Artificial Intelligence-Driven Patient Selection for Preoperative Portal Vein Embolization for Patients with Colorectal Cancer Liver Metastases
Related Experiment Video
Updated: Jun 5, 2025
Portal Vein Injection of Colorectal Cancer Organoids to Study the Liver Metastasis Stroma
Published on: September 3, 2021
Tom N Kuhn1, William D Engelhardt2, Vinzent H Kahl3
1Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Department of Diagnostic and Interventional Radiology, University Düsseldorf, Medical Faculty, Düsseldorf, Germany.
Purpose:
To develop a machine learning algorithm to improve hepatic resection selection for patients with metastatic colorectal cancer (CRC) by predicting post-portal vein embolization (PVE) outcomes.
Materials And Methods:
This multicenter retrospective study (2000-2020) included 200 consecutive patients with CRC liver metastases planned for PVE before surgery. Data on radiomic features and laboratory values were collected. Patient-specific eigenvalues for each liver shape were calculated using a statistical shape model approach. After semiautomatic segmentation and review by a board-certified radiologist, the data were split 70%/30% for training and testing. Three machine learning algorithms predicting the total liver volume (TLV) after PVE, sufficient future liver remnant (FLR%), and kinetic growth rate (KGR%) were trained, with performance assessed using accuracy, sensitivity, specificity, area under the curve (AUC), or root mean squared error. Significance between the internal and external test sets was assessed by the Student t-test. One institution was kept separate as an external testing set.
Results:
A total of 114 (76 men; mean age, 56 years [SD± 12]) and 37 (19 men; mean age, 50 years ± [SD± 11]) patients met the inclusion criteria for the internal validation and external validation, respectively. Prediction accuracy and AUC for sufficient FLR% or liver growth potential (KGR%> 0%) were high in the internal testing set-85.81% (SD ± 1.01) and 0.91 (SD ± 0.01) or 87.44% (SD ± 0.10) and 0.66 (SD ± 0.03), respectively. Similar results occurred in the external testing set-79.66% (SD ± 0.60) and 0.88 (SD ± 0.00) or 72.06% (SD ± 0.30) and 0.69 (SD ± 0.01), respectively. TLV prediction showed discrepancy rates of 12.56% (SD ±4.20%; P = .86) internally and 13.57% (SD ± 3.76%; P = .91) externally.
Conclusions:
Machine learning-based models incorporating radiomics and laboratory test results may help predict the FLR%, KGR%, and TLV as metrics for successful PVE.
