Search Menu
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information
Search Scholarly Publications

Title: Different CT slice thickness and contrast‐enhancement phase in radiomics models on the differential performance of lung adenocarcinoma

Abstract

Abstract Background To investigate the effects of computed tomography (CT) reconstruction slice thickness and contrast‐enhancement phase on the differential diagnosis performance of radiomic signature in lung adenocarcinoma. Methods A total of 187 patients who had been pathologically confirmed with lung adenocarcinoma and nonadenocarcinoma were divided into a training cohort ( n  = 149) and validation cohort ( n  = 38). All the patients underwent contrast‐enhanced CT and the images were reconstructed with different slice thickness. The radiomic features were extracted from different slice thickness and scan phase. The logistic regression (LR) algorithm was used to build a machine learning model for each group. The area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve and DeLong test was used to evaluate its discriminating performance. Results Finally, 34 image features and five semantic features were selected to establish a radiomics model. Based on the three contrast‐enhanced CT phases and four reconstruction slice thickness, 12 groups of radiomics models showed good discrimination ability with the AUCs range from 0.9287 to 0.9631, sensitivity range from 0.8349 to 0.9083, specificity range from 0.825 to 0.925 in the training group. Similar results were observed in the validation group. However, there was no statistical significancemore » between the different CT scan phase groups and different slice thickness ( p  > 0.05). Conclusions The radiomic analysis of contrast‐enhanced CT can be used for the differential diagnosis of lung adenocarcinoma. Moreover, different slice thickness and contrast‐enhanced scan phase did not affect the discriminating ability in the radiomics models.« less

Authors:
 [1];  [2];  [3];  [3];  [2];  [4];  [5];  [6];  [1]; ORCiD logo [5]
+ Show Author Affiliations
  1. Department of Radiology, Shandong Provincial Hospital Shandong University Jinan Shandong China
  2. Department of Radiology Shandong Provincial Hospital Affiliated to Shandong First Medical University Jinan Shandong China
  3. Department of Research Collaboration, R&,D center Beijing Deepwise &, League of PHD Technology Co., Ltd Beijing China
  4. Department of Thoracic Surgery, Chengxin Hospital Yuncheng Shandong China
  5. Department of Thoracic Surgery Shandong Provincial Hospital, Shandong University Jinan Shandong China
  6. Department of Thoracic Surgery Shandong Provincial Hospital Affiliated to Shandong First Medical University Jinan Shandong China
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1867291
Alternate Identifier(s):
OSTI ID: 1867292
Resource Type:
Published Article
Journal Name:
Thoracic Cancer
Additional Journal Information:
Journal Name: Thoracic Cancer Journal Volume: 13 Journal Issue: 12; Journal ID: ISSN 1759-7706
Publisher:
Wiley-Blackwell
Country of Publication:
Australia
Language:
English

