Ran Cao1, Yue Dong2, Xiaoyu Wang3, Meihong Ren4, Xingling Wang5, Nannan Zhao6, Tao Yu7, Lu Zhang8, Yahong Luo9, E-Nuo Cui10, Xiran Jiang11. 1. Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, 110122, China. Electronic address: 837052361@qq.com. 2. Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, China. Electronic address: dyy1026@sina.com. 3. Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, China. Electronic address: wangxyz007@hotmail.com. 4. Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, 110122, China. Electronic address: 2985403792@qq.com. 5. Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, China. Electronic address: 986193242@qq.com. 6. Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, China. Electronic address: nanazhao888@outlook.com. 7. Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, China. Electronic address: dryutao@hotmail.com. 8. Department of Human Resource, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, 110042, China. Electronic address: zl_0304@126.com. 9. Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, China. Electronic address: luoyahong8888@hotmail.com. 10. School of Computer Science and Engineering, Shenyang University, Shenyang, 110044, China. Electronic address: nuonuo8896@163.com. 11. Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, 110122, China. Electronic address: xrjiang@cmu.edu.cn.
Abstract
RATIONALE AND OBJECTIVES: Preoperative identifications of epidermal growth factor receptor (EGFR) mutation subtypes based on the MRI image of spinal metastases are needed to provide individualized therapy, but has not been previously investigated. This study aims to develop and evaluate an MRI-based radiomics nomogram for differentiating the exon 19 and 21 in EGFR mutation from spinal bone metastases in patients with primary lung adenocarcinoma. MATERIALS AND METHODS: A total of 76 patients underwent T1-weighted and T2-weighted fat-suppressed MRI scans were enrolled in this study, 38 were positive for EGFR mutation in exon 19 and 38 were in exon 21.MRI imaging features were extracted and selected from each MRI pulse sequence, and used to form the radiomics signature. A radiomics nomogram was developed integrating the radiomics signature and important clinical factors with receiver operating characteristic, calibration and decision curve analysis to assess the nomogram. Clinical characteristics were analyzed with Mann-Whitney U and Chi-Square tests to identify the most important factors. RESULTS: A total of 6 features were selected as the most discriminative predictors from the two MRI pulse sequences. The nomogram integrating the combined radiomics signature, age and CEA level generated good prediction performance in the training (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.90 vs. 0.87 vs. 0.59) and validation (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.88 vs. 0.86 vs. 0.72) cohort. DCA analysis confirmed the potential clinical utility of the nomogram. CONCLUSION: This study demonstrated that MRI features from spinal bone metastases can be used to prognosticate EGFR mutation subtypes in exon 19 and 21. The developed pre-treatment nomogram can potentially guide treatments for lung adenocarcinoma patients.
RATIONALE AND OBJECTIVES: Preoperative identifications of epidermal growth factor receptor (EGFR) mutation subtypes based on the MRI image of spinal metastases are needed to provide individualized therapy, but has not been previously investigated. This study aims to develop and evaluate an MRI-based radiomics nomogram for differentiating the exon 19 and 21 in EGFR mutation from spinal bone metastases in patients with primary lung adenocarcinoma. MATERIALS AND METHODS: A total of 76 patients underwent T1-weighted and T2-weighted fat-suppressed MRI scans were enrolled in this study, 38 were positive for EGFR mutation in exon 19 and 38 were in exon 21.MRI imaging features were extracted and selected from each MRI pulse sequence, and used to form the radiomics signature. A radiomics nomogram was developed integrating the radiomics signature and important clinical factors with receiver operating characteristic, calibration and decision curve analysis to assess the nomogram. Clinical characteristics were analyzed with Mann-Whitney U and Chi-Square tests to identify the most important factors. RESULTS: A total of 6 features were selected as the most discriminative predictors from the two MRI pulse sequences. The nomogram integrating the combined radiomics signature, age and CEA level generated good prediction performance in the training (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.90 vs. 0.87 vs. 0.59) and validation (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.88 vs. 0.86 vs. 0.72) cohort. DCA analysis confirmed the potential clinical utility of the nomogram. CONCLUSION: This study demonstrated that MRI features from spinal bone metastases can be used to prognosticate EGFR mutation subtypes in exon 19 and 21. The developed pre-treatment nomogram can potentially guide treatments for lung adenocarcinoma patients.
Authors: Eliodoro Faiella; Domiziana Santucci; Alessandro Calabrese; Fabrizio Russo; Gianluca Vadalà; Bruno Beomonte Zobel; Paolo Soda; Giulio Iannello; Carlo de Felice; Vincenzo Denaro Journal: Int J Environ Res Public Health Date: 2022-02-08 Impact factor: 3.390