Abstract #4747

Using machine learning to evaluate values of six diffusion models to predict the efficacy of neoadjuvant chemotherapy for esophageal cancer

Long Cui¹, Bingmei Bai², Chenglong Wang¹, Yang Song³, Shengyong Li¹, Haijie Wang¹, Jinrong Qu², and Guang Yang¹

¹Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China, ²Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China, ³MR Scientific Marketing, Siemens Healthcare, Shanghai, China

Synopsis

Keywords: Cancer, TumorWe assessed the performance of diffusion models for assessing response to neoadjuvant chemotherapy (NACT) using machine learning. Firstly, features were extracted from the region of interest on different parametric maps of different diffusion models for esophageal squamous cell carcinoma (ESCC) patients and changes of the parameters (Δ parameter) before and after NACT (pre-NACT and post-NACT) were calculated. Then different Δ-NACT models and pre-NACT models were built for using features from different diffusion models. The results demonstrated that diffusion models may be used to predict the efficacy of NACT in ESCC patients.

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