Citation Formats

Wang, Yang, Liu, Fang, Mo, Yan, Huang, Chencui, Chen, Yingxin, Chen, Fuliang, Zhang, Xiangwei, Yin, Yunxin, Liu, Qiang, and Zhang, Lin. Different CT slice thickness and contrast‐enhancement phase in radiomics models on the differential performance of lung adenocarcinoma. Australia: N. p., 2022. Web. doi:10.1111/1759-7714.14459.
Copy to clipboard
Wang, Yang, Liu, Fang, Mo, Yan, Huang, Chencui, Chen, Yingxin, Chen, Fuliang, Zhang, Xiangwei, Yin, Yunxin, Liu, Qiang, & Zhang, Lin. Different CT slice thickness and contrast‐enhancement phase in radiomics models on the differential performance of lung adenocarcinoma. Australia. https://doi.org/10.1111/1759-7714.14459
Copy to clipboard
Wang, Yang, Liu, Fang, Mo, Yan, Huang, Chencui, Chen, Yingxin, Chen, Fuliang, Zhang, Xiangwei, Yin, Yunxin, Liu, Qiang, and Zhang, Lin. Wed . "Different CT slice thickness and contrast‐enhancement phase in radiomics models on the differential performance of lung adenocarcinoma". Australia. https://doi.org/10.1111/1759-7714.14459.
Copy to clipboard
@article{osti_1867291,
title = {Different CT slice thickness and contrast‐enhancement phase in radiomics models on the differential performance of lung adenocarcinoma},
author = {Wang, Yang and Liu, Fang and Mo, Yan and Huang, Chencui and Chen, Yingxin and Chen, Fuliang and Zhang, Xiangwei and Yin, Yunxin and Liu, Qiang and Zhang, Lin},
abstractNote = {Abstract Background To investigate the effects of computed tomography (CT) reconstruction slice thickness and contrast‐enhancement phase on the differential diagnosis performance of radiomic signature in lung adenocarcinoma. Methods A total of 187 patients who had been pathologically confirmed with lung adenocarcinoma and nonadenocarcinoma were divided into a training cohort ( n  = 149) and validation cohort ( n  = 38). All the patients underwent contrast‐enhanced CT and the images were reconstructed with different slice thickness. The radiomic features were extracted from different slice thickness and scan phase. The logistic regression (LR) algorithm was used to build a machine learning model for each group. The area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve and DeLong test was used to evaluate its discriminating performance. Results Finally, 34 image features and five semantic features were selected to establish a radiomics model. Based on the three contrast‐enhanced CT phases and four reconstruction slice thickness, 12 groups of radiomics models showed good discrimination ability with the AUCs range from 0.9287 to 0.9631, sensitivity range from 0.8349 to 0.9083, specificity range from 0.825 to 0.925 in the training group. Similar results were observed in the validation group. However, there was no statistical significance between the different CT scan phase groups and different slice thickness ( p  > 0.05). Conclusions The radiomic analysis of contrast‐enhanced CT can be used for the differential diagnosis of lung adenocarcinoma. Moreover, different slice thickness and contrast‐enhanced scan phase did not affect the discriminating ability in the radiomics models.},
doi = {10.1111/1759-7714.14459},
journal = {Thoracic Cancer},
number = 12,
volume = 13,
place = {Australia},
year = {2022},
month = {5}
}
Copy to clipboard
Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1111/1759-7714.14459
Copyright Statement
Other availability
Search WorldCat Search WorldCat to find libraries that may hold this journal
Save / Share:
Export Metadata
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.

Works referenced in this record:

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping
journal, May 2020

Radiomics and radiogenomics in lung cancer: A review for the clinician
journal, January 2018

Radiomics: Images Are More than Pictures, They Are Data
journal, February 2016

CT-based radiomics signature for differentiating solitary granulomatous nodules from solid lung adenocarcinoma
journal, November 2018

Intraclass correlations: Uses in assessing rater reliability.
journal, January 1979

Spatial registration and normalization of images
journal, January 1995

  • Friston, Karl. J.; Ashburner, J.; Frith, C. D.
  • Human Brain Mapping, Vol. 3, Issue 3
  • DOI: 10.1002/hbm.460030303

Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer
journal, September 2019

Potential Application of Radiomics for Differentiating Solitary Pulmonary Nodules
journal, January 2016

Lung Cancer 2020
journal, March 2020

Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach
journal, September 1988

  • DeLong, Elizabeth R.; DeLong, David M.; Clarke-Pearson, Daniel L.
  • Biometrics, Vol. 44, Issue 3
  • DOI: 10.2307/2531595

Radiomics: the process and the challenges
journal, November 2012

The 2015 World Health Organization Classification of Lung Tumors
journal, September 2015

  • Travis, William D.; Brambilla, Elisabeth; Nicholson, Andrew G.
  • Journal of Thoracic Oncology, Vol. 10, Issue 9
  • DOI: 10.1097/JTO.0000000000000630

Radiomics Approach to Prediction of Occult Mediastinal Lymph Node Metastasis of Lung Adenocarcinoma
journal, July 2018

  • Zhong, Yan; Yuan, Mei; Zhang, Teng
  • American Journal of Roentgenology, Vol. 211, Issue 1
  • DOI: 10.2214/AJR.17.19074

Radiomics: the bridge between medical imaging and personalized medicine
journal, October 2017

  • Lambin, Philippe; Leijenaar, Ralph T. H.; Deist, Timo M.
  • Nature Reviews Clinical Oncology, Vol. 14, Issue 12
  • DOI: 10.1038/nrclinonc.2017.141

B-spline interpolation technique for digital signal processing
conference, July 2015

  • Svoboda, Marcus; Matiu-Iovan, Liliana; Frigura-Iliasa, Flaviu Mihai
  • 2015 International Conference on Information and Digital Technologies (IDT)
  • DOI: 10.1109/DT.2015.7222998
    Sort by:
    [ × clear filter / sort ]

    Similar Records in DOE PAGES and OSTI.GOV collections: