The use of machine learning and other sophisticated models to aid in prediction and decision making has become widely popular across a breadth of disciplines. Within the greater diagnostic radiology, radiation oncology, and medical physics communities promising work is being performed in tissue classification and cancer staging, outcome prediction, automated segmentation, treatment planning, and quality assurance as well as other areas. In this article, machine learning approaches are explored, highlighting specific applications in machine and patient-specific quality assurance (QA). Machine learning can analyze multiple elements of a delivery system on its performance over time including the multileaf collimator (MLC), imaging system, mechanical and dosimetric parameters. Virtual Intensity-Modulated Radiation Therapy (IMRT) QA can predict passing rates using different measurement techniques, different treatment planning systems, and different treatment delivery machines across multiple institutions. Prediction of QA passing rates and other metrics can have profound implications on the current IMRT process. Here we cover general concepts of machine learning in dosimetry and various methods used in virtual IMRT QA, as well as their clinical applications.

The potential of gold nanoparticles (GNPs) in therapeutic and diagnostic cancer applications is becoming increasingly recognized. These biologically compatible particles can be easily synthesized, tuned to different sizes, and functionalized by conjugation to various biologically useful materials. Efficient and specific delivery to tumor tissue can then be accomplished either by passive accumulation in leaky tumor vessels and tissue, or by directly targeting tumor-specific biomarkers. Tumor-localized GNPs can serve as both adjuvants for enhancing the efficacy of radiation therapy and also as contrast agents for various imaging modalities. In this review, we will discuss recent advancements and future potential in the application of GNP as both a radiosensitizer and an imaging contrast agent. Due to their versatility and biocompatibility, gold nanoparticles may represent a novel theranostic adjuvant for radiation applications in cancer management.