MOTIVATION: Advances in high throughput technologies enabled large access to various types of omics. Each omics provides a partial view of the underlying biological process. Integrating multiple omics layers would help have a more accurate diagnosis....
Characterizing cancer presents a delicate challenge as it involves deciphering complex biological interactions within the tumor's microenvironment. Clinical trials often provide histology images and molecular profiling of tumors, which can help under...
BACKGROUND: Medical mistrust is a barrier to optimal cancer care. Analyzing social media posts where patients voice mistrust provides an opportunity to understand its variations and derive potential ways to address medical mistrust.
Applications of artificial intelligence (AI) and machine learning (ML) are rapidly developing to support the diagnosis and classification of pathology specimens. These tools rely on digitization of pathology glass slides as whole slide images, allowi...
American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting
Jun 1, 2025
The integration of artificial intelligence (AI) and machine learning (ML) in oncology clinical trials is rapidly evolving alongside the broader field. For example, AI-driven adaptive trial designs may allow for real-time modifications based on emergi...
International journal of biological macromolecules
Jun 1, 2025
Cysteine cathepsins have been discovered to be substantially expressed in multiple types of cancer. They play a key role in the progression and growth of these cancers, rendering them appealing targets for nanoscale delivery and noninvasive diagnosti...
Antibody-drug conjugates (ADCs) have revolutionized oncology by enabling the delivery of cytotoxic agents. However, persistent limitations in payload diversity and emerging drug-resistance mechanisms have spurred investigations into innovative payloa...
American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting
Jun 1, 2025
Artificial intelligence (AI) is transforming multidisciplinary oncology at an unprecedented pace, redefining how clinicians detect, classify, and treat cancer. From earlier and more accurate diagnoses to personalized treatment planning, AI's impact i...
This study achieved cancer type and survival time prediction by transforming transcriptomic features into feature maps and employing deep learning models. Using transcriptomic data from 27 cancer types and survival data from 10 types in the TCGA data...
European journal of oncology nursing : the official journal of European Oncology Nursing Society
Jun 1, 2025
PURPOSE: Artificial intelligence (AI) systems in palliative care have garnered attention and popularity in recent years. Understanding patient and family needs is crucial for developing and implementing AI systems in palliative care. Few studies in p...
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