Abstract #4719

The Radiomic Signature as a Prognostic Biomarker for Locally Advanced Rectal Cancer

Yuchen Zhang¹, Yankai Meng², Hongmei Zhang², Chunwu Zhou², Di Dong³, Mengjie Fang³, Yali Zang³, Zhenyu Liu³, Jie Tian⁴, Di Dong³, Di Dong³, and Di Dong³

¹University of Electronic Science and Technology of China, Beijing, China, ²Department of Radiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China., Beijing, China, ³CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing P.R. China; University of Chinese Academy of Sciences, Beijing P.R. China., Beijing, China, ⁴CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China

Radiomics uses a large number of medical imaging features and can demonstrate voxel-wise intratumor heterogeneity. We calculated the radiomic signature for each patient using a weighted linear combination of the radiomic features selected by machine learning methods. The study endpoint was DFS, defined as the interval between TME surgery and disease progression, which included tumor local recurrence, distant metastasis, or death, or the date of the last follow-up visit (censored). The association between the radiomic signature and DFS was explored. Then, the three models were built to estimate the DFS in patients.

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