Skin Cancer

An online cutaneous melanoma risk screen tool.

To develop and deploy a publicly accessible online risk estimation tool for cutaneous melanoma that prioritizes high recall to minimize missed diagnoses, using real-world clinical photographs rather than dermoscopic images, and to bridge the gap between artificial intelligence research and clinical implementation. A 'segmentation-first, then-classification' strategy was adopted. Lesions were segme...

Artificial intelligence-based prediction of claudin 18.2 expression and immune phenotype from routine histology to guide treatment decisions in patients with gastric cancer.

BACKGROUND: First-line treatment of gastric cancer is evolving with the integration of immune checkpoint inhibitors (ICIs) and targeted agents, complicating biomarker stratification. Claudin 18.2 (CLDN18.2) is an established target for zolbetuximab; however, immunohistochemistry (IHC) is limited by tissue requirements, cost, and turnaround time. Artificial intelligence (AI) analysis of hematoxylin...

Targeting galactose metabolic reprogramming overcomes immunotherapy resistance in KRAS-mutant lung adenocarcinoma: Integrative multi-omics and machine learning approaches.

BACKGROUND: KRAS-mutant lung adenocarcinoma (LUAD) is associated with aggressive phenotypes and therapy resistance, which highlights an urgent need to...

Validation of AI-enabled surrogate models in quantitative systems pharmacology: a practical, context-of-use-driven review.

Quantitative systems pharmacology (QSP) models support model-informed drug development but are computationally expensive, limiting workflows requiring...

AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.

BACKGROUND: Neoantigens-tumor-specific peptides generated by somatic mutations-are central targets of effective anticancer T cell immunity and underpi...

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 ...

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...

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...

Emerging artificial intelligence advances in oncology: latest updates from the 2026 AACR annual meeting.

Advances presented at the 2026 American Association for Cancer Research (AACR) Annual Meeting highlight a shift from standalone artificial intelligenc...

Multidimensional analysis of tumour immune microenvironment in HCC: prognostic significance and implications for immunotherapy.

BACKGROUND: The combination of immune checkpoint inhibitors (ICIs) and anti-angiogenic agents represents the standard first-line therapy for patients ...

New approach methodologies (NAMs) for preclinical and translational evaluation of mRNA-lipid nanoparticle (LNP) therapeutics.

Messenger RNA-lipid nanoparticle (mRNA-LNP) therapeutics have emerged as a versatile drug modality, enabling in vivo protein expression for vaccines, ...

A machine-learning-derived multi-cell death mode signature predicts melanoma prognosis and reveals an immune-cold tumor microenvironment.

BACKGROUND: Regulated cell death programs influence melanoma progression and antitumor immunity, yet a robust prognostic model integrating multiple ce...

Leveraging multimodal cancer immunotherapy to amplify the efficacy of oncolytic viruses.

Oncolytic viruses (OVs) represent a versatile platform for cancer immunotherapy, capable of selectively infecting and lysing tumor cells while trigger...

Forging New Pathways in Oncology: Strategic Insights from the 17th Annual Frontiers in Cancer Science Conference.

The 17th Annual Frontiers in Cancer Science (FCS) conference (2025) highlighted the convergence of multiomics, computational biology, and ancestry-spe...

Single-cell and machine learning-based neural regulation signature for prognosis prediction and immunotherapy response in lung adenocarcinoma.

OBJECTIVE: Lung adenocarcinoma (LUAD) molecular heterogeneity limits traditional prognostic models. Given the emerging role of neural regulation (NR) ...

Explainable machine learning to predict immunotherapy outcomes in metastatic renal cell carcinoma - Meet-URO 15-AI study.

Metastatic renal cell carcinoma remains clinically challenging because of heterogeneous outcomes and limited predictive biomarkers for immunotherapy. ...

ADC Target Profiling in NSCLC: Generalizable AI Separates TROP-2 and cMET Phenotypes.

PURPOSE: Antibody-drug conjugates (ADC) targeting trophoblast cell surface antigen 2 (TROP-2) and cMET are entering clinical trials in non-small cell ...

AI in clinical diagnostics in dermatology: applications, validation, and real-world use cases.

Artificial intelligence (AI) has moved from proof-of-concept studies in dermatology to selective, real-world clinical use, particularly in image-based...

Preclinical and Virtual Models of Mucosal Melanoma: Bridging Translational Gaps in a Rare and Lethal Cancer.

Mucosal melanoma (MM) is a rare and lethal subtype of melanoma, disproportionately affecting Asian populations and exhibiting distinct clinicopatholog...

Metabolic reprogramming of myeloid cells in cancer: from lactate-NAMPT axis to AI-guided therapeutics.

Myeloid cells-including macrophages, monocytes, neutrophils and dendritic cells-are metabolically plastic sentinels that shape the tumor microenvironm...