Prediction of CD3 T-cell infiltration status in colorectal liver metastases: a radiomics-based imaging biomarker


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Colorectal cancer (CRC) continues to be a leading cause of cancer-related death in the developed world due to metastatic progression of the disease. In an effort to improve the understanding of tumor biology and developing prognostic tools, it was found that CD3+ tumor infiltrating lymphocytes (TIL) had a very strong prognostic value in primary CRC as well as in colorectal liver metastases (CLM). Quantification of TILs remains labor intensive and requires tissue samples, hence being of limited use in the pre-operative period or in the context of non-operable disease. Computed tomography (CT) images however are widely available for patients with CLM. In this study, we propose a pipeline to predict CD3 T-cell infiltration in CLM from pre-operative CT images. Radiomic features were extracted from 58 automatically segmented CLM lesions. Subsequently, dimensionality reduction was performed by training an autoencoder (AE) on the full feature set. We then used AE bottleneck embeddings to predict CD3 T-cell density, stratified into two categories: CD3hi and CD3low. For this, we implemented a 1D convolutional neural network (1D-CNN) and compared its performance against five machine learning models using 5-fold cross-validation. Results showed that the proposed 1D-CNN outperformed the other trained models achieving a mean accuracy of 0.69 (standard deviation [SD], 0.01) and a mean area under the receiver operating curve (AUROC) of 0.75 (SD, 0.02) on the validation set. Our findings demonstrate a relationship between CT radiomic features and CD3 tumor infiltration status with the potential of noninvasively determining CD3 status from pre-operative CT images.
radiomics, tumor infiltrating lymphocytes, machine learning, colorectal liver metastasis
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