What are the advantages of incorporating detailed imaging features like liver shape in the predictive model? TK: The importance of it lies in its predictive value, meaning that those features are very important for our machine-learning algorithms. And that was true not only for the FLR% model but also the kinetic growth percentage model and the total liver volume as well.
The study mentions that this model was tested at multiple institutions. How did it perform across these diff erent hospitals? TK: Our model was trained from four high-volume cancer centers, three from the U.S. and one from Japan. We used three institutions for training and testing and then we kept one institution separate for external validation. We were able to show that external validation did not reveal any statistically signifi cant diff erence, meaning that the external testing was basically as good as the internal testing. This indicated the generalizability of our model. Most of the studies in general do not have an external validation, and this is basically a real-world test from a separate institution that helps to underline the performance of a model under real- world conditions.
30 IRQ | SPRING 2025
How reliable is it for predicting treatment outcomes in patients from diverse backgrounds or with varying medical histories? TK: Based on our data, it is reliable. So, for example, one of our institutions was from Japan and they perform portal vein embolization (PVE) very differently than it is performed in the U.S. Under these circumstances, there was no real difference between the institutions on key markers or accuracy. Therefore, our model was very robust based on the diverse backgrounds of patients.
Do you and your team have any next steps or plans for follow-up with this study? TK: We defi nitely do, though I think the importance of this study lies in its applicability.
One thing that we are working currently on is integrating that into real-world clinical decisions. So basically, clinicians can use that tool to make predictions. I think what is important, too, is that the basis of this machine learning algorithm can be used and applied to other techniques of liver intervention, such as ALPPS or radiation lobectomy. It could be used for HCC patients as well.
Is there anything else you feel readers should know? TK: What makes our work truly remarkable is its practical applicability. For instance, take our AI-driven liver model—it enables predictive simulations, off ering a baseline liver shape alongside projections for 2 and 4 weeks after a hypothetical PVE. This means that without performing any procedures, we can visualize how the liver might change over time, providing surgeons with invaluable insights for preoperative planning.
Moreover, our model allows for a dynamic 4D simulation, illustrating the liver’s morphological evolution in real time. This capability is particularly exciting, as it grants clinicians a powerful tool to assess potential outcomes before any intervention takes place—ultimately aiding in the selection of patients who would benefi t most from PVE.
References
1. Shindoh J et al. Optimal future liver remnant in patients treated with extensive preoperative chemotherapy for colorectal liver metastases. Ann Surg Oncol. 2013 Aug;20(8):2493–2500. doi: 10.1245/s10434-012-2864-7. ePub 2013 Feb 3. PMID: 23377564; PMCID: PMC3855465.
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40
