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Title: 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]; ORCiD logo [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

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 Vol. 13 Journal Issue: 12; ISSN 1759-7706
Publisher:
Wiley-BlackwellCopyright Statement
Country of Publication:
Australia
Language:
English

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