Objectives: To analyze whether CT image normalization can improve 3-year recurrence-free survival (RFS) prediction performance in patients with non-small cell lung cancer (NSCLC) relative to the use of unnormalized CT images. Methods: A total of 106 patients with NSCLC were included in the training set. For each patient, 851 radiomic features were extracted from the normalized and the unnormalized CT images, respectively. After the feature selection, random forest models were constructed with selected radiomic features and clinical features. The models were then externally validated in the test set consisting of 79 patients with NSCLC. Results: The model using normalized CT images yielded better performance than the model using unnormalized CT images (with an area under the receiver operating characteristic curve of 0.802 vs 0.702, p = 0.01), with the model performing especially well among patients with adenocarcinoma (with an area under the receiver operating characteristic curve of 0.880 vs 0.720, p < 0.01). Conclusions: CT image normalization may improve prediction performance among patients with NSCLC, especially for patients with adenocarcinoma. Key Points: • After CT image normalization, more radiomic features were able to be identified. • Prognostic performance in patients was improved significantly after CT image normalization compared with before the CT image normalization. • The improvement in prognostic performance following CT image normalization was superior in patients with adenocarcinoma.
|Number of pages||10|
|Publication status||Published - 2022 Dec|
Bibliographical noteFunding Information:
This research was supported by D&P BIOTECH Inc. and partially supported by the Yonsei Signature Research Cluster Program of 2022 (2022-22-0002), the KIST Institutional Program(Project No.2E31051-21-204), the Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korean Government (MSIT) Artificial Intelligence Graduate School Program, Yonsei University (2020-0-01361), and the Graduate School of YONSEI University Research Scholarship Grants in 2018. The authors sincerely thank In Yong Park for his diligent proofreading of this paper.
This research was funded by D&P BIOTECH Inc.
© 2022, The Author(s), under exclusive licence to European Society of Radiology.
All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging