Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy

• Published in: EBioMedicine 2019-06, Vol.44, p.289-297
• Main Authors: Xie, Congying, Yang, Pengfei, Zhang, Xuebang, Xu, Lei, Wang, Xiaoju, Li, Xiadong, Zhang, Luhan, Xie, Ruifei, Yang, Ling, Jing, Zhao, Zhang, Hongfang, Ding, Lingyu, Kuang, Yu, Niu, Tianye, Wu, Shixiu
• Format: Article
• Language: English
• Subjects: Aged; Aged, 80 and over; Algorithms; Chemoradiotherapy - methods; Chemoradiotherapy - standards; Esophageal Neoplasms - diagnosis; Esophageal Neoplasms - mortality; Esophageal Neoplasms - therapy; Female; Humans; Image Processing, Computer-Assisted; Imaging; Male; Middle Aged; Neoplasm Grading; Neoplasm Staging; Oesophageal cancer; Prognosis; Prognostic model; Proportional Hazards Models; Radiation therapy; Radiometry; Radiomics; Radiotherapy, Image-Guided; Research paper; ROC Curve; Tomography, X-Ray Computed; Treatment Outcome; Tumor Burden
• ISSN: 2352-3964; 2352-3964
• DOI: 10.1016/j.ebiom.2019.05.023

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. Independent patient cohorts from two hospitals were included 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 clinical characteristics and copy number alterations(CNAs) in the validation dataset. 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. The proposed sub-regional radiomics model could predict the overall survival risk for patients with OSCC treated by definitive concurrent chemoradiotherapy. This work was supported by the Zhejiang Provincial Foundation for Natural Sciences, National Natural Science Foundation of China.