AI Medical Compendium Topic:
Biomarkers, Tumor

Clear Filters Showing 841 to 850 of 1021 articles

Inside a Metastatic Fracture: Molecular Bases and New Potential Therapeutic Targets.

Cancer medicine
INTRODUCTION: Bone metastases and pathological fractures significantly impact the prognosis and quality of life in cancer patients. However, clinical and radiological features alone have been shown to fail to predict skeletal related events of a bone...

Selection of neuroendocrine markers in diagnostic workup of neuroendocrine neoplasms: The real-world data and machine learning model algorithms.

Cancer cytopathology
BACKGROUND: Accurate diagnosis of neuroendocrine neoplasms (NENs) is challenging, especially in poorly differentiated neuroendocrine carcinomas (NECs). This study was aimed to search the best or best combination of neuroendocrine markers in the diagn...

Multiomics evaluation and machine learning optimize molecular classification, prediction of prognosis and immunotherapy response for ovarian cancer.

Pathology, research and practice
BACKGROUND: Ovarian cancer (OC), owing to its substantial heterogeneity and high invasiveness, has historically been devoid of precise, individualized treatment options. This study aimed to establish integrated consensus subtypes of OC using differen...

Machine learning models for enhanced diagnosis and risk assessment of prostate cancer with Ga-PSMA-617 PET/CT.

European journal of radiology
OBJECTIVE: Prostate cancer (PCa) is highly heterogeneous, making early detection of adverse pathological features crucial for improving patient outcomes. This study aims to predict PCa aggressiveness and identify radiomic and protein biomarkers assoc...

Toward Accurate Deep Learning-Based Prediction of Ki67, ER, PR, and HER2 Status From H&E-Stained Breast Cancer Images.

Applied immunohistochemistry & molecular morphology : AIMM
Despite improvements in machine learning algorithms applied to digital pathology, only moderate accuracy, to predict molecular information from histology alone, has been achieved so far. One of the obstacles is the lack of large data sets to properly...

Interpretable machine learning for thyroid cancer recurrence predicton: Leveraging XGBoost and SHAP analysis.

European journal of radiology
PURPOSE: For patients suffering from differentiated thyroid cancer (DTC), several clinical, laboratory, and pathological features (including patient age, tumor size, extrathyroidal extension, or serum thyroglobulin levels) are currently used to ident...

Utility of comprehensive genomic profiling combined with machine learning for prognostic stratification in stage II/III colorectal cancer after adjuvant chemotherapy.

International journal of clinical oncology
BACKGROUND AND PURPOSE: Accurate recurrence risk evaluation in patients with stage II and III colorectal cancer (CRC) remains difficult. Traditional histopathological methods frequently fall short in predicting outcomes after adjuvant chemotherapy. T...

Unveiling the role of PANoptosis-related genes in breast cancer: an integrated study by multi-omics analysis and machine learning algorithms.

Breast cancer research and treatment
BACKGROUND: The heterogeneity of breast cancer (BC) necessitates the identification of novel subtypes and prognostic models to enhance patient stratification and treatment strategies. This study aims to identify novel BC subtypes based on PANoptosis-...

Identification of a telomere-related gene signature for the prognostic and immune landscape prediction in head and neck squamous cell carcinoma by integrated analysis of machine learning and Mendelian randomization.

Medicine
Telomere-related genes (TRGs) are vital in diverse tumor types. Nevertheless, there is a notable lack of in-depth research concerning their significance in head and neck squamous cell carcinoma (HNSCC). In this context, the present study aims to asse...