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Proceedings Paper

Evaluation of 1D, 2D and 3D nodule size estimation by radiologists for spherical and non-spherical nodules through CT thoracic phantom imaging
Author(s): Nicholas Petrick; Hyun J. Grace Kim; David Clunie; Kristin Borradaile; Robert Ford; Rongping Zeng; Marios A. Gavrielides; Michael F. McNitt-Gray; Charles Fenimore; Z. Q. John Lu; Binsheng Zhao; Andrew J. Buckler
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Paper Abstract

The purpose of this work was to estimate bias in measuring the size of spherical and non-spherical lesions by radiologists using three sizing techniques under a variety of simulated lesion and reconstruction slice thickness conditions. We designed a reader study in which six radiologists estimated the size of 10 synthetic nodules of various sizes, shapes and densities embedded within a realistic anthropomorphic thorax phantom from CT scan data. In this manuscript we report preliminary results for the first four readers (Reader 1-4). Two repeat CT scans of the phantom containing each nodule were acquired using a Philips 16-slice scanner at a 0.8 and 5 mm slice thickness. The readers measured the sizes of all nodules for each of the 40 resulting scans (10 nodules x 2 slice thickness x 2 repeat scans) using three sizing techniques (1D longest in-slice dimension; 2D area from longest in-slice dimension and corresponding longest perpendicular dimension; 3D semi-automated volume) in each of 2 reading sessions. The normalized size was estimated for each sizing method and an inter-comparison of bias among methods was performed. The overall relative biases (standard deviation) of the 1D, 2D and 3D methods for the four readers subset (Readers 1-4) were -13.4 (20.3), -15.3 (28.4) and 4.8 (21.2) percentage points, respectively. The relative biases for the 3D volume sizing method was statistically lower than either the 1D or 2D method (p<0.001 for 1D vs. 3D and 2D vs. 3D).

Paper Details

Date Published: 4 March 2011
PDF: 7 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79630D (4 March 2011); doi: 10.1117/12.878265
Show Author Affiliations
Nicholas Petrick, U.S. Food and Drug Administration (United States)
Hyun J. Grace Kim, Univ. of California, Los Angeles (United States)
David Clunie, CoreLab Partners, Inc (United States)
Kristin Borradaile, CoreLab Partners, Inc (United States)
Robert Ford, Princeton Radiology Associates (United States)
Rongping Zeng, U.S. Food and Drug Administration (United States)
Marios A. Gavrielides, U.S. Food and Drug Administration (United States)
Michael F. McNitt-Gray, Univ. of California, Los Angeles (United States)
Charles Fenimore, National Institute of Standards and Technology (United States)
Z. Q. John Lu, National Institute of Standards and Technology (United States)
Binsheng Zhao, Columbia Univ. Medical Ctr. (United States)
Andrew J. Buckler, Buckler Biomedical LLC (United States)

Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers; Bram van Ginneken, Editor(s)

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