Aditya P. Apte

This work presents a public project on the Cancer Genomics Cloud (CGC) platform for reproducible, out-of-the-box application of analysis workflows for pre-trained radiological and radiotherapy AI models; the first and only such project supporting analysis of...

Early assessment of radiotherapy response is essential for adaptive treatment planning and digital-twin development. Longitudinal quantification of tumor volume and mass changes depends on reliable deformable image registration (DIR), which remains challengin...

Dynamic Contrast-Enhanced (DCE) MRI provides functional information on tumor vascularity and perfusion, useful in characterizing tumors and gauging treatment response. We introduce an open-source extension to pyCERR for extracting non-parametric features of c...

Differentiating radiation necrosis (RN) from tumor recurrence (TR) after radiotherapy remains challenging using conventional MRI. We investigated dynamic contrast-enhanced MRI (DCE-MRI) analysis using unbalanced regularized optimal mass transport (urOMT) to q...

To quantify and visualize tumor microenvironment transport behavior in longitudinal breast DCE-MRI acquired during neoadjuvant chemotherapy (NACT), and to develop image-based biomarkers for predicting therapeutic response