Oncology/Hematology

Artificial Intelligence Among U.S. Hematology Oncology Fellows: A Multicenter Survey of Education, Attitudes, and Clinical Use.

PURPOSE: A prior national survey of U.S. hematology/oncology (H/O) fellowship curricula demonstrated substantial heterogeneity and limited protected didactic time. Since then, artificial intelligence (AI), including large language models (LLM) and ambient tools, has become increasingly integrated into trainee education and clinical practice. We conducted a multi-center survey to assess the use of ...

Cancer workforce-a global crisis: a Lancet Oncology Commission.

The shortage of well-trained personnel to deliver cancer care and conduct research remains a major obstacle to reducing disparities in cancer survival between high-income countries and low-income and middle-income countries (LMICs). This Commission set out to provide actionable guidance for strengthening the global cancer workforce with an emphasis on reducing these disparities. To better understa...

TF-DWGNet: a directed weighted graph neural network with tensor fusion for multi-omics cancer subtype classification.

Integration and analysis of multi-omics data provide valuable insights for improving cancer subtype classification. However, such data are inherently ...

Spatially resolved single cell analysis suggests an APOE NCF1 associated immunosuppressive niche and its prognostic signature in thyroid cancer.

BACKGROUND: Thyroid carcinoma (TC) presents a rising global incidence, with a subset of cases progressing aggressively despite standard therapies. The...

Integrating machine learning and spatiotemporal transcriptomics to build a diagnostic model for osteosarcoma metastasis and to decipher the role of necroptosis genes.

OBJECTIVE: To develop a diagnostic model for osteosarcoma metastasis and elucidate the spatiotemporal role of necroptosis genes. METHODS: Multi-omics ...

Techniques and outcomes review of the endoscopic management of gastrointestinal bleeding.

INTRODUCTION: Gastrointestinal (GI) bleeding is a frequent and potentially life-threatening emergency that imposes a substantial healthcare burden wor...

Assessing Image Quality and Diagnostic Performance of Quadruple-Low Coronary CT Angiography with Deep Learning in High-BMI Patients.

RATIONALE AND OBJECTIVES: To examine the feasibility of a quadruple-low protocol in coronary computed tomography angiography (CCTA) assisted by the de...

Integrating handcrafted and deep learning MRI signatures: an interpretable framework for predicting chemotherapy benefit in glioma.

The molecular and spatial heterogeneity of gliomas severely limits accurate prediction of postoperative adjuvant chemotherapy efficacy, representing a...

Automated leukemia detection from microscopic images using deep transfer learning with explainable AI-based analysis.

Leukemia, a life-threatening hematological malignancy, necessitates early and accurate diagnosis for effective treatment. Traditional microscopic exam...

Exploratory Comparison of Meld, Meld-Na, and Meld 3.0 Scores for Prognostic Assessment at Diagnosis in Hepatocellular Carcinoma Using Machine Learning Approaches.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver tumor. Despite efforts to mitigate risk factors and implement surveillance...

Advancing child health: forecasting the next great research achievements.

Advances in pediatric research have improved the lives of infants, children, adolescents, and society at large. A decade ago the American Academy of P...

Screening of the key gene PAMR1 in breast cancer and its immunological and pharmacogenomic characteristics.

Breast cancer (BC) is a highly heterogeneous malignancy with a complex pathogenesis involving multiple molecular pathways and cellular interactions. T...

Deep learning-based cervical cancer T-staging using MRI: multi-structure segmentation and classification.

AIMS: Cervical cancer has high incidence and mortality, seriously threatening women's survival and quality of life. Radiologists currently rely mainly...

Extracellular vesicle and particle biomarkers in cancer: a machine learning blueprint for liquid biopsy.

The combination of machine learning and liquid biopsy is rapidly promoting the development of precision cancer diagnosis. Extracellular vesicles and p...

Development and external validation of multiple machine learning-based models for breast cancer-specific survival prediction in postoperative patients with invasive breast cancer: a study based on the SEER database and an external cohort.

BACKGROUND: Patients with invasive breast cancer (IBC) account for the vast majority of breast cancer cases and exhibit significant heterogeneity; hen...

Organoids as next-generation models for investigating intracranial tumours.

Tumour organoids have emerged as important tools in brain tumour research, addressing long-standing limitations of conventional two-dimensional cultur...

What's next for VI-RADS? Updates and future perspectives from the ACR VI-RADS steering committee.

Since the introduction of the Vesical Imaging-Reporting and Data System (VI-RADS), MRI has become an important imaging modality in the management of p...

ABCC2 as a novel therapeutic target in lung adenocarcinoma: a machine learning-driven discovery linking ammonia metabolism to prognosis and drug resistance.

BACKGROUND: Ammonia, long regarded as a metabolic waste product, has recently been recognized as a pivotal oncometabolite in the tumor microenvironmen...

Relationship between ileocolic arterial stump length and lymph node yield - a retrospective study using machine learning.

PURPOSE: In right-sided colon cancer surgery, ileocolic artery stump length may reflect the extent of mesenteric resection and lymph node harvest. Thi...

Decoding the microbiome: artificial intelligence-targeted gut microenvironment breakthroughs in personalized cancer therapy.

The gut microbiome functions as a key regulator of tumorigenesis and progression, thereby modulating tumor development and treatment outcomes (includi...