Oncology/Hematology

Investigating tryptophan metabolism in colorectal cancer using Single-cell RNA sequencing based on machine learning techniques.

BACKGROUND: Colorectal cancer (CRC) is characterized by genetic variation, epigenetic alterations, microenvironmental imbalance, and metabolic reprogramming. Currently, abnormalities amino acid metabolism has been shown to play an important role in the occurrence and progression of CRC. METHODS: AUCell, UCell, singscore, ssGSEA and AddModuleScore algorithms were used to determine the pattern of tr...

Artificial intelligence-detected HER2 strong-positive tumor proportion predicts FISH positivity and treatment response in breast cancer.

Human epidermal growth factor receptor 2 (HER2)-targeted therapies have revolutionized breast cancer treatment, necessitating standardized HER2 testing. However, current immunochemistry-based HER2 assessment faces challenges due to subjectivity and variability among observers, prompting the guidance of artificial intelligence (AI). We evaluated AI's efficacy in HER2 status assessment and treatment...

Hierarchical Flower-Like UiO-66@TiO2 Heterostructure for On-Plate Exosome Enrichment and In Situ Metabolic Fingerprinting Toward Prostate Cancer Diagnosis.

The efficient capture and sensitive detection of exosomes from complex biological samples remain critical challenges for liquid biopsy-based cancer di...

Machine Learning-Assisted SERS Quantification of Sialylated Alpha-Fetoprotein: From Single-Cell Analysis to Hepatocellular Carcinoma Risk Assessment.

Sialylated alpha-fetoprotein (sAFP) is a very potential marker for the pathogenesis exploration and clinical assessment of hepatocellular carcinoma (H...

Vascular reprogramming in cancer: engineering the tumor microenvironment.

Tumor vasculature has traditionally been viewed as structurally and functionally abnormal and, therefore, is primarily targeted for inhibition. Howeve...

Concordance between multidisciplinary tumor board decisions and AI-based recommendations in endometrial cancer: impact of discordance direction on clinical outcomes.

BACKGROUND: The clinical relevance of concordance between multidisciplinary tumor board (MDT) decisions and artificial intelligence (AI)-based treatme...

Evaluating the Accuracy of ChatGPT-4o in Addressing Complex Clinical Questions Based on NCCN Guidelines for Rectal Adenocarcinoma.

INTRODUCTION: The management of rectal adenocarcinoma requires navigation of complex, branching guideline pathways encompassing neoadjuvant sequencing...

From tumor-centric to ecosystem-based hypotheses in brain tumor research and care: Proceedings from The second Brain Tumor Meeting by the Sea organized by Julie Gavard and Eric Chevet, held in St Malo (France), 23-25 March 2026.

Brain tumors remain among the most lethal cancers, largely due to their remarkable heterogeneity, plasticity, and resistance to therapy. The second Br...

Integrative single-cell and multi-omics analysis of ZBTB21-mediated serine metabolism in colorectal cancer: from metabolic reprogramming to immune microenvironment modulation.

BACKGROUND: Colorectal cancer (CRC) exhibits pronounced biological diversity, a feature increasingly attributed to alterations in cellular metabolic r...

Macrophage heterogeneity in breast cancer microenvironment and development of a prognostic model: exploring TFF1 as a novel therapeutic target.

BACKGROUNDS: Breast cancer (BRCA) represents the most prevalent malignancy globally, with projections indicating 3.2 million new cases anticipated by ...

Intraoperative technological advances and new frontiers in precision glioma surgery.

BACKGROUND: Diffuse gliomas remain among the most surgically challenging tumors, characterized by their infiltrative nature, proximity to eloquent bra...

AI-enabled clinical decision support in breast cancer care: a blinded multicenter benchmarking study comparing medically specialized with a general-purpose system.

Medically specialized AI systems that have obtained regulatory clearance as medical devices can be deployed for patient-specific clinical decision sup...

Identification of exosome-related genes signature based on bioinformatics and machine learning for prognostic prediction in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is a prevalent malignant tumor with increasing incidence and mortality rates worldwide. Exosomes are secretory ves...

Metabolic phenotypes of doxorubicin-induced cardiotoxicity among patients with breast cancer.

BACKGROUND: Doxorubicin (DOX)-based chemotherapy has improved survival outcomes in breast cancer patients but is often limited by doxorubicin-induced ...

A novel prognostic zinc finger gene model for hepatocellular carcinoma via machine learning.

BACKGROUND: Hepatocellular carcinoma (HCC) is a highly lethal malignancy with poor prognosis, and effective biomarkers for predicting immunotherapy re...

Single-cell spatial landscape of aggrephagy activity stratifies hepatocellular carcinoma neutrophils and delivers a 5-gene diagnostic panel for patient stratification.

BACKGROUND: Hepatocellular carcinoma (LIHC) features a complex tumor microenvironment (TME) where tumor-associated neutrophils (TANs) show significant...

Rethinking cancer screening in Italy: equity, performance and innovation.

OBJECTIVES: To examine the current performance of cancer screening programs in Italy, evaluate the evolving regulatory and clinical guideline framewor...

Advances in CT and MRI for Yttrium-90 Radioembolization of Hepatocellular Carcinoma.

RATIONALE AND OBJECTIVES: Transarterial radioembolization (TARE) is increasingly used for patients with hepatocellular carcinoma (HCC) across Barcelon...

A Pulmonary Ultrasound Dataset for Tumor Segmentation and Classification.

Pulmonary tumor diagnosis with ultrasound is clinically valuable yet technically challenging. Although deep learning has shown promise in supporting s...

Enhancing brain tumor detection through deep learning and explainable AI techniques.

Brain tumors are a leading cause of cancer-related mortality, and manual MRI screening remains time-consuming and observer-dependent. Deep learning (D...