Kyle J. Lafata, PhD

Mechanistic tumor growth models provide a framework for linking observed tumor kinetics to underlying biological processes; however, their application to real preclinical datasets is limited by incomplete longitudinal measurements and uncertainty in model par...

To propose a federated learning (FL) framework incorporating a novel deep ensemble strategy for multi-institutional brain metastasis (BM) segmentation, improving performance in limited local datasets while preserving privacy by avoiding large-scale data trans...

Standardizing the assessment of the Tumor Microenvironment (TME) is critical for personalizing therapy in Head and Neck Squamous Cell Carcinoma (HNSCC). As part of a larger multiomics effort to discover multiscale biomarkers, we developed a mathematical frame...

Understanding chemoradiation resistance remains a major barrier to improving outcomes in head and neck cancer. We developed a multiscale framework linking tumor architecture, cellular network topology, and immune context to therapeutic response. This approach...

To develop an in-silico tumor model that incorporates nutrient-driven growth and radiotherapy response to generate spatio-temporal proliferating (P), quiescent (Q), and necrotic (N) cell maps for radiomics-based heterogeneity analysis.

Personalized oncology requires bridging the gap between macro-scale imaging and micro-scale cellular architecture. We developed a radiopathomic fusion framework to integrate PET/CT metabolic heterogeneity with graph-based topological modeling of the tumor mic...

Head and neck squamous cell carcinoma (HNSCC) is a clinically aggressive malignancy with heterogeneous responses to chemoradiation, driven in part by dynamic immune remodeling. While immune infiltration has been studied at different time points, the spatiotem...

Murine models of head and neck squamous cell carcinoma (HNSCC) are routinely monitored using external caliper measurements to estimate tumor volume, most often relying on simplified geometric assumptions such as cylindrical approximations. While convenient, t...

To develop a multi-parametric MRI (mp-MRI) radiomics framework for predicting post-resection glioblastoma (GBM) survival by integrating conventional MR modalities with a quantum mechanics–inspired imaging representation.

To develop and evaluate a federated learning (FL) framework for brain metastasis (BM) segmentation that integrates an uncertainty score into a novel FL objective, improving segmentation robustness and potentially performance when training on limited-size data...

Therapy Physics

To investigate mechanisms of chemoradiation resistance in murine head and neck squamous cell carcinoma using a multiscale framework integrating metabolic imaging (μPET), tissue heterogeneity (μCT), and cellular topology (digital pathology).

To develop an in silico clinical trial framework based on mathematical modeling of tumor response to chemoradiation therapy as a prognostic indication of patient outcomes.