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

Journal Article · · Thoracic Cancer
 [1];  [2];  [3];  [3];  [2];  [4];  [5];  [6];  [1];  [5]
  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
+ Show Author Affiliations
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.

Sponsoring Organization:
USDOE
OSTI ID:
1867291
Alternate ID(s):
OSTI ID: 1867292
Journal Information:
Thoracic Cancer, Journal Name: Thoracic Cancer Journal Issue: 12 Vol. 13; ISSN 1759-7706
Publisher:
Wiley-BlackwellCopyright Statement
Country of Publication:
Australia
Language:
English

References (21)

Spatial registration and normalization of images journal January 1995
Lung Cancer 2020 journal March 2020
Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer journal September 2019
Tumor segmentation analysis at different post-contrast time points: A possible source of variability of quantitative DCE-MRI parameters in locally advanced breast cancer journal May 2020
Radiomics and radiogenomics in lung cancer: A review for the clinician journal January 2018
CT-based radiomics signature for differentiating solitary granulomatous nodules from solid lung adenocarcinoma journal November 2018
Radiomics: the process and the challenges journal November 2012
Intraclass correlations: Uses in assessing rater reliability. journal January 1979
Radiomics: the bridge between medical imaging and personalized medicine journal October 2017
CT Slice Thickness and Convolution Kernel Affect Performance of a Radiomic Model for Predicting EGFR Status in Non-Small Cell Lung Cancer: A Preliminary Study journal December 2018
Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule journal October 2016
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening journal August 2011
The 2015 World Health Organization Classification of Lung Tumors journal September 2015
B-spline interpolation technique for digital signal processing conference July 2015
Computerized Texture Analysis of Persistent Part-Solid Ground-Glass Nodules: Differentiation of Preinvasive Lesions from Invasive Pulmonary Adenocarcinomas journal October 2014
Radiomics: Images Are More than Pictures, They Are Data journal February 2016
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping journal May 2020
Radiomics Approach to Prediction of Occult Mediastinal Lymph Node Metastasis of Lung Adenocarcinoma journal July 2018
Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach journal September 1988
Quantitative Image Quality and Histogram-Based Evaluations of an Iterative Reconstruction Algorithm at Low-to-Ultralow Radiation Dose Levels: A Phantom Study in Chest CT journal January 2018
Potential Application of Radiomics for Differentiating Solitary Pulmonary Nodules journal January 2016

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