AIMC Topic: Biomarkers, Tumor

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Association of artificial intelligence-based immunoscore with the efficacy of chemoimmunotherapy in patients with advanced non-squamous non-small cell lung cancer: a multicentre retrospective study.

Frontiers in immunology
PURPOSE: Currently, chemoimmunotherapy is effective only in a subset of patients with advanced non-squamous non-small cell lung cancer. Robust biomarkers for predicting the efficacy of chemoimmunotherapy would be useful to identify patients who would...

Classifying driver mutations of papillary thyroid carcinoma on whole slide image: an automated workflow applying deep convolutional neural network.

Frontiers in endocrinology
BACKGROUND: Informative biomarkers play a vital role in guiding clinical decisions regarding management of cancers. We have previously demonstrated the potential of a deep convolutional neural network (CNN) for predicting cancer driver gene mutations...

A deep learning framework for hepatocellular carcinoma diagnosis using MS1 data.

Scientific reports
Clinical proteomics analysis is of great significance for analyzing pathological mechanisms and discovering disease-related biomarkers. Using computational methods to accurately predict disease types can effectively improve patient disease diagnosis ...

Identification of novel markers for neuroblastoma immunoclustering using machine learning.

Frontiers in immunology
BACKGROUND: Due to the unique heterogeneity of neuroblastoma, its treatment and prognosis are closely related to the biological behavior of the tumor. However, the effect of the tumor immune microenvironment on neuroblastoma needs to be investigated,...

Integrated machine learning to predict the prognosis of lung adenocarcinoma patients based on SARS-COV-2 and lung adenocarcinoma crosstalk genes.

Cancer science
Viruses are widely recognized to be intricately associated with both solid and hematological malignancies in humans. The primary goal of this research is to elucidate the interplay of genes between SARS-CoV-2 infection and lung adenocarcinoma (LUAD),...

Detection of carcinoembryonic antigen specificity using microwave biosensor with machine learning.

Biosensors & bioelectronics
Early diagnosis and screening of tumor markers are essential for effective cancer treatment and improve the treatment efficiency and prognosis of tumor recurrence and metastasis. In this study, a split-ring resonator (SRR) circuit based on an interdi...

Combination of plasma-based lipidomics and machine learning provides a useful diagnostic tool for ovarian cancer.

Journal of pharmaceutical and biomedical analysis
Ovarian cancer (OC), the second leading cause of death among gynecological cancers, is often diagnosed at an advanced stage due to its asymptomatic nature at early stages. This study aimed to explore the diagnostic potential of plasma-based lipidomic...

Ki-67 evaluation using deep-learning model-assisted digital image analysis in breast cancer.

Histopathology
AIMS: To test the efficacy of artificial intelligence (AI)-assisted Ki-67 digital image analysis in invasive breast carcinoma (IBC) with quantitative assessment of AI model performance.

Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC.

BioFactors (Oxford, England)
Most patients with non-small cell lung cancer (NSCLC) are diagnosed at an advanced stage of the disease, which complicates treatment due to a heightened risk of metastasis. Consequently, the timely identification of biomarkers associated with lymph n...

Identification of ferroptosis-related genes associated with diffuse large B-cell lymphoma via bioinformatics and machine learning approaches.

International journal of biological macromolecules
Ferroptosis has emerged as a critical mechanism in the development and progression of various tumors, particularly diffuse large B-cell lymphoma (DLBCL). However, the thorough characterization of ferroptosis-related genes in DLBCL remains inadequatel...