Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …

Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the
same drugs or surgical methods in patients with the same tumor may have different curative …

Multiomics data including imaging radiomics and various types of molecular biomarkers
have been increasingly investigated for better diagnosis and therapy in the era of precision …

The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has
given rise to the prominence of the tumor microenvironment (TME) as a critical area of …

The field of oncology has witnessed an extraordinary surge in the application of big data and
artificial intelligence (AI). AI development has made multiscale and multimodal data fusion …

Differentiating benign renal oncocytic tumors and malignant renal cell carcinoma (RCC) on
imaging and histopathology is a critical problem that presents an everyday clinical …

Contemporary deep learning-based decision systems are well-known for requiring high-
volume datasets in order to produce generalized, reliable, and high-performing models …

Objective This study aims to assess the value of biomarker based radiomics to predict IDH
mutation in gliomas. The patient cohort consists of 160 patients histopathologicaly proven of …

Multiple myeloma (MM) is one of the most common hematological malignancies affecting the
bone marrow. Radiomics analysis has been employed in the literature in an attempt to …

Background Multi-omics research has the potential to holistically capture intra-tumor
variability, thereby improving therapeutic decisions by incorporating the key principles of …