Abstract #3488

Predicting the efficacy of neoadjuvant chemo-radiotherapy for advanced rectal cancer using random forest model and a paired-difference up-sampling strategy under small sample size

Jie Kuang¹, QingLei Shi², Gaofeng Shi¹, Xu Yan³, and LI Yang¹

¹The Fourth Hospital of Hebei Medical University, Shijiazhuang, China, ²Siemens Healthcare, MR Scientific Marketing, Beijing, China, ³Siemens Healthcare, MR Scientific Marketing, Shanghai, China

In this study, we adopted a paired-difference strategy, which can improve training efficiency of random forest (RF) model with a small sample size. Through optimizing in normalization, dimensional reduction, and features selection steps, a higher accuracy was achieved in predicting the efficacy of chemo-radiotherapy for advanced rectal cancer.

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