Publications [#326853] of Ehsan Samei

Papers Published
  1. Robins, M; Solomon, J; Hoye, J; Smith, T; Ebner, L; Samei, E, Inter-algorithm lesion volumetry comparison of real and 3D simulated lung lesions in CT, Proceedings of SPIE, vol. 10132 (January, 2017) [doi] .

    Abstract:
    © 2017 SPIE. The purpose of this study was to establish volumetric exchangeability between real and computational lung lesions in CT. We compared the overall relative volume estimation performance of segmentation tools when used to measure real lesions in actual patient CT images and computational lesions virtually inserted into the same patient images (i.e., hybrid datasets). Pathologically confirmed malignancies from 30 thoracic patient cases from Reference Image Database to Evaluate Therapy Response (RIDER) were modeled and used as the basis for the comparison. Lesions included isolated nodules as well as those attached to the pleura or other lung structures. Patient images were acquired using a 16 detector row or 64 detector row CT scanner (Lightspeed 16 or VCT; GE Healthcare). Scans were acquired using standard chest protocols during a single breath-hold. Virtual 3D lesion models based on real lesions were developed in Duke Lesion Tool (Duke University), and inserted using a validated image-domain insertion program. Nodule volumes were estimated using multiple commercial segmentation tools (iNtuition, TeraRecon, Inc., Syngo.via, Siemens Healthcare, and IntelliSpace, Philips Healthcare). Consensus based volume comparison showed consistent trends in volume measurement between real and virtual lesions across all software. The average percent bias (± standard error) shows -9.2±3.2% for real lesions versus -6.7±1.2% for virtual lesions with tool A, 3.9±2.5% and 5.0±0.9% for tool B, and 5.3±2.3% and 1.8±0.8% for tool C, respectively. Virtual lesion volumes were statistically similar to those of real lesions ( < 4% difference) with p > .05 in most cases. Results suggest that hybrid datasets had similar inter-algorithm variability compared to real datasets.