In a new paper published in Cell Reports Medicine, the UCL team outlines how they, in collaboration with a University of Cambridge team, developed a new type of treatment-predicting computer program that uses an individual's detailed genetic fingerprint to explore which treatments, if any, may kill their cancer—especially when standard treatments have failed.
In developing their model, specifically to recommend treatment options for triple-negative breast cancer (TNBC), a hard-to-treat cancer, the UCL team instead used a form of AI called "symbolic AI" which relies on logic rather than prediction and where its workings can be clearly seen.
To test the effectiveness of their approach, the team built computer-generated tumors—or "digital twins"—that aped the real-life tumors of 31 patients.
They then compared the results of the computer simulations with those from the clinical trial and found the computer program correctly predicted whether treatment would work in 74% of cases.
The digital twins also suggested ways to help patients whose tumors had not responded well to treatment.
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