NDLI: Application of contrast-enhanced CT radiomics in prediction of early recurrence of locally advanced oesophageal squamous cell carcinoma after trimodal therapy

Content Provider	Springer Nature : BioMed Central
Author	Tang, Sun Ou, Jing Liu, Jun Wu, Yu-ping Wu, Chang-qiang Chen, Tian-wu Zhang, Xiao-ming Li, Rui Tang, Meng-jie Yang, Li-qin Tan, Bang-guo Lu, Fu-lin Hu, Jiani
Abstract	Background Early recurrence of oesophageal squamous cell carcinoma (SCC) is defined as recurrence after surgery within 1 year, and appears as local recurrence, distant recurrence, and lymph node positive and disseminated recurrence. Contrast-enhanced computed tomography (CECT) is recommended for diagnosis of primary tumor and initial staging of oesophageal SCC, but it cannot be used to predict early recurrence. It is reported that radiomics can help predict preoperative stages of oesophageal SCC, lymph node metastasis before operation, and 3-year overall survival of oesophageal SCC patients following chemoradiotherapy by extracting high-throughput quantitative features from CT images. This study aimed to develop models based on CT radiomics and clinical features of oesophageal SCC to predict early recurrence of locally advanced cancer. Methods We collected electronic medical records and image data of 197 patients with confirmed locally advanced oesophageal SCC. These patients were randomly allocated to 137 patients in the training cohort and 60 in the test cohort. 352 radiomics features were extracted by delineating region-of-interest (ROI) around the lesion on CECT images and clinical signature was generated by medical records. The radiomics model, clinical model, the combined model of radiomics and clinical features were developed by radiomics features and/or clinical characteristics. Predicting performance of the three models was assessed with area under receiver operating characteristic curve (AUC), accuracy and F-1 score. Results Eleven radiomics features and/or six clinical signatures were selected to build prediction models related to recurrence of locally advanced oesophageal SCC after trimodal therapy. The AUC of integration of radiomics and clinical models was better than that of radiomics or clinical model for the training cohort (0.821 versus 0.754 or 0.679, respectively) and for the validation cohort (0.809 versus 0.646 or 0.658, respectively). Integrated model of radiomics and clinical features showed good performance in predicting early recurrence of locally advanced oesophageal SCC for both the training and validation cohorts (accuracy = 0.730 and 0.733, and F-1score = 0.730 and 0.778, respectively). Conclusions The integrated model of CECT radiomics and clinical features may be a potential imaging biomarker to predict early recurrence of locally advanced oesophageal SCC after trimodal therapy.
Related Links	https://cancerimagingjournal.biomedcentral.com/counter/pdf/10.1186/s40644-021-00407-5.pdf
Ending Page	10
Page Count	10
Starting Page	1
File Format	HTM / HTML
ISSN	14707330
DOI	10.1186/s40644-021-00407-5
Journal	Cancer Imaging
Issue Number	1
Volume Number	21
Language	English
Publisher	BioMed Central
Publisher Date	2021-05-26
Access Restriction	Open
Subject Keyword	Oncology Cancer Research Imaging Radiology Nuclear Medicine Esophageal neoplasms Carcinoma Squamous Cell Tomography X-ray computed Recurrence Therapeutics
Content Type	Text
Resource Type	Article
Subject	Radiological and Ultrasound Technology Radiology, Nuclear Medicine and Imaging Oncology
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	4.3/2023

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