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Thomas E. Yankeelov, Ph.D.

AIMBE College of Fellows Class of 2014
For outstanding contributions to the development and applications of advanced quantitative imaging biomarkers in cancer and other diseases

AI Digital Twins Get Better and Better: Cancer in Crosshairs

Via Medscape | September 16, 2026

Electrophysiologist Jonathan Chrispin, MD, wanted to do more.

He was standing in the electrophysiology lab at Johns Hopkins University in Baltimore, catheter in hand, about to ablate a patient’s ventricular tachycardia (VT) — a potentially fatal arrhythmia caused by electrical circuits looping through scar tissue left behind by a heart attack. In a standard procedure, Chrispin would spend 5-8 hours mapping the scar point by point and burning through it with a 3.5 mm catheter tip. The success rate hovers around 50%-60%. Many patients come back for a second ablation. Some come back for a third or fourth.

But this time, the targets had already been chosen. Before Chrispin entered the room, a team led by biomedical engineer Natalia Trayanova, PhD, had built a computational replica of the patient’s heart from a contrast-enhanced MRI scan — scar, border zones, healthy muscle, all mapped in three dimensions… Continue reading.

Predicting cancer’s response to therapy | Research News @ Vanderbilt | Vanderbilt University

Via Vanderbilt News | February 24, 2013

Neoadjuvant chemotherapy (NAC) is increasingly used in women with locally advanced breast cancer before surgical treatment. Early assessment of response to NAC would allow clinicians to identify patients who are not responding and adjust their therapy.

Thomas Yankeelov, Ph.D., Ingram Associate Professor of Cancer Research, and colleagues obtained two types of magnetic resonance imaging – diffusion-weighted (DW-MRI) and dynamic contrast-enhanced (DCE-MRI) – for 28 patients before treatment, after one cycle, and after completion of all cycles of NAC. They used the imaging data in a logistic model of tumor growth to calculate a proliferation rate after one cycle and from that, to predict the tumor cell number after NAC completion.