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

NCT05711914 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 300

Last updated 2023-02-03

No results posted yet for this study

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

Conditions

  • Non-small Cell Lung Cancer (NSCLC)

Sponsors & Collaborators

  • MEDEXPRIM

    collaborator UNKNOWN

  • GRATICULE

    collaborator UNKNOWN

  • Centre Hospitalier Universitaire de Nīmes

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
100 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-01-31
Primary Completion
2022-03-31
Completion
2022-12-31

Countries

  • France

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05711914 on ClinicalTrials.gov