Journal
EBIOMEDICINE
Volume 44, Issue -, Pages 289-297Publisher
ELSEVIER
DOI: 10.1016/j.ebiom.2019.05.023
Keywords
Oesophageal cancer; Radiomics; Prognostic model; Radiation therapy; Imaging
Funding
- Zhejiang Provincial Foundation for Natural Sciences [LZ15H220001]
- National Natural Science Foundation of China [81672994]
- Zhejiang Provincial Natural Science Foundation of China [LR16F010001]
- Zhejiang University Education Foundation ZJU-Stanford Collaboration Fund
- National Key Research Plan by the Ministry of Science and Technology of China [2016YFC0104507]
- Natural Science Foundation of China [81871351]
- Hangzhou Agriculture and Social Developmental Research Program [20172016A04]
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Background: Evaluating clinical outcome prior to concurrent chemoradiotherapy remains challenging for oesophageal squamous cell carcinoma (OSCC) as traditional prognostic markers are assessed at the completion of treatment. Herein, we investigated the potential of using sub-region radiomics as a novel tumour biomarker in predicting overall survival of OSCC patients treated by concurrent chemoradiotherapy. Methods: Independent patient cohorts from two hospitals were induded for training (n = 87) and validation (n = 46). Radiomics features were extracted from sub-regions clustered from patients' tumour regions using K-means method. The LASSO regression for 'Cox' method was used for feature selection. The survival prediction model was constructed based on the sub-region radiomics features using the Cox proportional hazards model. The clinical and biological significance of radiomics features were assessed by correlation analysis of dinical characteristics and copy number alterations(CNAs) in the validation dataset. Findings: The overall survival prediction model combining with seven sub-regional radiomics features was constructed. The C-indexes of the proposed model were 0.729 (0.656-0.801, 95% CI) and 0.705 (0.628-0.782, 95%CI) in the training and validation cohorts, respectively. The 3-year survival receiver operating characteristic (ROC) curve showed an area under the ROC curve of 0.811 (0.670-0.952, 95%CI) in training and 0.805 (0.638-0.973, 95%CI) in validation. The correlation analysis showed a significant correlation between radiomics features and CNAs. Interpretation: The proposed sub-regional radiomics model could predict the overall survival risk for patients with OSCC treated by definitive concurrent chemoradiotherapy. (C) 2019 The Authors. Published by Elsevier B.V.
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