The use of deep learning (DL) to improve cone‐beam CT (CBCT) image quality has gained
popularity as computational resources and algorithmic sophistication have advanced in …

Background: Over half a million individuals are diagnosed with head and neck cancer each
year globally. Radiotherapy is an important curative treatment for this disease, but it requires …

Recently, the concept of radiomics has emerged from radiation oncology. It is a novel
approach for solving the issues of precision medicine and how it can be performed, based …

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 …

Lung malignancies have been extensively characterized through radiomics and deep
learning. By providing a three-dimensional characterization of the lesion, models based on …

Currently, deep learning (DL) has achieved the automatic prediction of dose distribution in
radiotherapy planning, enhancing its efficiency and quality. However, existing methods …

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been
applied to many areas in different domains such as health care and drug design. Cancer …

Deep learning (DL) algorithms have rapidly become a robust tool for analyzing medical
images. They have been used extensively for medical image segmentation as the first and …

Purpose Detailed and accurate absorbed dose calculations from radiation interactions with
the human body can be obtained with the Monte Carlo (MC) method. However, the MC …

Early regression—the regression in tumor volume during the initial phase of radiotherapy
(approximately 2 weeks after treatment initiation)—is a common occurrence during …