Patients with locally advanced, triple-negative breast cancer (TNBC) typically receive neoadjuvant chemotherapy (NAT) to downstage the tumor and improve the outcome of subsequent breast conservation surgery. In this study, we integrated quantitative magnetic resonance imaging (MRI) data with biology-based mathematical modeling to address the currently unmet need for accurate prediction of TNBC response to NAT on an individual patient basis.
Specifically, dynamic contrast-enhanced MRI and diffusion-weighted MRI was acquired in 56 patients before, after two, and after four cycles of Adriamycin/Cytoxan (A/C), and again after Taxol as part of the ARTEMIS (NCT02276443) trial.
A biology-based mathematical model was established based on the reaction-diffusion equation to characterize the mobility of tumor cells, tumor proliferation, and treatment-induced cell death. Pre- and mid-treatment images of the individual patient were used for model calibration on a patient-specific basis; thus, the methodology represents a significant step away from population-based predictions, and towards individual-based predictions.
The personalized model accurately predicted the spatiotemporal response of TNBC to NAT and achieved high accuracy and specificity for predicting the final pathological status for each individual patient.
Ongoing effort to extend and refine this project include:
1) integrate mechanism-based model with deep learning approaches to enable pre-treatment prediction
2) quantify the uncertainty in our model prediction
3) speed up the computational simulation with reduced-order models
4) adapt the image-guided modeling to cancer in other sites
Specifically, dynamic contrast-enhanced MRI and diffusion-weighted MRI was acquired in 56 patients before, after two, and after four cycles of Adriamycin/Cytoxan (A/C), and again after Taxol as part of the ARTEMIS (NCT02276443) trial.
A biology-based mathematical model was established based on the reaction-diffusion equation to characterize the mobility of tumor cells, tumor proliferation, and treatment-induced cell death. Pre- and mid-treatment images of the individual patient were used for model calibration on a patient-specific basis; thus, the methodology represents a significant step away from population-based predictions, and towards individual-based predictions.
The personalized model accurately predicted the spatiotemporal response of TNBC to NAT and achieved high accuracy and specificity for predicting the final pathological status for each individual patient.
Ongoing effort to extend and refine this project include:
1) integrate mechanism-based model with deep learning approaches to enable pre-treatment prediction
2) quantify the uncertainty in our model prediction
3) speed up the computational simulation with reduced-order models
4) adapt the image-guided modeling to cancer in other sites
