Big Data

Machine learning (ML) is transforming cancer imaging by enhancing diagnosis, automating image analysis, and improving treatment planning. This review explores key ML applications, including tumor detection, radiomics, multi-modal imaging, and therapy monitoring. We discuss fundamental ML techniques, deep learning architectures, and data preprocessing strategies essential for medical imaging. ML-driven approaches have improved tumor segmentation, feature extraction, and computer-aided diagnosis across various cancer types. In cancer therapy, artificial intelligence (AI) aids radiotherapy planning, treatment response prediction, and real-time image-guided interventions. However, challenges such as data scarcity, model bias, and regulatory hurdles limit clinical adoption. Emerging solutions include explainable AI, federated learning for data privacy, and quantum computing for advanced imaging. Addressing these challenges through interdisciplinary collaboration will accelerate AI integration into clinical practice, enhancing cancer diagnosis and treatment.

A machine learning model developed by researchers at the Johns Hopkins Kimmel Cancer Center filters out the biological noise in liquid biopsy samples, helping clinicians better match therapies to their patients’ tumors.

Cancer patients are particularly susceptible to Drug–Drug Interactions (DDIs) due to frequent polypharmacy in oncology care. Co-administered drugs can increase toxicity or reduce effectiveness, potentially causing serious adverse events—for example, QTc-prolonging Tyrosine Kinase Inhibitors with CYP3A4 inhibitors can lead to torsade de pointes. Traditional DDI identification methods are time-consuming and costly, relying mainly on in vitro and in vivo wet lab experiments, clinical studies, or post-marketing surveillance. Many Machine Learning (ML) and Deep Learning (DL)-based DDI prediction models have been developed in recent decades to accelerate the identification of DDIs. We systematically reviewed ML- and DL-based DDI prediction models involving anticancer drugs. Key features of anticancer drugs involved and details of prediction models, such as the prediction tasks (existence or types of DDI) and performance, were summarised, as well as a list of newly predicted DDIs. Additionally, verification through up-to-date DrugBank and Drugs.com confirmed 22 of 96 newly predicted potential DDI drug pairs, demonstrating the practical value of these techniques. By understanding the current DDI prediction studies from both methodological and clinical standpoints, novel approaches may be tailored to the unique characteristics of oncology drugs, thereby enhancing the clinical relevance and applicability of DDI predictions.

Artificial intelligence is enabling a new class of biomarkers by integrating histology, molecular data, imaging, and clinical records to generate scalable, biologically grounded insights for precision oncology.

In a landmark move for European oncology, Gustave Roussy, the continent's leading cancer center, has announced the deployment of GrayOS, a sophisticated care orchestration platform. This makes the prestigious French institution the first in Europe to adopt the technology from developer Gray Oncology Solutions to streamline its complex radiation therapy operations, signaling a major step toward a more efficient and patient-centric future for cancer treatment.

Many cancer patients are experiencing the crippling effects created by the pandemic. One example of this is illustrated by a recent survey in which a group of radiation oncologists in the U.S. describe how their patients presented with more advanced cancers over the course of the pandemic.1 Shutdowns and ongoing concerns about COVID-19 have delayed screenings and interrupted treatment for millions of people globally. And because early diagnosis and treatment are crucial to survival rates, the increase in cancer deaths could be staggering.

Artificial intelligence has been utilized in a variety of capacities to fundamentally transform oncology care since its inception. Technology that initially started out as a “hallucination-prone punch line” per Matthew Matasar, MD, chief of the Division of Blood Disorders at Rutgers Cancer Institute and Cancer Survivorship editorial advisory board member for ONCOLOGY, has since evolved into an “increasingly serious” facet of oncology care with a myriad of applications.

Head and neck cancers present significant challenges in diagnosis, treatment planning, and prognostication due to their heterogeneous nature and anatomical complexity. Artificial intelligence, particularly convolutional neural networks, has emerged as a transformative tool for developing clinical decision support systems that enhance precision in oncology. However, the breadth of AI applications in HNC has often obscured specific insights into their clinical impact. This umbrella review critically evaluates the role of AI-supported CDSS, with a focus on CNN-based models, in improving diagnostic accuracy, guiding treatment decisions, and refining prognostic predictions in HNC.

The extensive heterogeneity of cancer across biological scales necessitates a holistic approach beyond single-analyte methods. Integrating multi-omics data — from genomics to proteomics — with multimodal information, such as clinical records and medical imaging, offers a comprehensive, systems-level view of tumorigenesis. Artificial intelligence (AI) has emerged as the essential technology to decipher these complex, high-dimensional datasets, powering substantial advances in early diagnosis, precise patient stratification, prediction of therapeutic response and the elucidation of mechanisms of drug resistance. To translate these powerful predictive models into practice, explainable AI is critical for building clinical trust and generating novel, testable biological hypotheses. While challenges in data accessibility and model generalizability persist, the field is advancing toward patient-specific digital twins, promising to simulate individual disease trajectories and optimize treatments, thereby heralding a new era of precision oncology.

Drug discovery has been constrained by extended timelines and high costs, as the cumulative requirements of preclinical validation, multi-phase clinical trials, and regulatory approval have been imposed. Recently, computational modeling has been explored as a supportive approach to accelerate the identification and refinement of therapeutic candidates.

Whole-slide imaging is now a validated component of modern pathology practice, supported by increasingly robust foundation models that generalize across stains and scanners and by updated Clinical Laboratory Improvement Amendments (CLIA) guidance that clarifies digital and remote workflows. Just as importantly, the same biopsy can now support triage, prognosis, molecular testing, and even treatment selection without additional tissue handling. This convergence of validated digital slides, stronger AI backbones, and clearer regulatory footing sets the stage for the next shift: AI that doesn’t just detect cancer but informs treatment decisions.

An artificial intelligence (AI) algorithm is capable of estimating a woman's risk of developing breast cancer in the next four years, according to a study published in The Lancet Digital Health. The tool identified women at high risk of developing breast cancer, and nearly one in ten of those who scored in the top 2% according to the algorithm were diagnosed within four years, despite having been discharged from hospital. The tool used mammograms from nearly 400,000 women and was then tested with data from nearly 96,000 women in Australia. The results were confirmed in a Swedish population of more than 4,500 women.