AIMC Topic: Biomarkers, Tumor
Clear Filters Showing 161 to 170 of 1131 articles
Development of Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer Patients Using Clinical Risk Factors, Patient-Reported Outcomes, and Serum Cytokine Biomarkers.
Clinical breast cancer
Mar 5, 2025
BACKGROUND: Radiation dermatitis (RD) is a significant side effect of radiotherapy experienced by breast cancer patients. Severe symptoms include desquamation or ulceration of irradiated skin, which impacts quality of life and increases healthcare co...
Discovery of mutations predictive of survival benefit from immunotherapy in first-line NSCLC: A retrospective machine learning study of IMpower150 liquid biopsy data.
Computers in biology and medicine
Mar 4, 2025
Predictive biomarker identification in cancer treatment has traditionally relied on pre-defined analyses, limiting discoveries to expected biomarkers and potentially overlooking novel ones predictive of therapy response. In this work, we develop a no...
Liquid Biopsy
Carcinoma, Non-Small-Cell Lung
Clinical Trials, Phase III as Topic
Randomized Controlled Trials as Topic
Middle Aged
Mutation
Bevacizumab
Paclitaxel
Retrospective Studies
Lung Neoplasms
Aged
Humans
Female
Immunotherapy
Machine Learning
Biomarkers, Tumor
Antineoplastic Combined Chemotherapy Protocols
Male
Automated Electrical Detection of Proteins for Oral Squamous Cell Carcinoma in an Integrated Microfluidic Chip Using Multi-Frequency Impedance Cytometry and Machine Learning.
Sensors (Basel, Switzerland)
Mar 4, 2025
Proteins can act as suitable biomarkers for the prognosis and diagnosis of certain conditions and can help us gain an understanding of the fundamental processes that occur inside an organism. In this work, we present a fully automated machine learnin...
Identification of novel diagnostic and prognostic microRNAs in sarcoma on TCGA dataset: bioinformatics and machine learning approach.
Scientific reports
Mar 4, 2025
The discovery of unique microRNA (miR) patterns and their corresponding genes in sarcoma patients indicates their involvement in cancer development and suggests their potential use in medical management. MiRs were identified from The Cancer Genome At...
Machine-learning diagnostics of breast cancer using piRNA biomarkers.
Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals
Mar 4, 2025
BACKGROUND AND OBJECTIVES: Prior studies have shown that small non-coding RNAs (sncRNAs) are associated with cancer occurrence or development. Recently, a newly discovered class of small ncRNAs known as PIWI-interacting RNAs (piRNAs) have been found ...
Machine Learning-Aided Identification of Fecal Extracellular Vesicle microRNA Signatures for Noninvasive Detection of Colorectal Cancer.
ACS nano
Mar 3, 2025
Colorectal cancer (CRC) remains a formidable threat to human health, with considerable challenges persisting in its diagnosis, particularly during the early stages of the malignancy. In this study, we elucidated that fecal extracellular vesicle micro...
MRI radiomics based on machine learning in high-grade gliomas as a promising tool for prediction of CD44 expression and overall survival.
Scientific reports
Mar 3, 2025
We aimed to predict CD44 expression and assess its prognostic significance in patients with high-grade gliomas (HGG) using non-invasive radiomics models based on machine learning. Enhanced magnetic resonance imaging, along with the corresponding gene...
Machine learning analysis identified NNMT as a potential therapeutic target for hepatocellular carcinoma based on PCD-related genes.
Scientific reports
Mar 3, 2025
Programmed cell death (PCD) plays a critical role in cancer biology, influencing tumor progression and treatment response. This study aims to investigate the role of PCD-related genes in hepatocellular carcinoma (HCC), identifying potential prognosti...
Machine learning identifies clinical tumor mutation landscape pathways of resistance to checkpoint inhibitor therapy in NSCLC.
Journal for immunotherapy of cancer
Mar 3, 2025
BACKGROUND: Immune checkpoint inhibitors (CPIs) have revolutionized cancer therapy for several tumor indications. However, a substantial fraction of patients treated with CPIs derive no benefit or have short-lived responses to CPI therapy. Identifyin...
Enhancing Personalized Chemotherapy for Ovarian Cancer: Integrating Gene Expression Data with Machine Learning.
Asian Pacific journal of cancer prevention : APJCP
Mar 1, 2025
OBJECTIVE: Ovarian cancer's complexity and heterogeneity pose significant challenges in treatment, often resulting in suboptimal chemotherapy outcomes. This study aimed to leverage machine learning algorithms, gene selection, and gene expression dat...
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