Literature DB >> 35371962

Reproducibility of radiomic features of pulmonary nodules between low-dose CT and conventional-dose CT.

Yufan Gao1, Minghui Hua1, Jun Lv1, Yanhe Ma1, Yanzhen Liu1, Min Ren2, Yaohua Tian3, Ximing Li4, Hong Zhang1.   

Abstract

Background: The reproducibility of radiomic features is essential to lung cancer detection. This study aimed to investigate the reproducibility of radiomic features of pulmonary nodules between low-dose computed tomography (LDCT) and conventional-dose computed tomography (CDCT).
Methods: A total of 105 patients with 119 pulmonary nodules [39 ground-glass nodules (GGNs) and 80 solid nodules] who underwent LDCT and CDCT were retrospectively studied between September 2019 and November 2020. Pulmonary nodules were manually segmented and 1,125 radiomic features (shape, first-order intensity, texture, wavelet, and Laplacian of the Gaussian features) were extracted from both LDCT and CDCT images. The concordance correlation coefficient (CCC) was used to evaluate the reproducibility of these radiomic features.
Results: Of the 1,125 radiomic features considered, 35.5% (399 of 1,125) and 41.5% (467 of 1,125) were reproducible (CCC ≥0.85) for GGNs and solid nodules, respectively. The intensity, texture, and wavelet features of solid nodules were more reproducible than those of GGNs. The mean CCC values for intensity and texture features of solid nodules were of 0.85 and above, whereas the mean values for those of GGNs were of less than 0.85. After Gaussian kernel (σ =2) preprocessing, the CCC of intensity and texture features of GGNs improved from 0.77 to 0.90, and 84.9% (79 of 93) of the radiomic features were reproducible (mean CCC increase from 0.84±0.13 to 0.92±0.08 for intensity features, and from 0.75±0.15 to 0.89±0.11 for texture features). Wavelet features had the lowest CCCs for both GGNs and solid nodules. Conclusions: The majority of the radiomic feature classes of solid pulmonary nodules have a high level of reproducibility between LDCT and CDCT. However, LDCT should not be used as an alternative to CDCT in the radiomic study of GGNs. 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.

Entities:  

Keywords:  Radiomics; dose; low-dose computed tomography (LDCT); pulmonary nodule

Year:  2022        PMID: 35371962      PMCID: PMC8923849          DOI: 10.21037/qims-21-609

Source DB:  PubMed          Journal:  Quant Imaging Med Surg        ISSN: 2223-4306


  33 in total

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Journal:  Radiology       Date:  2002-12       Impact factor: 11.105

2.  Applying CT texture analysis to determine the prognostic value of subsolid nodules detected during low-dose CT screening.

Authors:  Q Sun; Y Huang; J Wang; S Zhao; L Zhang; W Tang; N Wu
Journal:  Clin Radiol       Date:  2018-11-27       Impact factor: 2.350

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Journal:  Radiology       Date:  2018-04-24       Impact factor: 11.105

4.  Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.

Authors:  Hyuna Sung; Jacques Ferlay; Rebecca L Siegel; Mathieu Laversanne; Isabelle Soerjomataram; Ahmedin Jemal; Freddie Bray
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5.  Deep Learning-based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses.

Authors:  Jooae Choe; Sang Min Lee; Kyung-Hyun Do; Gaeun Lee; June-Goo Lee; Sang Min Lee; Joon Beom Seo
Journal:  Radiology       Date:  2019-06-18       Impact factor: 11.105

6.  Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas.

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Journal:  Radiology       Date:  2014-08-01       Impact factor: 11.105

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Journal:  Eur J Radiol       Date:  2020-04-20       Impact factor: 3.528

Review 8.  Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art.

Authors:  Geewon Lee; Ho Yun Lee; Hyunjin Park; Mark L Schiebler; Edwin J R van Beek; Yoshiharu Ohno; Joon Beom Seo; Ann Leung
Journal:  Eur J Radiol       Date:  2016-09-10       Impact factor: 3.528

9.  Reproducibility of radiomics for deciphering tumor phenotype with imaging.

Authors:  Binsheng Zhao; Yongqiang Tan; Wei-Yann Tsai; Jing Qi; Chuanmiao Xie; Lin Lu; Lawrence H Schwartz
Journal:  Sci Rep       Date:  2016-03-24       Impact factor: 4.379

10.  Radiomics: Images Are More than Pictures, They Are Data.

Authors:  Robert J Gillies; Paul E Kinahan; Hedvig Hricak
Journal:  Radiology       Date:  2015-11-18       Impact factor: 11.105

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  1 in total

1.  18F-FDG PET/CT radiomic analysis for classifying and predicting microvascular invasion in hepatocellular carcinoma and intrahepatic cholangiocarcinoma.

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Journal:  Quant Imaging Med Surg       Date:  2022-08
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