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 …

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 …

Artificial intelligence, and in particular deep learning using convolutional neural networks,
has been used extensively for image classification and segmentation, including on medical …

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 …

Introduction: Deep Learning (DL) is a machine learning technique that uses deep neural
networks to create a model. The application areas of deep learning in radiation oncology …

This paper reviews objective methods for prognostic modelling of cancer tumours located
within radiology images, a process known as radiomics. Radiomics is a novel feature …

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 …

Background: The future of artificial intelligence (AI) heralds unprecedented change for the
field of radiation oncology. Commercial vendors and academic institutions have created AI …

The interest in machine learning (ML) has grown tremendously in recent years, partly due to
the performance leap that occurred with new techniques of deep learning, convolutional …