AIMC Topic: Immune Checkpoint Inhibitors
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Probing the origins of programmed death ligand-1 inhibition by implementing machine learning-assisted sequential virtual screening techniques.
Molecular diversity
Jul 20, 2023
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...
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
May 31, 2023
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...
Deep learning for predicting the risk of immune checkpoint inhibitor-related pneumonitis in lung cancer.
Clinical radiology
Jan 14, 2023
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).
Deep learning captures selective features for discrimination of microsatellite instability from pathologic tissue slides of gastric cancer.
International journal of cancer
Aug 27, 2022
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...
A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology.
Nature communications
May 19, 2022
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 ...
A Pan-Cancer Analysis of Predictive Methylation Signatures of Response to Cancer Immunotherapy.
Frontiers in immunology
Dec 9, 2021
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...
Predicting response to immunotherapy plus chemotherapy in patients with esophageal squamous cell carcinoma using non-invasive Radiomic biomarkers.
BMC cancer
Oct 30, 2021
OBJECTIVES: To develop and validate a radiomics model for evaluating treatment response to immune-checkpoint inhibitor plus chemotherapy (ICI + CT) in patients with advanced esophageal squamous cell carcinoma (ESCC).
Esophageal Squamous Cell Carcinoma
Humans
Tomography, X-Ray Computed
Female
Biomarkers, Tumor
Support Vector Machine
Male
Bias
Esophageal Neoplasms
Antineoplastic Combined Chemotherapy Protocols
Combined Modality Therapy
Middle Aged
Treatment Outcome
Carboplatin
Immune Checkpoint Inhibitors
Antibodies, Monoclonal, Humanized
Docetaxel
Nomograms
Raman Spectroscopy and Machine Learning Reveals Early Tumor Microenvironmental Changes Induced by Immunotherapy.
Cancer research
Oct 13, 2021
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...
Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade.
Communications biology
Jul 15, 2021
Anti-PD-1 immunotherapy has recently shown tremendous success for the treatment of several aggressive cancers. However, variability and unpredictability in treatment outcome have been observed, and are thought to be driven by patient-specific biology...
Clinical decision support algorithm based on machine learning to assess the clinical response to anti-programmed death-1 therapy in patients with non-small-cell lung cancer.
European journal of cancer (Oxford, England : 1990)
Jun 26, 2021
OBJECTIVE: Anti-programmed death (PD)-1 therapy confers sustainable clinical benefits for patients with non-small-cell lung cancer (NSCLC), but only some patients respond to the treatment. Various clinical characteristics, including the PD-ligand 1 (...
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