Doohyun Park1, Daejoong Oh1,2, MyungHoon Lee2, Shin Yup Lee3,4, Kyung Min Shin5, Johnson Sg Jun2, Dosik Hwang6,7,8,9. 1. School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea. 2. D&P BIOTECH Inc., Seoul, Republic of Korea. 3. Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea. 4. Lung Cancer Center, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea. 5. Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea. 6. School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea. dosik.hwang@yonsei.ac.kr. 7. Center for Healthcare Robotics, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, Republic of Korea. dosik.hwang@yonsei.ac.kr. 8. Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea. dosik.hwang@yonsei.ac.kr. 9. Department of Radiology and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, Republic of Korea. dosik.hwang@yonsei.ac.kr.
Abstract
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.
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.
Authors: Ho Yun Lee; So Won Lee; Kyung Soo Lee; Ji Yun Jeong; Joon Young Choi; O Jung Kwon; So Hee Song; Eun Young Kim; Jhingook Kim; Young Mog Shim Journal: J Thorac Oncol Date: 2015-12 Impact factor: 15.609