Keywords: Radiomics, Radiomics, rectal cancer;neoadjuvant therapy;overall survival
Motivation: Use MR radiomics features as a potential factor for predicting overall survival (OS) after neoadjuvant therap in rectal cancer.
Goal(s): To explore the ability of the intratumoral and peritumoral radiomics features to predict the OS.
Approach: A nomogram that combines clinical features, intratumor and peritumor radiomics features from T2WI using Machine Learning to predict OS was developed, validated using the Kaplan-Meier survival curve.
Results: There was a significant statistical difference in the label scores between the high-risk and low-risk groups divided by median survival time as the cutoff value. The C-index of the training and test cohort was 0.798, 0.772 respectively.
Impact: The radiomics model of peritumor radiomics features as the intratumor model can predict the OS in rectal cancer with neoadjuvant therapy. Combined with clinical features and the radiomics model of intratumor radiomics features, high accuracy can be obtained.
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