AI Medical Compendium Topic

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Biomarkers, Tumor

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Cellular Senescence in Hepatocellular Carcinoma: Immune Microenvironment Insights via Machine Learning and In Vitro Experiments.

International journal of molecular sciences
Hepatocellular carcinoma (HCC), a leading liver tumor globally, is influenced by diverse risk factors. Cellular senescence, marked by permanent cell cycle arrest, plays a crucial role in cancer biology, but its markers and roles in the HCC immune mic...

Artificial intelligence-based biomarkers for treatment decisions in oncology.

Trends in cancer
The development of new therapeutic strategies such as immune checkpoint inhibitors (ICIs) and targeted therapies has increased the complexity of the treatment landscape for solid tumors. At the current rate of annual FDA approvals, the potential trea...

Epigenetic profiling for prognostic stratification and personalized therapy in breast cancer.

Frontiers in immunology
BACKGROUND: The rising incidence of breast cancer and its heterogeneity necessitate precise tools for predicting patient prognosis and tailoring personalized treatments. Epigenetic changes play a critical role in breast cancer progression and therapy...

Construction of an anaplastic thyroid cancer stratification signature to guide immune therapy selection and validation of the pivotal gene HLF through experiments.

Frontiers in immunology
INTRODUCTION: While most thyroid cancer patients have a favorable prognosis, anaplastic thyroid carcinoma (ATC) remains a particularly aggressive form with a median survival time of just five months. Conventional therapies offer limited benefits for ...

Artificial intelligence in gastrointestinal cancers: Diagnostic, prognostic, and surgical strategies.

Cancer letters
GI (Gastrointestinal) malignancies are one of the most common and lethal cancers globally. The dawn of precision medicine and developing technologies have reduced the mortality rates for GI malignancies, underscoring the main role of early detection ...

Integrated multi-omics analysis identifies a machine learning-derived signature for predicting prognosis and therapeutic vulnerability in clear cell renal cell carcinoma.

Life sciences
AIMS: Clear cell renal cell carcinoma (ccRCC) shows considerable variation within and between tumors, presents varying treatment responses among patients, possibly due to molecular distinctions. This study utilized a multi-center and multi-omics anal...

Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancer.

Breast cancer research : BCR
Recent evidence indicates that endocrine resistance in estrogen receptor-positive (ER+) breast cancer is closely correlated with phenotypic characteristics of epithelial-to-mesenchymal transition (EMT). Nonetheless, identifying tumor tissues with a m...

Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma.

Scientific reports
The connection between metabolic reprogramming and tumor progression has been demonstrated in an increasing number of researches. However, further research is required to identify how metabolic reprogramming affects interpatient heterogeneity and pro...

The KMeansGraphMIL Model: A Weakly Supervised Multiple Instance Learning Model for Predicting Colorectal Cancer Tumor Mutational Burden.

The American journal of pathology
Colorectal cancer (CRC) is one of the top three most lethal malignancies worldwide, posing a significant threat to human health. Recently proposed immunotherapy checkpoint blockade treatments have proven effective for CRC, but their use depends on me...