Ensemble Tabnet Predicting a T-Cell/MHC-I-Based Immune Profile Biomarker for Colorectal Liver Metastases from CT Images

• Published in: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) pp. 1 - 5
• Main Authors: Saber, Ralph, Henault, David, Rebolledo, Rolando, Turcotte, Simon, Kadoury, Samuel
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.04.2023
• Subjects: attentive interpretable tabular learning; Biological system modeling; Colorectal cancer liver metastases; Computed tomography; ensemble learning; Feature extraction; immune profiling; Immune system; Liver; Machine learning; noninvasive prognostic biomarker; Training
• ISSN: 1945-8452
• DOI: 10.1109/ISBI53787.2023.10230665

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 exhibits the CD3 Low MHC High immunological profile using preoperative CT images. To this end, we propose an ensemble network combining multiple Attentive Interpre table 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.