AIMC Topic: Immune Checkpoint Inhibitors

Clear Filters Showing 11 to 20 of 119 articles

Development and validation of interpretable machine learning models for predicting AKI risk in patients treated with PD-1/PD-L1: a retrospective study.

BMC medical informatics and decision making
BACKGROUND: Anti-programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immunotherapy has revolutionized cancer treatment. However, it can cause immune-related adverse events, including acute kidney injury (AKI). Such adverse e...

Immune checkpoint inhibitor-related pneumonitis: From guidelines to the front lines.

Respiratory investigation
Immune checkpoint inhibitors (ICIs) have changed cancer treatment, evoking durable responses in various cancers. However, their immune-mediated mechanisms can lead to unique toxicities known as immune-related adverse events (irAEs), among which ICI-r...

Leveraging readily available clinical data with machine learning to predict first-line immunotherapy outcomes in non-small cell lung cancer.

International immunopharmacology
BACKGROUND: Immune checkpoint inhibitors (ICIs) are essential first-line treatments for recurrent or metastatic non-small cell lung cancer (NSCLC). However, predicting their effectiveness and the occurrence of immunotherapy-related adverse events (ir...

Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC.

Nature communications
Immune checkpoint inhibitors (ICIs), either as monotherapy (ICI-Mono) or combined with chemotherapy (ICI-Chemo), improves survival in advanced non-small cell lung cancer (NSCLC). However, prospective guidance for choosing between these options remain...

Circulating cytokine profiling and clustering identify biomarker predicting efficacy of ICI in combination with chemotherapy.

Cancer letters
The combination of chemotherapy can enhance the efficacy of immune checkpoint inhibitors (ICIs), but requires precise patient stratification and biomarker screening. Cytokines influence immunotherapy outcomes, and multiplex cytokine profiling aids in...

Integrative habitat analysis and multi-instance deep learning for predictive model of PD-1/PD-L1 immunotherapy efficacy in NSCLC patients: a dual-center retrospective study.

BMC medical imaging
BACKGROUND: PD-1/PD-L1 immunotherapy represents the primary treatment for advanced NSCLC patients; however, response rates to this therapy vary among individuals. This dual-center study aimed to integrate habitat radiomics and multi-instance deep lea...

Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis.

Scientific reports
This study developed an immune-related long non-coding RNAs (lncRNAs)-based prognostic signature by integrating multi-omics data and machine learning algorithms to predict survival and therapeutic responses in breast cancer patients. Utilizing transc...

Predictive value of machine learning for radiation pneumonitis and checkpoint inhibitor pneumonitis in lung cancer patients: a systematic review and meta-analysis.

Scientific reports
Some studies have developed machine learning (ML) models for the prediction of pneumonitis following immunotherapy and radiotherapy for patients with lung cancer (LC). However, the prediction accuracy of these models remains a topic of debate. Thus, ...

Histopathologic deep learning model for predicting tumor response to hepatic arterial infusion chemotherapy plus TKIs and ICIs in large hepatocellular carcinoma.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: While triplet therapy (HTI), which combines hepatic arterial infusion chemotherapy (HAIC) with tyrosine kinase inhibitors and immune checkpoint inhibitors, is widely used in the treatment of large hepatocellular carcinoma (HCC), there are...