We used a novel 2-paramenter pharmacokinetic modeling framework that allows biosignatures to be extracted from dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) studies that contain 3-6 timepoints spaced 60-90 seconds apart. These parameters, referred to as P1 and P2, represent leakiness from vessels into the extravascular space and vice versa.
Currently, a lack of tests to differentiate patients likely to respond to IO vs. poor responders precludes a tailored approach to immunotherapy. Here we describe an imaging biomarker that allows physicians to target breast cancer patients with the highest likelihood of response to immunotherapies.
Despite chemotherapeutic advances, surgery remains a putative treatment modality for breast cancer. The type of surgery chosen, and its ultimate clinical and cosmetic consequences, depends on a surgeon’s ability to accurately assess a tumor’s size, distribution, and position in the breast relative to anatomical landmarks.
In order to accurately evaluate the potential success of various surgical options, a surgeon must mentally translate these 2D images into more realistic, 3D image to visualize breast and tumor morphologies.
Independent validation of a novel, non-invasive approach to predict pathologic complete response (pCR) in a blinded, prospectively-run single center trial
We developed the TumorScope engine, a software platform that utilizes pretreatment diagnostic data to build a computational tumor model that simulates in vivo tumor characteristics and interactions, incorporating morphology, metabolism, vascularity, and nutrient and drug delivery.
pCR Score: A Novel Prognostic Method to Estimate the Predictive Probability of pCR in Early-Stage Breast Cancer Patients.
To drive further utility, we now investigate a pCR score as a continuous outcome (0-100) to establish a prognostic system that evaluates the predictive probability that a patient will achieve pCR with any SOC NAT regimen.