Lina Zhao1, Jie Gong2, Yibin Xi3, Man Xu1, Chen Li3, Xiaowei Kang3, Yutian Yin1, Wei Qin4, Hong Yin3, Mei Shi5. 1. Department of Radiation Oncology, Xijing Hospital, Air Force Medical University, Xi'an, China. 2. Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, 710126, China. 3. Department of Radiology, Xijing Hospital, Air Force Medical University, Xi'an, China. 4. Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, 710126, China. wqin@xidian.edu.cn. 5. Department of Radiation Oncology, Xijing Hospital, Air Force Medical University, Xi'an, China. mshi82@hotmail.com.
Abstract
OBJECTIVES: To establish and validate a radiomics nomogram for prediction of induction chemotherapy (IC) response and survival in nasopharyngeal carcinoma (NPC) patients. METHODS: One hundred twenty-three NPC patients (100 in training and 23 in validation cohort) with multi-MR images were enrolled. A radiomics nomogram was established by integrating the clinical data and radiomics signature generated by support vector machine. RESULTS: The radiomics signature consisting of 19 selected features from the joint T1-weighted (T1-WI), T2-weighted (T2-WI), and contrast-enhanced T1-weighted MRI images (T1-C) showed good prognostic performance in terms of evaluating IC response in two cohorts. The radiomics nomogram established by integrating the radiomics signature with clinical data outperformed clinical nomogram alone (C-index in validation cohort, 0.863 vs 0.549; p < 0.01). Decision curve analysis demonstrated the clinical utility of the radiomics nomogram. Survival analysis showed that IC responders had significant better PFS (progression-free survival) than non-responders (3-year PFS 84.81% vs 39.75%, p < 0.001). Low-risk groups defined by radiomics signature had significant better PFS than high-risk groups (3-year PFS 76.24% vs 48.04%, p < 0.05). CONCLUSIONS: Multiparametric MRI-based radiomics could be helpful for personalized risk stratification and treatment in NPC patients receiving IC. KEY POINTS: • MRI Radiomics can predict IC response and survival in non-endemic NPC. • Radiomics signature in combination with clinical data showed excellent predictive performance. • Radiomics signature could separate patients into two groups with different prognosis.
OBJECTIVES: To establish and validate a radiomics nomogram for prediction of induction chemotherapy (IC) response and survival in nasopharyngeal carcinoma (NPC) patients. METHODS: One hundred twenty-three NPCpatients (100 in training and 23 in validation cohort) with multi-MR images were enrolled. A radiomics nomogram was established by integrating the clinical data and radiomics signature generated by support vector machine. RESULTS: The radiomics signature consisting of 19 selected features from the joint T1-weighted (T1-WI), T2-weighted (T2-WI), and contrast-enhanced T1-weighted MRI images (T1-C) showed good prognostic performance in terms of evaluating IC response in two cohorts. The radiomics nomogram established by integrating the radiomics signature with clinical data outperformed clinical nomogram alone (C-index in validation cohort, 0.863 vs 0.549; p < 0.01). Decision curve analysis demonstrated the clinical utility of the radiomics nomogram. Survival analysis showed that IC responders had significant better PFS (progression-free survival) than non-responders (3-year PFS 84.81% vs 39.75%, p < 0.001). Low-risk groups defined by radiomics signature had significant better PFS than high-risk groups (3-year PFS 76.24% vs 48.04%, p < 0.05). CONCLUSIONS: Multiparametric MRI-based radiomics could be helpful for personalized risk stratification and treatment in NPCpatients receiving IC. KEY POINTS: • MRI Radiomics can predict IC response and survival in non-endemic NPC. • Radiomics signature in combination with clinical data showed excellent predictive performance. • Radiomics signature could separate patients into two groups with different prognosis.
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