AI Medical Compendium Topic:
Biomarkers, Tumor

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Cuproptosis gene-related, neural network-based prognosis prediction and drug-target prediction for KIRC.

Cancer medicine
BACKGROUND: Kidney renal clear cell carcinoma (KIRC), as a common case in renal cell carcinoma (RCC), has the risk of postoperative recurrence, thus its prognosis is poor and its prognostic markers are usually based on imaging methods, which have the...

Development of fecal microbial diagnostic marker sets of colorectal cancer using natural language processing method.

The International journal of biological markers
BACKGROUND: Cancer screening and early detection greatly increase the chances of successful treatment. However, most cancer types lack effective early screening biomarkers. In recent years, natural language processing (NLP)-based text-mining methods ...

Microsatellite Instability, Mismatch Repair, and Tumor Mutation Burden in Lung Cancer.

Surgical pathology clinics
Since US Food and Drug Administration approval of programmed death ligand 1 (PD-L1) as the first companion diagnostic for immune checkpoint inhibitors (ICIs) in non-small cell lung cancer, many patients have experienced increased overall survival. To...

Machine learning-based delta check method for detecting misidentification errors in tumor marker tests.

Clinical chemistry and laboratory medicine
OBJECTIVES: Misidentification errors in tumor marker tests can lead to serious diagnostic and treatment errors. This study aims to develop a method for detecting these errors using a machine learning (ML)-based delta check approach, overcoming limita...

Deep learning-enabled breast cancer endocrine response determination from H&E staining based on ESR1 signaling activity.

Scientific reports
Estrogen receptor (ER) positivity by immunohistochemistry has long been a main selection criterium for breast cancer patients to be treated with endocrine therapy. However, ER positivity might not directly correlate with activated ER signaling activi...

Rapid intraoperative multi-molecular diagnosis of glioma with ultrasound radio frequency signals and deep learning.

EBioMedicine
BACKGROUND: Molecular diagnosis is crucial for biomarker-assisted glioma resection and management. However, some limitations of current molecular diagnostic techniques prevent their widespread use intraoperatively. With the unique advantages of ultra...

A deep learning model predicts the presence of diverse cancer types using circulating tumor cells.

Scientific reports
Circulating tumor cells (CTCs) are cancer cells that detach from the primary tumor and intravasate into the bloodstream. Thus, non-invasive liquid biopsies are being used to analyze CTC-expressed genes to identify potential cancer biomarkers. In this...

Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients.

Cancer medicine
BACKGROUND: Cancer biomarkers play a pivotal role in the diagnosis, prognosis, and treatment response prediction of the disease. In this study, we analyzed the expression levels of RhoB and DNp73 proteins in rectal cancer, as captured in immunohistoc...

Translation of tissue-based artificial intelligence into clinical practice: from discovery to adoption.

Oncogene
Digital pathology (DP), or the digitization of pathology images, has transformed oncology research and cancer diagnostics. The application of artificial intelligence (AI) and other forms of machine learning (ML) to these images allows for better inte...

Enlightening the path to NSCLC biomarkers: Utilizing the power of XAI-guided deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The early diagnosis of Non-small cell lung cancer (NSCLC) is of prime importance to improve the patient's survivability and quality of life. Being a heterogeneous disease at the molecular and cellular level, the biomarkers r...