A digital twin can replicate a human patient and test every treatment path in minutes, predicting what will work best. Just how good are they?
Clinician and patient alike face, really, the ultimate challenge when making those decisions. They have to consider the patient’s individual circumstances, available treatment options, potential side effects, relevant clinical data such as the patient’s genetic profile and cancer specifics, and more.
Asghar is currently working with colleagues to develop digital twins that could eventually help solve the aforementioned cancer scenario — a doctor and patient decide the best course of cancer treatment. But their applications are manifold, particularly in clinical research. Their current model gives predictions for RECIST , treatment response, and survival. In addition to collecting data from ongoing clinical trials, they’ve used retrospective data, such as from the Cancer Tumor Atlas, to test the model.
The final stage of their work, before it becomes widely available to clinicians, will be to integrate it into a clinical trial in which some clinicians use the model to make decisions about treatment vs some who don’t use the model. By studying patient outcomes in both groups, they will be able to determine the value of the digital twin program they created.
Digital twins could also assist in decision-making based on a patient’s priorities and real-life circumstances. “Maybe your priority is not necessarily to shrink this at all costs...maybe your priority is some mix of that and also quality of life,” Macklin said, referring to potential side effects. Or if someone lives 3 hours from the nearest cancer center, a digital twin could help determine whether less frequent treatments could still be effective.
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