513588
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KEYWORDS
Tumors
Liver
Image registration
Biopsy
Anatomy
Image segmentation
Liver cancer
Radiotherapy
Deformation
Natural surfaces
Biological research
Cancer
Rigid registration
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Derek J. Doss, Jon S. Heiselman, Ma Luo, Logan W. Clements, Michael I. Miga, Daniel Brown, Filip Banovac, "Quantitative imaging analysis to guide biopsy for molecular biomarkers," Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109512V (8 March 2019); https://doi.org/10.1117/12.2513588
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