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 …

Radiomics relies on the extraction of a wide variety of quantitative image-based features to
provide decision support. Magnetic resonance imaging (MRI) contributes to the …

Strong rationale and a growing number of preclinical and clinical studies support combining
radiotherapy and immunotherapy to improve patient outcomes. However, several critical …

Brain tumor segmentation is a critical task for patient's disease management. In order to
automate and standardize this task, we trained multiple U-net like neural networks, mainly …

Histopathological image segmentation is a challenging and important topic in medical
imaging with tremendous potential impact in clinical practice. State of the art methods rely on …

Radiomics extracts high-throughput quantitative data from medical images to contribute to
precision medicine. Radiomic shape features have been shown to correlate with patient …

Background Combining radiotherapy (RT) with immuno-oncology (IO) therapy (IORT) may
enhance IO-induced antitumor response. Quantitative imaging biomarkers can be used to …

Image registration and segmentation are the two most studied problems in medical image
analysis. Deep learning algorithms have recently gained a lot of attention due to their …

Objectives To differentiate Glioblastomas (GBM) and Brain Metastases (BM) using a
radiomic features-based Machine Learning (ML) classifier trained from post-contrast three …

Purpose While there is still a significant need to identify potential biomarkers that can predict
which patients are most likely to respond to immunotherapy treatments, radiomic …