Marios A Gavrielides1, Qin Li, Rongping Zeng, Kyle J Myers, Berkman Sahiner, Nicholas Petrick. 1. Division of Imaging and Applied Mathematics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Bldg. 62, Rm.4114, Silver Spring, MD 20993. Electronic address: marios.gavrielides@fda.hhs.gov.
Abstract
RATIONALE AND OBJECTIVES: The change in volume of lung nodules is being examined as a measure of response to treatment. The aim of this study was to determine the minimum detectable change in nodule volume with the use of computed tomography. MATERIALS AND METHODS: Four different layouts of synthetic nodules with different shapes but with the same size (5, 8, 9, or 10 mm) for each layout were placed within an anthropomorphic phantom and scanned with a 16-detector-row computed tomography scanner using multiple imaging parameters. Nodule volume estimates were determined using a previously developed matched-filter estimator. Analysis of volume change was then conducted as a detection problem. For each nodule size, the pooled distribution of volume estimates was shifted by a percentage c to simulate a changing nodule, while accounting for standard deviation. The value of c resulting in a prespecified area under the receiver operating characteristic curve (AUC) was deemed the minimum detectable change for that AUC value. RESULTS: Both nodule size at baseline and choice of slice collimation protocol had an effect on the value of minimum detectable growth. For AUC = 0.95, the minimum detectable nodule growth in volume when using the thin-slice collimation protocol (16 × 0.75 mm) was 17%, 19%, and 15% for nodule sizes of 5, 8, and 9 mm, respectively. CONCLUSIONS: Our results indicate that an approximate bound for detectable nodule growth in subcentimeter nodules may be relatively small, on the order of 20% or less in volume for a thin-slice CT acquisition protocol. Published by Elsevier Inc.
RATIONALE AND OBJECTIVES: The change in volume of lung nodules is being examined as a measure of response to treatment. The aim of this study was to determine the minimum detectable change in nodule volume with the use of computed tomography. MATERIALS AND METHODS: Four different layouts of synthetic nodules with different shapes but with the same size (5, 8, 9, or 10 mm) for each layout were placed within an anthropomorphic phantom and scanned with a 16-detector-row computed tomography scanner using multiple imaging parameters. Nodule volume estimates were determined using a previously developed matched-filter estimator. Analysis of volume change was then conducted as a detection problem. For each nodule size, the pooled distribution of volume estimates was shifted by a percentage c to simulate a changing nodule, while accounting for standard deviation. The value of c resulting in a prespecified area under the receiver operating characteristic curve (AUC) was deemed the minimum detectable change for that AUC value. RESULTS: Both nodule size at baseline and choice of slice collimation protocol had an effect on the value of minimum detectable growth. For AUC = 0.95, the minimum detectable nodule growth in volume when using the thin-slice collimation protocol (16 × 0.75 mm) was 17%, 19%, and 15% for nodule sizes of 5, 8, and 9 mm, respectively. CONCLUSIONS: Our results indicate that an approximate bound for detectable nodule growth in subcentimeter nodules may be relatively small, on the order of 20% or less in volume for a thin-slice CT acquisition protocol. Published by Elsevier Inc.
Authors: Andrew J Buckler; Jovanna Danagoulian; Kjell Johnson; Adele Peskin; Marios A Gavrielides; Nicholas Petrick; Nancy A Obuchowski; Hubert Beaumont; Lubomir Hadjiiski; Rudresh Jarecha; Jan-Martin Kuhnigk; Ninad Mantri; Michael McNitt-Gray; Jan H Moltz; Gergely Nyiri; Sam Peterson; Pierre Tervé; Christian Tietjen; Etienne von Lavante; Xiaonan Ma; Samantha St Pierre; Maria Athelogou Journal: Acad Radiol Date: 2015-09-14 Impact factor: 3.173
Authors: Qin Li; Yongguang Liang; Qiao Huang; Min Zong; Benjamin Berman; Marios A Gavrielides; Lawrence H Schwartz; Binsheng Zhao; Nicholas Petrick Journal: Med Phys Date: 2016-12 Impact factor: 4.071
Authors: Marc Horner; Stephen M Luke; Kerim O Genc; Todd M Pietila; Ross T Cotton; Benjamin A Ache; Zachary H Levine; Kevin C Townsend Journal: J Verif Valid Uncertain Quantif Date: 2019
Authors: Marios A Gavrielides; Benjamin P Berman; Mark Supanich; Kurt Schultz; Qin Li; Nicholas Petrick; Rongping Zeng; Jenifer Siegelman Journal: Quant Imaging Med Surg Date: 2017-12