AIMC Topic: Biomarkers, Tumor

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Advanced pathological subtype classification of thyroid cancer using efficientNetB0.

Diagnostic pathology
BACKGROUND: Thyroid cancer is a prevalent malignancy requiring accurate subtype identification for effective treatment planning and prognosis evaluation. Deep learning has emerged as a valuable tool for analyzing tumor microenvironment features and d...

A deep-learning model for quantifying circulating tumour DNA from the density distribution of DNA-fragment lengths.

Nature biomedical engineering
The quantification of circulating tumour DNA (ctDNA) in blood enables non-invasive surveillance of cancer progression. Here we show that a deep-learning model can accurately quantify ctDNA from the density distribution of cell-free DNA-fragment lengt...

Multi-Omics Deep-Learning Prediction of Homologous Recombination Deficiency-Like Phenotype Improved Risk Stratification and Guided Therapeutic Decisions in Gynecological Cancers.

IEEE journal of biomedical and health informatics
Homologous recombination deficiency (HRD) is a well-recognized important biomarker in determining the clinical benefits of platinum-based chemotherapy and PARP inhibitor therapy for patients diagnosed with gynecologic cancers. Accurate prediction of ...

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
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...

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)
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
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
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 ...

Considerations regarding the selection, sampling, extraction, analysis, and modelling of biomarkers in exhaled breath for early lung cancer screening.

Journal of pharmaceutical and biomedical analysis
Lung cancer (LC) is the deadliest cancer due to the lack of efficient screening methods that detect the disease early. This review, covering the years 2011 - 2025, summarizes state-of-the-art LC screening through analysis of volatile organic compound...