Predicting Response to PD-1 Checkpoint Blockade Using Deep Learning Analysis of Imaging and Clinical Data

Conditions: Non-small Cell Lung Cancer (NSCLC)

Sex: All

Ages: 18 Years – 100 Years

Enrollment: 300

Sponsor: Centre Hospitalier Universitaire de Nīmes

Location: France

Summary

Immunotherapy has transformed cancer treatment with the PD-1 class of checkpoint inhibitors - pembrolizumab and nivolumab -- demonstrating durable responses in Stage IV metastatic tumors such as non-small cell lung cancer and melanoma. Despite these numerous successes, PD-1/PD-L1 checkpoint blockade therapies do have a number of shortcomings.Many approaches to predict response to PD-1/PD-L1 checkpoint therapy have been investigated with limited success. Recent efforts exploring the utility of quantitative imaging biomarkers to predict response to PD-[L]1 immunotherapy have shown promise. The purpose of this retrospective multicenter study is to develop a multi-omic classifier to predict response to PD-1/PD-L1 checkpoint blockade for mutation negative (EGFR, ALK and ROS1) NSCLC

Eligibility Criteria

Inclusion Criteria:Patients between 18 and 100 years of age -Exclusion Criteria: Patient under 18 years of age-

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View on ClinicalTrials.gov

Source: ClinicalTrials.gov (NCT05711914). StuddyBuddy aggregates publicly available trial information.