Literature DB >> 21145028

Volume-doubling time of pulmonary nodules with ground glass opacity at multidetector CT: Assessment with computer-aided three-dimensional volumetry.

Seitaro Oda1, Kazuo Awai, Kohei Murao, Akio Ozawa, Daisuke Utsunomiya, Yumi Yanaga, Koichi Kawanaka, Yasuyuki Yamashita.   

Abstract

RATIONALE AND
OBJECTIVES: To investigate the volume-doubling time (VDT) of histologically proved pulmonary nodules showing ground glass opacity (GGO) at multidetector CT (MDCT) using computer-aided three-dimensional volumetry.
MATERIALS AND METHODS: We retrospectively evaluated 47 GGO nodules (mixed n = 28, pure n = 19) that had been examined by thin-section helical CT more than once. They were histologically confirmed as atypical adenomatous hyperplasia (AAH, n = 13), bronchioloalveolar carcinoma (BAC, n = 22), and adenocarcinoma (AC, n = 12). Using computer-aided three-dimensional volumetry software, two radiologists independently performed volumetry of GGO nodules and calculated the VDT using data acquired from the initial and final CT study. We compared VDT among the three pathologies and also compared the VDT of mixed and pure GGO nodules.
RESULTS: The mean VDT of all GGO nodules was 486.4 ± 368.6 days (range 89.0-1583.0 days). The mean VDT for AAH, BAC, and AC was 859.2 ± 428.9, 421.2 ± 228.4, and 202.1 ± 84.3 days, respectively; there were statistically significant differences for all comparative combinations of AAH, BAC, and AC (Steel-Dwass test, P < .01). The mean VDT for pure and mixed GGO nodules was 628.5 ± 404.2 and 276.9 ± 155.9 days, respectively; it was significantly shorter for mixed than pure GGO nodules (Mann-Whitney U-test, P < .01).
CONCLUSION: The evaluation of VDT using computer-aided volumetry may be helpful in assessing the histological entities of GGO nodules.
Copyright © 2011 AUR. Published by Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 21145028     DOI: 10.1016/j.acra.2010.08.022

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  28 in total

1.  Application of CT-PSF-based computer-simulated lung nodules for evaluating the accuracy of computer-aided volumetry.

Authors:  Ayumu Funaki; Masaki Ohkubo; Shinichi Wada; Kohei Murao; Toru Matsumoto; Shinji Niizuma
Journal:  Radiol Phys Technol       Date:  2012-03-25

2.  Persistent pulmonary subsolid nodules with solid portions of 5 mm or smaller: Their natural course and predictors of interval growth.

Authors:  Jong Hyuk Lee; Chang Min Park; Sang Min Lee; Hyungjin Kim; H Page McAdams; Jin Mo Goo
Journal:  Eur Radiol       Date:  2015-09-18       Impact factor: 5.315

Review 3.  Pulmonary subsolid nodules: what radiologists need to know about the imaging features and management strategy.

Authors:  Hyungjin Kim; Chang Min Park; Jae Moon Koh; Sang Min Lee; Jin Mo Goo
Journal:  Diagn Interv Radiol       Date:  2014 Jan-Feb       Impact factor: 2.630

Review 4.  Management of ground-glass opacities: should all pulmonary lesions with ground-glass opacity be surgically resected?

Authors:  Yoshihisa Kobayashi; Tetsuya Mitsudomi
Journal:  Transl Lung Cancer Res       Date:  2013-10

5.  Software performance in segmenting ground-glass and solid components of subsolid nodules in pulmonary adenocarcinomas.

Authors:  Julien G Cohen; Jin Mo Goo; Roh-Eul Yoo; Chang Min Park; Chang Hyun Lee; Bram van Ginneken; Doo Hyun Chung; Young Tae Kim
Journal:  Eur Radiol       Date:  2016-04-05       Impact factor: 5.315

6.  Pure ground-glass nodules: are they really indolent?

Authors:  Julien G Cohen; Gilbert R Ferretti
Journal:  J Thorac Dis       Date:  2017-09       Impact factor: 2.895

7.  What CT characteristics of lepidic predominant pattern lung adenocarcinomas correlate with invasiveness on pathology?

Authors:  Emily A Aherne; Andrew J Plodkowski; Joseph Montecalvo; Sumar Hayan; Junting Zheng; Marinela Capanu; Prasad S Adusumilli; William D Travis; Michelle S Ginsberg
Journal:  Lung Cancer       Date:  2018-02-03       Impact factor: 5.705

8.  Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning-assisted nodule segmentation.

Authors:  Lin-Lin Qi; Bo-Tong Wu; Wei Tang; Li-Na Zhou; Yao Huang; Shi-Jun Zhao; Li Liu; Meng Li; Li Zhang; Shi-Chao Feng; Dong-Hui Hou; Zhen Zhou; Xiu-Li Li; Yi-Zhou Wang; Ning Wu; Jian-Wei Wang
Journal:  Eur Radiol       Date:  2019-09-04       Impact factor: 5.315

9.  Volume doubling time of lung cancers detected in a chest radiograph mass screening program: Comparison with CT screening.

Authors:  Maki Kanashiki; Takuji Tomizawa; Iwao Yamaguchi; Koichi Kurishima; Nobuyuki Hizawa; Hiroichi Ishikawa; Katsunori Kagohashi; Hiroaki Satoh
Journal:  Oncol Lett       Date:  2012-06-28       Impact factor: 2.967

10.  Natural history of pathologically confirmed pulmonary subsolid nodules with deep learning-assisted nodule segmentation.

Authors:  Lin-Lin Qi; Jian-Wei Wang; Lin Yang; Yao Huang; Shi-Jun Zhao; Wei Tang; Yu-Jing Jin; Ze-Wei Zhang; Zhen Zhou; Yi-Zhou Yu; Yi-Zhou Wang; Ning Wu
Journal:  Eur Radiol       Date:  2020-11-21       Impact factor: 5.315

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