Other Cancers

Therapeutic Hybrid Intelligence with Neural and Knowledge-based Expert Reasoning for SRS (THINKERS): a mixture-of-experts AI model for vestibular schwannoma.

BACKGROUND: Outcome prediction after Gamma Knife radiosurgery (GKRS) for vestibular schwannoma remains largely guided by tumor size, Koos grade, baseline symptoms, cochlear dose constraints, and institutional experience rather than individualized estimates of tumor progression and functional outcomes. We developed THINKERS-VS, a mixture-of-experts (MoE) artificial intelligence framework for progre...

Evolution of artificial intelligence in breast cancer pathology: a bibliometric analysis.

BACKGROUND: Breast cancer is the most common malignant tumor affecting women, and pathology serves as the primary method for its diagnosis. In recent years, artificial intelligence (AI) has increasingly been applied to the pathological diagnosis of breast cancer. This study, therefore, aims to conduct a bibliometric analysis of the literature on artificial intelligence in breast cancer pathology (...

SPADE: A Deep Learning Framework for Spatial Mapping and Quantitative Cell-Cell Interaction Inference.

Spatial transcriptomics (ST) enables the study of tissue architecture by resolving gene expression in space, but current ST platforms are constrained ...

A selective deep learning framework for pressure injury staging with calibrated confidence and automated clinical documentation.

OBJECTIVES: Pressure injuries are common chronic wounds that require accurate staging to guide management. Deep learning has shown promise for automat...

Deep Learning of CT Imaging Predicts PD-L1 Expression and Immunotherapy Response in Metastatic NSCLC: A Multi-Center Study.

Immune checkpoint inhibitors (ICIs) benefit only a subset of patients with metastatic non-small cell lung cancer (NSCLC), but current selection relies...

Mapping the TCR landscape: computational tools empowering translational immunology and therapy design.

T cell receptors (TCRs) are central to adaptive immunity, yet their vast sequence and structural diversity present a significant challenge to fully un...

End-to-End PET/CT Interpretation and Quantification with an LLM-Orchestrated AI Agent: A Real-World Pilot Study.

Although deep learning models have improved individual PET analysis, image processing, and quantification tasks, end-to-end automation from raw DICOM ...

[Reirradiation: A new therapeutic paradigm in oncology].

Reirradiation (reRT) has become an essential therapeutic option for selected patients with locoregional recurrences, when surgery or systemic therapie...

Human serum albumin profiling by top-down analysis enables multi-class liver fibrosis staging: a cross-platform validation study.

Chronic liver disease (CLD) affects millions worldwide, yet accurately staging its progression without liver biopsy remains a major clinical challenge...

Deep Learning on Tumor Habitat Sub-regions vs. Whole-Tumor ROI: A Comparative Study of Fusion Strategies for Breast Cancer Molecular Subtype Prediction.

This study aimed to compare deep learning models based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) whole-tumor and habitat regio...

Multifaceted role of histopathology in liver transplantation for hepatocellular carcinoma: A narrative review towards precision prognosis.

Hepatocellular carcinoma is a leading cause of cancer mortality globally. Liver transplantation is considered the best curative treatment for selected...

Inferring translational efficiency from transcriptomes improves noncanonical neoantigen prioritization and cancer patient stratification.

Accurate assessment of protein translation is crucial for understanding disease variant functions, but mRNA-protein discrepancy limits transcriptomics...

Deep integration of clinical metadata with [18F]FDG PET/CT imaging for histological subtyping in non-small cell lung cancer: a multi-center study.

PURPOSE: To develop and validate a multimodal deep learning framework that integrates clinical metadata with [18F]FDG PET/CT imaging to resolve overla...

ULK1's role in cancer progression and its emerging therapeutic potential.

Macroautophagy (autophagy) enables cellular stress adaptation by degrading damaged components; ULK1, a serine/threonine kinase, initiates this process...

Machine learning algorithms develop a tumor-educated platelets-related gene signature to predict colorectal cancer prognosis and therapy response.

Tumor-educated platelets (TEPs) have recently emerged as an important component of liquid biopsy, yet the clinical relevance in colorectal cancer (CRC...

Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.

Cancers of unknown primary (CUP) refer to a highly heterogeneous group of metastatic tumors whose primary site remains undetectable despite comprehens...

Automated classification of clinical T staging pulmonary oncology from chest CT reports: a natural language processing framework for real-world data utilization.

BACKGROUND: Clinical Tumor, Node, and Metastasis (cTNM) classification is vital for predicting treatment efficacy and prognosis in patients with cance...

Intelligent three-dimensional imaging in pathology: Applications and developments.

Conventional two-dimensional (2D) pathology relies on a limited number of tissue sections and therefore provides information from isolated planes, whi...

Visceral adipose tissue inflammation in endometrial cancer.

BACKGROUND: Obesity is the largest risk factor for endometrial cancer. Body Mass Index (BMI) does not fully capture obesity's metabolic and inflammato...

Single-cell transcriptomic analysis reveals the T lymphocyte microenvironment and identifies HOPX as a hub gene in membranous nephropathy.

BACKGROUND: Membranous nephropathy (MN) is an autoimmune disease characterized by immune complex deposition and progressive renal function impairment....