Cancer is a fatal disease and the second most cause of death worldwide. Treatment of
cancer is a complex process and requires a multi-modality-based approach. Cancer …

As we enter the era of digital interdependence, artificial intelligence (AI) emerges as a key
instrument to transform health care and address disparities and barriers in access to …

Simple Summary Within the rapidly evolving landscape of Machine Learning in the medical
field, this paper focuses on the forefront advancements in neuro-oncological radiology. More …

Purpose Manual cohort building from radiology reports can be tedious. Natural Language
Processing (NLP) can be used for automated cohort building. In this study, we have …

Studies of dataset development in machine learning call for greater attention to the data
practices that make model development possible and shape its outcomes. Many argue that …

Background Existing research on medical data has primarily focused on single time-points
or single-modality data. This study aims to collect all data generated during radiotherapy …

Radiomics is a progressive field aiming to quantitatively assess the diversity within and
between tumors using image analysis. It holds tremendous promise for tracking tumor …

Personalized medicine holds immense potential for transforming healthcare by tailoring
treatments to individual patients, but its realization is hindered by privacy concerns and data …

Radiomics and radiogenomics are still new fields to be explored in oncology, although there
are several platforms and tools already developed, or in development. This review aimed to …

This study assessed the use of pre-trained language models for classifying cancer types as
lung (class1), esophageal (class2), and other cancer (class0) in radiology reports. We …