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Immune Checkpoint Inhibitors

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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...

Ct-based subregional radiomics using hand-crafted and deep learning features for prediction of therapeutic response to anti-PD1 therapy in NSCLC.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To develop and externally validate subregional radiomics for predicting therapeutic response to anti-PD1 therapy in non-small-cell lung cancer (NSCLC).

From pixels to patient care: deep learning-enabled pathomics signature offers precise outcome predictions for immunotherapy in esophageal squamous cell cancer.

Journal of translational medicine
BACKGROUND: Immunotherapy has significantly improved survival of esophageal squamous cell cancer (ESCC) patients, however the clinical benefit was limited to only a small portion of patients. This study aimed to perform a deep learning signature base...

Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types.

Journal for immunotherapy of cancer
BACKGROUND: The inflamed immune phenotype (IIP), defined by enrichment of tumor-infiltrating lymphocytes (TILs) within intratumoral areas, is a promising tumor-agnostic biomarker of response to immune checkpoint inhibitor (ICI) therapy. However, it i...

Investigation of deep learning model for predicting immune checkpoint inhibitor treatment efficacy on contrast-enhanced computed tomography images of hepatocellular carcinoma.

Scientific reports
Although the use of immune checkpoint inhibitors (ICIs)-targeted agents for unresectable hepatocellular carcinoma (HCC) is promising, individual response variability exists. Therefore, we developed an artificial intelligence (AI)-based model to predi...

Identification and Characterization of Immune Checkpoint Inhibitor-Induced Toxicities From Electronic Health Records Using Natural Language Processing.

JCO clinical cancer informatics
PURPOSE: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet their use is associated with immune-related adverse events (irAEs). Estimating the prevalence and patient impact of these irAEs in the real-world data setting is c...

Machine learning-based integration develops a stress response stated T cell (Tstr)-related score for predicting outcomes in clear cell renal cell carcinoma.

International immunopharmacology
BACKGROUND: Establishment of a reliable prognostic model and identification of novel biomarkers are urgently needed to develop precise therapy strategies for clear cell renal cell carcinoma (ccRCC). Stress response stated T cells (Tstr) are a new T-c...

Validated machine learning tools to distinguish immune checkpoint inhibitor, radiotherapy, COVID-19 and other infective pneumonitis.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND: Pneumonitis is a well-described, potentially disabling, or fatal adverse effect associated with both immune checkpoint inhibitors (ICI) and thoracic radiotherapy. Accurate differentiation between checkpoint inhibitor pneumonitis (CIP) rad...

Clinical Validation of Artificial Intelligence-Powered PD-L1 Tumor Proportion Score Interpretation for Immune Checkpoint Inhibitor Response Prediction in Non-Small Cell Lung Cancer.

JCO precision oncology
PURPOSE: Evaluation of PD-L1 tumor proportion score (TPS) by pathologists has been very impactful but is limited by factors such as intraobserver/interobserver bias and intratumor heterogeneity. We developed an artificial intelligence (AI)-powered an...

Deep learning based digital pathology for predicting treatment response to first-line PD-1 blockade in advanced gastric cancer.

Journal of translational medicine
BACKGROUND: Advanced unresectable gastric cancer (GC) patients were previously treated with chemotherapy alone as the first-line therapy. However, with the Food and Drug Administration's (FDA) 2022 approval of programmed cell death protein 1 (PD-1) i...