Cancers represent complex ecosystems comprising tumor cells and a multitude of non-
cancerous cells, embedded in an altered extracellular matrix. The tumor microenvironment …

The increasing availability of biomedical data from large biobanks, electronic health records,
medical imaging, wearable and ambient biosensors, and the lower cost of genome and …

In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …

Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …

Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict
patient outcomes, and inform treatment planning. Here, we review recent applications of ML …

Immunotherapy is used to treat almost all patients with advanced non-small cell lung cancer
(NSCLC); however, identifying robust predictive biomarkers remains challenging. Here we …

During the diagnostic process, clinicians leverage multimodal information, such as the chief
complaint, medical images and laboratory test results. Deep-learning models for aiding …

Patients with high-grade serous ovarian cancer suffer poor prognosis and variable response
to treatment. Known prognostic factors for this disease include homologous recombination …

The rapid development of precision medicine in recent years has started to challenge
diagnostic pathology with respect to its ability to analyze histological images and …

With biotechnological advancements, innovative omics technologies are constantly
emerging that have enabled researchers to access multi-layer information from the genome …