AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Immune Checkpoint Inhibitors

Showing 61 to 70 of 81 articles

Clear Filters

A Pan-Cancer Analysis of Predictive Methylation Signatures of Response to Cancer Immunotherapy.

Frontiers in immunology
Recently, tumor immunotherapy based on immune checkpoint inhibitors (ICI) has been introduced and widely adopted for various tumor types. Nevertheless, tumor immunotherapy has a few drawbacks, including significant uncertainty of outcome, the possibi...

Raman Spectroscopy and Machine Learning Reveals Early Tumor Microenvironmental Changes Induced by Immunotherapy.

Cancer research
Cancer immunotherapy provides durable clinical benefit in only a small fraction of patients, and identifying these patients is difficult due to a lack of reliable biomarkers for prediction and evaluation of treatment response. Here, we demonstrate th...

Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: a machine learning approach.

Journal for immunotherapy of cancer
BACKGROUND: Treatment with immune checkpoint inhibitors (ICIs) has been associated with an increased rate of cardiac events. There are limited data on the risk factors that predict cardiac events in patients treated with ICIs. Therefore, we created a...

A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology.

Nature communications
Epstein-Barr virus-associated gastric cancer (EBVaGC) shows a robust response to immune checkpoint inhibitors. Therefore, a cost-efficient and accessible tool is needed for discriminating EBV status in patients with gastric cancer. Here we introduce ...

Deep learning captures selective features for discrimination of microsatellite instability from pathologic tissue slides of gastric cancer.

International journal of cancer
Microsatellite instability (MSI) status is an important prognostic marker for various cancers. Furthermore, because immune checkpoint inhibitors are much more effective in tumors with high level of MSI (MSI-H), MSI status is routinely tested in multi...

Deep learning for predicting the risk of immune checkpoint inhibitor-related pneumonitis in lung cancer.

Clinical radiology
AIM: To develop and validate a nomogram model that combines computed tomography (CT)-based radiological factors extracted from deep-learning and clinical factors for the early predictions of immune checkpoint inhibitor-related pneumonitis (ICI-P).

Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study.

The Lancet. Digital health
BACKGROUND: Only around 20-30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tu...

Probing the origins of programmed death ligand-1 inhibition by implementing machine learning-assisted sequential virtual screening techniques.

Molecular diversity
PD-L1 is a key immunotarget involved in binding to its receptor PD-1. PD-L1/PD-1 interface blocking using antibodies (or small molecules) is the central area of interest for tumor suppression in various cancers. Blocking the PD-L1/PD-1 pathway in the...