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Colorectal cancer liver metastases (CLM) are the most common type of distantmetastases originating from the abdomen and are characterized by a highrecurrence rate after curative resection. It has been previously reported thatCLM presenting a low cluster of differentiation 3 (CD3) positive T-cellinfiltration density concurrent with a high major histocompatibility complexclass I (MHC-I) expression were associated with poor clinical outcomes. In thisstudy, we attempt to noninvasively predict whether a CLM exhibit theCD3LowMHCHigh immunological profile using preoperative CT images. To this end,we propose an ensemble network combining multiple Attentive InterpretableTabular learning (TabNet) models, trained using CT-derived radiomic features. Atotal of 160 CLM were included in this study and randomly divided between atraining set (n=130) and a hold-out test set (n=30). The proposed model yieldedgood prediction performance on the test set with an accuracy of 70.0% [95%confidence interval 53.6%-86.4%] and an area under the curve of 69.4%[52.9%-85.9%]. It also outperformed other off-the-shelf machine learningmodels. We finally demonstrated that the predicted immune profile wasassociated with a shorter disease-specific survival (p = .023) andtime-to-recurrence (p = .020), showing the value of assessing the immuneresponse.

Prediction of a T-cell/MHC-I-based immune profile for colorectal liver metastases from CT images using ensemble learning