We developed an ensembled suite of convolutional neural networks (core components of SimBioSys TumorSight) that segmented tumor and other tissues, in and around the breast (chest, adipose, gland, vasculature, skin). We sought to validate model results against the expertise of two breast-specialized radiologists.
DCE-MRI-derived microvasculature mapping and outcome prediction in patients with breast cancer.
Here, we show how MV maps — computed from standard-of-care (SOC) imaging — improve upon ctDNA measurements in predicting pathologic complete response (pCR) following NAT as well as distant recurrence free survival (DRFS).
Association of biophysics-based biomarker with tumor volumetric response in breast cancer treated with immunotherapy.
Using an integrative computational approach, we developed both the TumorIO biomarker and the TumorIO Score to predict response to IO therapy in breast cancer patients. Here, we assess the volumetric response in IO-treated patients and its relationship with the TumorIO Score.
Next generation immuno-oncology tumor profiling using a biophysics-based biomarker to predict immunotherapy response in early-stage breast cancer
Here, we applied an integrative computational approach to develop the IOScope biomarker, which predicts IO response in ESBC.
A rapid, non-invasive test to determine HER2-low status in triple negative breast cancer (TNBC) patients
The potential to expand the current treatment for HER2-low and -zero patients, highlights the limitations of traditional HER2 assessment, including HER2 intra-tumoral heterogeneity, differential IHC pathology staining, and inter-reader variability. We therefore sought to address how to optimally identify HER2-low TNBC patients that may benefit from HER2 ADCs.
Computational speed-up of large-scale, single-cell model simulations via a fully integrated SBML-based format
Here, we developed two solutions to communication bottlenecks that speed-up simulation by ∼4-fold for hybrid stochastic-deterministic simulations and by over 100-fold for fully deterministic simulations.
Novel computational biology modeling system can accurately forecast response to neoadjuvant therapy in early breast cancer
Here, we perform a study in an independent cohort of early-stage and locally advanced breast cancer patients to forecast tumor response to NAT and assess the stability of a previously validated biophysical simulation platform.
Next generation immuno-oncology tumor profiling using a rapid, non-invasive, computational biophysics biomarker in early-stage breast cancer
Here, we perform a study in an independent cohort of early-stage and locally advanced breast cancer patients to forecast tumor response to NAT and assess the stability of a previously validated biophysical simulation platform.
Validation of prognostic platform to further refine identification High Risk Patients indicated for Chemotherapy Free Treatment in Early-Stage Breast Cancer
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.
Development of a Novel Imaging Biomarker to Ascertain Responsiveness to Immunotherapy
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.