AIMC Topic: Immunotherapy

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Machine learning developed regulatory T cells-related signature for prognosis and immunotherapy benefit in oral squamous cell carcinoma.

American journal of otolaryngology
BACKGROUND: Oral squamous cell carcinoma (OSCC) is one of the most common malignancies with poor clinical outcome. Regulatory T cells (Tregs) have a dual role in maintaining immune homeostasis and suppressing anti-tumor immunity. The role of Tregs re...

Artificial intelligence networks for assessing the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features: a systematic review and meta-analysis.

BMC gastroenterology
BACKGROUND AND AIM: Artificial intelligence (AI) networks offer significant potential for predicting immunotherapy outcomes in gastrointestinal cancers by analyzing genetic mutation profiles. Their application in prognosis remains underexplored. This...

Mapping the rapid growth of multi-omics in tumor immunotherapy: Bibliometric evidence of technology convergence and paradigm shifts.

Human vaccines & immunotherapeutics
This study aims to fill the knowledge gap in systematically mapping the evolution of omics-driven tumor immunotherapy research through a bibliometric lens. While omics technologies (genomics, transcriptomics, proteomics, metabolomics)provide multidim...

Role of artificial intelligence in advancing immunology.

Immunologic research
Artificial intelligence (AI) has revolutionized various biomedical fields, particularly immunology, by enhancing vaccine development, immunotherapies, and allergy treatments. AI helps identify potential vaccine candidates and predict how the body rea...

Machine learning based radiomic models outperform clinical biomarkers in predicting outcomes after immunotherapy for hepatocellular carcinoma.

Journal of hepatology
BACKGROUND & AIMS: Atezolizumab plus bevacizumab (A/B) is a first-line therapy for unresectable hepatocellular carcinoma (HCC). Only a small proportion of patients respond to treatment. This study integrated radiomic and clinical data derived from ro...

Optimizing Immunotherapy: The Synergy of Immune Checkpoint Inhibitors with Artificial Intelligence in Melanoma Treatment.

Biomolecules
Immune checkpoint inhibitors (ICIs) have transformed melanoma treatment; however, predicting patient responses remains a significant challenge. This study reviews the potential of artificial intelligence (AI) to optimize ICI therapy in melanoma by in...

Nasopharyngeal cancer screening and immunotherapy efficacy evaluation based on plasma separation combined with label-free SERS technology.

Analytica chimica acta
BACKGROUND: In recent years, significant progress has been made in the treatment of nasopharyngeal carcinoma (NPC). The application of immunotherapy, especially the use of Programmed Cell Death Protein 1 inhibitors, has demonstrated excellent therape...

Sonopermeation combined with stroma normalization enables complete cure using nano-immunotherapy in murine breast tumors.

Journal of controlled release : official journal of the Controlled Release Society
Nano-immunotherapy shows great promise in improving patient outcomes, as seen in advanced triple-negative breast cancer, but it does not cure the disease, with median survival under two years. Therefore, understanding resistance mechanisms and develo...

Identification of M1 macrophage infiltration-related genes for immunotherapy in Her2-positive breast cancer based on bioinformatics analysis and machine learning.

Scientific reports
Over the past several decades, there has been a significant increase in the number of breast cancer patients. Among the four subtypes of breast cancer, Her2-positive breast cancer is one of the most aggressive breast cancers. In this study, we screen...

Integrative Multi-Omics Analysis Reveals Molecular Subtypes of Ovarian Cancer and Constructs Prognostic Models.

Journal of immunotherapy (Hagerstown, Md. : 1997)
Ovarian cancer (OV) remains the most lethal gynecological malignancy. The aim of this study was to identify molecular subtypes of OV through integrative multi-omics analysis and construct machine learning-based prognostic models for predicting the ef...