Prediction Algorithm of Pathological Complete Response of Rectal Cancer Based on Weight Distribution
The purpose of this study is to establish a deep learning model for predicting the pathological complete response of rectal cancer patients after neoadjuvant chemoradiotherapy.The MR imaging data of 99 patients with rectal cancer were retrospectively analyzed,and the data set was divided according to the training group(71 cases)and the test group(28 cases).The approximate tumor area was segmented by U-Net positioning.In the prediction stage,nine basic prediction models were obtained by changing the convolution layers and slice size of the neural net-work,and the prediction score was modified by using the weight distribution method.Among the 9 models in the vali-dation group,when the slice size is 256*256,the model with 4 convolution layers has the best overall performance.The average accuracy,specificity and sensitivity in the 3-fold cross-validation are 0.714,0.717 and 0.708 respec-tively.The model constructed in this study can be used as an auxiliary tool to predict the pathological response of pa-tients with advanced colorectal cancer to neoadjuvant therapy.The prediction accuracy is good and can provide a ref-erence for clinical treatment.
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