Shafiee, Shayan

Introduction Radiation therapy for head and neck squamous cell carcinoma is constrained by radiotoxicity to normal tissue. We demonstrate 100 nm theranostic nanoparticles for image-guided radiation therapy planning and enhancement in rat head and neck squamous cell carcinoma models. Methods PEG conjugated theranostic nanoparticles comprising of Au nanorods coated with Gadolinium oxide layers were tested for radiation therapy enhancement in 2D cultures of OSC-19-GFP-luc cells, and orthotopic tongue xenografts in male immunocompromised Salt sensitive or SS rats via both intratumoral and intravenous delivery. The radiation therapy enhancement mechanism was investigated. Results Theranostic nanoparticles demonstrated both X-ray/magnetic resonance contrast in a dose-dependent manner. Magnetic resonance images depicted optimal tumor-to-background uptake at 4 h post injection. Theranostic nanoparticle + Radiation treated rats experienced reduced tumor growth compared to controls, and reduction in lung metastasis. Conclusions Theranostic nanoparticles enable preprocedure radiotherapy planning, as well as enhance radiation treatment efficacy for head and neck tumors.

Delta like canonical notch ligand 4 (Dll4) expression levels in tumors are known to affect the efficacy of cancer therapies. This study aimed to develop a model to predict Dll4 expression levels in tumors using dynamic enhanced near-infrared (NIR) imaging with indocyanine green (ICG). Two rat-based consomic xenograft (CXM) strains of breast cancer with different Dll4 expression levels and eight congenic xenograft strains were studied. Principal component analysis (PCA) was used to visualize and segment tumors, and modified PCA techniques identified and analyzed tumor and normal regions of interest (ROIs). The average NIR intensity for each ROI was calculated from pixel brightness at each time interval, yielding easily interpretable features including the slope of initial ICG uptake, time to peak perfusion, and rate of ICG intensity change after reaching half-maximum intensity. Machine learning algorithms were applied to select discriminative features for classification, and model performance was evaluated with a confusion matrix, receiver operating characteristic curve, and area under the curve. The selected machine learning methods accurately identified host Dll4 expression alterations with sensitivity and specificity above 90%. This may enable stratification of patients for Dll4 targeted therapies. NIR imaging with ICG can noninvasively assess Dll4 expression levels in tumors and aid in effective decision making for cancer therapy.