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IDEAL: Artificial Intelligence and Big Data for Early Lung Cancer Diagnosis Study

NCT03753724 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 1293

Last updated 2022-07-29

No results posted yet for this study

Summary

This study aims to test the use of novel CT image analysis techniques to enable a better characterisation of small pulmonary nodules. The study will incorporate solid and predominantly solid nodules of 5-15 mm scanned using a variety of scanner types, imaging protocols and patient populations. The investigators hope that the new image processing techniques will improve the accuracy of lung nodule analysis which will in turn reduce the number of unnecessary investigations for benign nodules and may increase the accuracy of the early diagnosis of lung cancer in malignant nodules. This study aims to test this novel analysis software to subsequently allow validation.

Conditions

  • Pulmonary Nodule, Solitary
  • Pulmonary Nodule, Multiple
  • Lung Cancer

Sponsors & Collaborators

  • Optellum Ltd.

    collaborator UNKNOWN

  • University of Oxford

    lead OTHER

Principal Investigators

  • Fergus Gleeson, Prof · University of Oxford/Oxford University Hospitals NHS Foundation Trust

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2018-08-29
Primary Completion
2022-03-31
Completion
2022-03-31

Countries

  • United Kingdom

Study Locations

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Entities

Diseases

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 NCT03753724 on ClinicalTrials.gov