Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …

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 …

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 …

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 …

In order to limit radiotherapy (RT)-related side effects, effective toxicity prediction and
assessment schemes are essential. In recent years, the growing interest toward artificial …

This comprehensive review addresses most organs at risk involved in planning optimization
for prostate cancer. It can be considered an update of a previous educational review that …

This paper presents a review of deep learning (DL) in multi-organ segmentation. We
summarized the latest DL-based methods for medical image segmentation and applications …

Background Accurate prediction of operative transfusions is essential for resource allocation
and identifying patients at risk of postoperative adverse events. This research examines the …

Advances in patient-specific information and biotechnology have contributed to a new era of
computational medicine. Radiogenomics has emerged as a new field that investigates the …

Background and purpose Curative radiotherapy for prostate cancer may lead to anorectal
side effects, including rectal bleeding, fecal incontinence, increased stool frequency and …