ArticleJournal of medical imaging (Bellingham, Wash.)2018
Interchangeability between real and three-dimensional simulated lung tumors in computed tomography: an interalgorithm volumetry study.
Article in Journal of medical imaging (Bellingham, Wash.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Using hybrid datasets consisting of patient-derived computed tomography (CT) images with digitally inserted computational tumors, we establish volumetric interchangeability between real and computational lung tumors in CT. Pathologically-confirmed malignancies from 30 thoracic patient cases from the RIDER database were modeled. Tumors were either isolated or attached to lung structures. Patient images were acquired on one of two CT scanner models (Lightspeed 16 or VCT; GE Healthcare) using standard chest protocol. Real tumors were segmented and used to inform the size and shape of simulated tumors. Simulated tumors developed in Duke Lesion Tool (Duke University) were inserted using a validated image-domain insertion program. Four readers performed volume measurements using three commercial segmentation tools. We compared the volume estimation performance of segmentation tools between real tumors in actual patient CT images and corresponding simulated tumors virtually inserted into the same patient images (i.e., hybrid datasets). Comparisons involved (1) direct assessment of measured volumes and the standard deviation between simulated and real tumors across readers and tools, respectively, (2) multivariate analysis, involving segmentation tools, readers, tumor shape, and attachment, and (3) effect of local tumor environment on volume measurement. Volume comparison showed consistent trends (9% volumetric difference) between real and simulated tumors across all segmentation tools, readers, shapes, and attachments. Across all cases, readers, and segmentation tools, an intraclass correlation coefficient = 0.99 indicates that simulated tumors correlated strongly with real tumors (
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