  Colorectal cancer liver metastases (CLM) are the most common type of distant
metastases originating from the abdomen and are characterized by a high
recurrence rate after curative resection. It has been previously reported that
CLM presenting a low cluster of differentiation 3 (CD3) positive T-cell
infiltration density concurrent with a high major histocompatibility complex
class I (MHC-I) expression were associated with poor clinical outcomes. In this
study, we attempt to noninvasively predict whether a CLM exhibit the
CD3LowMHCHigh immunological profile using preoperative CT images. To this end,
we propose an ensemble network combining multiple Attentive Interpretable
Tabular learning (TabNet) models, trained using CT-derived radiomic features. A
total of 160 CLM were included in this study and randomly divided between a
training set (n=130) and a hold-out test set (n=30). The proposed model yielded
good 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 learning
models. We finally demonstrated that the predicted immune profile was
associated with a shorter disease-specific survival (p = .023) and
time-to-recurrence (p = .020), showing the value of assessing the immune
response.
