Abstract Machine learning technology has a growing impact on radiation oncology with an
increasing presence in research and industry. The prevalence of diverse data including 3D …

With the era of big data, the utilization of machine learning algorithms in radiation oncology
is rapidly growing with applications including: treatment response modeling, treatment …

Machine learning (ML) has the potential to revolutionize the field of radiation oncology, but
there is much work to be done. In this article, we approach the radiotherapy process from a …

Abstract Machine learning is an evolving branch of computational algorithms that are
designed to emulate human intelligence by learning from the surrounding environment …

Machine learning approaches to problem-solving are growing rapidly within healthcare, and
radiation oncology is no exception. With the burgeoning interest in machine learning comes …

Artificial intelligence and machine learning have the potential to make cancer care more
accessible, efficient, cost-effective and personalized. However, meticulously planned …

Within artificial intelligence, machine learning (ML) efforts in radiation oncology have
augmented the transition from generalized to personalized treatment delivery. Although their …

Precision medicine relies on an increasing amount of heterogeneous data. Advances in
radiation oncology, through the use of CT Scan, dosimetry and imaging performed before …

Radiation oncology has always been deeply rooted in modeling, from the early days of
isoeffect curves to the contemporary Quantitative Analysis of Normal Tissue Effects in the …

Radiation therapy is a pivotal cancer treatment that has significantly progressed over the last
decade due to numerous technological breakthroughs. Imaging is now playing a critical role …