Machine learning (ML) models have been increasingly used in clinical oncology for cancer diagnosis, outcome predictions, and informing oncological therapy planning. The early identification and prompt treatment of cancer, revolutionized by rapid and precise analysis of radiological and pathological images of tissues using ML algorithms, can improve the likelihood of survival and quality of care provided to cancer patients.

Oncology research poses several challenges, from detection and diagnosis to treatment and prognosis. To address some of these challenges, teams are turning to artificial intelligence and machine learning, which have the capabilities to revolutionize many human endeavors, including cancer research.

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Advances in computational algorithms and tools have made the prediction of cancer patient outcomes using computational pathology feasible. However, predicting clinical outcomes from pre-treatment histopathologic images remains a challenging task, limited by the poor understanding of tumor immune micro-environments. In this study, an automatic, accurate, comprehensive, interpretable, and reproducible whole slide image (WSI) feature extraction pipeline known as, IMage-based Pathological REgistration and Segmentation Statistics (IMPRESS), is described.

Immune checkpoint inhibitors have demonstrated PFS and OS benefits compared to sunitinib in patients with metastatic clear cell RCC. Current research lines are using genomic and transcriptomic data to identify predictive biomarkers for response to treatment. Since healthcare domain datasets are heterogenous in nature with diverse patient populations, deep learning approaches are needed to help better select patients that would benefit from personalized approaches.

Artificial intelligence (AI) technology is integrated into the services and products we use every day in our personal and professional lives, although we do not think about it very often. Many of us may not be aware of the AI-fueled personal assistants and tools designed to improve our efficiency and productivity that are readily available, linked to or embedded within computer software programs we use routinely. However, as we continue to learn about applications for AI in oncology, we can better understand how these approaches solve problems and change how treatments are being developed and made available to patients.

Learning Genomics by Fundación ECO & Health in Code

A multi-modal artificial intelligence model may have the ability to accurately stratify patients with prostate cancer into risk subgroups.

Learning Genomics by Fundación ECO & Health in Code

Learning Genomics by Fundación ECO & Health in Code