In clinical breast cancer intervention, selection of the optimal treatment protocol based on predictive biomarkers remains an elusive goal. Here, we present a modeling tool to predict the likelihood of breast cancer response to neoadjuvant chemotherapy using patient-specific tumor vasculature biomarkers. A semiautomated analysis was implemented and performed on 3990 histological images from 48 patients, with 10–208 images analyzed for each patient. We applied a histology-based mathematical model to 30 resected primary breast cancer tumors and then evaluated a cohort of 18 patients undergoing neoadjuvant chemotherapy, collecting pre- and posttreatment pathology specimens and MRI data. We found that core biopsy samples can be used with acceptable accuracy to determine histological parameters representative of the whole tissue region. Analysis of model histology parameters obtained from tumor vasculature measurements, specifically diffusion distance divided by the radius of the drug-delivering blood vessel ( L/r _b ) and blood volume fraction (BVF), provides a statistically significant separation of patients obtaining a pathologic complete response (pCR) from those who do not. With this model, it is feasible to evaluate primary breast tumor vasculature biomarkers in a patient-specific manner, thereby allowing a precision approach to breast cancer treatment.

Current imaging approaches used to monitor tumor progression can lack the ability to distinguish true progression from pseudoprogression. Simultaneous metabolic 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) positron emission tomography (PET) and magnetic resonance imaging (MRI) offers new opportunities to overcome this challenge by refining tumor identification and monitoring therapeutic responses to cancer immunotherapy. In the current work, spatial and quantitative analysis of tumor burden were performed using simultaneous [¹⁸F]FDG-PET/MRI to monitor therapeutic responses to a novel silicified cancer cell immunotherapy in a mouse model of disseminated serous epithelial ovarian cancer. Tumor progression was validated by bioluminescence imaging of luciferase expressing tumor cells, flow cytometric analysis of immune cells in the tumor microenvironment, and histopathology. While PET demonstrated the presence of metabolically active cancer cells through [¹⁸F]FDG uptake, MRI confirmed cancer-related accumulation of ascites and tissue anatomy. This approach provides complementary information on disease status without a confounding signal from treatment-induced inflammation. This work provides a possible roadmap to facilitate accurate monitoring of therapeutic responses to cancer immunotherapies.