Using artificial intelligence (AI), European researchers have developed an algorithm that they say successfully detects molecular changes in tumor cells and tissues from microscopic slides in many different cancers. "What is quite remarkable is that our algorithm can automatically link the histological appearance of almost any tumor with a very broad set of molecular characteristics and with patient survival," said Moritz Gerstung, PhD, group leader at EMBL European Bioformatics Institute. Institute researchers collaborated on the study with scientists from the Wellcome Sanger Institute and Addenbrooke's Hospital in Cambridge, UK. The pan-cancer analysis is believed to be the largest to date to train computer vision to "see" and combine digital pathology with the genetic changes that occur in cells as malignancies take hold. Ordinarily, histopathologists examine the appearance of cancer tissue under a microscope first, then geneticists perform molecular sequencing separately to analyze changes in the genetic code.

CHICAGO--An artificial intelligence machine-learning program has demonstrated the ability to accurately forecast which head and neck cancer patients are likely to experience severe weight loss, necessitating the use of a feeding tube, researchers at MD Anderson Cancer Center in Houston told attendees at the 2019 ASTRO Annual Meeting (Abstract 141). It marks the first time that such a sophisticated "self-teaching" computer algorithm has accurately identified patients likely to develop problems eating, said Jay Reddy, MD, PhD, Assistant Professor of Radiation Oncology and lead author of the study. "With head and neck radiation, a lot of toxicity occurs; however it's not always clear which patients will experience serious side effects," he told a press conference. Reddy and his colleagues used machine learning models to analyze large datasets from three sources--electronic health records, an internal web-based patient charting tool, and the hospital's records and verification system--in an effort to discern and eventually predict patients with weight loss exceeding 10 percent of total body weight, the need for a feeding tube, and/or any unplanned hospitalization within 3 months of radiation. Machine learning is a relatively powerful application of artificial intelligence (AI)–think facial recognition software--by which a computer program can automatically learn and improve itself by analyzing large quantities of data.

SAN DIEGO--A novel smartphone app that uses artificial intelligence (AI)-based algorithms significantly reduced pain and pain-related hospital admissions in a group of patients with various metastatic, solid-organ cancers, according to results from a randomized clinical trial reported at the 2018 Palliative and Supportive Care in Oncology Symposium sponsored by ASCO (Abstract 76). The ePAL is a smartphone app that regularly monitors pain and uses AI to differentiate urgent from non-urgent issues in real time. It also collects and assesses patient-reported pain severity three times each day while providing daily tips on pain-reduction strategies. It is one of the first apps to utilize both patient-reported outcomes and AI clinical algorithms, according to the researchers. The app was developed and tested in 56 pain patients and a matched group of 56 control patients who received regular pain management care by investigators at the Massachusetts General Hospital (MGH) Cancer Center, the hospital's Division of Palliative Care, and Partners HealthCare Pivot Labs, which is a new center of excellence that focuses on human-centered preventive care and chronic pain management.

Every industry has buzzwords that fan out across culture and become synonymous with a job well done. The '80s business culture brought us "synergy," which is now used to describe any systems that work together well, albeit sometimes mockingly. During the 1990s, the dot-com boom came and brought with it "enterprise solutions," and now in 2018, it seems as though everyone is talking about artificial intelligence. The term artificial intelligence (AI) came about in 1956, and since then, AI has progressed greatly. The first advances in AI focused on the construction of neural networks.

"Immunotherapy has profoundly changed the management of multiple cancers," said Roger Sun, MD, PhD candidate under Eric Deutsch, MD, PhD, and Charles Ferté, MD, PhD, at the laboratory INSERM U1030 at Gustave Roussy in Villejuif, France. "However, most patients do not respond to this type of treatment. That is why we need to identify biomarkers that allow identification of patients who are most likely to respond to immunotherapy." Studies utilizing biopsy samples of tumor tissues have confirmed the link between immune-cell infiltration into tumors and patients' treatment responses; however, Sun noted, because cancers are heterogeneous, biopsies only reflect the local aspect of the tumor. "Medical computational imaging, also known as radiomics, is a new field of research that aims to translate standard imaging like CT, MRI, or PET into objective data and use them as biomarkers," he explained.