Inside access By Tom N. Kuhn, MD; Amy R. Deipolyi, MD, FSIR JVIR spotlight


Artifi cial intelligence– driven patient selection for preoperative portal vein embolization for patients with colorectal cancer liver metastases


Kuhn TN, et al. J Vasc Interv Radiol. 2025 Mar;36(3):477-488. doi: 0.1016/j.jvir.2024.11.025.


Read the full article on JVIR.


the right candidates due to variability in patients’ response. Traditional methods were very limited and based on clinical parameters. We tried, by integrating additional features into machine learning algorithms, to improve patient selection accuracy and therefore optimize surgical outcomes.


Tell us about you, your team and your institution. Tom N. Kuhn, MD: I’m a clinician scientist at the German Cancer Research Center with a focus on machine-learning- based predictions on clinical decision- making and procedure outcomes. Our team consists of interdisciplinary experts in the fi eld of IR, oncology and biomedical engineering. We’re working across multiple high-volume cancer centers. Our institution aims to integrate machine learning into clinical workfl ow to optimize patient selection and therefore improve patient outcomes.


Why did you choose to pursue this specifi c topic? TK: We thought that portal vein embolization is a very important treatment, which is crucial for patients with colorectal liver metastases who require a liver resection but have insuffi cient future liver remnant (FLR%). However, it is currently diffi cult to select


What key factors determine whether a patient with colorectal cancer liver metastases would be eligible for a successful PVE treatment? TK: That very much depends on the patient. For our cohort, most of our patients were heavily pretreated with chemotherapy, meaning they received chemotherapy for more than 12 weeks. Therefore, it was defined that the FLR% should be above 30% based on the paper by Shindoh et al.1


To identify


key factors, we performed a feature importance analysis of our machine learning algorithms.


For our patients, it was very important to look back on what features were important to make accurate predictions. If we look at the top 10 features that made our model accurate for the FLR% model, there were two markers that you generally use in clinical practice. For example, the baseline FLR% or the baseline left-liver volume. But the rest were features that are currently not used for clinical decision-making, meaning there were six radiomic features that were extracted from the CT scans


and two features from our statistical shape model approach where we used principal components as features. This underlies the importance of machine learning in this specifi c setting.


How did the machine learning model help identify the patients that were chosen? TK: That was very much based on the specific features and how important they were—meaning mostly features that are not visible to the naked eye were predictive for their specific model. And that’s basically the importance of machine learning in that specific setting.


How did machine learning and radiomics improve predictions compared to traditional methods that only considered a small number of clinical and imaging factors? TK: What we have seen before is that for that specific setting, radiomic features were very important for prediction accuracy and what we could show as well. What I think is a novelty of our study is that a statistical shape model, which helps to quantify liver morphology and their growth dynamics, helped in making those predictions as well. And those are all features that are usually not used before, and I think that really underlies the importance of machine learning to analyze complex patterns, especially across multiple variables.


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