Literature DB >> 29273229

HRCT morphological characteristics distinguishing minimally invasive pulmonary adenocarcinoma from invasive pulmonary adenocarcinoma appearing as subsolid nodules with a diameter of ≤3 cm.

X Yue1, S Liu3, S Liu3, G Yang4, Z Li3, B Wang5, Q Zhou6.   

Abstract

AIM: To differentiate retrospectively the morphological characteristics at high-resolution computed tomography (CT) between minimally invasive pulmonary adenocarcinoma (MIA) and invasive pulmonary adenocarcinoma (IAC) appearing as subsolid nodules (SNs) with a diameter of ≤3 cm and to provide information to help operative decision-making.
MATERIALS AND METHODS: The patient notes of 260 patients with SNs of ≤3 cm in diameter (98 with MIA and 162 with IAC) confirmed at surgery and histopathology from September 2008 to June 2012 were reviewed retrospectively at the Department of Radiology, Weifang Respiratory Disease Hospital. Sixty-seven patients had pure ground-glass nodules (PGGNs) and 193 had mixed ground-glass nodules (MGGNs). Patients were grouped according to the final pathology: minimally invasive MIA and IAC. The HRCT characteristics were compared between the two groups.
RESULTS: There were statistically significant differences in the pattern, shape, diameter of solid components, proportion of solid components, CT radiodensity values of the ground-glass and solid components, borders, margins, air bronchograms, microvascular signs, and pleural indentations of the nodules between the two groups (all p<0.05). Multivariate and receiver operating characteristic (ROC) analyses indicated significant predictors of MIAs were as follows: small lesion diameter (≤14.7 mm), solid components ≤7 mm, <50% of solid components, low CT radiodensity values of the solid components (≤-107 HU), air bronchograms in the ground-glass opacity components, and microvascular signs.
CONCLUSION: The morphological characteristics at high-resolution CT can be used to differentiate between MIAs and IACs appearing as SNs with a diameter of ≤3 cm and provide information to help operative decision-making.
Copyright © 2017. Published by Elsevier Ltd.

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Year:  2017        PMID: 29273229     DOI: 10.1016/j.crad.2017.11.014

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  4 in total

1.  Determining the invasiveness of pure ground-glass nodules using dual-energy spectral computed tomography.

Authors:  Ye Yu; Jie-Jun Cheng; Jian-Ying Li; Ying Zhang; Liao-Yi Lin; Feng Zhang; Jian-Rong Xu; Xiao-Jing Zhao; Hua-Wei Wu
Journal:  Transl Lung Cancer Res       Date:  2020-06

2.  Assessing invasiveness of subsolid lung adenocarcinomas with combined attenuation and geometric feature models.

Authors:  Constance de Margerie-Mellon; Ritu R Gill; Pascal Salazar; Anastasia Oikonomou; Elsie T Nguyen; Benedikt H Heidinger; Mayra A Medina; Paul A VanderLaan; Alexander A Bankier
Journal:  Sci Rep       Date:  2020-09-03       Impact factor: 4.379

3.  A radiomics nomogram for invasiveness prediction in lung adenocarcinoma manifesting as part-solid nodules with solid components smaller than 6 mm.

Authors:  Teng Zhang; Chengxiu Zhang; Yan Zhong; Yingli Sun; Haijie Wang; Hai Li; Guang Yang; Quan Zhu; Mei Yuan
Journal:  Front Oncol       Date:  2022-08-11       Impact factor: 5.738

4.  3D convolutional neural network for differentiating pre-invasive lesions from invasive adenocarcinomas appearing as ground-glass nodules with diameters ≤3 cm using HRCT.

Authors:  Shengping Wang; Rui Wang; Shengjian Zhang; Ruimin Li; Yi Fu; Xiangjie Sun; Yuan Li; Xing Sun; Xinyang Jiang; Xiaowei Guo; Xuan Zhou; Jia Chang; Weijun Peng
Journal:  Quant Imaging Med Surg       Date:  2018-06
  4 in total

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