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…
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).
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.
Here, we applied an integrative computational approach to develop the IOScope biomarker, which predicts IO response in ESBC.
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…
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.
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.
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.
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