CT radiomic features for predicting resectability of oesophageal squamous cell carcinoma as given by feature analysis: a case control study

• Published in: Cancer imaging 2019-10, Vol.19 (1), p.66-66, Article 66
• Main Authors: Ou, Jing, Li, Rui, Zeng, Rui, Wu, Chang-Qiang, Chen, Yong, Chen, Tian-Wu, Zhang, Xiao-Ming, Wu, Lan, Jiang, Yu, Yang, Jian-Qiong, Cao, Jin-Ming, Tang, Sun, Tang, Meng-Jie, Hu, Jiani
• Format: Article
• Language: English
• Subjects: Accuracy; Age; Analysis; Biological markers; Biomarkers; Biopsy; Cancer; Cancer therapies; Cancer treatment; Carcinoma; Case-Control Studies; CAT scans; Chemotherapy; Computed tomography; Decision making; Decision trees; Diagnosis; Diagnostic imaging; Esophageal cancer; Esophageal Neoplasms - diagnostic imaging; Esophageal Neoplasms - surgery; Esophageal Squamous Cell Carcinoma - diagnostic imaging; Esophageal Squamous Cell Carcinoma - surgery; Esophagectomy; Esophagectomy - methods; Esophagus; Feature extraction; Female; Humans; Lymphatic system; Male; Medical imaging; Medical records; Metastasis; Middle Aged; Neoadjuvant therapy; Patients; Radiation therapy; Radiomics; Regression analysis; Regression models; Squamous cell carcinoma; Support vector machines; Surgery; Surgical outcomes; Therapy; Tomography; Tomography, X-Ray Computed - methods; Training; Tumors
• ISSN: 1470-7330; 1740-5025; 1470-7330
• DOI: 10.1186/s40644-019-0254-0

Computed tomography (CT) is commonly used in all stages of oesophageal squamous cell carcinoma (SCC) management. Compared to basic CT features, CT radiomic features can objectively obtain more information about intratumour heterogeneity. Although CT radiomics has been proved useful for predicting treatment response to chemoradiotherapy in oesophageal cancer, the best way to use CT radiomic biomarkers as predictive markers for determining resectability of oesophageal SCC remains to be developed. This study aimed to develop CT radiomic features related to resectability of oesophageal SCC with five predictive models and to determine the most predictive model. Five hundred ninety-one patients with oesophageal SCC undergoing contrast-enhanced CT were enrolled in this study, and were composed by 270 resectable cases and 321 unresectable cases. Of the 270 resectable oesophageal SCCs, 91 cases were primary resectable tumours; and the remained 179 cases received neoadjuvant therapy after CT, shrank on therapy, and changed to resectable tumours. Four hundred thirteen oesophageal SCCs including 189 resectable cancers and 224 unresectable cancers were randomly allocated to the training cohort; and 178 oesophageal SCCs including 81 resectable tumours and 97 unresectable tumours were allocated to the validation group. Four hundred ninety-five radiomic features were extracted from CT data for identifying resectability of oesophageal SCC. Useful radiomic features were generated by dimension reduction using least absolute shrinkage and selection operator. The optimal radiomic features were chosen using multivariable logistic regression, random forest, support vector machine, X-Gradient boost and decision tree classifiers. Discriminating performance was assessed with area under receiver operating characteristic curve (AUC), accuracy and F-1score. Eight radiomic features were selected to create radiomic models related to resectability of oesophageal SCC (P-values