Big Data

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.

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.

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

Low-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalized future cancer risk assessment could focus approaches toward those most likely to benefit. We hypothesized that a deep learning model assessing the entire volumetric LDCT data could be built to predict individual risk without requiring additional demographic or clinical data.

Electronic nudges delivered to health care clinicians based on a machine learning algorithm that predicts mortality risk quadrupled rates of conversations with patients about their end-of-life care preferences, according to the long-term results of a randomized clinical trial published by Penn Medicine investigators in JAMA Oncology today. The study also found that the machine learning-triggered reminders significantly decreased use of aggressive chemotherapy and other systemic therapies at end of life, which research shows is associated with poor quality of life and side effects that can lead to unnecessary hospitalizations in their final days.

Too often, searching for the right cancer treatment is a guess-and-check process, as doctors move through a list of costly therapies that may or may not work until they find one that sticks. But a newly discovered biomarker that can be calculated by an artificial intelligence algorithm from a routine CT scan could one day make that process much more efficient.

A new study shows that an ensemble of machine-learning models can predict six-month mortality for patients with ovarian cancer using patient-reported outcome data.

Rhabdomyosarcoma (RMS) is an aggressive soft-tissue sarcoma, which primarily occurs in children and young adults. In this study, we utilized convolutional neural networks (CNN) to learn histologic features associated with driver mutations and outcome using hematoxylin and eosin (H&E) images of RMS.

Use of artificial intelligence in medical imaging has increased rapidly across health care specialties, and oncology is no exception. Researchers have developed AI tools to assist in detection and diagnosis of several cancers, such as those of the breast, colon, liver, lung and prostate.