Journal
EUROPEAN RADIOLOGY
Volume 31, Issue 5, Pages 2856-2865Publisher
SPRINGER
DOI: 10.1007/s00330-020-07423-2
Keywords
Tomography; X-ray computed; Adenocarcinoma of lung; Prognosis; Biomarkers; tumor
Funding
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - ICT and Future Planning [NRF-2019R1A2C1087524]
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The study found that the performance of radiomics models for predicting DFS in NSCLC patients was not significantly affected by CT slice thickness. The models showed consistent performance across different slice thickness datasets and the mixed slice thickness dataset, indicating that CT slice thickness does not impact the prognostic accuracy of radiomics models.
Objectives To investigate whether CT slice thickness influences the performance of radiomics prognostic models in non-small-cell lung cancer (NSCLC) patients. Methods CT images including 1-, 3-, and 5-mm slice thicknesses acquired from 311 patients who underwent surgical resection for NSCLC between May 2014 and December 2015 were evaluated. Tumor segmentation was performed on CT of each slice thickness and total 94 radiomics features (shape, tumor intensity, and texture) were extracted. The study population was temporally split into development (n = 185) and validation sets (n = 126) for prediction of disease-free survival (DFS). Three radiomics models were built from three different slice thickness datasets (Rad-1, Rad-3, and Rad-5), respectively. Model performance was assessed and compared in three slice thickness datasets and mixed slice thickness dataset using C-indices. Results In corresponding slice thickness datasets, the C-indices of Rad-1, Rad-3, and Rad-5 for prediction of DFS were 0.68, 0.70, and 0.68 in the development set, and 0.73, 0.73, and 0.76 in the validation set (p = 0.40-0.89 and 0.27-0.90, respectively). Performance of the models was not significantly changed when they were applied to different slice thicknesses data in the validation set (C-index, 0.73-0.76, 0.72-0.73, 0.75-0.76; p = 0.07-0.92). In the mixed slice thickness dataset, performances of the models were similar to or slightly lower than their performances in the corresponding slice thickness datasets (C-index, 0.72-0.75 vs. 0.73-0.76) in the validation set. Conclusions The performance of radiomics models for predicting DFS in NSCLC patients was not significantly affected by CT slice thickness.
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