Fengchang Yang1, Jiayi Zhang2, Liu Zhou3, Wei Xia2, Rui Zhang2, Haifeng Wei4, Jinxue Feng5, Xingyu Zhao2, Junming Jian2, Xin Gao6,7, Shuanghu Yuan8,9,10. 1. Department of Radiology, Shandong Cancer Hospital and Institute, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China. 2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No. 88 Keling Road, Suzhou New District, Suzhou, 215163, Jiangsu, China. 3. School of Mathematics and Information Engineering, Wenzhou University Oujiang College, Wenzhou, 325035, Zhejiang, China. 4. Department of Radiology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250012, Shandong, China. 5. Cheeloo College of Medicine, Shandong University, Jinan, 250000, Shandong, China. 6. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No. 88 Keling Road, Suzhou New District, Suzhou, 215163, Jiangsu, China. xingaosam@yahoo.com. 7. Jinan Guoke Medical Engineering and Technology Development Co., Ltd., Pharmaceutical Valley New Drug Creation Platform, Jinan New District, No. 3 Building, Jinan, 250101, Shandong, China. xingaosam@yahoo.com. 8. Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong University, No.440 Jiyan Road, Huaiyin District, Jinan, 250100, Shandong, China. yuanshuanghu@sina.com. 9. Shandong First Medical University and Shandong Academy of Medical Sciences, No. 6699 Qingdao Road, Jinan, 250117, Shandong, China. yuanshuanghu@sina.com. 10. Zhengzhou University, No. 100 Science Road, Zhengzhou, 450001, Henan, China. yuanshuanghu@sina.com.
Abstract
OBJECTIVES: The goal of this study was to evaluate the effectiveness of radiomics signatures on pre-treatment computed tomography (CT) images of lungs to predict the tumor responses of non-small cell lung cancer (NSCLC) patients treated with first-line chemotherapy, targeted therapy, or a combination of both. MATERIALS AND METHODS: This retrospective study included 322 NSCLC patients who were treated with first-line chemotherapy, targeted therapy, or a combination of both. Of these patients, 224 were randomly assigned to a cohort to help develop the radiomics signature. A total of 1946 radiomics features were obtained from each patient's CT scan. The top-ranked features were selected by the Minimum Redundancy Maximum Relevance (MRMR) feature-ranking method and used to build a lightweight radiomics signature with the Random Forest (RF) classifier. The independent predictive (IP) features (AUC > 0.6, p value < 0.05) were further identified from the top-ranked features and used to build a refined radiomics signature by the RF classifier. Its prediction performance was tested on the validation cohort, which consisted of the remaining 98 patients. RESULTS: The initial lightweight radiomics signature constructed from 15 top-ranked features had an AUC of 0.721 (95% CI, 0.619-0.823). After six IP features were further identified and a refined radiomics signature was built, it had an AUC of 0.746 (95% CI, 0.646-0.846). CONCLUSIONS: Radiomics signatures based on pre-treatment CT scans can accurately predict tumor response in NSCLC patients after first-line chemotherapy or targeted therapy treatments. Radiomics features could be used as promising prognostic imaging biomarkers in the future. KEY POINTS: The radiomics signature extracted from baseline CT images in patients with NSCLC can predict response to first-line chemotherapy, targeted therapy, or both treatments with an AUC = 0.746 (95% CI, 0.646-0.846). The radiomics signature could be used as a new biomarker for quantitative analysis in radiology, which might provide value in decision-making and to define personalized treatments for cancer patients.
OBJECTIVES: The goal of this study was to evaluate the effectiveness of radiomics signatures on pre-treatment computed tomography (CT) images of lungs to predict the tumor responses of non-small cell lung cancer (NSCLC) patients treated with first-line chemotherapy, targeted therapy, or a combination of both. MATERIALS AND METHODS: This retrospective study included 322 NSCLC patients who were treated with first-line chemotherapy, targeted therapy, or a combination of both. Of these patients, 224 were randomly assigned to a cohort to help develop the radiomics signature. A total of 1946 radiomics features were obtained from each patient's CT scan. The top-ranked features were selected by the Minimum Redundancy Maximum Relevance (MRMR) feature-ranking method and used to build a lightweight radiomics signature with the Random Forest (RF) classifier. The independent predictive (IP) features (AUC > 0.6, p value < 0.05) were further identified from the top-ranked features and used to build a refined radiomics signature by the RF classifier. Its prediction performance was tested on the validation cohort, which consisted of the remaining 98 patients. RESULTS: The initial lightweight radiomics signature constructed from 15 top-ranked features had an AUC of 0.721 (95% CI, 0.619-0.823). After six IP features were further identified and a refined radiomics signature was built, it had an AUC of 0.746 (95% CI, 0.646-0.846). CONCLUSIONS: Radiomics signatures based on pre-treatment CT scans can accurately predict tumor response in NSCLC patients after first-line chemotherapy or targeted therapy treatments. Radiomics features could be used as promising prognostic imaging biomarkers in the future. KEY POINTS: The radiomics signature extracted from baseline CT images in patients with NSCLC can predict response to first-line chemotherapy, targeted therapy, or both treatments with an AUC = 0.746 (95% CI, 0.646-0.846). The radiomics signature could be used as a new biomarker for quantitative analysis in radiology, which might provide value in decision-making and to define personalized treatments for cancer patients.
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