Skin Cancer

Radiomics for predicting microsatellite instability-high status in colorectal cancer: a systematic review and meta-analysis.

OBJECTIVE: Microsatellite instability (MSI) has emerged as a key predictive biomarker for chemotherapy and immunotherapy response, and as a prognostic indicator in colorectal cancer (CRC). The current clinical standard for MSI detection relies on polymerase chain reaction (PCR) or immunohistochemical analysis of tumor biopsy specimens. CT, PET-CT, and MRI-based radiomics models present a promising...

Application of a machine learning-based PANoptosis-immune-related gene risk score model in prognostic stratification and immunotherapy benefit prediction for glioblastoma.

This study developed a risk score model using PANoptosis and immune-related genes to predict glioblastoma (GBM) prognosis. Utilizing TCGA data and 66 PANoptosis regulatory network-related genes, patients were clustered into two subtypes. Machine learning identified 11 core PANoptosis-Immune-Related Genes (PIRGs). Single-cell analysis revealed their dysregulated expression in GBM. External validati...

Integrating single-cell transcriptomics and machine learning to predict breast cancer prognosis and immunotherapy sensitivity: a study based on proliferating T cells.

Multi-Omics and Machine Learning Analyses Reveal PIK3CG, PRKCD, and TRIM22 as Potential Markers of Poor Prognosis and Immune Activation in Glioblastoma.

BACKGROUND: Glioblastoma (GBM) is one of the most aggressive brain tumors with a poor prognosis despite current treatment modalities. This study aimed...

A machine learning-driven framework integrating cell death and senescence signatures for multi-target drug design and immunotherapy optimization in ovarian cancer.

Ovarian cancer (OC) remains therapeutic challenge due to its complex molecular heterogeneity and therapy-induced adaptive resistance. While non-apopto...

Reinventing miRNA cancer therapy through immunomodulation and AI.

Despite the rational therapeutic premise of microRNA (miRNA) replacement or inhibition for cancer treatment, its clinical translation remains signific...

A macrophage co-expression signature enables robust prognostic prediction in glioblastoma.

BACKGROUND: Glioblastoma (GBM) exhibits profound cellular heterogeneity and a highly immunosuppressive microenvironment in which tumor-associated macr...

3D-printed real-time biosensing system integrating deep learning for label-free in vitro T cell culture analysis.

Recent advances in T cell-based immunotherapies highlight the urgent need for precise and dynamic monitoring across the entire cell culture pipeline. ...

Gene-level gut microbiome signatures as predictive biomarkers for response to immune checkpoint inhibitors across multiple cancer types.

Targeting programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) with immune checkpoint inhibitors (ICIs) has...

Deep Learning and Experimental Validation of BRCA1-Associated Protein-1 Missense Variant Pathogenicity in Uveal Melanoma.

OBJECTIVE: To benchmark the pathogenicity predictions of AlphaMissense, a deep learning model, against high-throughput functional scores from saturati...

Nanomaterials reshaping cancer radiotherapy: Radiosensitization mechanisms, delivery and theranostic platforms, multimodal synergy, and clinical translation strategies.

Radiotherapy (RT) remains a cornerstone of cancer management but is fundamentally constrained by normal tissue toxicity, intrinsic and acquired radior...

Crosstalk between immune and programmed cell death and their application in colorectal cancer risk stratification.

Programmed cell death (PCD) and cellular immunity play pivotal roles in colorectal cancer (CRC) progression; understanding their crosstalk and identif...

Optimal treatment selection for hepatocellular carcinoma in the era of immunotherapy.

Immunotherapy has revolutionized hepatocellular carcinoma (HCC) management, necessitating personalized strategies in current guidelines. Despite curat...

Clinician-deployable deep hypergraph model integrating clinical and CT radiomics predicts immunotherapy outcomes in NSCLC.

Current image-based deep learning models that predict the benefits of immunotherapy in non-small cell lung cancer (NSCLC) require high-performance har...

A Roadmap to Transform Lung Cancer Outcomes: Priorities in Biology, Therapeutic Innovation, Early Detection, Prevention and Interception.

Advances in targeted therapies, immunotherapy, and early detection have revolutionized lung cancer treatment and extended survival. Nonetheless, lung ...

Imaging-based machine learning for the diagnosis and prognosis of uveal melanoma: a systematic review and meta analysis.

[Current status and future perspectives of precision treatment for locally advanced rectal cancer].

The treatment paradigm for locally advanced rectal cancer is undergoing a fundamental transformation from the traditional fixed triad of "radiotherapy...

[Research progress in clinical pathology and molecular mechanisms of pancreatic adenosquamous carcinoma].

Pancreatic adenosquamous carcinoma (PASC) is a rare exocrine malignancy of the pancreas with an increasing incidence, histologically defined by the co...

Macrophage polarization as a therapeutic target in lung diseases: From pathogenesis to precision intervention.

Macrophages serve as core effector cells of the innate immune system, exerting dual regulatory roles in pulmonary homeostasis maintenance and disease ...

A network-based deep learning model integrating subclonal architecture for therapy response prediction in cancer.

Predicting treatment response remains challenging in oncology, particularly given the growing diversity of therapeutic options. Despite efforts using ...