Mr. Sean K. Pentinga Profile

Sean K. Pentinga

Assistant Researcher at Western University

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Proceedings Article | 6 March 2018 Paper

3D human lung histology reconstruction and registration to in vivo imaging

Sean A. Pentinga, Keith Kwan, Sarah Mattonen, Carol Johnson, Alexander Louie, Mark Landis, Richard Inculet, Richard Malthaner, Dalilah Fortin, George Rodrigues, Brian Yaremko, David Palma, Aaron Ward

Proceedings Volume 10581, 105810V (2018) https://doi.org/10.1117/12.2292210

KEYWORDS: Tissues, Lung, Computed tomography, Image registration, In vivo imaging, Pathology, Lung cancer, 3D image processing, Surgery, Cancer

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Stereotactic ablative radiotherapy (SABR) delivers high-dose-per-fraction radiotherapy to tumours and spares surrounding tissue. It is effective for early-stage non-small cell lung cancer. However, SABR causes radiationinduced lung injuries that mimic recurring cancer, confounding detection of recurrences and early salvage therapy. We have previously developed radiomics-based recurrence detection. However, our radiomics system needs to be validated against histologic markers of viable tumour post-SABR. In this paper, our goals were to develop semiautomatic (1) 2D reconstruction of pseudo whole-mount (PWM) tissue sections from scanned slides, (2) 3D reconstruction and registration of PWM sections to pre-surgery computed tomography (CT), and (3) quantitative registration error measurement. Lobectomy tissue sections on standard 1” × 3” slides were obtained from patients who underwent SABR. Our graphical user interface allows interactive stitching of the sections into PWMs. Using our developed 3D Slicer-based thin-plate spline warping tool, we performed 3D PWM reconstruction and registered them to CT via correspondence of homologous intrinsic landmarks. The target registration error for 229 fiducial pairs defining vessels and airways was calculated for 56 PWMs reconstructed from 9 patients. We measured a mean of 7.33 mm, standard deviation of 4.59 mm and root mean square of 8.65 mm. This proof-of-principle study demonstrates for the first time that it is feasible to register in vivo human lung CT images with histology, with no modifications to the clinical pathology workflow other than videography to document gross dissection. Ongoing work to automate this process will yield a tool for histologic lung imaging and radiomics validation.

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