Park, Doohyun Oh, Daejoong Lee, MyungHoon Lee, Shin Yup Shin, Kyung Min Jun, Johnson S. G. Hwang, Dosik 2024-01-19T10:32:55Z 2024-01-19T10:32:55Z 2022-12-15 2022-12 0938-7994 https://pubs.kist.re.kr/handle/201004/114237 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. English Springer Verlag Importance of CT image normalization in radiomics analysis: prediction of 3-year recurrence-free survival in non-small cell lung cancer Article 10.1007/s00330-022-08869-2 1 European Radiology, v.32, pp.8716 - 8725 European Radiology 32 8716 8725 N scie scopus 000803870100008 Radiology, Nuclear Medicine & Medical Imaging Radiology, Nuclear Medicine & Medical Imaging Article; Early Access TUMOR ADENOCARCINOMA Radiomics Prognosis Computed tomography Non-small cell lung cancer