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Abstract #1815

Rectal cancer: preoperative prediction of perineural invasion by machine learning modeling of multiregional radiomics features from multiparametric MRI

Yu Fu1, Xiangchun Liu1, Kan He1, Jianqing Sun2, Chunyu Zhang1, Xiaochen Huai2, and Huimao Zhang1

1Department of Radiology, The First Hospital of Jilin University, changchun 130021, China, 2Philips Healthcare, Beijing, China

Defined by tumor invasion of nervous structures and nerve sheaths, the presence of perineural invasion (PNI) is thought to indicate an increased risk for progressive disease in rectal cancers. Here, we developed and validated a radiomics model for individualized prediction of PNI in rectal cancer based on pre-procedure MRI. The Ridge Classifier is found to have the best prediction accuracy score (80.8%), its specificity, sensitivity and F1 score are 90.5%, 60.4%, and 67.0%, respectively. So, the radiomics features from MRI of rectal cancer is a useful tool for predicting PNI preoperatively and has marked discrimination accuracy.

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